Summary Figures List

Summary Figures List ............................................................................................................... I Tables List ............................................................................................................. XII 1. Introduction.......................................................................................................... 1 1.1 Air pollutants and their sources .................................................................................. 4 1.2 Tropospheric Ozone ................................................................................................. 12 1.2.1 Tropospheric ozone in large urban centres ...................................................... 16 1.3 The SPMA’s climate characterization ...................................................................... 29 1.4 Objectives ................................................................................................................. 34 2. Data and methodology ...................................................................................... 35 2.1 Characterization of the air pollution data source: the CETESB (Environmental Engineering State Company) .......................................................................................... 35 2.1.1 Pollutants monitoring networks ....................................................................... 38 2.1.2 Characterization of some CETESB measurement stations ............................. 43 2.1.3 Air quality standards........................................................................................ 60 2.2 Data........................................................................................................................... 65 2.2.1 Air pollution data ............................................................................................. 65 2.2.1.1 Data consistency .................................................................................. 67 2.2.2 Climatic data .................................................................................................... 68 2.3 Methodology............................................................................................................. 70 2.3.1 Ozone behaviour in the SPMA ........................................................................ 70 2.3.2 Analysis of atmospheric patterns influence on ozone concentrations in the SPMA ....................................................................................................................... 74 2.3.2.1 Selection of months with ozone anomalies and observed atmospheric anomalies .................................................................................... 76 2.3.2.2 Association of atmospheric patterns to ozone concentrations ............. 78 2.3.2.3 Atmospheric influence on tropospheric ozone daily variability .......... 81 3. Results .............................................................................................................. 82 3.1 Tropospheric ozone behaviour in SPMA ................................................................. 82 3.1.1 Yearly Means ................................................................................................... 84 3.1.2 Monthly Means ................................................................................................ 91 3.1.2.1 Tropospheric ozone seasonal cycle in the SPMA ............................... 98 3.1.3 Daily Means ................................................................................................... 110 3.1.3.1 The SPMA’s diurnal tropospheric ozone cycle ................................. 123 3.1.4 analysis of tropospheric ozone spatial distribution in the SPMA.................. 130 3.2 Months of ozone anomalies and the observed atmospheric anomalies .................. 135 3.3 Association of atmospheric patterns to ozone concentrations ................................ 151 3.3.1 Months with intense negative ozone anomalies. ........................................... 152 3.3.2 Months with intense positive ozone anomalies. ............................................ 180 3.4 Atmosphere’s interference in tropospheric ozone .................................................. 227 3.4.1 Months of category –2 ................................................................................... 228 3.4.2 Months of category 2 ..................................................................................... 245 4. Conclusions and Final Considerations ............................................................ 279 5. Bibliographical References .............................................................................. 284 ANNEX 1 ............................................................................................................. 297 ANNEX 2 ............................................................................................................. 304

ii ANNEX 3 ............................................................................................................. 308 ANNEX 4 ............................................................................................................. 309 ANNEX 5 ............................................................................................................. 310

I

Figures List Figure 01: Evolution of the light vehicles fleet in SPMA. ......................................... 6 Figure 02: Residence time and spatial scale of transport of some pollutants. ...... 10 Figure 03: Photochemical smog over Quebec, Canada. ....................................... 17 Figure 04: Pollutants concentrations evolution (y-axis) along the hours of the day (xaxis) in a typical smog episode.............................................................................. 18 Figure 05: Sample of damages to vegetation caused by ozone. ........................... 20 Figure 06: CO air quality indexes from some stations in the SPMA, from 2002 to 2006. 21 Figure 07: Mean annual concentrations to NO2 (g/m3) from some stations in SPMA, from 1998 to 2006. ................................................................................................ 22 Figure 08: PM air quality indexes from some stations in the SPMA, from 2002 to 2006. 22 Figure 09: Number of days with ozone concentrations above AQS (columns) and maximum hourly concentration (line). .................................................................... 23 Figure 10: O3 air quality indexes from some stations in SPMA, from 2002 to 2006.23 Figure 11: Number of AQS surpassings and severe ozone levels by month in the SPMA, from 2002 to 2006. .................................................................................... 25 Figure 12: Ozone in the SPMA – High concentration – 04/03/03 .......................... 26 Figure 13: Ozone in the SPMA – Low concentration – 10/09/03. .......................... 27 Figure 14: Localization of the SPMA monitoring stations from which data was used in this study ............................................................................................................... 39 Figure 15: Congonhas station surroundings. ......................................................... 45 Figure 16: Lapa station surroundings. ................................................................... 46 Figure 17: Osasco station surroundings. ............................................................... 46 Figure 18: São Caetano do Sul station surroundings. ........................................... 47 Figure 19: Mooca station surroundings. ................................................................ 48 Figure 20: Ibirapuera station surroundings. ........................................................... 49 Figure 21: Pico do Jaraguá station surroundings. The red arrow denotes the station site.

50

Figure 22: Parque D. Pedro II station surroundings. Approximate location is shown by the red box. ........................................................................................................... 52

II

Figure 23: Santana station surroundings. Approximate location is shown by the red box.

52

Figure 24: Nossa Senhora do Ó station surroundings. Approximate location is shown by the red box........................................................................................................ 53 Figure 25: Diadema station surroundings. Approximate location is shown by the red box.

54

Figure 26: Santo Amaro station surroundings. Approximate location is shown by the red box. 55 Figure 27: Santo André – Capuava station surroundings. Approximate location is shown by the red box. ........................................................................................... 55 Figure 28: São Miguel Paulista station surroundings. Approximate location is shown by the red box........................................................................................................ 56 Figure 29: Mauá station surroundings. Approximate location is shown by the red box. 57 Figure 30: Pinheiros station surroundings. Approximate location is shown by the red box.

58

Figure 31: Horto Florestal station surroundings. Approximate location is shown by the red box. 58 Figure 32: Yearly concentration in mean low concentration stations (< -2). ........ 86 Figure 33: Yearly concentration in mean average concentration stations (between -1 and +1). ............................................................................................................... 87 Figure 34: Yearly concentration for mean high concentration stations (> +1). .... 88 Figure 35: Yearly concentration for mean very high concentration stations (> +2).89 Figure 36: Number of O3 AQS surpassing and attention level, temporal evolution, per station in the SPMA – 2003 to 2007. ..................................................................... 90 Figure 37: Monthly ozone concentration evolution in Parque D. Pedro II, Santana and Pico do Jaraguá stations. The straight lines show the linear tendency for each station. 92 Figure 38: Monthly ozone concentration evolution in Congonhas station. The straight lines show the linear tendency. ............................................................................. 93 Figures 39a and 39b: Monthly ozone concentration evolution in Horto Florestal, Santo Amaro and Osasco stations. The straight lines show the linear tendency for each station. 94

III

Figure 40: Mean monthly value temporal evolution for the 17 stations, from 1996 to 2005. The red line indicates the mean linear tendency. Months highlighted in red and blue show, respectively, the highest and lowest mean concentration observed in each year.

95

Figure 41: Mean monthly value temporal evolution for Ibirapuera, Mauá, Mooca, Parque D. Pedro II, São Caetano do Sul e São Miguel Paulista stations, from 1996 to 2005. The red line indicates the mean linear tendency. Months highlighted in red and blue show, respectively, the highest and lowest mean concentration observed in each year.

96

Figure 42: Ozone (µg/m3), solar radiation (W/m2) and cloud covering (%) mean seasonal cycles in SPMA, from 1996-2005. Featuring the months of October (in red, yearly maximum), June (in blue, yearly minimum) and February (secondary maximum).............................................................................................................. 99 Figure 43a: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.................................................. 100 Figure 43b: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.................................................. 101 Figure 43c: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.................................................. 102 Figure 43d Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.................................................. 103 Figure 43e: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.................................................. 104 Figure 43f: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.................................................. 105 Figure 44: SPMA average solar radiation seasonal cycle (W/m 2) (blue line with yellow lozenges) and O3 average seasonal cycle in SPMA’s stations with a clear seasonal cycle.

108

Figure 45: SPMA average solar radiation seasonal cycle (W/m 2) (blue line with yellow lozenges) and O3 average seasonal cycle in SPMA’s stations with a different seasonal cycle.

109

Figure 46: O3 daily mean concentration standard deviation, in each SPMA’s monitoring station. The average value, 14.30 μg/m3, is shown by the line. ......... 110

IV

Figure 47a: O3 Daily mean concentrations in the low variability stations. Invalid or missing data are shown in blue. .......................................................................... 113 Figure 47b: O3 Daily mean concentrations in the low variability stations. Invalid or missing data are shown in blue. .......................................................................... 114 Figure 48a: O3 Daily mean concentrations in the medium variability stations. Invalid or missing data are shown in blue. .......................................................................... 116 Figure 48b: O3 Daily mean concentrations in the medium variability stations. Invalid or missing data are shown in blue. .......................................................................... 117 Figure 49a: O3 Daily mean concentrations in the high variability stations. Invalid or missing data are shown in blue. .......................................................................... 118 Figure 49b: O3 Daily mean concentrations in the high variability stations. Invalid or missing data are shown in blue. .......................................................................... 119 Figure 49c: O3 Daily mean concentrations in the high variability stations. Invalid or missing data are shown in blue. .......................................................................... 120 Figure 50: Ozone average daily cycle from the 17 stations in the SPMA ............ 124 Figure 51: NOx average daily cycle, Cerqueira César station, from the 12th of August to the 09th of September of 1997. The bars represent the standard deviation of concentrations hourly averages in sunny and cloudy days. ................................ 125 Figure 52: NO2 and ozone daily cycles on the 03rd and 04th of February, 1998. . 127 Figures 53a (above) and 53b (below): Solar Radiation average daily cycle (1999-2001, in blue) and ozone concentrations (1996-2005) in October and June, in Santana (red) and Osasco (yellow) stations............................................................................... 128 Figure 54: Annual distribution of the number of ozone concentration anomalies higher than 1. 138 Figure 55: Monthly distribution of positive ozone anomalies higher than 1σ, in the period 1996-2005, considering the 17 stations. ................................................... 139 Figure 56: Annual distribution of anomalies in O3 concentrations lesser than – 1.145 Figure 57: Monthly distribution of anomalies higher than 1σ, from 1996 to 2005, considering the 17 stations. ................................................................................. 146 Figure 58: Monthly evolution time series for RU, RAD and O3 anomalies. ......... 148 Figure 59: O3 concentration anomalies’ time series, considering the 17 stations. Months chosen for the atmospheric patterns analyses are indicated by the blue and red arrows. .......................................................................................................... 149

V

Figure 60: Passing of frontal systems on the Brazilian coast, April 1998. The city of Santos is indicated as the closest SMPA representative. .................................... 153 Figure 61a: Air relative and specific humidity in April 1998 and its respective anomalies. ........................................................................................................... 154 Figure 61b: Incoming shortwave solar radiation and OLR in April 1998 and its respective anomalies. .......................................................................................... 155 Figure 61c: Air temperature and surface air pressure in April 1998 and its respective anomalies. ........................................................................................................... 156 Figure 61d: Streamlines and wind direction and intensity in April 1998 and its anomaly. 157 Figure 61e: Air divergence over surface and in higher levels in April 1998 and its respective anomalies. .......................................................................................... 158 Figure 62: Passing of frontal systems on the Brazilian coast, JUL 2005. The city of Santos is indicated as the closest SMPA representative. .................................... 160 Figure 63a: Air relative and specific humidity in July 2005 and its respective anomalies. ........................................................................................................... 161 Figure 63b: Incoming shortwave solar radiation and OLR in July 2005 and its respective anomalies. .......................................................................................... 162 Figure 63c: Air temperature and surface air pressure in July 2005 and its respective anomalies. ........................................................................................................... 163 Figure 63d: Streamlines and wind direction and intensity in July 2005 and its anomaly. 164 Figure 63e: Air divergence over surface and in higher levels in July 2005 and its respective anomalies. .......................................................................................... 165 Figure 64: Passing of frontal systems on the Brazilian coast, November 1997. The city of Santos is indicated as the closest SMPA representative. ................................ 166 Figure 65: SACZ positioning on the Southeast coast (infrared channel), the 18th of November, 1997. ................................................................................................. 167 Figure 66a: Air relative humidity in November 1997 and its anomaly. ................. 168 Figure 66b: Incoming shortwave solar radiation and OLR in November 1997 and its respective anomalies. .......................................................................................... 168 Figure 66c: Air temperature and surface air pressure in November 1997 and its respective anomalies. .......................................................................................... 169

VI

Figure 66d: Streamlines and wind direction and intensity in November 1997 and its anomaly. .............................................................................................................. 170 Figure 66e: Air divergence over surface and in higher levels in November 1997 and its respective anomalies. .......................................................................................... 171 Figure 67: Passing of frontal systems on the Brazilian coast, January 2004. The city of Santos is indicated as the closest SMPA representative. .................................... 172 Figure 68: SACZ positioning over the Southeastern coast (brightness temperature), in the 25th of January, 2004. .................................................................................... 173 Figure 69: Precipitation anomaly in Brazil, January 2004. ................................... 174 Figure 70a: Air relative humidity in January 2004 and its respective anomaly. ... 175 Figure 70b: Incoming shortwave solar radiation and OLR in January 2004 and its respective anomalies. .......................................................................................... 176 Figure 70c: Air temperature and surface air pressure in January 2004 and its respective anomalies. .......................................................................................... 177 Figure 70d: Streamlines and wind direction and intensity in January 2004 and its anomaly. .............................................................................................................. 178 Figure 70e: Air divergence over surface and in higher levels in January 2004 and its respective anomalies. .......................................................................................... 179 Figure 71: Passing of frontal systems on the Brazilian coast, August 1999. The city of Santos is indicated as the closest SMPA representative. .................................... 181 Figure 72a: Air relative and specific humidity in August 1999 and its respective anomalies. ........................................................................................................... 182 Figure 72b: Incoming shortwave solar radiation and OLR in August 1999 and its respective anomalies. .......................................................................................... 183 Figure 72c: Air temperature and surface air pressure in August 1999 and its respective anomalies. ........................................................................................................... 184 Figure 72d: Streamlines and wind direction and intensity in August 1999 and its anomaly. .............................................................................................................. 185 Figure 72e: Air divergence over surface and in higher levels in August 1999 and its respective anomalies. .......................................................................................... 186 Figure 73: Passing of frontal systems on the Brazilian coast, March 2002. The city of Santos is indicated as the closest SMPA representative. .................................... 188

VII

Figure 74a: Air relative and specific humidity in March 2002 and its respective anomalies. ........................................................................................................... 189 Figure 74b: Incoming shortwave solar radiation and OLR in March 2002 and its respective anomalies. .......................................................................................... 190 Figure 74c: Air temperature and surface air pressure in March 2002 and its respective anomalies. ........................................................................................................... 191 Figure 74d: Streamlines and wind direction and intensity in Mach 2002 and its anomaly. .............................................................................................................. 192 Figure 74e: Air divergence over surface and in higher levels in March 2002 and its respective anomalies. .......................................................................................... 193 Figure 75: Passing of frontal systems on the Brazilian coast, February 2003. The city of Santos is indicated as the closest SMPA representative. ................................ 194 Figure 76: ULCs’ influence over a significant part of the Brazilian territory in February 2003.

196

Figure 77: Bolivian High positioning over South America, February 2003. .......... 196 Figure 78a: Air relative and specific humidity in February 2003 and its respective anomalies. ........................................................................................................... 197 Figure 78b: Incoming shortwave solar radiation and OLR in February 2003 and its respective anomalies. .......................................................................................... 198 Figure 78c: Air temperature and surface air pressure in February 2003 and its respective anomalies ........................................................................................... 199 Figure 78d: Streamlines and wind direction and intensity in February 2003 and its anomaly. .............................................................................................................. 200 Figure 78e: Air divergence over surface and in higher levels in February 2003 and its respective anomalies. .......................................................................................... 201 Figure 79: Passing of frontal systems on the Brazilian coast, October 2002. The city of Santos is indicated as the closest SMPA representative. .................................... 203 Figure 80: Daily precipitation in the SPMA in October 2002. ............................... 204 Figure 81a: Air relative humidity in October 2002 and its respective anomaly. ... 205 Figure 81b: Incoming shortwave solar radiation and OLR in October 2002 and its respective anomalies. .......................................................................................... 205 Figure 81c: Air temperature and surface air pressure in October 2002 and its respective anomalies. .......................................................................................... 206

VIII

Figure 81d: Streamlines and wind direction and intensity in October 2002 and its anomaly. .............................................................................................................. 207 Figure 81e: Air divergence over surface and in higher levels in October 2002 and its respective anomalies. .......................................................................................... 208 Figure 82: Passing of frontal systems on the Brazilian coast, January 2001. The city of Santos is indicated as the closest SMPA representative. .................................... 210 Figure 83: Precipitation in the SPMA, January 2001. .......................................... 210 Figure 84: Average brightness temperature over South America on the 05t h and 10th of January 2001, showing the SACZ episode. ........................................................ 211 Figure 85a: Air relative humidity in January 2001 and its respective anomaly. ... 212 Figure 85b: Incoming shortwave solar radiation and OLR in January 2001 and its respective anomalies. .......................................................................................... 212 Figure 85c: Air temperature and surface air pressure in January 2001 and its respective anomalies. .......................................................................................... 213 Figure 85d: Streamlines and wind direction and intensity in January 2001 and its anomaly. .............................................................................................................. 214 Figure 85e: Air divergence over surface and in higher levels in January 2001 and its respective anomalies. .......................................................................................... 215 Figure 86: Passing of frontal systems on the Brazilian coast, April 2000. The city of Santos is indicated as the closest SMPA representative. .................................... 217 Figure 87: Precipitation in the SPMA in April 2000. ............................................. 218 Figure 88: Precipitation anomaly in the SE region of Brazil in April 2000. ........... 218 Figure 89a: Air relative humidity in April 2000 and its respective anomaly. ......... 219 Figure 89b: Incoming shortwave solar radiation and OLR in April 2000 and its respective anomalies. .......................................................................................... 220 Figure 89c: Air temperature and surface air pressure in April 2000 and its respective anomalies. ........................................................................................................... 221 Figure 89d: Streamlines and wind direction and intensity in April 2000 and its anomaly. 222 Figure 89e: Air divergence over surface and in higher levels in April 2000 and its respective anomalies. .......................................................................................... 223

IX

Figure 90: Ozone anomalies temporal series, highlighting months of category –2 (dark blue) associated to previous months of negative anomalies or decreasing tendencies (light blue)............................................................................................................ 224 Figure 91: Ozone anomalies monthly time series, highlighting months with negative (green) and positive (red) solar radiation anomalies in which ozone anomalies greater than 2σ. 225 Figure 92a: Tropospheric ozone and relative air humidity daily averages in April 1998. Frontal systems’ passing days are indicated by the arrow. ................................. 230 Figure 92b: OLR and air temperature daily averages in April 1998. Frontal systems’ passing days are indicated by the arrow. ............................................................ 231 Figure 92c: Surface pressure and wind speed daily averages in April 1998. Frontal system passing days are indicated by the arrow. ................................................ 232 Figure 92d: Solar radiation and precipitation daily averages in April 1998. Frontal system passing days are indicated by the arrow. ................................................ 233 Figure 92e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in April 1998. ........................................................................................ 234 Figure 93a: Tropospheric ozone and relative air humidity daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow. ................................. 237 Figure 93b: OLR and air temperature daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow. ............................................................ 238 Figure 93c: Surface pressure and Wind speed daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow. ............................................. 239 Figure 93d: Solar radiation and precipitation daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow. ............................................. 240 Figure 93e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in July 2005. ........................................................................................ 241 Figure 94a: Monthly evolution of wind direction and speed in Ibirapuera and Mooca stations in July 2005. The dotted line marks the days of frontal systems’ passing.242 Figure 94b: Monthly evolution of wind direction and speed in Pinheiros, Santana and Santo Amaro stations in July 2005. The dotted line marks the days of frontal systems’ passing.243 Figure 94c: Monthly evolution of wind direction and speed in São Caetano do Sul station in July 2005. The dotted line marks the days of frontal systems’ passing.244

X

Figure 95a: Tropospheric ozone and relative air humidity daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow. ....................... 248 Figure 95b: OLR and air temperature daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow. ............................................................ 249 Figure 95c: Surface pressure and wind speed daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow. ............................................. 250 Figure 95d: Solar radiation and precipitation daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow. ............................................. 251 Figure 95e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in August 1999. .................................................................................... 252 Figure 96a: Monthly evolution of wind direction and speed in Ibirapuera and Osasco stations from the 1st to the 16th of August 1999. The dotted line marks the days of frontal systems’ passing. ..................................................................................... 253 Figure 96b: Monthly evolution of wind direction and speed in Parque D. Pedro II station from the 1st to the 16th of August 1999. The dotted line marks the days of frontal systems’ passing. ..................................................................................... 254 Figure 96c: Monthly evolution of wind direction and speed in Ibirapuera and Osasco stations from the 17th of August to the 1st of September 1999. The dotted line marks the days of frontal systems’ passing. ................................................................... 255 Figure 96d: Monthly evolution of wind direction and speed in Parque D. Pedro II station from the 17th of August to the 1st of September 1999. The dotted line marks the days of frontal systems’ passing.......................................................................... 256 Figure 97a: Tropospheric ozone and relative air humidity daily averages in March 2002. Frontal systems’ passing days are indicated by the arrow. ....................... 259 Figure 97b: OLR and air temperature daily averages in March. Frontal systems’ passing days are indicated by the arrow. ............................................................ 260 Figure 97c: Surface pressure and wind speed daily averages in March 2002. Frontal systems’ passing days are indicated by the arrow. ............................................. 261 Figure 97d: Solar radiation and precipitation daily averages in March 2002. Frontal systems’ passing days are indicated by the arrow. ............................................. 262 Figure 97e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in March 2002. ..................................................................................... 263

XI

Figure 98a: Monthly evolution of wind direction and speed in Mooca and Parque D. Pedro II stations in March 2002. The dotted line marks the days of frontal systems’ passing.264 Figure 98b: Monthly evolution of wind direction and speed in Pinheiros, Santana, São Caetano do Sul and São Miguel Paulista stations in March 2002. The dotted line marks the days of frontal systems’ passing. ........................................................ 265 Figure 98c: Monthly evolution of wind direction and speed in Santo André - Capuava station in March 2002. The dotted line marks the days of frontal systems’ passing.266 Figure 99a: Tropospheric ozone and relative air humidity daily averages in February 2003. Frontal systems’ passing days are indicated by the arrow. ....................... 270 Figure 99b: OLR and air temperature daily averages in February 2003. Frontal systems’ passing days are indicated by the arrow. ............................................. 271 Figure 99c: Surface pressure and wind speed daily averages in February 2003. Frontal systems’ passing days are indicated by the arrow. ................................. 272 Figure 99d: ULC’s formation and paths in South America in February 2003. ...... 273 Figure 99e: Solar radiation and precipitation daily averages in February. Frontal systems’ passing days are indicated by the arrow. ............................................. 274 Figure 99f: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in February 2003.................................................................................. 275 Figure 100a: Monthly evolution of wind direction and speed in Mooca and Parque D. Pedro II stations in February 2003. The dotted line marks the days of frontal systems’ passing.276 Figure 100b: Monthly evolution of wind direction and speed in Santana, Santo Amaro and São Caetano do Sul stations in February 2003. The dotted line marks the days of frontal systems’ passing. ..................................................................................... 277 Figure 100c: Monthly evolution of wind direction and speed in São Miguel Paulista station in February 2003. The dotted line marks the days of frontal systems’ passing. 278

XII

Tables List Table 01: Average emission factors of vehicles in the SPMA fleet: 1983-1989 (g/km) .................................................................................................................................. 5 Table 02: Average emission factors of vehicles in the SPMA fleet: 2006 .................. 5 Table 03: Mean global balance of tropospheric ozone, in Tg O3/year...............14 Table 04: Measurement stations used: configuration and localization ................... .41 Table 05: Stations ranking considering soil use and exposed population .......................................................................................................................44 Table 06: Stations ranking considering representativeness……................................45 Table 07: Air Quality National Standards –CONAMA nº 03 Resolution, of 28/06/90 ......................................................................................................................62 Table 08: Air quality indexes to main air pollutants according to CETESB .....................................................................................................................64 Table 09: Main air pollutants according to CETESB, air quality indexes and health effects..................................................................................................65 Table 10: Years of temporal series start and end of each SPMA station .........................................................................................................................67 Table 11: Number of ozone AQS surpassings in the SPMA in each month, from 1999 to 2005..........................................................................................................75 Table 12: Classification criteria of intense O3 concentration monthly episodes .................................................................................................................................................77 Table 13: Stations classifying by concentration comparison ..............................................................................88 Table 14: Tendency line angular coefficient for the 17 analyzed stations and their evaluation periods....................................................................95 Table 15: Monitoring stations divided by average standard deviation of ozone daily concentration in the SPMA........................................................119 Table 16: Invalid data percentage by year in some CETESB stations within the SPMA .......................................................................................................................128 Table 17: Exposition, seasonal cycle and concentration level features of the CETESB stations...............................................................................................139 Table 18: Months with ozone concentration anomaly average value higher than 1 of the corresponding month (category 1)...............................................................145 Table 19: Motnhs with ozone concentration anomaly average value higher than 2 of the corresponding month (category 2)......................................................................146 Table 20: Climatic anomalies in months of ozone positive anomalies higher than 1...............................................................................149 Table 21: Months with average O3 concentrations lower than –1 of the corresponding month (category -1).....................................................................152 Table 22: Months with average O3 concentrations lower than –1.5 of the corresponding month (category -2).............................................................152 Table 23: Climatic anomalies in months of ozone negative anomalies higher than 1...............................................................................155

1 1. Introduction The second half of the 20th century depicts a time of deep technological and social changes in the history of mankind. After the end of World War II the technological production has grown at an exponentially accelerated pace. This time of great technological evolution, partially instigated by political issues, has also induced improvement in health issues. These combined scenarios have contributed to a greater growth of the world’s population as a whole, along with a growing process of urbanization of this increasing population.

The cities, once consolidated as political, scientific, financial, and population centres of human life, already offered access to these technological improvements that lead to superior life quality of their populations. Therefore, searching for better life conditions, expressed in terms of improved accessibility, health, transport, employment, and consumption, part of the rural population started migrating towards the cities, leading to the inception of the phenomenon known as “Rural Exodus” (CARAMANO E ABRAMOVAY, 1999). This consists in a massive leave of rural regions inhabitants toward urban regions, where they often play the role of extra unqualified manpower or appeal to not formalized activities. An increased portion of the population migrated to the cities, so that the global urban population suffered a seamless growth since that time. Clearly, this phenomenon happened in a variety of ways in the diverse Earth regions (UNO, 2004).

Thus, cities have begun to gradually attract the attention of several fields of study, in order to understand and analyze their many issues; among them, social and environmental ones. Then, it has become essential to investigate the urban environment, where more than a half of the 6 billion human beings live today. This condition is especially aggravated in developing countries like Brazil. These countries have many of the biggest metropolises in the world which are those that present the worst social and environment conditions, with chronic problems (the urban/rural population rate in Brazil is 85%/15% – according to UNO in 2004). Thus, urban regions environmental problems have become a particularly important issue in the whole world, especially in developing countries, once they are able to influence

2 negatively the life quality of the majority of the global population, including the Brazilian one.

Atmospheric pollution, thus, comes to light in this context. Several human activities embodied in current usual social and productive scenarios generate, as byproduct, emissions of many materials in the atmosphere. These materials are composed of various fumes, and solid or liquid particles, which are harmful to health and environment. These activities, or pollution sources, are at the core of the technological-industrial development, which was accelerated since the middle of the 20th century, and include from paint solvents to big plant complexes and automotive fleets of whole cities. Therefore, the increase of pollution emissions followed the technological and population increases, bringing the context of the theme to be developed in this paper: atmospheric pollution in the Sao Paulo Metropolitan Area – SPMA.

Several factors are known to influence the negative impacts of air pollutants in the great metropolitan regions. Among them, one can discern factors related to social dynamics – such as cities’ size and their features and pace; the pollution emission rate, which, by its turn, is influenced by the available technology and legal measures intended to constrain or minimize these values; the prevalent soil use and whether the population is exposed to these substances or not (also related to the spatial distribution of the population), and if so, in which degree. Other factors, however, depend on natural processes and states, such as topographical variations within the urban area and its surroundings; atmosphere dynamics, which are determined by factors of various scales, from global circulation to altitude. Briefly, air contamination in big urban centres turns out to be a quite complex phenomenon, under the influence of several factors, not only by those originally natural, but from those derived from social sources as well (MONTEIRO,1975). This topic will be discussed in more details ahead.

This dissertation will evaluate the existent relationships between atmosphere dynamics and tropospheric ozone concentrations in the atmosphere of SPMA, due to the importance of the first in regulating the latter’s concentrations, through distinct

3 atmospheric processes that can lead to accumulation or dissipation of pollutants in the air and its intra or extra urban transport (CETESB, 2001; SÁNCHEZ-CCOYLLO et al, 2006, FRUEHAUF, 1998). In order to better understand the phenomenon, a general description will be provided for some important compounds and, then, tropospheric ozone will be described in a little more detail than the others.

This section will then firstly describe the main atmospheric pollutants, its sources and impacts on the environment. Afterwards, a deeper characterization of tropospheric ozone and the SPMA climate will be provided, and, finally, the main objectives will be discussed.

4 1.1 Air pollutants and their sources

Methodical description of air pollutants must always be oriented by the focus of the study in question. If all compounds, including organic, inorganic, solid, liquid, natural, anthropic, etc., would be considered, a pretended list would be extremely long and, nevertheless, incomplete (BRASSEUR et al, 1999). For example, according to Companhia de Tecnologia de Saneamento Ambiental do Estado de São Paulo (CETESB) (State of São Paulo´s environmental technologic company), atmospheric pollutants may be defined as “any substance present in the air which is able to make it improper, harmful or offensive to health; that is inconvenient to the general well-being, harmful to materials, fauna, flora and damaging to the use and enjoyment of public spaces and community activities1”. In this chapter, only a few compounds of meaningful environmental importance in big urban centres and their related sources will be described.

Main anthropogenic pollution sources can be classified today as fixed or mobile. Fixed sources are mainly related to industrial activities and garbage burning. Mobile sources include light and heavy-duty motor vehicles and motorcycles that massively move around in big cities; such as the automotive fleet of the SPMA, composed of approximately 7.5 million vehicles in 2006 (CETESB). In addition to the fleet’s size, its age is another feature that aggravates pollutant emissions in the SPMA: 53% of vehicles had been produced before 1996. Due to increasingly clean technologies implementation, and to higher requirement levels requested by environment legal bodies and society, pollutants emission factors have been reducing with time, and so the more aged vehicles pollute much more than new ones. Emission factors mean the amount of pollutants emitted (in g) per travelled kilometre (g/km); tables 01 and 02 show the evolution of these factors comparing numbers from the 1980’s to more recent ones:

Companhia de Tecnologia de Saneamento Ambiental – CETESB, 2001. Relatório de Qualidade do Ar no Estado de São Paulo (The State of Sao Paulo Air Quality Report – 2000). Page 21. 1

5 Table 01: Average emission factors of vehicles in the SPMA fleet: 1983-1989 (g/km). YEAR/ ALCOHOL GASOLINE C* MODEL CO HC NOx CO HC NOx Until 83 18 1.6 1.0 33 3.0 1.4 84/85 16.9 1.6 1.2 28 2.4 1.6 86/87 16 1.7 1.8 22 2.0 1.9 88 13.3 1.6 1.4 18.5 1.7 1.8 89 12.8 1.6 1.1 15.2 1.6 1.6 *Composed of 22% anhydrous alcohol and 78% Gasoline. Source: CETESB, The State of São Paulo Annual Air Quality Report – 1990. Table 02: Average emission factors of vehicles in the SPMA fleet: 2006 (g/km). TYPO OF

EMISSION FACTOR

VEHICLE

CO

HC

NOx

SOx

MP

GASOLINE C

10.83

1.12

0.74

0.08

0.08

ALCOHOL

19.80

2.12

1.28

-

-

FLEX

0.48

0.14

0.09

-

-

DIESEL

14.61

2.29

10.53

0.14

0.55

TAXI

0.80

0.44

0.90

-

-

MOTORBIKES

14.61

1.94

0.12

0.02

0.05

(ALCOHOL)

& OTHERS Source: CETESB, The State of São Paulo Annual Air Quality Report – 2006. By looking at present times’ values (table 2), it’s easy to perceive a considerable decrease in gasoline emission factors since those found in the 1980’s (table 1). On the other hand, alcohol-fuelled vehicles show either stability or even a slight increase in such factors during the same period. This decrease in gasolinefuelled vehicles accounts for PROCONVE (Motor Vehicles Air Pollution Control Program), which was implemented by CETESB in 1996 and defines maximum pollutant emission limits for motor vehicles.

Alcohol-fuelled vehicles though, are mainly old (more than fifteen years-old), forming a degrading fleet that compromises the motor’s effectiveness and general cleanness, leading to higher and higher emission factors. Fortunately, such vehicles constituted only about 15% of SPMA’s total light-duty motor vehicles fleet in 2006, as shown in figure 01:

6

Figure 01: Evolution of the light vehicles fleet in SPMA. Source: CETESB, 2006. * The decrease shown in 2005 is an outcome of PRODESP´s (State Data Processing Company) database update. Other anthropogenic pollutant sources include burning of biomass waste, such as firewood, coal, forests, plantations and other organic materials, which cast bulky quantities of VOCs to the atmosphere. VOCs are also emitted by sprays and paint solvents. In addition to exhaust pipe emissions, generated by combustion motors, motor vehicles also emit from the crank case and produce evaporative emissions (from lubricant oils and fuels – HC), and tires wear (particles). Besides that, the surroundings of large construction sites usually show high concentrations of particulate matter. Natural sources of pollutants that often influence cities’ atmospheric composition, specially in SPMA, include sea breeze (which brings ocean salts, like sodium) and resuspension of soil particles (iron, aluminium), among others. Therefore, it is possible to conclude that pollutants emission sources are very diverse. However, the myriad of compounds emitted by these sources themselves is much more numerous, for very often one source alone can be accounted for the emission of several compounds, as the case of motor vehicles, for example.

Pollutants emitted directly by one source are called primary pollutants; and the others resulting from reactions among pollutants with other compounds or

7 environmental variables (e.g. air moisture) are called secondary pollutants. As per its origin, pollutants can be organic, bonded to carbon (C) and hydrogen (H), like methane (CH4). They are emitted through biological processes, such as vegetal material decomposition, as the case of the chosen compound. Evidently, inorganic pollutants are not related to such processes (e.g. lead). According to its physical state, one can find pollutants in solid and liquid states, called aerosol (or particles); there are many in gaseous form or steam (lighter species) (SEINFELD, 2006, BRASSEUR, 1999).

Some of the most important primary pollutants, their sources and environmental importance are: -

SO2 (sulphur dioxide), produced by pyrite comprising coal burning, from electricity generation and industrial activity. Its importance is due to reactions with air humidity, that generate other sulphurous compounds important to acid rain, such as SO4-, and to industrial smog (whitish mist formed from industrial pollutants in conditions of high air humidity and low temperature) (BRASSEUR, 1999).

-

CO (carbon monoxide) and CO2 (carbon dioxide), by-products of vehicles motor combustion, among others; their main feature is the absorption of radiant energy transformed in to heat. Carbon compounds are very versatile and can react with several other compounds. Combining their high level of reactivity with their capacity of absorption of solar radiation they also take part in the process that generates photochemical smog (brownish gray mist resulted from a combination of pollutants of several origins with solar radiation, under conditions of high irradiation, temperature and air stability – FinlaysonPitts, 2000).

-

VOC (Volatile Organic Compounds), a usual denomination to a group of organic compounds generated by the use of fossil fuels, the evaporation of paint solvents and some cleaning products, as well as waste (incomplete oxidations) from reactions that take place in the engine of vehicles. In the SPMA, the main sources of these pollutants are vehicles moved with gasoline C and diesel, both from the exhaust pipe and from emission from crank case (where the lubricant oil is stored) and by processes of evaporation of these

8 fuels. Besides that, they are usually present in the atmosphere in regions where wildfires are regular occurrences. They are also important agents in the generation of tropospheric ozone and nitrogen dioxide (SEINFELD, 2006). -

Particulate Matter (PM10), a complex blend of suspended solid and liquid particles, often visible, like remains of dust, smoke, pollen, soil, and mists. Particles composition varies according to its origin, as already explained above. Particulate matter may carry any other pollutant dissolved and/or adsorbed on its surface. Particles with size under 10 μm can also be called Inhalable Particles – IP, for they can be inhaled and are therefore harmful to the respiratory system (CASTANHO, 1997). Besides that, they are also associated to environmental impacts related to suppression or modification of clouds, like those mentioned in the studies of ROSENFELD (2000), ARTAXO (2002, 2004) e KOREN et al. (2002, 2004).

-

Hydrocarbons, included in VOC. They are represented by several compounds containing hydrogen and carbon, emitted either by organic-related processes, such as plant metabolism wastes, and by anthropogenic sources like incomplete burning or evaporation of fossil fuels or cement production. Hydrocarbons also contribute to generation of tropospheric ozone and PAN (a kind of aldehyde which influences the formation of photochemical compounds) (PEREIRA, 2004).

-

NO (nitric oxide), emitted by motor vehicle combustion, is the precursor of nitrogen dioxide (NO2) (MOUVIER, 1995). Many secondary pollutants also have strong environmental impacts.

Secondary pollutants are those generated from reactions among pollutants or among pollutants and natural factors, such as solar radiation, for example. Some of the main secondary pollutants, their origin and environmental importance are:

-

H2SO4 (sulphuric acid), generated through reactions started by the oxidation of sulphur (SO3 + H2O → H2SO4); particles of sulphate in the atmosphere increase its albedo, such as in great volcanic outbursts (JACOB, 1999);

-

NO2 (nitrogen dioxide), is an outcome of NO oxidation, in a reaction usually present inside motor vehicles, or in the atmosphere after the emission of NO.

9 Together with some other factors like the sunlight, it is one of the main tropospheric O3 precursors and an important photochemical smog compound (NARSTO,2000); -

O3 (ozone), has a beneficial effect in the stratosphere while partially blocking ultraviolet solar radiation, which is harmful to life, but has an extremely toxic action in the troposphere. Usually it is used to analyze general photochemical compounds (CETESB, 2000). As the main focus of this dissertation, it will be described in more detail ahead;

-

HNO3 (nitric acid), generated by NOx (NO and NO2) oxidation with OH (hydroxyl). One of the main aid rain components, it is completely dissociated in the water, increasing its pH (CASTRO,1993);

-

OH (hydroxyl), generated through photolysis – dissociation by solar radiation – of O3, occurs in low concentration in the atmosphere, but with extreme high level of reactivity. It has a very important position for it is one of the main agents in the atmosphere oxidation and can be bonded with a myriad of compounds (NH3, NOx, CO, etc.), originating a vast number of other compounds and reactions. It is reputed as the atmosphere “detergent” due to its importance in the control of atmosphere’s oxidative capacity (BRASSEUR, 1999).

The life time of some pollutants are presented in figure 02:

10

Figure 02: Residence time and spatial scale of transport of some pollutants. Source: Adapted from Seinfeld et al, 1998. Currently, some of the most studied air pollutants include SO 2, PM, CO, NO2, and O3. The state of São Paulo’s environmental technology company (Companhia de Tecnologia de Saneamento Ambiental - CETESB) has specific regulations regarding all these pollutants, with definitions of concentrations considered Air Quality Standards (AQS); these concentrations are considered thresholds that, when exceeded, lead to harmful effects to health and environment. They will be explained ahead in more details.

Nowadays, the majority of pollutants in SPMA stands totally or partially controlled, especially primary pollutants (CETESB, 2001, 2005, CHIQUETTO, 2005). On the other hand, secondary pollutants are in a very different situation, because this group of pollutants is generated in the atmosphere through several interactions including the primary pollutants themselves, secondary pollutants, atmospheric variables (solar radiation, temperature, moisture, etc.), natural emissions, etc. Among secondary pollutants, one of the most important in SPMA is the tropospheric ozone,

11 which concentrations often exceed the AQS, compromising the health of the population that resides and circulates throughout the city (ANDRADE et al, 2004, CETESB, 2001; SOBRAL, 1997). Works such as the ones from Gonçalves et al (2004), Moseholm et al (1993) e White et al (1993) should be mentioned, when climatic variability, air pollution and health are considered. Gonçalves et al performed a study about the effects of SO2, PM and O3 and some atmospheric variables, namely long wave radiation (LWR, related to nebulosity and precipitation), mean temperature, solar radiation and water steam density, on child mortality in summer months, through a Principal Component Analysis. This study has found that in months of higher climatic variability (when an intense frontal system passes, or in consecutive days with low air relative humidity and high temperatures), climate variability itself was responsible for the majority of mortality cases; in contrast, in months when there were no great variations in climatic variability, it was suggested that a larger portion of children’s deaths could be attributed to harmful effects of atmospheric pollution on human health.

12 1.2 Tropospheric Ozone For a long time, this gas was not recognized as a proper substance, but, instead, as the “electricity smell”. With chemistry development in the 19th century its occurrence was detected and its structure was confirmed. In 1839, German chemist Christian Friedrich Schönbein wrote about a smell that followed water electrolysis and started to investigate this odour’s occurrence conditions; afterwards, he found that it was related to a compound bounded to oxygen. Jacques Louis-Soret determined its composition in 1865 (RUBIN, 2001).

Yet in the 19th century, tropospheric ozone aroused much curiosity when it used to be erroneously related to the track of epidemics. Since 1930, some measurements in different places of the world performed by scientists already produced some significant records of tropospheric ozone (such as Pic du Midi, in France, between 1874 and 1909), although isolated and with lack of precision. Only in the 1970’s an international integrated effort was started in order to understand in a deeper manner its occurrence in the troposphere, vertical and horizontal distribution, seasonality, generation, destruction, etc.

Ozone environmental importance comes, partially, from its ability to strongly absorb solar radiation, centred in 9.6 μm, and so O3 is considered a greenhouse gas, especially in the high troposphere. Its role in atmosphere chemical equilibrium is, however, more important than its radioactive features. While it is considered a trace gas because of its minute quantity, both ozone and OH (generated by photodissociation of O3 and sequential reaction with water steam in free troposphere2) are the most important oxidation agents in the troposphere, responsible for consuming the majority of the reduced gases, such as CO and HC and the majority of sulphurous and nitrous compounds (SEIFELD, 2006).

In the natural troposphere, the generation of O3 happens mainly by an array of reactions involving solar radiation (hv, λ < 420 nm) and peroxide radicals with NO, inside the natural cycle of NOx (JACOB, 1999): 2

O3+hv → O2 + O (1D) O (1D) + M → O + M H2O + O → 2OH (JACOB, 1999)

13

HO2 + NO → OH + NO2 or CH3O2 + NO → CH3O + NO2 Leading to NO2 + hv + O2 → NO + O3 Then, ozone can be consumed by NO NO + O3 → NO2 + O2 It can also generate OH radicals through photodissociation and reaction with water steam, as formerly described.

In the free troposphere, the production of O3 happens by the oxidation of CO: OH + CO → CO2 + H H + O2 + M → HO2 + M HO2 + NO → OH + NO2 NO2 + hv → NO + O O + O2 + M → O3 + M and the oxidation of methane (CH4) (BRASSEUR, 1999): OH + CH4 → CH3 + H2O CH3 + O2 + M → CH3O2 + M CH3O2 + NO → CH3O + NO2 CH3O + O2 → HO2 + CH2O HO2 + NO → OH + NO2 NO2 + hv → NO + O O + O2 + M → O3 + M

14 In the natural environment, there is a natural tropospheric ozone cycle, in which its formation and consumption is regulated within the tropospheric chemistry. The amounts of ozone are regulated in junction with other pollutants’ cycles, including OH and NOx. Considering the larger availability of NOx in the Northern Hemisphere, one could expect a higher concentration of O 3 in that hemisphere. On the other hand, dry deposition of ozone is quicker over land than on oceans, therefore preventing ozone accumulation at alarming levels in the Northern Hemisphere where 70% of Earth’s landmass lies. Anyway, from a general point of view, there are larger amounts of this pollutants in the NH than in the SH due to larger industrial emissions. While dry deposition speed is quicker over land than on the ocean, it slows down over leafless or low biologic activity vegetation, conditions that contribute to O3 transportation in winter. In the Southern Hemisphere, there is less ozone production than in the NH, but as the greater part of emissions occurs at tropical latitudes with large availability of solar radiation, the result is that in the NH, there is more ozone on middle-latitudes than on tropical ones, and the exact opposite prevails over the SH. Nevertheless, chemical and photochemical processes account for the mean tropospheric ozone global balance, as shown in table 03: Table 03: Mean global balance of tropospheric ozone, in Tg O3/year SOURCES* 3400-7500 -CHEMICAL FORMATION IN SITU

3000-4600

-TRANSPORT FROM

400-1100

STRATOSPHERE DRAINS

3400-7500

-CHEMICAL CONSUPTION IN SITU

3000-4200

-DRY DEPOSITION

500-1500

Source: JACOB, 1999.

According to table 03, tropospheric ozone chemical production on the surface surpasses the stratosphere transport from twofold to tenfold. Considering drains of tropospheric O3, chemical reactions in the troposphere are also responsible by its consumption in a ratio from twofold to eightfold than dry deposition. Clearly, these ratios vary according the features of the studied region. Relatively to mean global meridional distribution, there is fewer O3 in tropical regions than in medium latitudes

15 regions due to photochemical destruction, more efficient vertical distribution and lesser stratospheric interference.

Most measures of this pollutant are performed in the NH, which allows more accuracy in understanding this pollutant’s behaviour on surface in several different conditions and environments. For example, according to BRASSEUR, in 1999, there is a well-defined annual variation in middle-latitudes in the NH, where the O3 mixing ratio is 25-50% higher in spring-summer than in autumn-winter due to larger photochemical production. This factor, by its turn, is more important in this period due to interaction of largely available solar radiation and anthropogenic emissions of urban and industrialized areas, which are more numerous and spatially better distributed in the NH than in the SH. This annual cycle is also observed in medium latitudes in SH, though less evidently. According to hypothesis aroused by Liu et al (1987), this spring maximum is due to accumulation of O 3 precursors in winter, related to longer O3 residence time in the winter due to lesser photochemical destruction. While in the NH precursors tropospheric emissions are mostly originated from industrial activities, in SH they result mostly from biomass burning in Africa and South America in the dry season (winter), with a maximum in September-November period.

The time of residence of tropospheric ozone in the troposphere varies from five days to some weeks in summer, and can reach more than three months in winter in medium latitudes due to lesser photochemical activity and slower deposition speed on vegetation without leaves.

In recent years, a general increase of this pollutant in troposphere has been registered due to continuous emissions of NOx and VOC by human activities that interfere

with

atmospheric

compounds’ natural

SEINFELD, 2006, JACOB, 1999, MOUVIER, 1995).

cycles

(BRASSEUR,

1999,

16 1.2.1 Tropospheric ozone in large urban centres The former section has introduced the global distribution of tropospheric O3 and its annual variation. This frame is quite different when analyzing tropospheric ozone concentrations in big urban centres and their surroundings.

In North America, O3 concentration in non-urban areas usually dwell around 40 ppb. However, in greatly urbanized areas, characterized by industrial activities and intensive vehicles traffic (hence with high concentrations of emitted NOx and VOC), tropospheric O3 concentrations can greatly overpass these limits, reaching values beyond 100 ppb. In severe pollution episodes, concentration can exceed 200 ppb. The highest measured concentration of this gas was 680 ppb in the greater Los Angeles, in 1955, in a tragic and memorable incident of photochemical smog (NARSTO, 2000).

When present at high concentration on the surface, O 3 becomes a great environmental importance pollutant, precisely for its highly oxidant capacity that lead to harmful effects to population health, plants, ecosystems and materials.

As previously seen, production and consumption of O3 are governed by the amounts of some tropospheric compounds; in the large urban centres, especially NOx. Local atmospheric composition is changed in these places due to the emission of many pollutant substances that degrade air quality. Under such conditions, tropospheric O3 production is governed by complex interactions between its precursors

and

other

environmental

factors.

Additionally

to

higher

NOx

concentrations due to direct emissions, these places also present high concentration of VOC (section 1.1). These compounds, besides being important air pollutants themselves, also lead to more production of O3 for, through NO oxidation, generating more NO2, which, by photodissociation, provides an increase in ozone production, as previously explained. In the following array of equations, HR means any nonmethane HC, R stands for a sequence of HCs and M means any third compound (N2, O2): OH + RH → R + H2O

17 R + O2 + M → RO2 + M RO2 + NO → RO + NO2 RO + O2 → HO2 + carbonyl HO2 + NO → OH + NO2 2x(NO2 + hv → NO + O) 2x(O + O2 + M → O3 + M) This draft of O3 production from NOx and COV in the presence of sunlight was designed by Haagen-Smit in 1952 when studying the photochemical smog in Los Angeles. He indentified ozone, nitrous oxides, aldehydes, hydrocarbons, and peroxy acetyl nitrate (PAN) as the main photochemical smog components, additionally to high concentration of particulate material, responsible for the visibility reduction associated with smog. Thus, tropospheric ozone could be detected partially for this phenomenon, which produces a familiar brownish-gray mist above the cities (COLBECK e MACKENZIE, 1994):

Figure 03: Photochemical smog over Quebec, Canada. Source: http://www.criacc.qc.ca Pollutants’ concentrations evolution along the day in a typical smog episode is presented in figure 04:

18

Figure 04: Pollutants concentrations evolution (y-axis) along the hours of the day (x-axis) in a typical smog episode. Source: Adapted from Brasseur, 1999. As seen in figure 04, the generation of O3 in the urban environment is bounded to plentiful availability of NOx and VOC in the atmosphere. However, relationships between ozone and its precursors is quite complex. The amount of ozone produced depends on both the quantity and, mainly, the ratio between NOxs and VOCs.

If VOC availability is greater than NOx availability, OH radicals, also present in large quantities, will react mainly with VOCs; if NOxs predominate, these will react firstly. Thus, in atmospheres with low VOC/NOx ratio (NOx prevalence), ozone mitigation is more efficient if VOCs are controlled; this case is considered VOClimiting, meaning that ozone is easily limited by the VOCs reduction. That is because an excessively high NOx concentration leads to a reduction in ozone concentrations, either by the reaction with NO or by the increase of OH + NO2 reactions. This increase leads to fewer reactions of OH with HO2 and so, the ozone production via HO2 is also reduced (BRASSEUR, 1999). On the other hand, in atmospheres with high VOC/NOx ratio (VOCs prevalence), ozone reduction is more efficient if NOxs are controlled (SEINFELD, 2006). According to studies of the US Environmental Protection Agency (EPA), a given system is considered VOC-limiting if VOC/NOx ratio is lower than 8. When this ratio is from 8-15, the scenario is considered intermediate, and both VOC control and NOx control may convey positive results. If

19 the ratio is above 15, the system is considered NOx-limiting, which means that the reduction of NOx concentrations is more effective to minimize ozone concentrations (http://www.epa.gov – NARSTO, 2000).

Additionally, meteorological conditions are also known to influence this scenario. Most severe air pollution episodes happen in conditions of significant atmospheric stability, with low air vertical mixing. According to a document from North American Research Strategy for Tropospheric Ozone (NARSTO), Annex 4, O3 concentrations increase as mixing conditions become more difficult. However, NOx concentrations are known to increase as atmosphere vertical stability increases; this directly influences O3 concentrations. Therefore, stagnation conditions tend to change VOC/NOx ratio and its evolutions with time. If vertical blend is extremely weak, the system stays as VOC-limiting because sunset happens before enough quantities of NOx are processed to change the regimen.

Consequently, many different situations and variables impact tropospheric ozone concentrations in a given place, as latitude, features of atmospheric circulation in many scales, and atmospheric constitution, the latter more significantly modified in large urban areas where great precursor emissions occur due to human activities.

High O3 concentrations in these places become harmful due to the great oxidant capacity of this pollutant, which is toxic to living organisms’ tissues. On human health, O3 impacts include skin premature aging, infections resistance decreasing, and eyes and upper respiratory tract irritation (MOUVIER, 1995). Moreover, it was proved that ozone also damages vegetation, because it causes burning over plant’s leaves, which even impacting crop production (NARSTO, 2000 – as is presented in figure 05), what grant it strong social-economic importance.

20

Figure 05: Sample of damages to vegetation caused by ozone. Source: http://www.treehugger.com CETESB’s quality air reports bring measurements and representation of certain ozone values as “reference to protecting vegetation”. Methodology involved in measuring these limits is based on continued exposition of vegetation to ozone. The standard value is 40 ppb/hour. In other words, if 50 ppb of ozone are registered in the period of one hour, the standard (called AOT40) was surpassed by 10 ppb. According to CETESB, if in a period of 3 months the accumulated AOT value is equal or higher than 3000 ppb, crop productivity is damaged. Yet, if in a period of five days the AOT value is equal or higher than 200 ppb, the more sensible species show visible wounds (CETESB, 2001). Miriam Gutjahr, in her doctorate thesis by the University of São Paulo (2002), raised attention to the problems caused by this pollutant, for the damages to vegetation it causes can impact from farming production to very diverse ecosystems, because the ozone can be transported from urban centres where is produced to their surroundings.

Since it is a secondary pollutant, generated in the atmosphere due to emissions of other pollutants, its origin, transport and dispersion or consumption characteristics present very different patterns in comparison to other primary pollutants. Whereas most pollutants present a sharp decreasing or stability in its concentration in the São Paulo Metropolitan Area, tropospheric ozone’s tendency is less defined or linear. Its concentrations are extremely unstable and yet today are accounted for often surpassing AQS (CETESB, 2004). According Gutjahr,

21 “O3 pollution occurs in multiple temporal and spatial scales and its complete comprehension becomes complicated because of the overlapping of chemicals and meteorological interactions. This complexity has confounded the efforts to

optimize strategies to control precursors emission, especially those with 3 investigation founded on a limited amount of data” .

In recent years, reports from CETESB show that some pollutants concentrations in SPMA have been stable, allowing for good air quality in more than 90% of the days in the case of CO, for example.

Figure 06: CO air quality indexes from some stations in the SPMA, from 2002 to 2006. Source: CETESB, 2007. A similar situation occurs with NO2 concentrations, as can be observed in figure 07: 3

A Poluição do Ar em Paulínia (SP): Uma Análise Histórico-Geográfica do Clima, GUTJARH, M. R., Air Pollution in Paulínia (SP): A Historical-Geographical Climate Analysis, Doctorare Thesis by the University of São Paulo, Revised Edition, 2003, page 164 (free title translation by the author of this work).

22

Figure 07: Mean annual concentrations to NO2 (g/m3) from some stations in SPMA, from 1998 to 2006. Source: CETESB, 2007. PM concentrations, yet often responsible for Regular air quality (not optimized), present significant decreasing tendency, as seen in figure 08:

Figure 08: PM air quality indexes from some stations in the SPMA, from 2002 to 2006. Source: CETESB, 2007.

23 In contrast, when it comes to ozone concentrations, a different scenario is observed, as in figures 09 and 10:

Figure 09: Number of days with ozone concentrations above AQS (columns) and maximum hourly concentration (line). Source: CETESB, 2006.

Figure 10: O3 air quality indexes from some stations in SPMA, from 2002 to 2006. Source: CETESB, 2007. Inasmuch ozone is generated by pollutants emission that, in the SPMA, are mostly

originated

by

vehicles

emissions

(nitrous

and

hydrocarbons),

its

concentrations could be expected to be higher in regions with high concentrations of these pollutants. However, there is no factual correlation to support this incipient

24 supposition (CETESB, 2003; AZEVEDO, 2002; MARTINS, 2006). Indeed, many areas characterized by low vehicular pollutants indexes (theoretically, less polluted places), eventually show high ozone concentrations, such as Ibirapuera Park and Mauá, in the São Paulo Metropolitan Area. Particularly, Ibirapuera Park has caught authorities’ attention by showing one of highest ozone concentrations in the area (CETESB, 2004, MARTINS, 2006). A possible hypothesis to explain this fact lies in the vehicular pollutants transportation, which would be emitted on the surrounding big avenues (Av. Ibirapuera, Av. Brasil, Av. Vinte e Três de May) to the Park, and transformed in ozone in this process (MARTINS et al, 2004; CETESB, 2004), or even through pollutants transportation from more distant places (AZEVEDO, 2002). Another aforementioned characteristic of the ozone cycle is that both its formation and its consumption are associated to the presence of NOx. Thus, the presence of these compounds would be necessary to its formation (as well as HC compounds and solar radiation). However, NOx presence in the troposphere without solar radiation leads to ozone consumption by the same NOx (MOUVIER, 1999; SEINFELD, 2006; AZEVEDO, 2002), as previously explained.

Additionally, its concentration usually present higher daily peaks in the October-March period, precisely when the SPMA atmosphere presents higher instability (CETESB, 1994, 2006; ANDRADE, 1994). This instability comes from higher temperatures, higher air humidity and lower pressure in low atmospheric levels. These circumstances lead to a greater precipitation probability, and are prerequisites to the “favourable dispersion conditions”, defined by CETESB as atmospheric conditions in which pollutants dispersion is promoted by meteorological variables, avoiding severe air pollution episodes. While most pollutants record higher peaks in the Autumn-Winter period, featured by greater atmosphere stability, lower air temperature, lower solar radiation incidence, and frequent thermal inversion layers close to ground, ozone does not present the same behaviour, for is usual in this period to find low concentrations of this pollutant. This conclusion was mentioned in CETESB’s study called Comportamento Sazonal da Poluição do Ar em São Paulo – Análise de Quatorze Anos de Dados da RMSP e Cubatão: 1981 a 1994 (1994) (Annual Behaviour of Air Pollution in São Paulo – Analyse of Data of Fourteen Years from the SPMA and Cubatão), that evaluated measured pollutants concentrations

25 evolution by CETESB’s network during many years, in an attempt to discover yearly patterns, as well as in the study of Andrade (1994).

This information can be verified in figure 11:

Figure 11: Number of AQS surpassings and severe ozone levels by month in the SPMA, from 2002 to 2006. Source: CETESB, 2007. Figures 12 and 13 show, respectively, days with high and low O 3 concentrations in the SPMA in 2003. Coordinates are UTM and values expressed in the isolines are μg/m3.

26

Figure 12: Ozone in the SPMA – High concentration – 04/03/03 Source: CHIQUETTO, 2005.

27

Figure 13: Ozone in the SPMA – Low concentration – 10/09/03. Source: CHIQUETTO, 2005. These figures were not included to faithfully represent the pollutant’s spatial distribution in the city, but to denote a possible complex spatial distribution.

Considering these particular characteristics of ozone and its specific relationship to atmospheric conditions and its precursors, this dissertation proposes to study the influence of atmospheric patterns in ozone concentrations (measured by CETESB’s pollutants monitoring network, covering several districts of the SPMA) in different temporal scales, and their respective relations. This way, it is possible to conclude, considering some factors such as: 1) high concentration over the SPMA; 2) harmful effects to human health and environment; 3) specific behaviour considering other pollutants, that the study of tropospheric ozone occurrence in the SPMA presents great relevance to future actions to control it and, so, minimize these impacts, in order to allow a general improvement to the complicated environmental frame of SPMA and the life quality of its inhabitants. Ultimately, the objectives proposed in this study are seen as only a first approach to this problem, because, although atmospheric patterns identify the synoptic scale, it is not directly

28 represented in ozone data. However, this restriction will not be a methodological problem considering the comprehensive objectives of the study.

29 1.3 The SPMA’s climate characterization

The Greater São Paulo features among the world's five largest metropolitan areas. Its population reaches almost 20 millions inhabitants (19.7 millions, according an estimate from IBGE (Brazilian institute of Statistics and Geography), 2008) – and a significant part of it comes from other Brazilian regions and also from other countries. It includes 137 districts from 39 counties, according to EMPLASA (State’s Metropolitan Planning Company, 1994). It is situated at 2321’ latitude South and 4644’ longitude West, with a mean altitude of 750m, varying from 720m in the bottom of river valleys (Tietê and Pinheiros rivers) to 830m in its central plateau (Paulista Avenue).

It lays on the geomorphologic unit of Planalto Atlântico (Atlantic Plateau), over a sedimentary basin with fluvial-lacustrine origin, which originated in the Cenozoic Period (São Paulo’ sedimentary basin). Its terrain presents relatively low declivity, making intensive human occupation easy. Its limits include the mountain ranges of Cantareira to the North, Itapeti to the East, Mar to the South and the high massif of Itapecerica-São Roque to the West and Southwest (SAMPAIO, 2000). This terrain configuration, a valley enclosed by mountain ranges, makes pollutants dispersion difficult. This scenario often could be the cause or factor to contribute to the worst air pollution episodes recorded in SPMA.

In São Paulo, wind directions from S and SE are frequent, due to common incursion of cold fronts and the Atlantic sea breeze, with winds coming from the ocean to the continent; this wind direction also contributes to pollutants accumulation over the city, once many pollutants are emitted in regions situated Southeast of the city – the industrial complexes in the ABCD and Cubatão areas (LOMBARDO, 1985, SÁNCHEZ-CCOYLLO, 2000, SAMPAIO, 2000). São Paulo’s main climate feature is its high variability, precisely because it is situated in a subtropical region (yet on the tropic of Capricorn), showing characteristics both from altitude humid tropical climate and the perennially humid subtropical climates of Southern of Brazil. So, São Paulo’s climate is defined as an alternation between two major seasons: a hot and humid one (from October to

30 March) and another, colder and dryer (from April to September); any of the seasons can present more intense characteristics, depending on the locally present systems’ intensity. In a lesser temporal scale, this transient band can present “abrupt variations in pace and in the sequence of kinds or climate, when atmospheric states of intense heating followed by intense cooling are likely to occur in short temporal segments – days or weeks4”. Notwithstanding, compared to the rest of the Brazilian territory and its metropolitan regions, SPMA mean temperatures can be considered as generally mild, varying from 15.8C in July to 22.4C in February, according to INMET (Meteorology National Institute) data from 1960 to 1991 (Annex 5).

Pluviosity varies, in average, from 1400 to 1800 annual mm, with torrential rains episodes with accumulated daily precipitation over than 150 mm, responsible for frequent floods in riverine places and avenues, especially in summer months with high air humidity and temperature. Medium atmospheric pressure is 926 mb, with higher figures in the cool months and lower ones in warm months, according to the climatologic normal of INMET of 1961-1990. Temperature and precipitation climatology of São Paulo city from 1960 to 1990 is presented in Annex 5.

The main synoptic-scale weather atmospheric systems which predominate over the subtropical region where the Greater São Paulo lays are: The South Atlantic Subtropical High, Cold Fronts, High Pressure Areas following the Cold Fronts, and the South Atlantic Convergence Zone (SACZ). The dynamics of different weather systems prevalence determines the typical climate in a given region. These systems’ typical frequency and intensity prevalence defines the climatic variability (annual, interannual, interdecadal) in the South American subtropical region.

The South Atlantic Subtropical High is an air mass generated from the South Atlantic anticyclone high air pressure. It holds high air pressure. This anticyclone stays virtually stationary over South Atlantic Ocean at 30 0 latitude South and its position and intensity change mainly according to the season. In summer it is less 4

A Relação entre o clima e o abastecimento de água na RMSP. Uma proposta de modelagem conceitual da Bacia Hidrográfica da Represa Guarapiranga, MATEUS, R. S., Relations between Climate and the Water Supply in the SPMA), Masters Degree Qualification Report, Postgraduate Program in Physical Geography, Department of Geography, Humanities College, USP, São Paulo, 2005 (free title translation by the author of this work)

31 intense and has its centre biased toward the ocean because low pressures predominate over the continent as an outcome of summer heating. In winter, in contrast, it is able to influence a greater area and the system is often associated to many atmospheric blocking episodes in the south and southeast areas of the country. The atmospheric blocking will be explained ahead.

On the other hand, high pressure areas following the cold fronts feature low temperature and higher atmosphere stability, since they are originated in colder regions. In winter, high pressures are more intense and extend further to the north due to the great amount of related potential energy. They are responsible for the sharp temperature decrease in autumn and winter in a large centre-south region of Brazil, including the occurrence of frost in this region and snow in the mountain ranges in Southern Brazil (GALVANI e AZEVEDO, 2003).

The cold fronts arise, in general way, from the contrast between tropical and temperate climates, in order to reach thermodynamic equilibrium to latitudinal atmospheric differences. They represent cold air motion over warm air areas, developing high atmospheric instability, increasing atmospheric circulation and producing cloudiness and rain where they pass through. The SPMA region is affected by cold fronts virtually the whole year; in winter they precede the incursion of high pressure areas that bring lower temperatures, as aforementioned. These processes induce rain episodes that are important attenuators of the dry season. In summer, the fronts induce more intense rains due to great heat and humidity availability in the atmosphere and, in junction with strong convective activity and the South Atlantic Convergence Zone (SACZ), contribute to higher precipitation frequency and intensity in spring and summer (SAMPAIO, 2000).

The South Atlantic Convergence Zone (SACZ) occurs in summer and features a nebulosity band covering from the south of the Amazon forest to the central South Atlantic, responsible for great convective activity in this area, impacting specially Brazil’s South and Southeast with long time flood and/or dry occurrences (Casarim and Kousky, 1986, Kodama, 1991 and 1992). Its origin and persistence are associated to remote mechanisms, as the South Pacific Convergence Zone and other regional events, such as Amazonic convection and the localization, in altitude,

32 of the subtropical jet stream, which contributes to greater atmospheric instability. The SACZ is also related to the Chaco Low, a low pressure air mass centred in the central region of South America, which is intensified in summer, partly accounting for convective activity in the Amazonic region (Rocha and Gandu, 1996).

Other atmospheric systems, which influence the region with relatively lower frequency, are nevertheless important to the climatic composition of the southeast region of Brazil. The Mesoscale Convective Complexes are round-shaped systems, initially composed of cumulonimbi but very often featuring the prevalence of cirrus and stratus clouds in their mature development stages (SILVA DIAS, 1989). Systems of this kind reaching South America are generated at approximately 25 0 South, over Paraná and Paraguay rivers basins. According to Figueiredo and Scolar, in 1996, they impact mainly Brazil’s south region (70% of total) and southeast region (30%). They occur mostly in spring and winter, with a life cycle of 10 to 20 hours, presenting its maximum extent at dawn (Velasco and Fritsch, 1987). Its origin is bounded to the Andes air outflow, from north to east, with great transport of Amazonic region humidity and heat toward the South, in low levels, that converge with the nocturne katabatic local outflow in the Paraná and Paraguay rivers basins and a region with subtropical jet stream maximum speed (Silva Dias, 1996).

In the South Atlantic, atmospheric blocking lasts for a period of 8 to 9 days. Its annual frequency maximum occurs in the end of winter and the onset of spring, with a secondary maximum in autumn. They feature an almost stationary anticyclone with large amplitude and intensity, and promote high pressure and air temperature on the surface, with high atmospheric stability. Once settled, blockings tend to stabilize over the region, diverting cold fronts extra-tropical cyclones paths that just pass by the border of the blocked area. Its origin, as Lejenas and Oakland (1983) research demonstrates, is bounded to 1) furcation of subtropical jet in high levels, which intensifies the air subsidence in latitudes 50-600 S, generating anticyclones in higher than the usual latitudes and breaking the west zonal circulation pattern; and to 2) geopotential height standard deviation anomalies at the level of 500 hPa around latitude 60º S. In this case, the more intense the anomalies, more intensive are blocking episodes (Marques and Rao, 1996).

33 Some Instability Lines also impact the SPMA region. Indeed, Instability Lines include a kind of convective process with different origins, affecting both middle and tropical latitudes. They feature many sizes cumulonimbi groups assembled in line or curve shapes, and their origin are associated to mesoscale circulation. Sea, continental or valley-mountain breeze configurations, or even prefrontal and postfrontal circulation, may be associated to the Instability Line formation. It propagates through the convergence of the border line subsiding air with air coming from outside, towards the low pressure in the system centre (CERQUEIRA, 2006). Their extensions vary from hundreds to thousands kilometres and can last from some hours to one day. In the tropics they are characterized mainly by the intensity of precipitation

events,

with

average

wind

speeds

from

12

to

25m/s

(www.master.iag.usp.br).

Additionally to atmospheric systems activity, with their natural variations, the São Paulo city’s climate suffers anthropic interferences, due to the SPMA magnitude and structure, leading to an urban climate conception.

34 1.4 Objectives The general objective of this dissertation is to describe the tropospheric ozone behaviour in SPMA in diverse temporal scales (daily, monthly and annual), using pollution data collected by CETESB. Additionally, an analysis of atmospheric patterns observed in episodes/situations of extreme conditions will be presented. Therefore, synoptic patterns of atmospheric variables in Centre-South of Brazil, especially in SPMA, will be analyzed, in order to try to generalize the occurrence of these O 3 extreme concentrations. Because O3 is a secondary pollutant, such as HC and aldehydes, its formation is dependent on other atmosphere processes, and so, its occurrence and variability require deep investigation. The results obtained in this study must contribute to the development of researches associated to the theme and as subsidy to the elaboration of administrative actions proper to its control. The formation of secondary pollutants in the atmosphere, especially the formation of O 3, is very complex in terms of atmospheric chemistry, and many studies of scientists, like chemists, meteorologists and physicists, have been carried out in order to understand these processes.

As a specific objective, there is a study of the pollutant spatial distribution in São Paulo city through the comparison of concentrations measured in many different CETESB’s monitoring stations in the SPMA. This study must contribute to a better understand of the occurrence of tropospheric ozone in several places of the study area.

35 2. Data and methodology

2.1 Characterization of the air pollution data source: the CETESB (Environmental Engineering State Company) To characterize São Paulo’s atmospheric pollution and its temporal evolution, data from CETESB’s manual and telemetric pollutants monitoring network will be used. This section briefly describes CETESB, its history, functions, and the stations network location and general characterization.

Considering the great menace to human health, to animals, to vegetation, and to materials presented by excessive pollutants concentrations in the great world metropolises, continuing and effective monitoring must be carried out as a way to collect enough information and data to take the proper precautions and corrections. In São Paulo, the responsible official organization by such monitoring is CETESB, which belongs to the state of São Paulo Environment Secretary. It was created in 1968 to originally deal with sanitary and water pollution issues. Only in the 1970s, when air quality was continually worsening as a result of increasing industrial activity, CETESB becomes responsible for atmospheric pollution control and reduction. Therefore, it becomes responsible by creating actions to control air pollution, to restrain activities in case of severe episodes and require cleaner equipments, for elaborating long term control plans, zoning legislation, air quality standards and environmental licensing. According to CETESB, “Environmental licensing, for example, constitutes a valuable resource to the development of a preventive control politics of environmental quality. More than a simple legal formality, licensing allows the imposition of rules to installation and operation of terrain sales, industries, constructions, and other undertakings which constitute potential pollution sources . Authorizations to such activities are granted only after complete satisfaction of all technical 5 requirements legally stablished” .

In the following decades, the pollutants monitoring stations networks were installed. The first pollutants to be measured were smoke (FMC – which is used as an indicator of carbonaceous materials) and SO2, which in the 1970s was one of the main pollutants present in São Paulo atmosphere (CHIQUETTO, 2005). The 5

From the CETESB website: www.cetesb.sp.gov.br

36 measurement of these elements started in 1973 (afterwards, this network would also measure Total Suspended Particulates – TSP) with manual monitoring stations in the city, whose data allowed the implementation of several measures against industrial pollution that reached alarming levels in the 70s in São Paulo (while in Cubatão, a close industrial area, the situation was even worse). Nowadays, industrial pollution responds to approximately only 10% of total city’s pollution (CETESB, 2000), which proves the effectiveness of these actions, with rare cases of surpassing or even approximation of AQS by pollutants of these sources, such as SO2 and some hydrocarbons. During the 1970s, and even until today, CETESB publishes an air quality daily bulletin, made available to press and population, in order to disclose quality indexes and the occurrence of severe pollution episodes in the respective day, with concentrations data measured in the city. In these bulletins, CETESB informs in a daily basis the air quality levels measured in all stations, allowing the verification of any AQS surpassing and also making this information available to media and population.

These bulletins are of two kinds: stations ones and pollutants ones. Stations daily bulletins bring information from a specific station and the elements it measures. Pollutants daily bulletins bring information about an specific pollutant from all stations, in junction with information about the air quality index related to this pollutant (nowadays, besides the daily bulletins, CETESB also makes hourly data, related pollutants concentrations and also meteorological variables such as air temperature, relative humidity, global radiation, etc., available through the internet). Additionally, some publications, related to pollutants concentrations were published by CETESB, as, for example Formação e ocorrência de oxidantes fotoquímicos na região da Grande São Paulo (Formation and occurrence of photochemical oxidants in Greater São Paulo region) (1979 – CETESB’s own authorship), of great worth in the process of characterization of atmospheric pollutants at that time. Besides the daily bulletins, CETESB publishes the Relatório Anual de Qualidade do Ar (Annual Air Quality Report) since the 1980s, which summarize pollutants measuring of the related year and compare them with the measuring collected in the previous four years.

37 However, although attentions were focused on industrial pollution, which were in a reducing pace, in the 1980s the city saw a big increase in the amount of vehicles which virtually duplicated along that decade, as can be observed in Figure 01. Nowadays, the total vehicles fleet in the SPMA is roughly estimated in 7.5 million of unities.

Other areas of the state of São Paulo, despite not presenting an outstanding number of vehicles as the SPMA, also present air quality problems due diverse reasons, as industrial activity (Cubatão), biomass burning (Limeira) and vehicles (Campinas). Therefore, while increasing the number of stations in the SPMA, CETESB also started to implement stations in these other places in the state. They will be detailed ahead.

During this period, an intensive increase of vehicular pollutants emissions is seen; from primary, as CO and NOx, to secondary, as O 3 and NO2. In 1981, automatic network monitoring stations were implemented and became responsible by measuring IP (Inhalable Particles), CO, O3, NO2, and also SO2. Thenceforth, CETESB publishes air quality annual reports which summarize the concentrations of these pollutants along the year, and make them available to the general public in order to disclose better information about the situation of atmospheric pollution in the state. Additionally, several studies have been carried out, such as “Comportamento Sazonal da Poluição do Ar em São Paulo – análise de 14 anos de dados da RMSP e Cubatão, 1981 a 1994” (Seasonal Air Pollution Behaviour in São Paulo – analyse of 14 years data of SPMA and Cubatão, 1981 to 1994), which are important tools to better understand pollution phenomena in São Paulo.

38 2.1.1 Pollutants monitoring networks

CETESB has two pollutants monitoring networks operating in the SPMA: the manual one, which has been working since 1972, is set by 18 stations, 8 of them measuring SO2 and smoke and 10, Particulate Matter. There other networking is the telemetric (automatic) one, with 22 stations in the SPMA to measure CO, NO 2, SO2, PI and O3, in operation since 1981. They are situated in more than 20 different places, most of them in the city central area, its surroundings and the ABC region (Santo Andre, São Bernardo and São Caetano do Sul cities), which denotes the objective of monitoring industrial sources placed in the early beginning of air pollution monitoring. Some stations monitor only some pollutants; the information about the items measured by each of them are presented in table 04 (page 41). For example, only the stations localized in the city of São Caetano do Sul and in Parque D. Pedro II had measured MNHC concentrations, which are important pollutants, emitted by both mobile and fixed sources, due to the harmful effects to human health and the generation of tropospheric ozone, as can be seen in section 1.2.1. Tropospheric ozone in large urban centres. The measurement of these compounds ended in 2002 in CETESB’s network due to operational difficulties related to high costs and equipment maintenance. The localization of the SPMA stations from which data was used in this study is presented in figures 14 e 15:

39

Figure 14: Localization of the SPMA monitoring stations from which data was used in this study Source: CETESB, 2006 There are other regions in the state of São Paulo presenting high industrial concentrations besides the São Paulo metropolitan area. In the 1970s, Cubatão was consolidated as a regional industrial centre; therefore, a greater level of economic development was seen in the region, bringing together severe environmental problems. Therefore, three stations of telemetric network were settled in the city: in the city’s centre and in districts next to industrial areas (Vila Nova and Vila Parisi), in order to measure concentrations of pollutants emitted by industrial activities. Some other regions of the state subsequently raised authorities’ attentions due to the high level or air pollution, as the case of Campinas, Paulínia, Sorocaba and São José dos Campos. These areas featured great urban and industrial expansion in the last years, with a growth rate superior to the observed in the SPMA, making them vulnerable to the same pollution problems. Besides industrialization, there are problems related to other activities such the burning of sugar cane and waste. More stations are being

40 implemented in several of the state’s regions as a consequence of the continued effort to understand the growing problem of air pollution. Table 04 has information about the localization of many CETESB stations in the State of São Paulo and the related items – pollutants and atmospheric variables – measured in each one of them.

41 Table 04: Measurement stations used: configuration and localization (red point denotes monitoring of the given parameter; blue dash denotes a not monitored parameter). IP – Inhalable Particles SO2 – Sulphur dioxide NO – Nitrogen oxide NO2 – Nitrogen dioxide MONITORING STATIONS Configuration IP

SO2 NO NO2

-

-

•

•

•

•

-

•

•

•

•

•

•

•

•

•

•

-

-

-

•

-

•

•

•

-

-

-

•

-

-

-

•

•

•

•

•

•

•

•

•

-

-

-

•

-

-

-

IP

SO2 NO NO2

•

-

-

-

•

-

•

•

•

-

-

-

•

•

•

•

•

•

•

•

NOx – Nitrogen oxides CO – Carbon monoxide O3 – Ozone RH – Relative Humidity

TM – Temperature WS – Wind speed WD – Wind direction

Location NO CO O3 RH TM WS WD - STATE CAPITAL CITY x Horto Florestal* • • Rua do Horto, 931 Pico do Jaraguá* • • Estrada Pico do Jaraguá, s/n - Parque Estadual do Jaraguá Congonhas • • • • Al. Dos Tupiniquins, 157 Ibirapuera • • • • • • • Parque do Ibirapuera, setor 25 Santana • • • Av. Santos Dumont, 1019 Lapa • • • • Av. Embaixador Macedo Soares, 7995 Mooca • • • Rua Bresser, 2341 Nossa Senhora do Ó • R. Capitão José Aranha do Amaral, 80 Parque D. Pedro • • • • • • • Parque Dom Pedro II, 319 Pinheiros • • • Rua Frederico Hermann Jr, 345 Santo Amaro • • • • Av. Padre José Maria, 355 São Miguel Paulista • • • • • Rua Diego Calado, 166 NO CO O3 RH TM WS WD - GRANDE SÃO PAULO x Diadema • Rua Benjamim Constant, 3 Mauá • • Rua Vitorino Dell’Antonia, 150 Santo André – Capuava • • • Rua Manágua,2 São Caetano do Sul • • • • • • • Rua Aurélia s/n (EMI F. Pessoa V. Paula) Osasco • • • • • Av. dos Autonomistas c/ Rua S. Maurício

*Mobile Station

Source: CETESB, 2006. Available at: http://www.cetesb.sp.gov.br/Ar/ar_indice_padroes.asp

42 As aforementioned, the stations in São Paulo were initially localized next to the city centre and to regions of high industrial concentration. However, while in the decade of 1970 these areas were responsible by the worst episodes of air quality, nowadays they account by only 10% of the total São Paulo pollution because many and effective actions have been implemented and new technologies been developed to deal with the problem. Today, most of the areas covered by CETESB network stations feature high indexes of vehicular pollution, as big riverine avenues along Pinheiros and Tietê rivers and even downtown São Paulo. During the 1970s, the downtown region was a target of environmental concern because, besides very intense vehicular pollutants concentrations, also high indexes of pollutants coming from industrial sources were usual, once the common southeast winds could carry pollutants originated in regions on the southeast of SPMA, such as ABC region and Cubatão, to the centre of São Paulo city (SÁNCHEZ-CCOYLLO, 2000).

43 2.1.2 Characterization of some CETESB measurement stations

The amount and spatial distribution of air quality monitoring stations are fundamental in the evaluation of that monitoring in a given place. Figures 07 and 08 demonstrate that CETESB monitoring stations are reasonably well distributed in the SPMA. Indeed, a larger amount of stations, strategically localized, would provide a better representation of air quality in the SPMA, but one must to consider many details related to local representativeness in the consideration about stations implementation. For example, a station localized at the side of an expressway, with intensive vehicles flow, would collect just pollution locally generated. On the other hand, a station which would be more distant from these great emission centres has a role in demonstrating the background concentration, the concentration representing the whole urban area away from the sources. For example, it is necessary to take in account local topography and atmospheric circulation in prevalent urban scale, besides many other practical issues. Mouvier (1995) says that, in France, “...specialized government bodies implemented an automatic stations network in many places reputedly representative [...]6”, pointing out to the fact that sometimes the monitoring station is not properly positioned to represent what is expected. Commonly, the process of the station’s installation involves a great deal of bureaucratic and political requirements and preferences, as well equipments security, which lead to restrictions in the array of available places to choose from. To a better understanding of these issues in the air quality monitoring networks in SPMA, CETESB applied a representativeness scale to all stations, in which each station is ranked according to the kind of information it discloses about air pollution in the city, presented in table 05:

6

A poluição atmosférica, MOUVIER , G., Atmospheric Pollution, Biblioteca Básica de Ciência e Cultura, Instituto Piaget, Lisboa, 1995, p 40. (free title translation by the author of this work)

44 Table 05: Stations ranking considering soil use and exposed population Station characterization Commercial Residential

Industrial Urban/background concentration

Near roads (vehicular)

Rural

Closed environment (indoor)

Description Measures population exposition in central urban areas, typical commercial areas, with intense pedestrians and vehicles traffic; Measures population exposition in residential districts and cities suburban areas; Localized in areas where industrial sources are important to the observed concentrations, both for long term evaluation and watching for concentration peaks; Localized in urban areas not near to specific sources, in order to represent background concentrations of the urban area as a whole; Localized near intense traffic roads (avenues, streets, motorways) to measure vehicles’ emission influence; Localized as far as possible from vehicular, industrial and urban sources, in order to measure rural areas concentrations; To measure concentrations in domestic and work environment (except occupational environment)

This rating was adapted from US Environmental Protection Agency (EPA) and World Health Organization (WHO) guidelines. Another classification applied to the stations, from similar sources, focuses stations spatial representativeness and can be seen in table 06:

45 Table 06: Stations ranking considering representativeness. Representativeness scale Microscale Medium scale

District scale Urban scale Regional scale National and global scales

Scope area Concentrations including few meters size areas (until 100 metres); Concentrations for blocks urban areas (few blocks with similar characteristics) with dimensions between 100 and 500 metres; Concentrations in city areas (districts) presenting uniform activity with dimensions between 500 to 4.000 metres; Concentrations of cities or metropolitan regions, with dimensions between 4 to 50 km; Generally, concentrations in a rural areas, with reasonably similar geography and dimensions from tens to hundreds kilometres; Concentrations related to a country or the planet as a whole, respectively.

Considering CETESB station’s localization and disposition, most of them present up to the district scale representativeness, once the network number of stations is enough to provide representativeness from several places in SPMA. Congonhas station, for example, is considered a vehicular one, suffering direct influence from the intensive vehicles flow present in Bandeirantes and Washington Luís avenues, as can be seen in figure 16:

Figure 15: Congonhas station surroundings. Source: CETESB, 2004. Another station considered as vehicular is Lapa station, localized at the side of Marginal do Tietê avenue, as can be seen in figure 16:

46

Figure 16: Lapa station surroundings. Source: CETESB, 2004. The next figure shows the localization of Osasco station, another vehicular one.

Figure 17: Osasco station surroundings. Source: CETESB, 2006.

47

This station, localized aside Autonomistas Avenue, represents the pollution generated by this avenue; however, is influenced by intense industrial activity in the region, considered by Osasco municipality an Exclusively Industrial Use Zone (ZI/08). Notwithstanding in the last years the region have lost its industrial characterization, yet many industrial plants are in operation there.

Figure 18: São Caetano do Sul station surroundings. Source: CETESB, 2003. São Caetano do Sul station is not localized near to intensive traffic flow, once it is 150m far from Av. Goiás; its representativeness is considered industrial and residential.

48

Figure 19: Mooca station surroundings. Source: CETESB, 2007. Another mixed kind station is Mooca, localized in a leisure area (soccer field inside a green area), enclosed in a mixed soil use region, with industrial, commercial, and residential uses, 70m far from Bresser Street, with intensive vehicles flow.

49

Figure 20: Ibirapuera station surroundings. Source: CETESB, 2004. Ibirapuera

station

is

considered

representative

of

the

background

concentrations of the SPMA, since it is localized inside an urban park, more than 250m away from avenues with intensive vehicles flow. Soil use around the park is essentially residential:

50

Figure 21: Pico do Jaraguá station surroundings. The red arrow denotes the station site. Source: CETESB, 2007. Finally, Pico do Jaraguá mobile station was settled far from great emission centres, on the eastern slope of Jaraguá mountain, turned to the SPMA, near the peak. It also represents background pollution of this immense urban area, and possibly receives influence of pollutants transportation from Rod via dos Bandeirantes (motorway) and other places. There is much more to discuss about the CETESB’s pollutants monitoring network in the SPMA (its distribution, equipments, representativeness, etc). It is important to emphasize the necessity of analysis and reflections in order to guarantee the construction of a more representative network, especially considering stations spatial distribution in the city and its related representativeness in the context of each district. The current network that monitors air quality in the SPMA is reasonably well distributed; but only recently, since 2002, the data generated by the

51 network present higher percentage of valid data and are organized in a way to simplify scientific research with them. Besides that, considering the stations used in this study, only the ones described in this chapter with more details have some kind of publication available, with station details and characterization. This kind of document is important due to the information provided about the station representativeness, and the performing of characterization studies is recommended to all monitoring network stations in order to allow a deeper understanding of each station and, thus, allow a comprehensive and detailed knowledge about pollutants temporal evolution and spatial distribution. Additionally to CETESB’s published documents, more information may be used in order to characterize some stations, such as the studies of Sampaio (2000) and Azevedo (2002). In this context, Sampaio’s study is important once it brings a characterization of CETESB’s air quality monitoring network stations, while Azevedo’s study investigates the relationship of ozone concentrations with urban activities intensity in the stations surroundings. Moreover, some satellite images from the surroundings of the stations were obtained from the Maplink7 portal on the web. According to the previously mentioned study (SAMPAIO), Parque D. Pedro II station is situated in a region near traffic corridors with intensive vehicle (Av. Do Estado) and people (25 de March St.) activities, and also near to the big homonymous bus terminal. Besides the bus terminal, the proximity with the municipal market intensifies heavy vehicles traffic in the area. Thus, this station is representative of an area with intensive urban activity, mainly commercial, with some few residences. So, this station is important due to the intensive urban activity and vehicular pollution emission in its neighbourhoods, and also to present topographical conditions unfavourable to pollutants dispersion, once it is located in the valley of Tamanduateí river:

7

http://maplink.uol.com.br/

52

Figure 22: Parque D. Pedro II station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ Santana station, however, is localized in a very different area. It is not directly under the influence of emission sources, as seen in figure 23:

Figure 23: Santana station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ The station is located in a military area, predominantly residential with some commercial activity. Notwithstanding localized in a area without intensive urban activity, is suffers influence of pollution emitted by very large avenues in the neighbourhood, specially Marginal do Tietê, Santos Dumont and Av. Cruzeiro do Sul.

53 “On the other hand, Nossa Senhora do Ó station is located in a site with poor green area, although with few urban activity. The region is referred as a “peripheral district, with little vegetation and where free spaces are often used as garbage dumping site. It is a residential and commercial region, with houses 8 built in small allotments ”:

The station is located in a school, fairly distant from the main region emission sources as the avenues Min. Petrônio Portela, João Paulo I (450m) and Itaberaba (600m).

Figure 24: Nossa Senhora do Ó station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ Diadema station is located in a predominantly industrial area, with reduced local sources influence. A very significant green area can be found in its neighbourhood:

8

Análise crítica da rede de monitoramento da qualidade do ar da CETESB na Grande São Paulo. SAMPAIO SILVA, R. Critic Analysis of CETESB’s Air Quality Monitoring Network in the SPMA. Final Graduation Work, FFLCH-USP, 2000. p. 88 (free title translation by the author of this work)

54

Figure 25: Diadema station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ The only significant vehicular pollution source (Imigrantes Motorway) is relatively distant, as in figure 25. Localized in an area that features many kinds of soil use, the importance of Santo Amaro station is due to the intensive vehicles and peoples flow in its surroundings. It is influenced both by Santo Amaro bus terminal and also by the intense commercial activity of the local commercial centre, close to the underground station, and also a green area in the Centro Educacional de Santo Amaro Joerg Bruder (school centre) is present, where it is localized.

55

Figure 26: Santo Amaro station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ Santo André – Capuava station is located in a mostly industrial area, thus suffering direct influence from the petrochemical pole, although with some residential soil use in its surroundings:

Figure 27: Santo André – Capuava station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/

56 In figure 27, part of the petrochemical complex can be seen at the right of Av. Presidente Arthur da Costa e Silva. In the region of São Miguel Paulista station, residential soil use predominates, with some industrial activity in the neighbourhoods:

Figure 28: São Miguel Paulista station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/

There is no significant topographic variation between the station site and the avenues seen in figure 29, that are Pires do Rio and Nordestina; however, due to the reasonable distance from the monitoring station, their influence is of less importance. Mauá station is located in an area with reduced urban activity according to Azevedo’s study (2002): :

57

Figure 29: Mauá station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ A significant green area and predominantly residential soil use can be seen in figure 30, and the only nearest avenue (Av. Capitão João) does not represent a so intensive vehicles flow as the avenues next to the stations localized in the centre of the São Paulo urban area. It suffers direct influence of the nearby ABC industrialized regions. Pinheiros station is located in CETESB’s office in São Paulo, in Alto de Pinheiros district, an arboureous and high standard residential area, suffering direct influence from vehicles flow on Av. Frederico Hermann Júnior and also some influence from nearby avenues Marginal do Pinheiros and Pedroso de Morais.

58

Figure 30: Pinheiros station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ Finally, Horto Florestal station is localized fairly far from great emission sources, near a large green area with predominantly residential soil use in the urbanized area in the surrounds of Horto Florestal park.

Figure 31: Horto Florestal station surroundings. Approximate location is shown by the red box. Source: http://maplink.uol.com.br/ Thus, one can realize that these stations represent the most diverse localizations within the SPMA. The present research allowed associating soil use in the stations surroundings with some variations in ozone concentration behaviour,

59 which affects this pollutant’s concentration in each monitoring station. These points will be discussed in item 3.1.4 (ozone spatial distribution in the SPMA).

60 2.1.3 Air quality standards

According to CETESB, the main objectives of air quality monitoring are: “To provide data to activate emergency actions in atmospheric stagnation periods (lack of wind or rain), when atmosphere pollutants levels may represent a risk to public health; To evaluate air quality according to established limits in order to protect the population’s health and well-being; To follow trends and changes in air quality due to alteration in pollutants 9 emission ”.

To improve the means to deal with air quality issues in a metropolis such as São Paulo, Air Quality Standards (AQS) had to be established. They represent a given concentration that is an upper threshold under which pollutants mean low or none risk to human health, environment, and materials. These standards were established, at a national level, by the Instituto Brasileiro de Meio Ambiente (IBAMA) (Brazilian Environmental Institute) and approved by Conselho Nacional de Meio Ambiente (CONAMA) (National Environmental Council). They are used to determine the urgency level presented by a given concentration of a pollutant.

Air quality monitoring includes observing pollutants concentration, comparing them with air quality standards, which are determined according to the maximum concentration allowed to the given pollutant, the pollutant’s emission rates and the removal systems in operation at the time.

These standards can be classified as primary or secondary. The air quality primary standards are pollutant concentrations that can affect population health if surpassed; they are regarded as the maximum concentration levels, in short and medium terms, usually defining daily or hourly standards. Secondary standards are pollutants concentrations that represent the minimum harm possible to fauna, flora, and population’s well-being in long term, which means annual air quality medium standards to be reached.

Pollutants chosen for monitoring by the CETESB monitoring network are those of great environmental importance and with higher atmospheric concentrations. They

61 are: sulphur dioxide (SO2), Particulate Matter (PM), divided in Total Suspended Particulates (TSP), smoke (SMK) and Inhalable Particles (IP), in other words, particles lesser than 10 µm, carbon monoxide (CO), ozone (O3), also used to measure photochemical oxidants concentration in general, and nitrogen dioxide (NO2). In order to divulge a station air quality index result, the worst case is always used (the greatest measured concentration among pollutants in that station). Air quality primary and secondary national standards to the aforementioned pollutants are shown in Table 07, according to the CONAMA No 3 resolution, of 28/06/90. Table 07: Air Quality National Standards –CONAMA nº 03 Resolution, of 28/06/90. Pollutant

Sample time

Primary 3 standard µg/m

Secondary 3 standard µg/m

Measurement method

Total Suspended Particulates (TSP)

24 hours* AGA**

240 80

150 60

Big volumes sampler

Inhalable Particles Sulphur dioxide

24 hours* AAA*** 24 hours* AAA 24 hours* AAA

Carbon monoxide

1 hour* 8 hours*

Ozone Nitrogen dioxide

1 hour* 1 hour AAA

150 60 150 50 365 80 40,000 (35 ppm) 10,000 (9 ppm) 160 320 100

100 40 150 50 100 40 40,000 (35 ppm) 10,000 (9 ppm) 160 190 100

Smoke

Reflectance Inertial separation/filtration Pararosalinic

Non-dispersive infrared Chemical luminescence Chemical luminescence

* Must not be exceeded more than once a year ** Annual Geometric Average *** Annual Arithmetic Average Source: CETESB, available at http://www.cetesb.sp.gov.br/Ar/ar_indice_padroes.asp CETESB’s methodology to air quality monitoring lead to the development of air quality indexes. These indexes depend on comparing pollutant concentration to air quality primary standard and are divided in six levels: Good air quality (1 to 50% of primary AQS), Average (51 to 100%), Improper (101 to 199%), Bad air quality (200 to 299%), Foul (300 to 399%) and Critical (above 400%). The tree last ones are enough severe and its occurrence indicates the need of specific legal in-time actions in order to decrease pollutant concentration. They are called Attention State (to Bad air 9

From the CETESB website: www.cetesb.sp.gov.br

62 quality), Alert (to Foul air quality) and Emergency (to Critical air quality), respectively. In situations of Attention State, voluntary restriction of polluting activities is recommended, as, for example, vehicles traffic rotation. In Alert State, however, reduction of such activities is mandatory, and in Emergency vehicles concentration is forbidden, as well mandatory interruption of industrial plant activities. The Critical State features a very high pollutant concentration during a short time period in unfavourable conditions to dispersion. In October of 2007 a World Health Organization (WHO) meeting established international AQSs to pollutants PM, NO2, and ozone. According to WHO, many standards which were established in many countries (including Brazil), are still harmful to human health and environment and therefore must be reformulated. In case of more restricted standards come to be adopted by Brazilian legislation, this change will not imply any methodological change to this research, once ozone concentration analysis in this wok is not directly focusing on surpassing AQS. Air quality indexes to different levels established by CETESB for each atmospheric pollutant are presented in Table 08. Health problems related to main pollutants caused by occurrence of these severe pollution levels are summarized in table 09.

63 Table 08: Air quality indexes to main air pollutants according to CETESB: ELEMENTS

QUALITY LEVEL 50% ATTENTION AQS AQS

ALERT

EMERGENCY

CRITICAL

SO2 – Sulphur dioxide (µg /m³)

80

365

800

1,600

2,100

2,620

IP – Inhalable Particles (µg /m³)

50

150

250

420

500

600

CO – Carbon monoxide (ppm)

4.5

9.0

15.0

30.0

40.0

50.0

O3 – Ozone (µg /m³)

80

160

200

800

1,000

1,200

NO2 – Nitrogen dioxide (µg /m³)

100

320

1,130

2,260

3,000

3,750

AQS – Air quality standard / µg – microgram / ppm - parts per million

Source: CETESB, available at http://www.cetesb.sp.gov.br/Ar/ar_indice_padroes.asp

64 Table 09: Main air pollutants according to CETESB, air quality indexes and health effects:

Source: CETESB, available at: http://www.cetesb.sp.gov.br/Ar/anexo/efeitos.pdf

65

2.2 Data 2.2.1 Air pollution data Data used for determining air quality were O3 concentrations collected by CETESB’s measurement stations network settled in the SPMA (figure 14). They were extracted from annual reports and daily bulletins published by CETESB, the last with pollutant hourly measurements. Pollutant concentration data were used to determine its variability in diverse temporal scales considered and to identify extreme concentrations periods. The reading of ozone data, analyzing of consistency, calculation of annual, monthly, and daily averages of their respective anomalies were performed through programs built in FORTRAN10 computational language, and these series graph’s were plotted with Grid Analysis and Display System (GrADS) and Excel.

The utilization of GrADS visualization software allows reading of data up to four dimensions: latitude, longitude, altitude and time. Mathematical operations among different sources data can be performed (with many functions applied to atmospheric sciences). Simultaneous data analysis is often used due to ease of overlapping different data “layers” (overlays), generating different charts that can be analysed simultaneously. GrADS is a chart and maps viewer programmable in FORTRAN computational language.

Ozone pollution data were obtained in .txt format in order to be copied to the computers of the Climatology and Biogeography Laboratory of the Geography Department of the Philosophy, Letters and Human Sciences Faculty at USP.

In their original format, these data are hourly ozone measurements from the 17 monitoring stations used in this study: Congonhas, Diadema, Horto Florestal, Ibirapuera, Lapa, Mauá, Mooca, Nossa Senhora do Ó, Osasco, Pico do Jaraguá, Pinheiros, Parque D. Pedro II, Santana, Santo Amaro, São Caetano do Sul, São Miguel Paulista and Santo André – Capuava. Indeed, Horto Florestal and Pico do 10

FORTRAN computational language was developed in 1956 as the first high level programming language (FREITAS, 2004). It was developed for specific scientific use and since then it has been

66 Jaraguá stations are not fixed stations that represent only these sites. CETESB has two mobile stations, which can be moved in order to monitor diverse sites according to air quality monitoring needs. For example, in Pico do Jaraguá mobile station measurements were taken only in the years 2002 and 2003. Nowadays, this station is denominated Itaquera for it is located in a district with this name, in east São Paulo. Start and end observation dates vary according to the station, as it is presented in table 10.

Table 10: Years of temporal series start and end of each SPMA station. Station Congonhas Diadema Horto Florestal Ibirapuera Lapa Mauá Mooca Nossa Senhora do Ó Osasco Pico do Jaraguá Pinheiros Parque D. Pedro II Santana Santo Amaro São Caetano do Sul São Miguel Paulista Santo André – Capuava

Start

End

24/05/1996 14/04/1999 17/08/2004 22/05/1996 11/08/1996 05/08/1996 01/01/1997 18/06/2004 05/06/1996 02/01/2002 01/09/1999 09/05/1996 01/05/1999 01/01/2003 30/07/1996 13/08/1996 26/10/2000

03/03/1999 31/12/2005 31/12/2005 31/12/2005 22/09/2000 31/12/2005 31/12/2005 31/12/2005 28/09/2001 18/12/2003 31/12/2005 19/12/2005 31/12/2005 31/12/2005 31/12/2005 17/02/2005 31/12/2005

largely used by professionals of several areas, including meteorology. FORTRAN language has been improved many times, and one of its most widespread versions is FORTRAN-77

67 2.2.1.1 Data consistency

In order to identify invalid or lacking data, a treatment of these data was performed. Data originally organized by CETESB present quality indicators denoting their validation or not. Despite the existence of this quality control, hourly values higher than 500 μg/m3 were still present and needed to be deleted and converted into lacking data. Annual, monthly, and daily averages were calculated considering the criteria adopted in this study to the amount of representative data in each scale. For yearly averages, a minimum of 8 months of valid data in a year were necessary; for the monthly average, 20 days of valid data, and, for the daily average, the minimum amount was 16 hours of valid data in a day.

A FORTRAN language program was developed in order to identify invalid values and to calculate averages and anomalies to deal with such large quantity of data.

68 2.2.2 Climatic data Climatic data to be used in this research were collected from the data array of Reanalysis II from National Centre for Environmental Prediction National Centre Atmospheric Research (NCEP/NCAR) (Kanamitsu et al, 2002), with spatial resolution of 2.5 degrees. The utilized variables, at surface level, were outgoing longwave radiation (OLR), atmospheric pressure, solar radiation, air temperature, relative and specific air humidity; at 250 hPa and at surface level, wind direction and intensity. From wind direction and intensity it was possible to calculate air convergence and divergence at surface level and at the top of the troposphere, as well the position of streamlines and identification of atmospheric fields which are favourable or not for atmospheric instability, inducing situations with larger or lesser cloud covering, precipitation and higher or lower pollutants dispersion probability at low atmospheric levels. Atmospheric fields were plotted in the geographically delimited area of 35 o S; 30º E; 10o S; and 60º W. These coordinates were chosen to allow the representation of atmospheric circulation in centre-south of Brazil, were the SPMA is inserted.

Reanalysis data are obtained with the execution of atmospheric model T62/28 level NCEP global spectral model (Kalnay et al., 1996) to past periods, based on observed data. Atmospheric models are built from atmospheric physic laws that generate equation groups to process the collected data and provide different results, depending on the initial data inserted. This can be done to perform a forecast (an approximation of future atmospheric conditions from observed previous data) or a diagnostic analysis (simulation of atmospheric conditions in a previous time from already observed data). The data array used in the study allows to, specially, diagnostic of previous atmospheric conditions in order to assess simulations or diagnostic studies of atmospheric variability. Reanalysis data can be obtained at the follow internet address: http://www.cdc. noaa.gov/cgi-bin/db_search/SearchMenus.pl.

Additionally to reanalysis data, atmosphere local circulation was also analyzed using wind speed and direction data obtained in CETESB’s stations. This data array allowed analysing lower troposphere conditions in more details in months with anomalous ozone concentrations. Monthly air temperature deviation data from Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) (Weather Forecast and

69 Climate Studies Centre) were also used in the preparation of the qualification report of this study.

Besides that, using Boletim Climanálise (Climatic analysis bulletin) reports, a monthly publication available at CPTEC internet site, which assess predominant atmospheric patterns in each month in Brazil, allowed more precise characterization of the predominant atmospheric patterns in each situation. Analysis of satellite images, temperature anomalies, precipitation, number of transient frontal systems in South America and other large scale variables, such as El Niño South Oscillation (ENSO – monthly variation of South Pacific Ocean surface temperature) allowed the collection of information about frontal systems intensity, blocking situations, the movement of atmospheric systems, etc.

70 2.3 Methodology 2.3.1 Ozone behaviour in the SPMA In order to understand this pollutant’s behaviour in the studied area, means in the three analysis scales proposed were calculated for each station.

For the annual scale, yearly means were calculated for each of the 17 stations, by adding up all hourly data from daily bulletins to calculate an average value to the whole year in each station. Thus, annual temporal series were built to all 17 ozone measurement stations used in this study. The comparison between yearly means time series allowed the identification of stations with different concentration levels.

Standard deviation of each station was calculated, then the standard deviations average of each station was calculated. Afterwards, the process was repeated to each station’s concentration means. With the averages of all stations, anomalies of each one were calculated, that compared to the average standard deviation, allowed the identification of four groups of stations: the low concentration ones (anomaly twofold or more lower than standard deviation), the medium ones (anomaly inside standard deviation), the high ones (anomaly higher than standard deviation) and very high ones (anomaly twofold higher than standard deviation).

In monthly scales, however, ozone monthly mean concentrations were used. Similarly to the annual means, monthly means were calculated using hourly data, and O3 monthly temporal series were built to each station. Afterwards, months with intense high and low O3 anomalies were analyzed. Besides monthly temporal series for each station, charts were built with monthly averages overlapped year by year, in other words, all years, from January to December, overlapped in the same scale for comparing interannual O3 behaviour. This procedure also allowed the detection of its seasonal cycle. In this context, indexes of Pearson linear correlation were calculated between some analyzed variables and O3 concentrations averages: 2

r =

 X Y 

2

 X  Y  i i

2

i

2

i

71 r = Pearson correlation coefficient (Chatfield, 1996) x,y = correlated variables

Finally, monthly averages were also used to analyse concentration linear tendencies for each station. Stations were grouped according to increase, stability or decrease tendencies, using the angular regression coefficient value in its regression line: Yt = a + bt where:Yt = forecasted temporal series value a = straight line linear coefficient b = straight line angular coefficient t = time Angular coefficient b is also called “tendency term” and, basically, is the increase in the average value by time unit11. For this reason, stations with higher angular coefficient were those which present a higher increasing tendency in their concentrations.

In daily scale, daily averages were calculated from hourly data. Daily temporal series were built for better visualization of O3 variability and the missing or invalid data were traced in order to better understand each station measurement quality and temporal distribution of missing data.

Selected reanalysis data present 2.5 degrees of resolution in zonal and meridional directions. Therefore, the SPMA embraces an area that comprises, as a maximum, two grade points. SPMA ozone concentration data come from 17 different stations and so there is not a proper concordance of spatial scales to comparison among atmospheric fields and ozone concentrations. So, it was decided to calculate spatial average to the whole SPMA with the data from the 17 stations, and work with an average temporal series in comparison with atmospheric fields. This study attempts to reach a more comprehensive understanding about atmospheric conditions and ozone concentrations, without a more detailed view of each station behaviour in face of atmosphere variability. Despite the great divergence among O 3 data from many stations, in general there is good correlation in the monthly scale. This was the case, for instance, in the replacement of Congonhas station by

11

GEM (Multiscale Study Group) website: ttp://www.icess.ucsb.edu/gem/modulo_1.htm

72 Ibirapuera station, which concentrations, despite very different in its absolute concentrations, show a correlation index of 0.63 in the temporal evolution of its monthly series during the year of 1996 (CETESB, 1997).

In order to analyse daily and monthly O3 variabilities, pollutant average daily cycles in each month were calculated for the whole period. With data from each station, values from a same hour of a month were added up to build an average daily cycle by month and, afterwards, the average among the same months of different years was calculated.

A similar process was performed to the calculation of average seasonal cycles of each station: after monthly average calculation, all average monthly values of a given month inside the series were added up to an average value to that month, and an average annual series, comprised by twelve months, was obtained for each station. Therefore, the more years of data one station present, the less extreme values it comprises in the average annual series. Thus, this procedure was performed only to stations with 5 or more measurement years, and so Horto Florestal, Pico do Jaraguá, Nossa Senhora do Ó and Santo Amaro stations were excluded. As an average seasonal variation is noticed in ozone behaviour along the year, this average annual series will be referred ahead as the ozone average seasonal cycle.

Finally, a map with ozone concentration spatial distribution in the SPMA was developed, which is important due to diverse population exposition levels and to O 3 spatial variability, using data from the 17 stations spread throughout the SPMA.

Firstly, averages values were calculated for each station, adding up all its data. Then, each station coordinates of Universal Transversal Mercator (UTM) and latitude/longitude were obtained through CETESB’s informative bulletins and the stations addresses (table 04). A “mask” of the SPMA area (a file with latitude and longitude points which delimitate a given area) with a region political map was processed in GrADS, and on that each station localization coordinates were inputted to insert points representing stations localization on the map. So, each point is related to a given O3 average concentration.

73

Cressman Analysis technique (CRESSMAN, 1959) was used to a better visualization of this pollutant’s spatial occurrence in the SPMA. This interpolation technique consists in estimating an approximate value to a point with no data, considering closer values; however, it differentiates from regular interpolation because Cressman Objective Analysis allows to a more precise result to a variable spatial distribution because it considers the amount of points and the distance among them. “To each station, an error is defined as the difference between the station value and a value obtained by the grid interpolation to that station. The correction factor is based on a weighted distance formula applied to all these errors inside the influence radius. Correction factors are applied to 12 each grid point before the next stage is performed. ”

This map is not intended to display the exact ozone concentration in each site of the SPMA, but to allow a general understanding of the pollutant spatial distribution over the different areas represented by each station, as aforementioned in section 4.1.2.

12

DOTY, B, 2006 The Grid Analysis and Display System, revised edition by Tom Holt from University of East Anglia and Mike Fiorino from Lawrence Livermore National Laboratory, 1995. Available at: http://www.master.iag.usp.br/ind.php?inic=00&prod=mapa

74

2.3.2 Analysis of atmospheric concentrations in the SPMA

patterns

influence

on

ozone

By means of ozone yearly behaviour in the SPMA analyses, it was found an increasing pollutant tendency in the studied period. More precisely, linear increasing tendency is observed in the 1996 to 2003 period; at the series ending, a negative linear tendency to O3 concentrations values is observed. This point denotes that, in the series ending, a decreasing of yearly values occurred, what, according to the Relatório de Qualidade do Ar no Estado de São Paulo (State of São Paulo Air Quality Report) may be linked with meteorological conditions. Nonetheless, a smaller number of AQS surpassings was observed in 2005.

Table 11: Number of ozone AQS surpassings in the SPMA in each month, from 1999 to 2005.

Source: CETESB, 2006. Therefore, before starting interpretation analysis between atmosphere and ozone behaviours, the tendency observed in the original O 3 data was removed in order to capture authentic variations of pollutant behaviour. To perform that, it was used the linear regression equation, previously explained. In order to remove the tendency of temporal series, the angular coefficient is multiplied by the time and the result is subtracted from the observed figure in each time. This way, temporal tendency is removed from the data series.

After removing the tendency from the ozone monthly time series, analysis of the relation among atmospheric patterns in ozone concentrations in the SPMA was done in three stages. The first stage was the detection of periods with positive and

75 negative ozone anomalies to define months of interest. It was observed that specific monthly climate anomalies were associated to these months of ozone high and low anomalous concentration. After that, months with positive and negative anomalies were subdivided in two new classes, according to O3 anomaly’s intensity. The second stage consisted in analysing the spatial behaviour of the climatic variables’ monthly averages and anomalies, through the building of maps containing these atmospheric variables in the months of interest.

The last stage involved the interpretation of pollutant mean daily values and analysed atmospheric variables, in order to reach a deeper understanding of possible atmospheric interferences in its concentrations in the synoptic time scale.

76 2.3.2.1 Selection of months with ozone anomalies and observed atmospheric anomalies

The temporal series of O3 concentrations monthly anomalies was built for the period 1996-2005. Months with positive and negative extreme O3 anomalies were detected based on monthly standard deviation. Thus, months with absolute O 3 anomaly value higher or lower than one standard deviation (1) were selected. Monthly anomaly is calculated as the difference between monthly average value and its climatologic mean, as indicated: vari’ = vari – varclim, where vari’ denotes the anomaly, vari the observed monthly value, and varclim the climatologic mean.

Monthly episodes with different O3 concentration intensities were subdivided in four categories: two categories with positive anomalies, with O 3 concentration anomaly value higher than 1 and 2, and two categories with negative anomalies, with anomaly value lower than -1 and -1.5, as shown in table 12. The selection criterion for months with negative anomalies lower than -2 was not satisfied; it was found, then, that O3 negative anomalies are less intense. Atmospheric and O 3 anomalies to all established categories (1, 2, -1 and -2) were analysed together, in order to define a relationship of junctioned variation. Table 12: Classification criteria of intense O3 concentration monthly episodes. category

criterion

1

[O3] > 1

2

[O3] > 2

-1

[O3] < -1

-2

[O3] < -1,5

In order to have an initial, less refined, interpretation of the relationship between atmospheric and O3 behaviour, it was decided to analyse the periods with

77 O3 anomalies. Naturally, it was supposed that O3 intense anomalies should present some interference by the atmosphere. Although the positive and higher concentrations surpass AQS and, therefore, represent greater harm to environment and population’s health, the analysis of episodes with O 3 concentration negative anomalies allow to elaborate a more comprehensive interpretation about information and hypotheses associated to pollutant variability. These analysis criteria allow a more precise assertion of the influence of atmosphere on ozone concentrations. For months of categories 1, 2, -1 and –2, a table was built containing climatic anomalies present at each month with the variables solar radiation, air temperature, pressure, air relative humidity and outgoing longwave radiation (OLR). Then, the percentage of positive and negative climatic anomalies associated to the selected months was calculated.

78 2.3.2.2

Association

of

atmospheric

patterns

to

ozone

concentrations

To more intense anomalies months (categories 2 and –2) regional atmospheric compositions were also used, in an attempt to suppose which atmospheric systems and monthly atmospheric patterns could be more closely associated to ozone concentrations in the SPMA.

These compositions were traced in GrADS using the data array provided by Reanalysis II, for the variables wind direction and intensity, air divergence field at low and high levels, streamlines, air temperature, shortwave solar radiation, air relative humidity (as well specific humidity to the months of August 1999, March 2002, February 2003, April 1998 and July 2005), surface atmospheric pressure and outgoing longwave radiation, in the area defined by the geographic coordinates (60ºW;35S) and (30ºW;10S), in order to represent the main atmospheric systems actuating in the synoptic scale on the study area.

Some options in the visualization program (GrADS) were chosen which allow a more clear view of variables spatial distribution, as the filling effect (shaded), besides the option of variable smoothing (smooth) in order to delete data considered as noise. Maps with monthly averages of variables along with their respective anomalies were plotted, which were obtained directly from Reanalysis or by subtracting the observed value of temporal series average (see previous section).

Besides Reanalysis II data, some information from the Climanálise (Climate analysis) bulletins were also included, such as frequency and intensity of frontal systems which reached areas in the SPMA latitude (in this case, the municipality of Santos – about 80 km, on the coast), precipitation anomalies in Brazil, positioning and duration of SACZ episodes and monthly precipitation rate. This information contributes to a more complete analysis of atmospheric patterns present in each analysis month. Also, some months with high ozone pollution, identified ahead, laid very close to categories 2 and –2 thresholds and were also included in this analysis in order to increase the number of observed cases and to grant more reliability to the conclusions.

79

A significant influence of atmospheric conditions over ozone concentrations had already been mentioned in the study of Andrade and Massambani (1994), in the characterization of this pollutant’s seasonal behaviour in the SPMA. Several CETESB’s publications, mainly state of São Paulo air quality reports, raise attention to the fact that ozone data must “be carefully analysed, once they represent mostly variations of meteorological conditions, which can be confirmed by significant differences between the same months in different years13”.

Even reductions in ozone concentrations in the years of 2004 and 2006 were influenced by a higher frequency of cloudy days, with lower maximum temperatures (CETESB, 2004, 2006). In 2007, however, the yearly air quality report in the state of São Paulo denotes a period of several hot and dry days in September and October, causing high ozone concentrations anomalies in the SPMA in comparison with previous years.

Other studies performed to a greater spatial scale including São Paulo metropolitan area (FREITAS, 2003; SANCHÉZ-CCOYLLO et al, 2006; RECUERO, 2003; BOIAN et al, 2005) and other Brazilian regions denote that not only local meteorological conditions, but even atmospheric circulation in regional scale, can influence ozone concentrations; either, in regional scale, by transportation of precursors pollutants emitted by biomass burning in the Centre-West and Centre of Brazil (Northeast Brazil outflow), or by transportation of vehicle or industrial pollutants from the own metropolitan area or other near regions (Northeast flux)).

To the Southeast USA, particularly Los Angeles, some studies point to the importance of atmospheric conditions on ozone concentrations, especially solar radiation, air temperature, vertical mixing ratio and surface circulation direction. According to Wise et al, (2005), atmospheric variability is accountable for about 4070% of ozone variability, reflecting specially in vertical mixing rate, once atmospheric variability in synoptic scale in this region is lower than the observed in other USA regions, reducing the weight of variations in radiation and temperature in this analysis 13

CETESB: The State of São Paulo Air Quality Report, 2006, p.59.

80 scale. Other studies (NARSTO, 2000) reinforce the importance either of atmospheric stability as of transportation in local and regional scales.

81 2.3.2.3 Atmospheric influence on tropospheric ozone daily variability In order to complete the atmospheric compositions analysis, previously explained, an analysis of the pollutant’s daily means in the months of category 2 e -2 was chosen. For this reason, O3 daily means were plotted, using hourly data from all stations. The variables air temperature, relative air humidity, atmospheric pressure, shortwave incoming radiation, longwave outgoing radiation, wind speed and precipitation rate were obtained from the Reanalysis II in the daily scale for the SPMA’s region and then used to build charts in Excel and GrADS. Besides this data set, precipitation data from the Biogeography and Climatology Laboratory in the Geography Department of FFLCH in University of São Paulo were utilized for March 2002, and wind direction and speed from some CETESB stations. Images and Satellite pictures showing the Bolivian High positioning, the duration and trajectory of Upper-Level Cyclonic Vortexes (ULCs) and the occurrence of the South Atlantic Convergence Zone (SACZ) in some of the discussed months were also obtained.

Charts were built and organized in a way to ease the analysis of day-to-day ozone concentrations evolution and the daily variability of the atmospheric variables in the same periods, comparing the charts of different variables in a similar time axis. This analysis was somewhat developed by Monteiro’s rhythmic analysis (1976), however, without mentioning the predominant atmospheric system. It was possible to see the atmospheric variability in the synoptic scale, and so, a subjective analysis of the influence of atmospheric systems on ozone concentrations was performed, and it will be seen in section 3.3 that this influence changes a lot in the different evaluated months. Hourly wind speed and direction data from CETESB stations were organized in Fortran computing language in order to visualize them in the form of shape vectors and to delete invalid data. Therefore, it was possible to follow up with wind direction shifting and the prevailing wind direction throughout the months. Such shifts are easily seen after the day of passing of frontal systems, seen on the graphs as a blue line. Pearson’s linear correlation indexes were calculated between ozone and some atmospheric variables in this time scale analysis, in a way to provide better theoretical support for the analysis performed.

82 3. Results 3.1 Tropospheric ozone behaviour in SPMA Tropospheric ozone measurement in the SPMA has begun in 1983 with four automatic network stations: Parque Dom Pedro II, Lapa, Congonhas and Mooca (CETESB, 1985). The first years of measurement are marked by a significant amount of invalid data, but within the following years, data quality improves, mainly due to the measurement’s range increase. Initially, Mooca station presented the highest ozone concentrations; however, due to the expansion and updating of the measurement work and equipment in 1996, several other stations have also started measuring it Mauá, São Miguel Paulista, Osasco, São Caetano do Sul and Ibirapuera. The configuration of a spatially more representative network, through these new stations, made it possible to realize that tropospheric ozone air contamination took place throughout many diverse areas in SPMA. In 1999, Santana, Pinheiros and Diadema stations have also started ozone measurement, as well as Santo André-Capuava in 2000 and Santo Amaro in 2002. More details about ozone measurement between 1996 and 2005 can be found in Table 10.

Since the fist years of measurement, this air pollutant has frequently exceeded AQS, becoming an environmental concern for local authorities. For instance, in 1986, there were 103 hours of exceeded ozone AQS in Mooca station, and in 1990, the maximum yearly value was 587μg/m3, which corresponds to the attention level (CETESB, 1990). Its concentrations have oscillated significantly since these early years, and these oscillations account partially for different atmospheric conditions and partially for changes in traffic regulations concerning emission levels and also to the insertion of new fuels or modification of existing ones (CETESB, 1994, ANDRADE, 1994). The introduction of ethanol as a fuel and the addition of it in regular gasoline (which starts to be known as “gasoline C” or “gasohol”, with up to 22% ethanol in its composition) have changed the nature and amount of substances emitted from car exhausts (more details in section 1.1). Nevertheless, there is no known set of reasons that explains the whole of tropospheric ozone variability. When comparing 1980s concentrations with the ones found in this decade, a certain decrease can be noticed, but they still represent a threat to the population and the environment. As an example, there is no defined tendency for ozone between 2000

83 and 2003, according to the 2005 CETESB’s Air Quality Report (figures 09 and 10 in section 1.2.1.). The study of Chiquetto, in 2005, has more details about the historical evolution of the measured atmospheric pollution in the SPMA.

84 3.1.1 Yearly Means In 1996, no station reached the yearly minimum representative value used in this work, which is at least eight months of valid data. In 1996, such value was not reached because a major air quality network maintenance and updating work was carried out by CETESB during this year, which interfered with the normal measurement activities (CETESB, 2000).

Figures 32 to 36 show some data variability among the stations; however, when analyzing yearly data, many details such as extreme concentration periods, more precise station variability, ozone seasonal behaviour, among others, remain on the undertow. Besides that, since there is no ozone yearly AQS, the only purpose for analyzing this data is to determine long-term tendencies within this data (CETESB, 2007), in an attempt to identify extreme yearly values and classify stations according to their yearly mean (which will also be discussed in the SPMA’s ozone spatial distribution analysis, section 3.4.1). The different ozone measuring stations have begun to operate in different periods, hence the difference in data availability (table 10). During data analyzing, yearly means were found to be represent station differences more clearly; in order to identify them better, ozone yearly time series in this section are grouped in four figures with the same time axis.

The standard deviation () mean was calculated for all stations, based on each station’s own standard deviation. By calculating the difference between each station’s standard deviation and the mean of all stations, it was possible to classify stations according to their concentration. As a result, four different concentration classes were detected (table 13):

- Low concentrations, with station anomaly value lesser or equal to -2 ; - Medium concentrations, with station anomaly value between -1 and +1; - High concentrations, with station anomaly value higher than +1 and - Very high concentrations, with station anomaly value higher than +2.

85 Table 13: Stations classifying by concentration comparison: low (blue), average (green), high (red) and very high (brown), comparing to the spatial average standard deviation ( = 3.99).

Station

Anomaly

Lapa

-18.09

Congonhas

-17.53

Pinheiros

-10.27

Osasco

-9.62

Parque D. Pedro II

-8.39

Nossa Sra. Do Ó

-7.56

Mooca

-1.14

Horto Florestal

1.95

Santo André – Capuava

2.39

Ibirapuera

3.79

São Miguel Paulista

4.01

Diadema

4.66

São Caetano do Sul

4.74

Mauá

7.68

Pico do Jaraguá

10.18

Santana

11.14

Santo Amaro

16.24

Congonhas, Lapa, Osasco, Pinheiros, Parque Dom Pedro II and Nossa Senhora do Ó stations have shown the lowest O3 concentrations, with minimum values around 10 µg/m3, and maximum up to 25µg/m3, except for Parque Dom Pedro II station that has presented yearly means between 25 a 28 µg/m 3 in 1999, 2002 and 2003.

86

Figure 32: Yearly concentration in mean low concentration stations (< -2). In figure 32, it is evident that ozone yearly time series from this group of stations remain below the average line (the thick black line) in all years. Nossa Senhora do Ó station has representative data in this time scale only in 2005.

Mooca, Ibirapuera, Santo André-Capuava and Horto Florestal stations present higher concentrations than the ones in the previous group. In general, their annual mean concentrations are found within 20-40 µg/m3. Surprisingly, Ibirapuera station ozone means depict similar values and temporal dynamics to this stations in this time scale (between 28 µg/m3 in 2004 and approximately 42 µg/m3, in1997 and 2002), despite being known in the literature for its high ozone concentrations. Therefore, it was also included in the group of stations shown in figure 33:

87

Figure 33: Yearly concentration in mean average concentration stations (between 1 and +1). Nonetheless, it can be seen in figure 33 that in most years of the time series, mean yearly ozone concentrations from this station remain above the SPMA’s spatial mean (thick black line). In order to divide all stations in low, medium, high and very high concentrations, the methodology of comparing each station’s yearly standard deviation with the yearly spatial standard deviation was used. This has probably incurred in the fact that Ibirapuera station, within these temporal and spatial frames, appeared as a medium concentration station, despite the fact that the numerical value of its standard deviation is closer to the numbers obtained for high concentration stations (table 13). Horto Florestal, Santo André-Capuava and Nossa Senhora do Ó stations, on the other hand, show yearly mean ozone concentrations close to the average line (thick black line). Horto Florestal station has valid data to produce a yearly average only for 2005, and it is quite similar to those found on all stations from this group this year.

São Miguel Paulista, Diadema, São Caetano do Sul and Mauá stations, however, show yearly mean values considered high according to the applied methodology, with minimum yearly means around 30 μg/m3, and maximum values between 40 and 45 μg/m3, as illustrated in figure 34.

88

Figure 34: Yearly concentration for mean high concentration stations (> +1). These stations show above-average yearly means in most years. Station Mauá, in 2001, behaved in a very different manner, presenting an increase of ozone concentrations while a decreased was observed in all other stations.

The last group of stations, Pico do Jaraguá, Santo Amaro and Santana, present the highest observed values within all analyzed stations, as shown in figure 35. Minimum yearly means were observed at about 40 µg/m3 and maximum at about 55 µg/m3, such as in Santo Amaro station in 2004 and Santana in 2005. However, Santo Amaro and Pico do Jaraguá present only three and two years of available data, respectively. In order to identify a possible concentration tendency in years beyond the studied time series (1996-2005), the 2007 São Paulo State Air Quality Report (CETESB, 2008) was used, from which 2006 and 2007 data were extracted.

89

Figure 35: Yearly concentration for mean very high concentration stations (> +2). The 2007 CETESB’s report figures (figure 36) show ozone concentrations evolution from 2003 to 2007, for several SPMA stations, according to the number of times the AQS was exceeded in each year. Regardless, this data has a similar variation to the yearly arithmetic mean, so one can be used to suppose the behaviour of the other one. According to this report, Diadema, Ibirapuera, Santo Amaro, Santo André-Capuava, Mooca, Santana and São Caetano do Sul stations still present similar concentration patterns to previous years. Thus, they can still be classified in a similar way that was suggested in this study, as long as considering only yearly means. In contrast, Pinheiros and Nossa Senhora do Ó stations exceeded the AQS more frequently than in previous years, and Mauá station, less frequently, as seen in figure 36. Each station’s O3 concentration temporal tendencies will be better evaluated in the next section, which looks into the monthly means.

90

Figure 36: Number of O3 AQS surpassing and attention level, temporal evolution, per station in the SPMA – 2003 to 2007. Source: CETESB (2008).

91 3.1.2 Monthly Means

Because they suppress seasonal and monthly variations, the previously analyzed yearly means make it easy to differentiate the average concentration for each station. Monthly means, on the other hand, allow an O 3 concentration yearly overview, in a way that seasonal/monthly variations (in which distinct atmospheric conditions are prevalent) are easily identified. The monthly minimum representative value (20 or more days with valid data each month) was attained for most stations in the majority of months.

Ozone monthly concentration linear tendency was calculated for each station, as described in section 2.3. Among the 17 stations analyzed, 8 showed an elevation tendency, 7, a decrease tendency and 2, stability in their monthly concentrations, as seen in table 14, figures 37 to 39 and Annex 1.

Table 14: Tendency line angular coefficient for the 17 analyzed stations and their evaluation periods.

Horto Florestal

Angular coefficient -0.4

Santo Amaro

-0.3

2003-2005

Osasco

-0.1

1996-2001

Ibirapuera

-0.09

1996-2005

Pinheiros

-0.09

1999-2005

Mauá

-0.06

1996-2005

São Caetano do Sul

-0.02

1996-2005

Congonhas

-0.01

1996-1999

Nossa Senhora do Ó

-0.01

2004-2005

Mooca

0.04

1997-2005

Diadema

0.05

1999-2005

Santo André – Capuava

0.06

2001-2005

Lapa

0.06

1996-2000

São Miguel Paulista

0.07

1996-2005

Parque D. Pedro II

0.1

1996-2005

Santana

0.2

1999-2005

Pico do Jaraguá

0.3

2002-2003

Station

Evaluation period 2004-2005

Although more stations with negative coefficients are shown in table 14 – which indicates more stations with an O3 decreasing tendency – the spatial mean

92 from all 17 stations shows a general increasing tendency, as it will be shown in figure 40. Congonhas and Nossa Senhora do Ó stations present a very slight decreasing tendency, which are not perceived in the final mean.

Annex 1 contains figures with monthly means for all stations.

No distinguishable logical features could be found to classify stations with a decreasing or increasing tendency, neither regarding their concentration level, spatial distribution or period of study.

As The stations which present the highest increasing ozone tendencies within the 17 stations studied were: Santana, Pico do Jaraguá and Parque D. Pedro II, as seen in figure 37.

Figure 37: Monthly ozone concentration evolution in Parque D. Pedro II, Santana and Pico do Jaraguá stations. The straight lines show the linear tendency for each station. As seen in figure 37, the increasing temporal tendency is clear and steady in the time series showed for station Parque D. Pedro II. In 1996 and 1997, its monthly means are seen in much lower values than in following years, especially 1998, 2002 and 2005. In this last year, its concentrations reach levels comparable to those of the other stations, classified as high ozone concentrations (section 2.3.2). But

93 unfortunately, a more detailed analysis of its time series is not possible due to the lack of valid data throughout it, especially at the end.

Santana station presents a shorter time series, but also demonstrates a clear increase in its monthly means, from 1999 to 2005. However, Pico do Jaraguá station has only two years of validate data, 2002 and 2003. In these two years, several SPMA stations showed high ozone concentrations, as it will be seen later on in the seasonal cycle analysis. Monthly concentrations increasing tendencies, in this context, are influenced by the high ozone concentrations of the second semester of 2002 (specially in October), and also by the small number of monthly values available, being so associated to low statistical significance (presumed but not calculated). In spite of having low statistical significance, these high ozone concentrations observed in Pico do Jaraguá stations are relevant due to the fact that this station is located in an environmental protection area within the SPMA, but away from the urban centre.

Congonhas station presents stability in its monthly concentrations within its analyzed period, from 1996 to 1999 (figure 38).

Figure 38: Monthly ozone concentration evolution in Congonhas station. The straight lines show the linear tendency.

94 Only Congonhas and Nossa Senhora do Ó stations present concentrations stability; all other stations show either increasing or decreasing tendencies, as informed by table 14. Some stations with significant decreasing concentration tendencies will be shown in figures 39 a (Santo Amaro, Horto Florestal) and b (Osasco).

Figures 39a and 39b: Monthly ozone concentration evolution in Horto Florestal, Santo Amaro and Osasco stations. The straight lines show the linear tendency for each station. Santo Amaro, Horto Florestal (figure 39a) and Osasco (figure 39b) stations show the most evident decreasing tendencies. These series were grouped in

95 different figures due to their measurement temporal differences: while Santo Amaro and Horto Florestal present data from 2003 to 2005, Osasco does so from 1996 to 2001. This station presents a not very strong reduction tendency (angular coefficient of – 0.1, approximately 1.2 μg/m3 decrease per year), but Santo Amaro station, very different values comparing the beginning and the end of its time series (angular coefficient of -0.3, approximately 3.6μg/m3 decrease per year). It also presents a concentration peak in February of 2003 (around 86 μg/m3), and a minimum in July of 2005 (around 25 μg/m3). Months with unusual mean concentrations will be discussed in more details in section 3.3 and 3.4. Horto Florestal station has valid data only in three years of measurement (2004 to 2006), with lower values in 2005, as seen in table 11.

Figure 40: Mean monthly value temporal evolution for the 17 stations, from 1996 to 2005. The red line indicates the mean linear tendency. Months highlighted in red and blue show, respectively, the highest and lowest mean concentration observed in each year. Considering the SPMA 17 stations average, there is an average increasing ozone concentration tendency. However, it doesn’t mean that there is a general ozone increase in the SMPA (in all stations). The spatial average is a result from observed concentrations in diverse stations set in different areas that work (or worked) in different periods. This “average” then is an overall mixed reflection of

96 different temporal-spatial boundaries. Therefore, one cannot only analyze the average of this entire set of stations as the only data source for the SPMA’s ozone concentrations temporal evolution.

In an attempt to calculate a more accurate ozone concentration tendency, the monthly mean was also calculated using only the stations with longer time series. (figure 41). Ibirapuera, Mauá, Mooca, Parque D. Pedro II, São Caetano do Sul and São Miguel Paulista stations were chosen for this calculation, because they have similar observation periods (1996-2005, except for Mooca station, which is 19972005).

Figure 41: Mean monthly value temporal evolution for Ibirapuera, Mauá, Mooca, Parque D. Pedro II, São Caetano do Sul e São Miguel Paulista stations, from 1996 to 2005. The red line indicates the mean linear tendency. Months highlighted in red and blue show, respectively, the highest and lowest mean concentration observed in each year. Through the data depicted in this figure, it can be concluded that there is no evident increasing or decreasing average ozone concentration tendency for the stations with longer time series (1996-2005).

Thus, the increasing tendency observed when looking into the spatial 17 stations average tendency is rather a consequence of the increasing number of

97 monitoring stations in the SPMA, not a real increase on the pollutant’s concentrations. Another possible conclusions drawn from this comparison is that ozone concentrations in SPMA in 1996 were probably higher than represented by that set of stations (figure 41), because they didn’t represent the SPAM as broadly as the 2005 network, when it had more than the double (13) of measuring stations in 1996 (6). As monitoring starts in new places, the real ozone spatial distribution and concentration is slowly revealed. This line of thinking can also be applied to previous times, before the 1996 air quality network improvements in the SPMA.

From this context, it is possible to say that tropospheric ozone is not a recent air pollution problem in the SPMA; instead, it was not properly known before 1996, mainly due to the technological and financial issues involved in implementing a proper air quality monitoring network. Or also due to other past priorities, such as other compounds, as seen in several air quality reports that had been issued by CETESB since 1983 and other air pollution studies in the SPMA published before 1996 (FRUEHAUF, 1988; SOBRAL, 1988; CHIQUETTO, 2005). What is regarded as recent in this work (from 1996 onwards) is simply a more accurate notion of the true ozone contamination in the air inhaled by the SPMA population.

98 3.1.2.1 Tropospheric ozone seasonal cycle in the SPMA Another important aspect which is revealed by the monthly means is the ozone seasonal cycle. Montero, in 1976, explained that atmospheric pollution comes mostly from anthropic activity, but is somewhat controlled by atmospheric phenomena. Therefore, it’s logical to suppose that the different atmospheric patterns throughout the year interact also in different ways with the spatial distribution of pollutants, resulting in seasonal patterns of pollutant behaviour as well. As explained on the 1st chapter (Introduction), tropospheric ozone occurrence depends on a particular combination of precursor pollutants, namely nitrogen oxides and VOC, under the photodissociation influence of sunlight. So, the intensity and frequency of the sunlight reaching the lower atmospheric levels is extremely important in determining ozone concentrations. By analyzing the figures showed in this section (specially figure 42), it’s possible to notice that minimum and maximum concentrations in the year follow a certain pattern, with the lowest monthly values usually observed from MAY-JUL and the highest from SEP-NOV. The seasonal variation of the tropospheric ozone is bonded to the quantity of incoming shortwave solar radiation in the lower atmospheric levels, as in Andrade et al. (1993) e CETESB (1994). This solar radiation presents a minimum around the end of autumn and a maximum in spring, as seen in figure 42.

99 Figure 42: Ozone (µg/m3), solar radiation (W/m2) and cloud covering (%) mean seasonal cycles in SPMA, from 1996-2005. Featuring the months of October (in red, yearly maximum), June (in blue, yearly minimum) and February (secondary maximum). As illustrated by this figure, tropospheric ozone seasonal cycle is evidently influenced by the seasonal cycles of incoming shortwave solar radiation and cloud covering. Pearson’s linear correlation coefficient between average monthly ozone values and solar radiation (1996-2005) is 0.98 and between ozone and cloud covering is -0.6. With increasing solar radiation and decreasing cloud covering, conditions for tropospheric ozone formation on the surface become optimized. The biggest difference between solar radiation and cloud covering occurs in August, September and October, when a significant increase in ozone concentrations is perceived. From November on, although, with a greater cloud covering over the SPMA, there is a decrease in this difference, implying on a reduction on average pollutant concentrations. At any rate, there are episodes of high ozone concentration in summer as well, such as in February, for example, when an ozone secondary maximum takes place. From May on, however, even with lower cloud covering, ozone concentrations drop due to lower incoming radiation. The association of ozone with other atmospheric variables is not as visible in this time scale.

Despite the existence of this average seasonal behaviour, not all monitoring network stations present the same dynamics. Besides yearly variations in the seasonal cycles, each site’s own conditions are paramount in determining O3 concentrations. Figures 43a to 43f show each station’s seasonal cycle in each year of analysis and its own temporal average.

100

Figure 43a: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.

101

Figure 43b: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.

102

Figure 43c: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.

103

Figure 43d Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.

104

Figure 43e: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line.

105

Figure 43f: Ozone monthly seasonal cycle, in each ear, for each station displayed. The average is displayed as the thick black line. Data from figures 43a to 43f were organized in a way to facilitate the observation of the ozone seasonal cycle. All monitored years are represented in all station graphs with the same colours. The average cycle can be found on the figures in the thick black line. Firstly, June minimums and October and February maximums stand out on most stations, especially in the average values (black line), which removes interannual variability. These graphs also show clearly concentrations

106 differences between different

months and

years,

besides station-to-station

differences, since all graphs are in the same temporal scale.

Ozone concentration interannual variation analysis reveals that the year of 2002 (light blue line) stands out with the highest yearly values (figures 43a to 43f). An ozone maximum is observed in October, in all monitoring stations. Other relevant months are: March 2003 (dotted cyan line), that stands out in all stations, September 2004 (dotted dark blue line), with high values observed in most stations, as well as January 2001 (purple line) and March 2002 (light blue line). Some months displaying lower concentrations are April 1998 (yellow line) and some months of the second semester of 1997 (light green line) and the year of 2004 (dotted dark blue line) for the most part. However, these lower concentration months do not show the same spatial consistency than the high concentrations months mentioned above. Some of them will be more accurately investigated in the following sections, in which atmospheric patterns related to extreme ozone concentrations are discussed.

More observations can be pointed out regarding the seasonal cycle, based on the presented figures. It does not only vary in each year, but also according to the chosen station, which means that there is both a temporal and spatial variability of the seasonal cycle. Additionally, it is possible to detect that not all stations represent this seasonal cycle in a well defined manner (June minimums, October and February maximums). Congonhas, Lapa and Osasco stations depict this difference.

In Congonhas station, the highest concentration values in 1997 and 1998 took place in February and December, respectively. In this station, there is no sufficient data for this evaluation in 1996 and 1999. In Lapa station, a clearer seasonal is seen only in 1999 and 2000. In 1997, the highest mean happened in February, and concentrations decreased until a June minimum, which was repeated in September and November; in 1998, the highest monthly mean occurred in December. Osasco station shows a clearer seasonal cycle only in 1999; in 1997, one can verify that JUN-AUG average values were higher than SEP-NOV ones. In 1998, lowest values occurred in March and April, but ozone mean seasonal behaviour was much more variable in 2000 and 2001. For instance, in 2001, the highest monthly mean took place in July. Also in Parque Dom Pedro II and Pinheiros stations, this seasonal cycle

107 is not well represented, except in 2002. In Parque D. Pedro II station, the analysis of the years of 1996 e 2005 is hindered by lack of data availability, and in Pinheiros station, this happens in the years of 1999 and 2003.

A significantly different situation can be found in other ozone monitoring stations. In Ibirapuera station, for example, the seasonal cycle occurred in every year, except for 1997, when FEV-MAY averages were higher than in SEP-NOV and in 1998, when higher concentrations occurred from August on, without the secondary February maximum. In São Miguel Paulista and Santo André-Capuava, seasonal cycle is also clear is all years, only exception being in 2003, when February monthly means were the highest. In Horto Florestal and Nossa Senhora do Ó station, there is little available data for this analysis. In Horto Florestal, the 50 µg/m3 maximum occurred in September 2004, suggesting that this contained one of the most polluted months of September seen on the time series, as verified in other seasonal cycles from other stations, especially in Nossa Senhora do Ó. Mooca and Santo AndréCapuava reflect the cycle clearly. Exceptions made for Mooca station in 1998, when MAY-JUL concentrations were higher than in March and April, in 2003; and in Santo André station, in which the highest means are found in February, July and October. Ozone concentration peaks are easily seen, in both stations, in September 2004 and October 2002.

In Diadema stations, the seasonal cycle is well represented in all years. In Santo Amaro and Pico do Jaraguá stations, there is little available data for representing this cycle, but the afore-mentioned months of February 2003 and October 2002 are evident. In Santana station, the seasonal cycle is perceived in all years but in 2000, when June concentrations are higher than in many other months of the time series in this station, including February 2003. In Mauá, many of these exceptional months are also easily seen, such as October 2002, September 2004, September 2003, March 2002 and January 2001.

The final part of this analysis divides stations in two groups, stations with a clear seasonal cycle, influenced by solar radiation (figure 44), and stations with a different seasonal cycle (45). Figures 44 and 45 show concisely what has just been discussed about the seasonal cycle, its spatial variability and representativeness.

108

Figure 44: SPMA average solar radiation seasonal cycle (W/m2) (blue line with yellow lozenges) and O3 average seasonal cycle in SPMA’s stations with a clear seasonal cycle. Nine out of the 17 stations clearly represent the seasonal cycle (figure 44): Ibirapuera, São Caetano do Sul, Mooca, Santana, Diadema, São Miguel Paulista, Santo André-Capuava, Lapa and Mauá. Nearly all present average monthly concentrations higher than the 17-stations average, except for Lapa station, with much lower concentrations, and Mooca, with values quite close to the average line. It is evident that the average solar radiation seasonal cycle plays a significant role in the ozone seasonal behaviour in these stations. As formerly described, there is a high correlation between ozone and solar radiation average seasonal cycles, but not all stations follow this pattern. Some stations present a cycle which is different from the usual. Pinheiros, Parque D. Pedro II, Osasco and Congonhas present a different seasonal cycle, and so do not have the same correlation to solar radiation in the seasonal scale, as shown in figure 45.

109

Figure 45: SPMA average solar radiation seasonal cycle (W/m 2) (blue line with yellow lozenges) and O3 average seasonal cycle in SPMA’s stations with a different seasonal cycle. These stations present either a slightly (Pinheiros) or completely different (Congonhas) seasonal cycle than the one observed for most SPMA stations. Seasonal cycles from Horto Florestal, Santo Amaro, Pico do Jaraguá and Nossa Senhora do Ó stations have not been included because they have got less than three years of data, which was considered statistically inappropriate for analysis.

Possible reasons for the varied pollutant concentrations behaviour among the stations, as shown in figures 43.a to 45, are discussed in section 3.1.4.

110 3.1.3 Daily Means

Ozone concentration daily mean analysis promotes a deeper understanding of each station’s data temporal variability. In figures 46 to 49c, the invalid and missing data are displayed in the hourly scale, along with ozone means in the daily scale. The complete time series are displayed, which means significantly different periods for each station, as shown in table 10. The result is 365 or 366 data per year, including valid and invalid values (standing for invalid or missing data). This way of representing allows the visualization of daily variability and also the quantity of invalid data in different periods, as well as the identification of long periods with no data.

When analyzing monthly and yearly data, ozone measuring stations were classified according to the pollutant’s mean concentration value, or its tendency and seasonal cycle representation, respectively. When analyzing daily-scale data, an attempt was made in order to check the variability difference between stations and the occurrence of missing or invalid data. The first analyzed aspect was the daily standard deviation, as seen in figure 46.

Figure 46: O3 daily mean concentration standard deviation, in each SPMA’s monitoring station. The average value, 14.30 μg/m3, is shown by the line.

111

In figure 46, station data reflect more clearly different levels of daily variability. It is evident that Congonhas stations has the lowest variability, in contrast to Santo Amaro, with the highest variability in the daily scale. Mooca station occupies an intermediate position in this classification. In the yearly scale, Congonhas station presents the second lowest concentration, and Santo Amaro, the highest. Mooca station is also in an intermediate position regarding the annual means. Therefore, through data from tables 13 (section 3.1.1) and 15, presented below, it is logical to conclude that most stations with low variability also present low average concentration.

112 Table 15: Monitoring stations divided by average standard deviation of ozone daily concentration in the SPMA.

Congonhas

Daily standard deviation 8.01

Osasco

9.81

Lapa

10.59

Pinheiros

11.97

Parque D. Pedro II

13.72

São Miguel Paulista

14.35

Nossa Senhora do Ó

14.74

Horto Florestal

14.77

Mooca

14.87

São Caetano do Sul

14.97

Santo André – Capuava

15.27

Santana

15.80

Diadema

15.97

Pico do Jaraguá

15.99

Mauá

16.33

Ibirapuera

17.02

Santo Amaro

19.00

Station

Monitoring stations can be grouped regarding the calculated standard deviation for daily ozone concentrations. There are stations with low variability, in which the standard deviation is lower than the spatial average (green colour in table 15), stations with medium variability, in which the standard deviation values are close to the average (red colour in table 15) and stations with high variability, in which standard deviation values are substantially higher than the spatial average (brown colour in table 15).

Regarding the volume of data in each station, Parque D. Pedro II station presents the higher number of missing data among the stations featuring low daily variability, as seen in figure 47b. The amount of missing data (shown in blue) ranges from 0 to 24, according to the number of invalid hours in a given day.

113

Figure 47a: O3 Daily mean concentrations in the low variability stations. Invalid or missing data are shown in blue.

114

Figure 47b: O3 Daily mean concentrations in the low variability stations. Invalid or missing data are shown in blue.

Congonhas station does not present any valid data at all after the middle of 1999, but the time series reaches 2001, when the station is deactivated (CETESB,

115 2001). The same happens to Lapa station, which does not present valid data after the end of 2000. Parque D. Pedro II station also features a significant amount of invalid data, mainly in 2000, 2004 and 2005. Among these stations, the ones with the higher data quality are Osasco and Pinheiros, especially Osasco, because there is a considerable amount of invalid data in Pinheiros, mainly in 1999, 2003 and 2004.

Regarding the daily variability, maximum concentrations rarely exceed 80 µg/m3 (figure 47a to7b), except for Parque D. Pedro II station, in the end of the time series, containing values close to 100 µg/m3. As a matter of fact, this station holds higher variability, comparing to others of the same group, with standard deviation equal to 13.72 μg/m3 (table 15). In the monthly mean analysis, this station shows one of the most evident increasing tendencies in the time series from 1996 to 2005. However, the great amount of invalid data at the end of the station’s time series (figure 47b) hinders a better evaluation.

The following stations were classified as medium variability stations: Horto Florestal, São Miguel Paulista, São Caetano do Sul, Mooca and Nossa Senhora do Ó. A lesser amount of invalid data is seen (figures 48a to 48b), comparing to the low variability stations, (figures 47a and 47b), except for Nossa Senhora do Ó and Horto Florestal stations, for their evaluation period begin theoretically in January 2004, but only have valid data after July (Nossa Senhora do Ó) or September (Horto Florestal) of that year.

116

Figure 48a: O3 Daily mean concentrations in the medium variability stations. Invalid or missing data are shown in blue.

117

Figure 48b: O3 Daily mean concentrations in the medium variability stations. Invalid or missing data are shown in blue. Either way, a higher amount of invalid data is more visible in the beginning or the end of the time series. Although Horto Florestal station presents maximum daily values around 70 μg/m3, other stations from this group present values between 80 and 100 µg/m3, and maximums around 120 μg/m3, in São Miguel Paulista station, in October 2002 (48a).

Santo André-Capuava, Ibirapuera, Mauá, Diadema, Santo Amaro, Santana and Pico do Jaraguá stations were classified as stations with the highest daily concentrations variability (figures 49a, 49b and 49c):

118

Figure 49a: O3 Daily mean concentrations in the high variability stations. Invalid or missing data are shown in blue.

119

Figure 49b: O3 Daily mean concentrations in the high variability stations. Invalid or missing data are shown in blue.

120

Figure 49c: O3 Daily mean concentrations in the high variability stations. Invalid or missing data are shown in blue.

Concerning the stations from the high variability daily concentrations group, higher concentration episodes surpass 100 µg/m3 in Mauá and Santana stations, reaching maximum daily values equal to 120 µg/m3 in Diadema station in 2002 and in Santo Amaro station in 2003. When looking at Ibirapuera station, a peak of 130 µg/m3 can also be seen in the beginning of the time series (1996), similar to peaks

121 found in Diadema (October 2002), Pico do Jaraguá (September 2003) and Santo Amaro (February and Mach 2003) stations. Ibirapuera’s station decreasing tendency is not so evident in the daily data time series (figure 49b) as in the yearly and monthly means time series (figure 33 and annex 1). Mauá and Santana stations also present high concentrations, but do not show as many extreme concentrations as the other stations in this group. The highest ozone daily concentration variability is observed in Santo Amaro station.

Regarding the invalid data frequency, it was possible to conclude that, the highest the variability in daily data, the lesser invalid data a station contains. A possible explanation could rely on the fact that the stations with high variability also present the highest concentration means, and so, are more relevant for representing this pollutant in the SPMA. Some of them (such as Pico do Jaraguá and Ibirapuera) belong to the “urban station” category, which measure the city background concentrations away from the vehicular and industrial emission sources (CETESB, 2004, 2007). Thus, due to the greater relevance of these stations in representing ozone concentrations in the SPMA, their equipment might have been kept in better technical conditions. Another fact is that the stations classified as high daily concentrations began their measuring work in latter periods in the time series, when improved technologies were available, which contributes to a better data quality. The opposite might have occurred to the low variability stations.

From 2002 on, there is almost no invalid data in the SPMA stations, as seen in table 16:

Table 16: Invalid data percentage by year in some CETESB stations within the SPMA. Blue squares indicate inactive years in the given station. Year/Station

1996 1997

1998

1999

2000

2001

Congonhas

NA

12.3

52.4

9.7

0.4

5.1

4.5

9.0

Diadema

2002

2003

2004

2005

Total

2.1

0.0

0.0

0.0

0.0

1.6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

2.8

16.7

Horto Florestal Ibirapuera

0.0

4.4

6.5

8.7

6.6

2.4

Lapa

0.0

9.6

14.0

7.8

30.4

0.0

Mauá

0.0

5.2

9.4

9.0

10.4

2.9

0.0

0.0

0.0

0.0

3.6

4.2

6.7

5.3

9.3

4.0

0.0

0.0

0.0

0.0

3.2

0.0

0.0

0.0

Mooca Nossa Senhora do Ó

10.3

122 Osasco

0.0

4.5

4.5

7.2

6.6

2.1

Parque D. Pedro II

0.0

4.2

11.8

5.0

ND

3.0

Pinheiros

0.9

3.0

3.3

Santana

12.9

4.6

2.0

Pico do Jaraguá

4.1 0.0

0.0

0.0

0.0

0.0 0.0

Santo Amaro

0.0

0.0

2.6

0.0

0.0

0.0

1.0

0.0

0.0

0.0

3.2

0.0

0.0

0.0

0.0

0.0

São Caetano do Sul

0.0

11.7

7.8

8.5

4.8

1.9

0.0

0.0

0.0

0.0

3.4

São Miguel Paulista

0.0

15.0

7.8

5.4

5.6

2.5

1.1

0.0

0.0

0.0

3.7

0.0

2.2

0.0

0.0

0.0

0.0

0.3

Santo

André

–

Capuava

*NA: not available

Percentages shown in table 16 consider only invalid data, not invalid and missing, as was presented in figures 47a to 49c. Therefore, it is possible to conclude that from 2002 onwards, almost all invalid data represented in figures 47a to 49c indicate, as a matter of fact, missing data. Missing data occurs when there is a period in which the station, for some reason, such as maintenance, technical problems, etc., is not working. Invalid data, found before 2002, come from incorrect measuring. This way, maintenance and upkeeping of the CETESB air quality monitoring networks are associated with a better data quality. Nonetheless, missing data in some periods after 2002 is still considerably frequent, in some stations, so this set of data is yet to become even more complete.

Some final comments can be made about data validity. The period with invalid or missing data in the beginning of the analysed period (1996 to 2005) refers, mostly, to the lack of information due to the fact that the station was not working, since time series were completed with values that indicated missing data so that it would be possible to work with all stations in the same database. For example, according to the CETESB Air Quality Report of the SPMA and the Countryside in 2000, Santana station began its measuring work on 06/05/1999, although ozone data registered values since the first day of that month, and the time series shown in the figures range from 01/01/1999 to 31/12/2005. Congonhas and Lapa stations show similar facts. The end of their time series demonstrates a high number of invalid data; this stations present very low ozone concentrations compared to other stations, and so, were gradually deactivated. This procedure promoted proper aiming at other SPMA’s regions, with worse ozone air quality indexes (CETESB, 2004). It was not possible to obtain data from October, November and December 2001.

123 3.1.3.1 The SPMA’s diurnal tropospheric ozone cycle In the daily scale, besides the daily means time series, a study of tropospheric ozone concentration hourly variation was performed. Pollutants normally follow a diurnal cycle, with concentrations varying according to different atmospheric conditions and emission patterns through the day; and it is important to recall that the emission patterns are directly influenced by the anthropic activities that originate them in the first place (MONTEIRO, 1976). So, one can expect that a vehicular pollutant will have typical concentration peaks according to the places and hours of the day when a greater vehicle flux is observed, such as the morning and late afternoon rushes (from about 7 to 10a.m. and from about 5 to 8 p.m., respectively) in the big avenues. These are the hours when the anti-traffic laws prevail, in order to decrease traffic congestion and pollutant emission at these hours, because at the other hours of the day, the emission of pollutants is less concentrated (CETESB, 2006).

Tropospheric ozone cycle, being a secondary pollutant, is certainly influenced by the different precursors emission rates throughout the day (MARTINS, 2006). Its daily cycle was calculated according to what is described in 2.3 Methodology.

Ozone average daily behaviour was already studied by Qin et al (2004) in Los Angeles, in which ozone quantities in the atmosphere are calculated for weekends and weekdays, suggesting different mean ozone quantities for these periods. Hypothesis suggests that there is less light scattering in weekends, due to lesser PM emission in these days, as well as less NOx emissions, leading to an increase in ozone concentrations for reasons aforementioned. Another previously cited study was carried out by Azevedo (2002) in the SPMA, in which O3 daily cycle is analyzed, with similar conclusions: weekly ozone variation is inversely proportional to the intensity of human activities. Results obtained in this work about the tropospheric ozone daily cycle were quite similar, confirming the results obtained in these mentioned researches. According to the results shown in figure 50, ozone average daily cycle presents a typical pattern: concentrations are generally lower at night and increase progressively

124 after 7 a.m. At around 3 p.m., there is a maximum in the pollutant concentration, according to results obtained by 13 stations in at least eight months in the year. After that, concentrations decrease. They reach a secondary minimum at around midnight and show a slight increase until the 4 a.m. nocturnal maximum, which was also detected by 13 stations in at least eight months in the year. According to 10 of the 17 stations, concentrations decrease again and reach a minimum at around 7 a.m., and then start rising again, hence restarting the cycle. To understand more clearly this ozone behaviour in the daily scale, an average daily cycle was calculated and is presented in figure 50:

Figure 50: Ozone average daily cycle from the 17 stations in the SPMA Some observations can be made about the typical behaviour of this pollutant that reflect its secondary and photochemical nature. Because the amount of solar radiation reaching the ground determines tropospheric ozone formation, higher concentrations are expected at more intense irradiation hours. Thus, ozone daily concentration peak takes place, in average, at around 3 p.m. in SPMA, a couple of hours after the peak of solar radiation incidence on the ground on a clear day. Moreover, in this hours, ozone precursors reach a minimum, for a great part of them

125 has already been converted into ozone. So, this NOx minimum also contributes for the ozone maximum, because there is less ozone consumption by NOx:

Figure 51: NOx average daily cycle, Cerqueira César station, from the 12th of August to the 09th of September of 1997. The bars represent the standard deviation of concentrations hourly averages in sunny and cloudy days. Source: Castanho, 1999.

Therefore, given the abundant availability of ozone precursors in the SPMA’s mixing layer, tropospheric ozone amounts in the daily scale is also strongly influenced by incoming shortwave solar radiation. In the morning, after the 7-8 a.m. rush, environmental and chemical conditions for ozone formation start to increase As previously explained, (section 1.2), NO slowly starts to turn into NO2, and NO2 photodissociation starts to take place and form ozone (ANDRADE ET AL, 2004). An example of the daily evolution of these pollutants can be seen in figure 04.

This tardiness happens because of reaction time of NOx photolysis (MASSAMBANI ET AL, 2004). Consequently, at around 3 a.m., after many hours with high incoming shortwave solar radiation, accumulated ozone quantities reach a peak. Due to the rotation of the Earth, incoming shortwave solar radiation starts to

126 decrease. This implies in a reduction in the formation of ozone. Besides that, NOx emission in the late afternoon rush (at around 6 p.m.), contributes to a greater consumption of ozone. Concentrations remains low during nigh time. Ozone reaches a minimum concentration at around 6-7 a.m., because at this time, there is already significant NOx emission which consumes ozone in the air. Additionally, solar radiation at this time is still low. Therefore, ozone is formed in slower rates and there is a deficit in its concentration, which is already low due to the absence of solar radiation at night. From about 9 a.m. onwards, greater sunlight availability contributes even more to increase ozone concentration, by removing potentially ozoneconsuming NOx from the air and increasing ozone concentration simultaneously, through the process of photodissociation.

The only part of the cycle that remains yet to be explained is the ozone nocturnal increase, the small “peak” in the early morning that occurs at about 3-4 a.m. in 14 stations. Since there is no solar radiation during this time, other processes must account for the increase of ozone concentrations in the early morning, such as the transport of other compounds, for example. One suggestion is that tropospheric ozone concentrations increase at this time due to a precursors minimum, as seen in figure 52.

127

Figure 52: NO2 and ozone daily cycles on the 03rd and 04th of February, 1998. Source: Castanho, 1999.

Generally, this is the typical behaviour observed in most stations, for most months. There are, though, variations according to these parametres. For example, most stations show daily average curves of higher concentrations in the SEP-NOV period, confirming the influence of the seasonal cycle previously mentioned. This difference can be seen when comparing figures 53a and 53b below.

128

Figures 53a (above) and 53b (below): Solar Radiation average daily cycle (19992001, in blue) and ozone concentrations (1996-2005) in October and June, in Santana (red) and Osasco (yellow) stations. Source: CHIQUETTO et al, 2007 It is evident, from the figures presented and the topics discussed previously, that solar radiation variability is of paramount influence on tropospheric ozone

129 concentrations both on the seasonal and daily time scales. Nocturnal maximum occurs around 3 or 4 a.m., and can be seen in figures 52, 53a and 53b. It happens mainly from SEP-NOV, the months with the highest pollutant concentration, in most stations, as aforementioned. Congonhas station did not present enough valid data at 4a.m. Lapa station does not present this secondary maximum as consistently, and in Pico do Jaraguá station it occurs around 2 or 3 a.m.

130 3.1.4 analysis of tropospheric ozone spatial distribution in the SPMA

In sections 3.1.1 to 3.1.3, the different ozone concentrations observed in the different monitoring stations were discussed. Analysis of the pollutant’s behaviour in these three scales allowed perceiving important differences in the average concentration level, data variability and seasonal cycle traits in the different monitoring stations in the SPMA. Therefore, it was possible to conclude, based on the results obtained, that tropospheric ozone concentrations in the analysed stations is not homogeneous in the SPMA. This spatial distribution of the pollutant will be briefly discussed in this section.

In sections 2.1.2, some differences among stations were defined, concerning their location, soil use in its surroundings (residential, industrial, commercial, etc.), and exposition to near emission sources, via analysis of some documents published by CETESB, works from other authors (SAMPAIO, 2000, AZEVEDO, 2002) and satellite photos containing the approximate location of the station and its surrounding area. These features are summarized in table 17:

131 Table 17: Exposition, seasonal cycle and concentration level features of the CETESB stations. Station

Direct exposition and local sources Seasonal Concentration level type

Cycle

Congonhas

Yes – vehicular

No

Low

Diadema

No – vehicular

Yes

High

Ibirapuera

No near emission sources

Yes

Medium

Horto Florestal

No – vehicular

-

Medium

Lapa

Yes – vehicular and industrial

Yes

Low

Mauá

No – industrial

yes

High

Mooca

No – vehicular and industrial

Yes

Medium

Nossa Senhora do Ó

No – vehicular

-

Low

Osasco

Yes – vehicular and No – industrial

No

Low

Pico do Jaraguá

No near emission sources

-

Very high

Pinheiros

Yes – vehicular

No

Low

Parque D. Pedro II

Yes – vehicular

No

Low

Santana

No – vehicular

Yes

Very high

Santo Amaro

No – vehicular

-

Very High

São Caetano do Sul

No – vehicular and industrial

Yes

Medium

São Miguel Paulista

No – vehicular

Yes

High

Santo André – Capuava

Yes – vehicular and industrial

Yes

Medium

Source: SAMPAIO (2000), CETESB (2004, 2006, 2007) and AZEVEDO (2002)

As mentioned in section 2.2.1, Horto Florestal, Pico do Jaraguá, Nossa Senhora do Ó and Santo Amaro stations had less than three years of data in the data set used in this study, and for that reason were considered statically improper for the average seasonal cycle analysis.

When analysing table 17, it is clear that all stations classified as low concentrations (according to the methodology applied in 3.1.1: Congonhas, Lapa, Osasco, Pinheiros and Parque D. Pedro II) are all situated in areas with intense urban activity and exposed to direct vehicular emissions. Other stations present variable degrees of exposition to vehicular and industrial pollution, but it is interesting to notice that even the stations located close to green areas such as Pico do Jaraguá, Parque do Ibirapuera and Horto Florestal, or even leisure areas or a significant amount of vegetation (Mauá, Santo Amaro, Santana) present higher concentrations compared to the ones situated directly at the avenues. It is important

132 to highlight that Diadema and Santana stations (of high and very high concentrations, respectively, according to the aforementioned methodology), although placed close to intense vehicle traffic roads, are not directly exposed to them, because they are not on the venue itself, but in green areas located nearby (figures 15 to 31). Despite the fact that it may not be perceivable in a first glance on the photo, more information about the stations’ site was gathered through other aforementioned studies, that shed a light on these issues, because CETESB unfortunately did not publish studies about all stations.

For this reason, it is evident that stations situated in areas with less urban activity, essentially, with reduced vehicles traffic, present higher ozone concentration and a clearer seasonal cycle.

It has already been discussed in this work how ozone formation over surface in urban centres depend on certain precursor gases, especially NOx and COV (mainly emitted by vehicles) and on the photodissociation effect induced by shortwave solar radiation, but that an excessive amount of these gases end up reacting in a way to consume ozone. For this reason, conditions for ozone accumulation on great avenues with intense vehicle flux are not favourable, due to the great amount of NOx which consume this gas, according to reactions described in sections 1.2 and 1.2.1.

Therefore, it is logical to conclude that the highest ozone concentrations are found exactly in places with plenty of green area or even leisure areas. Ibirapuera, Horto Florestal, Diadema and Pico do Jaraguá stations are located in urban parks inside the SPMA, true vegetation oasis in a huge and densely urbanized, overpopulated centre. Santana station is located in Campo de Marte, a small airport close to Marginal Tietê (an important freeway), but with a significant amount of green area. Other stations, such as Santo Amaro, Mooca and São Caetano do Sul, re located in leisure or educational areas, such as sportive and educational public centres and primary and secondary schools. The stations São Miguel Paulista, Nossa Senhora do Ó, Santo André-Capuava and Mauá are situated in predominantly residential areas, being the closest representatives of the air people breathe at their homes. But concentrations observed in schools, parks and leisure areas are equally important,

133 because common sense leads people to believe that these areas would have cleaner air. Ironically, the exact opposite is found for tropospheric ozone concentrations: avenues have lower concentrations of this pollutant than parks, leisure areas. This happens so because the air layers closest to the ground, in the avenues and areas with intense urban activity, are already saturated with other pollutant species. It is important to recall that stations situated far from the emission sources are regarded by CETESB as measuring background air pollution, the closest to the whole urban area average.

As a conclusion for what has been presented through this data set, it can be said that there are no “clean air havens” inside the SPMA. The extreme population growth, the demand for products and services, the frantic intensity of urban activity, all lead to a substantial amount of emissions of substances which are harmful to human health and the environment, which contaminate the whole air in the local scale. Within this context, atmospheric conditions play a vital role in the variability of this substance’s concentrations. In the figures in annex 3, a rather rough idea of this pollutant’s spatial distribution in the SPMA area is presented. According to what was described in section 2.3.1, the GrADS interpolation tool, which is based on the Cressman analysis (CRESSMAN, 1959) was used to generate this map.

In these figures, green numbers indicate location and identification number of the stations, while white numbers represent isolines of ozone concentration values μg/m3. Congonhas, Lapa and Ibirapuera stations correspond, in the picture, to numbers 1, 5 and 4, respectively. Their analyses allow seeing that the highest ozone concentrations, represented by the colours green, yellow, orange and red, take place in areas far from the city centre and from dense vehicle circulation. Osasco station (No 9) would be an exception to this rule, because it is relatively far and present low concentrations, however, it is known that it suffers direct influence from the pollutants in Autonomistas Avenue (Figure 15), which eventually decreases its average concentrations according to what has been previously explained.

134 In Pico do Jaraguá and Santo Amaro stations (No 10 and No 14, respectively) the highest average ozone concentrations were observed. Although Santo Amaro district is densely urbanized and has avenues with intense vehicles flux (Marginal Pinheiros, Av. Santo Amaro), the station is located inside a public educational and sportive centre, and so, far from the primary pollutant emission sources. Therefore, this map shows relevantly only the concentrations observed at the monitoring stations, but not the areas among stations, due to the fact that each area suffers direct influence from the prevailing soil use. The mobile station Pico do Jaraguá was installed on the Pico do Jaraguá (figure 21) mountain, in its eastern slope, thus representing its surroundings, which are less urbanized and away from the urban centre. The high average ozone concentrations observed might be due to ozone or its precursors transport from Bandeirantes motorway or other important roads by southeast winds that, according to several CETESB’s studies (2003, 2004, 2005, 2006, 2007), are the most usual in several network stations in the SPMA, and are also associated to high ozone concentrations in many of them (CETESB 2003, 2004, 2005, 2006, 2007).

As a last conclusion to be drawn from this section, it is reasonable to say that in order to reach a better understanding of ozone’s spatial distribution in the SPMA, one should consider coupling information from observations made at monitoring stations with soil use information in the city, specially regarding the amount of vehicles in the great avenues (AZEVEDO, 2002) and also with atmospheric motion in the intra-urban scale. Tarifa and Azevedo’s study (2001), about the division of the metropolis in diverse micro-climatic unities, according to aspects as the soil use acquire greater importance in this context, in which local atmospheric compositions are of prime importance in determining ozone concentrations.

135 3.2 Months of ozone anomalies and the observed atmospheric anomalies As explained in the previous section, months of anomalous ozone concentrations were selected in order to analyse the atmospheric anomalies observed in each of them. Comparison to the standard deviation allowed classifying months in two categories, as indicted in previously. This information is summarized in tables 18, 19, 21 e 22:

136

Table 18: Months with ozone concentration anomaly average value higher than 1 of the corresponding month (category 1). The most intense anomalies in each month are highlighted in red and yellow:

137 Table 19: Months with ozone concentration anomaly average value higher than 2 of the corresponding month (category 2)

In the considered period (1996-2005), 22 months presented positive ozone anomaly higher than 1, using the monthly standard deviation as the classification criterion, that can be seen in Table 18 (above). The highest number of cases occurred in the years of 2002 and 2003. Three months in the time series, August 1999, March 2002 and February 2003 (in red), present ozone concentration anomaly higher than 2, as seen in Table 19. Other months with high concentrations, quite close to this border, are April 2000, January 2001, October 2002 and September 2004, highlighted in yellow.

Inbetween 1996 and 1998, there were no cases of concentration anomalies higher than 1, except for May 1997 (Table 18). In 2001, 2004 and 2005 only on event of concentration anomaly higher than 1 was observed in each year. The highest number of concentration anomalies higher than1 was observed between 1999 and 2003.

138

Figure 54: Annual distribution of the number of ozone concentration anomalies higher than 1. Particularly, in 2002, six cases were recorded, and in 2003, five. The year 2000 follows with four cases and 1999, with three. From these cases, only three are higher than 2: August 1999, March 2002 and February 2003, which will be discussed in more details ahead.

139

Figure 55: Monthly distribution of positive ozone anomalies higher than 1σ, in the period 1996-2005, considering the 17 stations. When analysing figure 55, it is clear that, within the time series considered, positive ozone anomalies higher than 1σ were more frequent in April and May. None of these anomalies, however, were classified as category 2, what demonstrates that the anomalies observed in these months, despite more frequent, are less intense. When comparing these occurrence of O3 anomalies situations to climatic anomalies, certain prevalent atmospheric patterns can be observed together with ozone concentrations positive anomalies higher than 1. The number of positive or negative climatic conditions anomalies in these extreme cases (total of 22 cases) is indicated in Table 20.

140 Table 20: Climatic anomalies in months of ozone positive anomalies higher than 1.

According to the information in Table 20, significant positive ozone anomalies (higher than 1) are frequently associated to positive anomalies of solar radiation (RAD), air temperature (TEMP), surface atmospheric pressure (SLP) and outgoing longwave radiation (OLR), and to negative anomalies of air relative humidity (RU). Fourteen (63.3%) out of the 22 cases of positive anomalies higher than 1 were associated to positive anomalies of RAD, TEMP, SLP and OLR and to negative anomalies of RU. From the 22 ozone positive anomalies higher than 1σ, 18 cases (80%) were associated to positive anomalies of RAD and OLR and to RU negative anomalies. Therefore, it seems that the variables associated tor radiation, either solar or from the Earth, and to relative air humidity are well associated to high ozone concentrations than TEMP and SLP, although these still bear some connection to the positive deviations of ozone concentrations.

In the months when ozone concentration anomalies higher than 1 were identified, the climatic anomalies values were the following (Table 18): solar radiation, highest value in April 2000, with +33.70 W/m2, and the lowest, in January 1999, with negative anomaly of –24.70 W/m2; air temperature, highest in October 2002, with +3.53°C, and the lowest, May 2003, with – 0.91°C; atmospheric pressure – the highest observed anomaly in January 2001, +2.03 hPa, and the lowest, in September 1999, – 1.14 hPa; outgoing longwave radiation – the highest value also in April 2000, +21.61 W/m2, and the lowest in January 1999, -16.40 W/m2.

From these values, it can be seen that the positive climatic anomalies that are associated to ozone concentration positive deviations are always more extreme than the negative ones, for RAD, TEMP, SLP and OLR (table 18). RU presented the

141 lowest anomaly in October 2002, -13.46%, and the highest in November 2000, +4.98%.

Negative anomalies of RAD, TEMP, SLP and OLR (and positives for RU) associated to the referred positive ozone anomalies are less frequent and less intense than the positive ones (for RU, the opposite signal prevails – positive anomalies are more frequent and intense than negative ones for RU). This negative anomalies (or positive for RU) correspond to only 37% of the cases, considering all variables, 20% of cases, considering only RAD, OLR and RU, and 35% less intense, in average, compared to anomalies with the opposite signal.

As a conclusion for this analysed set of data, it can be stated that when atmospheric conditions of more intense solar radiation, higher temperature and pressure, less cloudiness (higher OLR) and lower air relative humidity occur, there are higher chances that ozone concentrations will be higher than the average value for the given month. Particularly, relative air humidity anomalies were negative in 20 out of 22 cases in which ozone concentration anomalies was higher than 1, which stands for 90% of the cases.

In months of positive ozone anomaly superior to 2 (category 2) (August 1999, March 2002 and February 2003, Table 12), the second highest OLR anomaly can be observed, +15.63 W/m2, in August 1999. In February 2003, a very significant RAD anomaly was observed, +31.11 W/m2, that although is the third highest anomaly for this variable in this time series, is considerably close to the highest values, in April 2000 and January 2001 (+33.7 and 32.48 W/m2, respectively, table 18). Other months in which high atmospheric anomalies were observed include January 2001 (RAD and SLP) and October 2002 (TEMP and RU), that, although are not included in category 2, are also highlighted in Table 18 as months with high concentration.

This initial classification of months that had been performed without removing the increasing ozone concentration tendency from the time series had indicated these same months as category 2. Nonetheless, after tendency removal and calculation review, it was possible to verify that the high concentrations observed in January 2001 and October 2002 were influenced not only by atmospheric conditions,

142 but also by the increasing ozone concentration tendency in the SPMA. In contrast, August 1999 and March 2002, previously considered as category 1, moved up to category 2 after removing the tendency. Thus, one conclusion that can be drawn from this analysis is that the increasing pollutant tendency not only masks the real monthly variability of ozone concentrations as well as the relative importance of the high concentrations in each month of the studied time series. But for the negative anomalies (categories –1 and –2), a smaller number of cases were observed (Table 21):

143

Table 21: Months with average O3 concentrations lower than –1 of the corresponding month (category -1).

Table 22: Months with average O3 concentrations lower than –1.5 of the corresponding month (category -2).

144 When analysing O3 concentrations negative anomalies’ intensity, it was verified that no month presented anomaly lesser than –2, thus, the classification criterion for dividing months according to anomaly intensity was changed for negative anomalies. Months with anomaly lesser than–1.5  were classified as category –2. Even though, only two months were able to fit into this category: April 1998 and July 2005, which will be discussed in more details ahead.

In the evaluated period (1996-2005), 14 months were classified as category 1, which means O3 concentration anomaly lesser than -1, which accounted for 12% of the total months within the time series. Months of category -1 were 35% less frequent than months of category 1. Just like was performed for months of positive anomalies, an internal classification was used to separate months with different anomaly intensities. The months of April 1998 and July 2005, highlighted in blue, were considered as category –2 (table 22). Other months with significantly negative deviation are November 1997 and January 2004, highlighted in green in table 21.

Figure 56 show a higher number of cases of category -1 in years in which months of category 1 were observed to occur in lesser frequency, what is evident in the beginning and at the end of the temporal series (1997, 1998, 2004 and 2005). There are less months of category -1 than months of category 1, what shows that, when considering O3 negative anomalies, a considerable part of cases present values between the average and -1.

145

Figure 56: Annual distribution of anomalies in O3 concentrations lesser than – 1. Three cases of anomalies lesser than -1 in the boundary years of the temporal series, 1997, 1998, 2004, and 4 cases in 2005 (Figure 56), in contrast with positive anomalies, that were observed specially in the middle of the time series, between 1999 and 2003 (Figure 56). In 2001, only a single case such as this was observed, in May. There were no other cases of O3 concentrations anomalies lesser than -1 (which means no category –1 and –2 months), as shown in table 21. The month with the highest number of cases is December. There weren’t any cases of negative anomalies lesser than –1σ in months of October and February, as can be seen in Figure 57:

146

Figure 57: Monthly distribution of anomalies higher than 1σ, from 1996 to 2005, considering the 17 stations. Table 23 shows the climatic anomalies that occur in the class -1 months:

Table 23: Climatic anomalies in months of ozone negative anomalies higher than 1.

Initially, it is possible to notice that the climatic variables associated to ozone negative anomalies lesser than –1 do not suggest a significant influence on ozone concentrations, in comparison with what was observed for the positive anomalies (table 20). Variables TEMP and RU, for instance, present exactly the same quantity (7 cases) in months of category -1. There is a slightly higher number of negative RAD anomaly (8 cases; 57%) than positive anomalies of this variable (6 cases). For SLP, 8 positive anomaly cases and 5 negative anomaly cases were registered, indicating similar behaviour pattern to positive ozone anomalies, therefore, opposite to what

147 would be expected of negative anomalies. The variable that behaved in the most easily defining manner in months of category -1 was OLR, which presented negative anomalies in 9 out of 14 cases, which means 64% of cases, indicating more cloudiness in these cases. This may have had a greater impact on O3 formation and concentrations. According to table 23, it is possible to see that the lowest RAD anomaly occurred in November 1997, with –31.27 W/m2, and the highest, in December 2005, with +21.85 W/m2. The variable SLP presented its lowest anomaly, -2.65 hPa, in September 1997, and the highest, +1.75 hPa, in June 2004. For OLR, the lowest anomaly in the time series occurred in November 1997, with –17.19 W/m2, and the highest in December 2005, with +15.7 W/m2.

The highest RU anomaly occurred in August 1998, +4.49%, and the lowest, in September 1997, - 8.28%. TEMP had its lowest anomaly in December 2005, – 1.46°C, and the highest in September 1997, +2.04°C. When considering months of category -1, negative RAD, SLP, OLR and RU anomalies are more intense than positive ones, and positive TEMP anomalies are more intense than negative ones. Therefore, these observations suggest that RAD and OLR negative anomalies have a greater influence over O3 negative anomalies, in accordance to what has been observed for class 1 months.

The same number of positive and negative anomalies were observed for variables RU and TEMP in months of category -1. However, TEMP positive anomalies are more intense than negative ones, and, when considering RU anomalies, negative anomalies are more intense. From the months that had been classified as category –2 previously to the increasing tendency removal from the time series, April 1998 was the only one that remained in this category. This means that in this month, ozone concentrations were truly low. After removing the tendency, the month of July 2005 was also added to category -2. The anomalous negative behaviour of this month had been masked by the pollutant’s increase in the time series.

When analysing variables behaviour in cases of ozone positive concentration anomalies, it was possible to conclude that these anomalies are, mostly, associated

148 to positive TEMP, RAD, OLR and SLP anomalies, RU negative anomalies. One could expect that negative anomalies of this pollutant would be associated to exact opposite atmospheric patterns, which would be negative anomalies of TEMP, RAD, OLR and SLP and positive RU anomalies. However, atmospheric variables in the months of April 1998 and July 2005, which are category –2, do not happen in this expected manner, except for variables OLR and RAD, which are positive in those two months. Therefore, it is suggested that increasing cloudiness and decreasing incoming radiation on the surface have a greater impact over negative tropospheric ozone anomalies; however, this variables’ behaviour probably do not account solely for determining ozone concentrations negative anomalies, and there must be other ozone removal mechanisms that influence in a way to decrease this pollutant’s concentrations under these circumstances.

Figure 58 shows the temporal evolution of monthly ozone, RAD and RU anomalies, since these were the variables that were better associated to ozone in this time scale, from the previous analysis.

Figure 58: Monthly evolution time series for RU, RAD and O3 anomalies. By the analysis of figure 58, it is possible to detect that most positive ozone anomalies occur together with positive RAD and negative RU anomalies. An opposite

149 pattern is seen for ozone negative anomalies – negative RAD anomalies together with positive RU anomalies. The Pearson correlation index between monthly ozone anomalies and RAD was 0.42 and between ozone and relative humidity was –0.4.

As a final conclusion for this section, it is possible to say that O3 variability is somewhat influenced by the observed atmospheric conditions, mainly when the pollutant’s positive anomalies are considered. Nevertheless, the temporal series’ increasing tendency must also be considered, which can mask important anomalies and their interpretations. On the other hand, regarding negative ozone anomalies, there must be other processes involved, probably streaming from atmospheric chemistry. These processes are mentioned in section 1.2.1 Tropospheric ozone in large urban centres. Figure 59 shows ozone anomalies’ monthly time series, after removing the tendency.

Figure 59: O3 concentration anomalies’ time series, considering the 17 stations. Months chosen for the atmospheric patterns analyses are indicated by the blue and red arrows. Months shown in this figure are category 2 (red arrows) and –2 (blue), and also other months which had quite similar anomalies to the thresholds chosen for these

150 categories. For negative anomalies, this months are November 1997, April 1998, January 2004 and July 2005. For positive ones, August 1999, April 2000, January 2001, March 2002, October 2002, February 2003 and September 2004. They shall be discussed in more details in the next section.

151 3.3 Association of atmospheric patterns to ozone concentrations

The following section is intended to analyse average atmospheric fields during months with positive and negative ozone anomalies greater than 2, organized in categories 2 and –2, and also some other specific months. Evaluating these fields allows for a more complete look on the average atmospheric behaviour during the chosen months, inserting them in this context. It is meant to deepen the analysis performed in the previous section, which only mentioned punctual climatic anomalies associated to negative and positive ozone concentration anomalies in the SPMA. Besides months of categories 2 and –2, the months of October 2002, January 2001, April 2000 and September 2004 were also chosen. Apart from the fact that their concentrations alone are classified as category 1, they were higher than the month’s average and quite close to those observed in months of category 2. Specially, excessive ozone concentrations in October 2002 were responsible for AQS (Improper air quality) and attention level surpassings (Bad air quality) in several CETESB monitoring stations, in several days, including weekends (CETESB, 2003). Moreover, O3 anomaly recorded in this month was higher than 1,9σ, which is in fact, really close to what was registered for category 2 months. As a matter of fact, before removing the increasing tendency from the time series, this month belonged to category 2. November 1997 and January 2004 were also included due to their low concentration anomalies, quite similar to what was observed for months of category – 2, in an effort to increase the number of cases for analysis of months with low ozone concentrations. .

The following atmospheric compositions were mostly plotted for surface level. Whenever there is no indication in the subtitle, it is assumed that the level on the figure is surface level.

152

3.3.1 Months with intense negative ozone anomalies.

April 1998 Analysing the atmospheric fields for April 1998 (figures 61a to 61e), it is possible to notice that there were no great climatic anomalies in this month, comparing to other months of categories –2 and +2. However, this month was featured by OLR and surface air pressure, as shown by figures 61b and 61c. These anomalies were associated to a greater intensity of the frontal systems 14 over Southern Brazil, where the highest values of negative anomalies were registered, and also the Southeast of the country. For the SPMA, there was a slightly positive OLR anomaly, +2,28W/m2, according to table 18 in the previous section. Although there were some anomalies of the mentioned variables, most analysed variables had values close to zero in the SPMA region, what means an atmospheric situation closer to what would be expected in this month for this region.

Nevertheless, negative OLR anomalies are observed over all southern Brazil, being associated to frontal systems stationarity which was greater than the climatological mean, according to the Climanálise bulletin of April 199812.

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Figure 60: Passing of frontal systems on the Brazilian coast, April 1998. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0498/index.html This region of OLR negative anomalies extends over the state of São Paulo, showing a higher number of frontal systems passing for this area. According to the Climanálise bulletin for this month, four systems passed through and influenced between latitudes 20ºS and 25S, where the SPMA is located. Despite the fact that the average for this month in this latitude is six systems, these systems were intense enough to induce greater cloudiness and precipitation rates over the Centre-south of Brazil in this month.

Such aspects might have had influenced on the negative values of O3 concentrations that were observed in this month, as indicated in Table 18. Atmospheric motion in high levels in this month (figure 61e) provided enough anomalous atmospheric instability in lower levels. In the state of São Paulo region, the atmosphere seems to be in a transition region between the patterns of air humidity, OLR and temperature observed in the South and the Northeast of Brazil.

154

Figure 61a: Air relative and specific humidity in April 1998 and its respective anomalies.

155

Figure 61b: Incoming shortwave solar radiation and OLR in April 1998 and its respective anomalies.

156

Figure 61c: Air temperature and surface air pressure in April 1998 and its respective anomalies.

157

Figure 61d: Streamlines and wind direction and intensity in April 1998 and its anomaly.

158

Figure 61e: Air divergence over surface and in higher levels in April 1998 and its respective anomalies.

159 July 2005

Cold fronts passing over the South and Southeast of Brazil in July 2005 were associated to positive divergence anomalies over the South of Brazil, while regions north of the SPMA show anomalies close to zero (SPMA) or negative (figures 62e). This behaviour indicates that the Southern region of the country were more closely associated to divergent air motion in lower atmospheric levels. Surface air pressure, OLR and solar radiation over the state of São Paulo presented positive anomalies in this month, and temperature and relative humidity, negative anomalies, which were determined by colder air masses, with higher air pressure following the frontal systems. All atmospheric variables observed in July 2005 are associated to a greater intensity of frontal systems over Southern and Southeastern Brazil (figures 62a to 62e). As a matter of fact, according to the Climanálise bulletin for this month15, precipitation rate positive anomalies were recorded in the Western part of the São Paulo state, induced by the frequent passing of frontal systems strong enough to affect the countryside of the state. Four frontal systems, well distributed throughout the month, passed over the SPMA region in July 2005 (figure 62).

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Figure 62: Passing of frontal systems on the Brazilian coast, JUL 2005. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0705/index.html Similar to what was observed in April 1998, the number of frontal systems in July 2005 was lesser than the average (which is seven systems), but these systems were of considerable intensity, according to the Climanálise bulletin for this month. It is known that ozone’s annual cycle has a minimum in the period MAY-JUL, associated to lower temperatures and solar radiation incidence on the surface in these months. For this reason, it is possible to conclude that, even with a slightly positive solar radiation anomaly and lesser cloudiness in these months, only these factors do not contribute to an increase on tropospheric O3. Since this month was also featured to frequent lower levels instability, associated to the passing of frontal systems, it is more likely that this factor has contributed in a more significant way for the occurrence of O3 negative anomalies. It is important to bear in mind that the

161 possible suggestions mentioned here for ozone variability are made only from the viewpoint of the physical-dynamic aspects of the atmosphere.

Figure 63a: Air relative and specific humidity in July 2005 and its respective anomalies.

162

Figure 63b: Incoming shortwave solar radiation and OLR in July 2005 and its respective anomalies.

163

Figure 63c: Air temperature and surface air pressure in July 2005 and its respective anomalies.

164

Figure 63d: Streamlines and wind direction and intensity in July 2005 and its anomaly.

165

Figure 63e: Air divergence over surface and in higher levels in July 2005 and its respective anomalies.

166 November 1997 During November 1997 significantly low concentrations were also registered, with negative anomalies close to the -1,5σ threshold criterion used for classifying more intense events (category –2). In figure 64, one can see that there were seven frontal systems (which is the climatologic mean) in this month that passed over the city of Santos, that probably influenced atmospheric conditions over the SPMA, for most of them moved on beyond 23S.

Figure 64: Passing of frontal systems on the Brazilian coast, November 1997. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/1197/index.html Based on the solar radiation field (figure 66b), it is evident that this variable had negative anomalies over most Centre-south. Surface pressure anomalies (figure 66c) also indicate the high frequency of frontal systems, associated to the formation of low pressure areas. This synoptical situation was also characterized by the formation of SACZ around the middle of the month as shown by figure 65:

167

Figure 65: SACZ positioning on the Southeast coast (infrared channel), the 18th of November, 1997. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/1197/figIV.html According to the Climanálise bulletin16 the SACZ episode lasted from the 14th to the 19th of November. Following the SACZ occurrence, this month also presented relative humidity positive anomalies (figure 66a). A greater part of the Southeastern region, including the state of São Paulo, was featured by air convergence in lower atmospheric levels (figure 66e), with maximum values in the Northwestern Minas Gerais, Southwest Bahia and Eastern Goiás, supported by divergence negative anomalies. In higher levels, positive air convergence on surface can be observed over the states of São Paulo and Paraná, giving dynamic support to the lower level conditions.

For this reason, atmospheric conditions in this month are unfavourable to the production and concentration of O3 in the lower atmospheric levels, because of the negative solar radiation anomalies, induced by the frequent passing of cold fronts and the formation of SACZ, and also due to the dynamic instability in the lower atmospheric levels, contributing to pollutant dispersion and lesser accumulation of precursors. 16

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168

Figure 66a: Air relative humidity in November 1997 and its anomaly.

Figure 66b: Incoming shortwave solar radiation and OLR in November 1997 and its respective anomalies.

169

Figure 66c: Air temperature and surface air pressure in November 1997 and its respective anomalies.

170

Figure 66d: Streamlines and wind direction and intensity in November 1997 and its anomaly.

171

Figure 66e: Air divergence over surface and in higher levels in November 1997 and its respective anomalies.

172 January 2004

When analysing figure 67, no frontal systems passing is observed over the SPMA region in January 2004 (the climatologic mean is six systems):

Figure 67: Passing of frontal systems on the Brazilian coast, January 2004. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0104/index.html Nonetheless, by the information contained in table 21, it is possible to see that ozone concentrations were lesser than –1σ. It does not happen so due to the frequent passing of frontal systems, as observed in other months of category –2 or similar one, but might have happened due to the formation and persistence of a SACZ system during the month. According to the Climanálise bulletin for this month, three SACZ episodes were observed in January 2004 over the Brazilian territory, and two of them have reached SPMA latitude. Particularly, the last episode of the month had a rather vertical position, as seen in figure 68:

173

Figure 68: SACZ positioning over the Southeastern coast (brightness temperature), in the 25th of January, 2004. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0104/zcas.html These SACZ episodes induced positive precipitation anomalies in several areas of the Centre-south of Brazil, even with flooding episodes in the SPMA and other metropolitan areas hit by these intense precipitation episodes17. Precipitation anomalies in Brazil for this month are shown figure 69:

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174

Figure 69: Precipitation anomaly in Brazil, January 2004. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0104/zcas.html A precipitation anomaly of approximately 100mm can be observed in the SPMA region. Precipitation created by any thermodynamic process (frontal, convective, orographic), despite not directly removing ozone from the atmosphere, does remove ozone precursors, contributing to hinder ozone formation. Additionally, the nebulosity associated to precipitation events partly blocks incoming solar radiation, thus also contributing to the decreasing of ozone concentrations. For this reason, it is possible to conclude that synoptical events that affect precipitation and cloudiness patterns can also interfere with O3 concentrations. Atmospheric compositions do not indicate relative air humidity anomaly in this month (figure 70a), as well as normal cloudiness (slightly positive OLR anomaly – figure 70b), but there is a more intense solar radiation anomaly. Air temperature presented negative deviations, as well as atmospheric pressure (figure 70c), what might have contributed to the dispersion of ozone formed over surface. A SACZ

175 episode might also be associated to the SE wind direction anomaly, which had an intensity anomaly of approximately 2 m/s (figure 70d).

Streamlines positioning indicates an air convergence area over the Brazilian countryside, and figure 70e indicates air convergence over surface and divergence in altitude, a little less intense than the region’s average, yet still contributing for the constant atmospheric instability observed over the SPMA during this month.

Figure 70a: Air relative humidity in January 2004 and its respective anomaly.

176

Figure 70b: Incoming shortwave solar radiation and OLR in January 2004 and its respective anomalies.

177

Figure 70c: Air temperature and surface air pressure in January 2004 and its respective anomalies.

178

Figure 70d: Streamlines and wind direction and intensity in January 2004 and its anomaly.

179

Figure 70e: Air divergence over surface and in higher levels in January 2004 and its respective anomalies.

180 3.3.2 Months with intense positive ozone anomalies.

August 1999 By analysing figures 72a and 72e, it is possible to notice that positive anomalies of OLR, shortwave radiation, surface pressure and negative anomalies of air relative and specific humidity configurate the high pressure system in the centre of the continent, as described in the Climanálise bulletin of August 1999. This bulletin also informs that the frontal systems climatological average of August is of seven systems; however, in figure 71, it can be seen that August 1999 received only three systems. Positive surface pressure anomalies happen all over the continent and an even more intense one can be seen over the surface, which roughly represents the South Atlantic Anticyclone. In figure 72d, this system’s positioning and influence are shown, as well as a divergent air region over the continent. “In August, most of Brazil’s Southeast region received no precipitation. Frontal systems did not reach the region very often due to the predominance of a high air pressure system over Central Brazil, which contributed to scarce cloudiness. Moreover, low relative air humidity indexes were recorded, with percentages as low as 20% in some areas during the afternoon, especially in the end of the month. These circumstances contributed to increased wildfire occurrences. Negative precipitation anomalies were detected in São Paulo, Rio de Janeiro and Espírito Santo. Mean temperature ranged from 16 to 20ºC, thus featuring positive anomalies throughout the region, even 2ºC in the South of São Paulo. When it comes to the maximum temperature, it is possible to see that it ranged from 24 to 30ºC. Especially in the end of the month, high maximum 18 temperature and low relative air humidity values have been observed .”

It is possible to see that there is air divergence in the SPMA region (figure 72e). This conditions is, although, close to the average for this month. In higher tropospheric levels, dynamically supporting these conditions, there is strong air convergence, considerably anomalous for this month, with anomalies up to –4.5*10-6 m/s. The high pressure system’s influence over the SPMA, with drier air coming from its centre inside the continent, particularly in the Goiás region, and from another one, which corresponds to the South Atlantic Anticyclone, were probably responsible 18

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181 for the greater air stability over the SPMA region, hence making it harder for frontal systems to reach and influence that region. This incurred in major pollutants concentration over the SPMA, particularly in the second half of the month, according to the Climanálise bulletin for this month:

Figure 71: Passing of frontal systems on the Brazilian coast, August 1999. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0899/index.html Concurrently, positive OLR and radiation anomalies (figure 70b) favoured ozone production and concentration in the troposphere over the SPMA.

182

Figure 72a: Air relative and specific humidity in August 1999 and its respective anomalies.

183

Figure 72b: Incoming shortwave solar radiation and OLR in August 1999 and its respective anomalies.

184

Figure 72c: Air temperature and surface air pressure in August 1999 and its respective anomalies.

185

Figure 72d: Streamlines and wind direction and intensity in August 1999 and its anomaly.

186

Figure 72e: Air divergence over surface and in higher levels in August 1999 and its respective anomalies.

187 March 2002

By looking at the atmospheric compositions for this months, negative air relative humidity anomalies are seen in the (figure 74a). Even though there is a positive specific air humidity anomaly in the studied region, relative air humidity presented negative anomalies, probably linked to the positive temperature anomalies (figure 74c). OLR and radiation positive anomalies are seen over the SPMA (figure 74b), which probably are connected to a greater ozone production at surface level. There is also slightly a positive surface pressure anomaly and NE predominant wind directions (figure 74d). Based on the streamlines and air divergence figures (74e), it can be perceived that air divergence was taking place over the SPMA’s surface, as well as in higher tropospheric levels, but they were close to the expected for this month.

These conditions suggest a month with plenty of sunshine, higher temperatures and lower air relative humidity, notably when looking at figure 73:

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Figure 73: Passing of frontal systems on the Brazilian coast, March 2002. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0302/index.html Based on the above figure, only one frontal system reached SPMA in this month, in contrast to the climatological average which is seven systems (according to Climanálise bulletin). In the light of the importance of frontal systems for precipitation events in the SPMA, the hypothesis of several consecutive days with high solar radiation and atmospheric pressure, low relative air humidity and scarce precipitation having an influence on ozone concentrations is bolstered. This context is probably responsible for the high ozone anomaly observed in this month, enough to classify it as category 2, as verified in table 12. This hypothesis will be more carefully checked in section 3.4.

189

Figure 74a: Air relative and specific humidity in March 2002 and its respective anomalies.

190

Figure 74b: Incoming shortwave solar radiation and OLR in March 2002 and its respective anomalies.

191

Figure 74c: Air temperature and surface air pressure in March 2002 and its respective anomalies.

192

Figure 74d: Streamlines and wind direction and intensity in Mach 2002 and its anomaly.

193

Figure 74e: Air divergence over surface and in higher levels in March 2002 and its respective anomalies.

194 February 2003 Concerning the atmospheric fields, the month of February 2003 was considerably similar to March 2002, with positive anomalies in surface pressure, temperature, radiation and OLR, and negative anomalies in air relative humidity, besides air divergence over surface (figures 78a to 78e), also with NE winds. Several Upper Level Cyclonic Vortexes (ULCs) were active, including one that moved from Bahia state to São Paulo state, where it hindered cold fronts incursion; consequently, there was weak frontal activity. Ultimately, there was no SACZ activity:

Figure 75: Passing of frontal systems on the Brazilian coast, February 2003. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0203/index.html Figure 75 shows that only two frontal systems influenced in the SPMA in this month (February’s climatological average in the SPMA’s latitude is of six systems), in the 17th and 21st, in a way that the first two weeks of this month and the last week

195 had no frontal systems influence. According to the Climanálise bulletin for this month19, “…upper level cyclonic vortexes’ influence, associated to increased surface pressure, blocked the frontal systems, which was unfavourable to precipitation events. Considering the historical average, negative anomalies prevailed over the Brazilian Southeast region, except for very isolated areas”.

In figure 76, ULCs’ influence is seen over a significant part of the Brazilian territory:

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196

Figure 76: ULCs’ influence over a significant part of the Brazilian territory in February 2003. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0203/index.html Although figure 76 indicates some cloudiness over the SPMA, ULCs were very intense and spatially extended, acting over the ocean in the synoptic scale, hence the weak frontal activity over the SPMA, especially by the end of the month.

According to the aforementioned Climanálise bulletin, the ULCs influence was due to shifted position of the Bolivian High that influenced the position of air convergence in upper levels over Northeast Brazil, incurring in air divergence over the surface.

Figure 77: Bolivian High positioning over South America, February 2003. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0203/altabolivia.html Therefore, positive anomalies of OLR, radiation, temperature and surface pressure and negative anomalies of air relative humidity, seen in figures 78a to 78e demonstrate the ULCs’ influence, due to the shifted position of the Bolivian high, which contributed to the pollutant’s accumulation in this month over the studied area.

197

Figure 78a: Air relative and specific humidity in February 2003 and its respective anomalies.

198

Figure 78b: Incoming shortwave solar radiation and OLR in February 2003 and its respective anomalies.

199

Figure 78c: Air temperature and surface air pressure in February 2003 and its respective anomalies

200

Figure 78d: Streamlines and wind direction and intensity in February 2003 and its anomaly.

201

Figure 78e: Air divergence over surface and in higher levels in February 2003 and its respective anomalies.

202 October 2002 Tropospheric ozone’s seasonal cycle, with a minimum around June and a maximum in October, has been previously discussed. October 2002 is one of the most polluted months in the time series considered (1996 to 2005), in absolute terms. Excessively high ozone concentrations were observed in the SPMA in any time scale, as seen in figures 32 to 35 in section 3.1.1 and figures 43a to 43f in section 3.1.2.1. Besides the pollutant’s increasing tendency in the considered period, atmospheric conditions have also contributed to this pollution event in the SPMA, as it will be shown in figures 81a to 81e.

Relative air humidity presents intense negative anomalies in the centre of the country, with values of approximately –10% in a large area that extends itself from the SPMA to beyond 10S latitude (centre of Tocantins state), the limit of the maps used in this section (figure 81a). Through the figures 81b and 81c, intense positive solar radiation, OLR and air temperature anomalies can be seen in this area, indicating the occurrence of many dry, hot and sunny days. Surface atmospheric pressure, though, is close to the climatological mean in the SPMA.

Prevailing atmospheric motion indicates an air convergence area in the continent, centred over Goiás state. The SPMA is slightly influenced by this area, as seen in figure 81d. A curious record for this month is that seven frontal systems reached SPMA’s latitude (figure 79):

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Figure 79: Passing of frontal systems on the Brazilian coast, October 2002. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/1002/index.html However, according to the Climanálise bulletin for this month: “Tem frontal systems have been observed in October, in the whole country, although most of them has influenced only up to the south of São Paulo state20”.

Moreover, this very document informs that there was a negative precipitation anomaly throughout the whole Southeast region. It can be seen that, in fact, the first three systems of the month did not surpassed this latitude (south of São Paulo state), probably indicating less intense systems. In short, in the light of the fact that only the systems of the 21st, 25th e 29th days of the month had some influence on the SPMA, there were practically twenty days without any frontal systems influence at all (figure 79). This hypothesis is bolstered by data from figure 80: 20

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204

Figure 80: Daily precipitation in the SPMA in October 2002. Source: Reanalysis II. This figure shows that there has been precipitation in nine days of the month, but only four recorded precipitation volumes over 5mm. The most intense precipitation episode, of 17 mm, took place on a day featured by the activity of the most intense frontal system of the month (figure 79). It is evident that frontal systems in this month were not intense, since October’s climatological mean for 20 to 25S latitude is of seven systems (Climanálise bulletin).

So, while an incipient look on these figures may suggest good pollutant dispersion conditions, what is found is the exact opposite, due to the weak intensity of the systems that reached the SPMA in this month. For this reason, once again there are atmospheric patterns similar to those seen in other category 2 months, which probably have eased ozone’s production and concentration over the SPMA’s surface.

205

Figure 81a: Air relative humidity in October 2002 and its respective anomaly.

Figure 81b: Incoming shortwave solar radiation and OLR in October 2002 and its respective anomalies.

206

Figure 81c: Air temperature and surface air pressure in October 2002 and its respective anomalies.

207

Figure 81d: Streamlines and wind direction and intensity in October 2002 and its anomaly.

208

Figure 81e: Air divergence over surface and in higher levels in October 2002 and its respective anomalies.

209 January 2001

This month presented positive solar radiation, OLR, surface pressure positive anomalies and slight air temperature positive deviations, and also negative relative air humidity anomalies (figures 85a to 85e).

These anomalies had a similar spatial configuration to that found in April 1998, which means, the state of São Paulo is featured as a transition region between prevailing opposite atmospheric conditions in Southern and Northeastern Brazil. In the south, air relative humidity positive anomalies (figure 85a) and OLR and radiation negative anomalies (figure 85b) are found, with less than normal air divergence (figure 85e). Therefore, in the SPMA, predominance of low air relative humidity, higher temperature and lesser cloudiness in this month, what might have contributed to the positive ozone anomalies. Prevailing wind direction was NE (figure 85d), as previously observed in several months of high ozone concentration.

Regarding frontal activity and precipitation, Climanálise bulletins in this month indicate the influence of four frontal systems in January 2001 in the SPMA’s latitude, but that were featured by low intensity (figure 82):

210 Figure 82: Passing of frontal systems on the Brazilian coast, January 2001. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0101/index.html According to the Climanálise bulletin in this month, “[...] seven frontal systems influenced this month [...] these systems were of low intensity and organized little convection in the continent’s countryside21”. This number is the climatological mean for this month; however, only four reached SPMA’s latitude. It is reasonable to suppose that, eve if this systems reached Santos’ latitude, they would probably not influence the SPMA enough to disperse the registered high ozone concentrations. The frontal systems’ interference over atmospheric conditions on the continent was reduced due to the influence of the South Atlantic Anticyclone 22, which also hindered local convection. Although there were many precipitation events in the month (figure 83), it was still less than the average23 due to the aforementioned factors:

Figure 83: Precipitation in the SPMA, January 2001. Source: Reanalysis 2. 21

http://climanalise.cptec.inpe.br/~rclimanl/boletim/0101/index.html http://climanalise.cptec.inpe.br/~rclimanl/boletim/0101/ativ_convectiva.html 23 http://climanalise.cptec.inpe.br/~rclimanl/boletim/0101/sistemas_frontais.html 22

211

Comparing the precipitation and frontal systems passing figures, it is possible to see that only the cold front of the 25th is associated to an intense precipitation event in the month, in contrast to other systems, on the 12th, 21st-22nd and 30th. Negative precipitation anomalies took place even with a SACZ episode in the beginning of January 2001, which did not affect the SPMA and so, could not make up for the relatively scarce events of the month:

Figure 84: Average brightness temperature over South America on the 05th and 10th of January 2001, showing the SACZ episode. Source: www.cptec.inpe.br For this reason, it is suggested that the prevailing atmospheric conditions in this month, with solar radiation, temperature and surface pressure positive anomalies and air divergence, relative air humidity and precipitation negative anomalies; together with weak intensity of the frontal systems, influenced by the South Atlantic Anticyclone, induced a synoptic situation of favourable conditions for tropospheric ozone production and concentration in the SPMA.

212

Figure 85a: Air relative humidity in January 2001 and its respective anomaly.

Figure 85b: Incoming shortwave solar radiation and OLR in January 2001 and its respective anomalies.

213

Figure 85c: Air temperature and surface air pressure in January 2001 and its respective anomalies.

214

Figure 85d: Streamlines and wind direction and intensity in January 2001 and its anomaly.

215

Figure 85e: Air divergence over surface and in higher levels in January 2001 and its respective anomalies.

216 April 2000

In April 2000, positive anomalies of the variables solar radiation, OLR, surface pressure and slightly positive temperature anomalies, and also negative deviations of air relative humidity (figures 89a to 89c), but centred on the São Paulo state. Strong air divergence on surface levels can be noted (figure 89e), centred over the Paraná state that influence conditions on the SPMA region, strengthened by strong air convergence in upper levels as well, in this region. Wind direction was similar to the observed in other category 2 months, which is NE (figure 89d).

One more time, conditions of little nebulosity, low air humidity and higher temperatures are observed, bolstering positive tropospheric ozone anomalies in the SPMA.

Frontal activity in April 2000 was limited to only three systems in the SPMA, but the climatological mean for the SPMA in April is six systems (according to the Climanálise bulletin). Although these systems have reached lower latitudes, including some states from NE Brazil, their influence on the atmospheric conditions on the SPMA was limited:

217

Figure 86: Passing of frontal systems on the Brazilian coast, April 2000. The city of Santos is indicated as the closest SMPA representative. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0400/index.html According to the Climanálise bulletin for this month, “Frontal systems in April (2000) were weak, leading only to some cloudiness on the coast and changes in wind direction and temperature in the countryside of the Southeast and Centre-west regions of Brazil24“.

Figures 87 and 88, that depicts precipitation and its anomaly, show that there was some precipitation, specially in the frontal system’s passing days (1 and 19), but precipitation remained under the average, with negative anomalies through all the Southeast region, including values close to –100mm in the SPMA, not contributing to ozone’s (or its pollutants) dispersion: 24

http://www.cptec.inpe.br/products/climanalise/0400/index.html

218

Figure 87: Precipitation in the SPMA in April 2000.

Figure 88: Precipitation anomaly in the SE region of Brazil in April 2000. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0400/r_sudeste.html

219

Even the third frontal system in April 2000, which reached the coast of Bahia, only generated precipitation in the South of Brazil, occurring only in cloudiness in the SE. For this reason, one can conclude that both the frontal systems’ intensity and frequency influence monthly tropospheric ozone concentrations in the SPMA. Particularly in the months of January 2001 and April 2000, significantly high O3 positive anomalies were observed. Its concentration over the surface was favoured due to solar radiation positive anomalies and inefficiency of atmospheric removing mechanisms in many time scales. Daily evolution of ozone concentrations in months of category 2 and –2 (April 1998, July 2005, August 1999, March 2002, February 2003) will be described in further details in the next section.

Figure 89a: Air relative humidity in April 2000 and its respective anomaly.

220

Figure 89b: Incoming shortwave solar radiation and OLR in April 2000 and its respective anomalies.

221

Figure 89c: Air temperature and surface air pressure in April 2000 and its respective anomalies.

222

Figure 89d: Streamlines and wind direction and intensity in April 2000 and its anomaly.

223

Figure 89e: Air divergence over surface and in higher levels in April 2000 and its respective anomalies. Finally, considering the fact that the atmospheric patterns regulated more visibly months with positive ozone deviations, an attempt was made to look for other reasons, possibly able to influence negative anomalies. In this context, ozone concentrations were observed in months preceding the analysis-selected ones, in order to infer if there was a possible influence of this previous months over categories 2 and –2 months. For months of category 2, no pattern was observed, but for–2 category months, it was possible to notice that, in general, ozone concentrations had already presented negative anomalies or decreasing tendencies before months with more intense negative anomalies (-2 category), as shown in figure 90:

224

Nov/97

Jan/04 Jul/05 Abr/98

Figure 90: Ozone anomalies temporal series, highlighting months of category –2 (dark blue) associated to previous months of negative anomalies or decreasing tendencies (light blue). By analysing this figure, it is possible to see that the months highlighted in dark blue (category -2) are always preceded by other months with negative anomalies or close to zero, or with a decreasing tendency, highlighted in light blue. So, it is possible to suggest that ozone concentrations from the previous months can contribute to more intense negative anomalies in a given month, specially bearing in mind what was mentioned in section 1.1 and according to data from Figure 02 (Residence time and spatial scale of transport of some pollutants). This figure shows that tropospheric ozone’s residence time in the atmosphere ranges from five days to some weeks in summer in the Northern Hemisphere (up to three months in winter in middle-latitudes). Clearly it does not occur in all SPMA’s stations, due to different climate patterns and mainly to intense vehicles traffic, as explained in section 1.2.1, but it is logical to suggest that ozone concentrations, specially in the background stations, are somewhat influenced by concentrations observed in the previous months, once atmospheric variability does exist, no matter human society’s calendar conventions.

225 Based on the fact that the variable which best correlates to ozone in the monthly scale is shortwave solar radiation, an attempt was made in order to analyse months with intense positive and negative solar radiation anomalies (above +30W/m2 or below -30W/m2) which did not present intense ozone anomalies.

Set/04 Mar/97 Jan/97

Fev/98

Fev/05 Out/05

Figure 91: Ozone anomalies monthly time series, highlighting months with negative (green) and positive (red) solar radiation anomalies in which ozone anomalies greater than 2σ. In figure 91, two (September 2004 and February 2005) out of three months with intense solar radiation anomalies also recorded positive ozone anomalies, but they did not reach category 2. Actually, only September 2004 has reached anomalies higher than 1σ. It can be suggested then (except for March 1997), that despite the fact that the months highlighted in red in the figure have presented intense solar radiation anomalies (higher than +30w/m2), they did not present positive ozone anomalies as intense as category 2 months because they occurred right after months with negative anomalies, highlighted in orange. Nevertheless, an increase in ozone anomalies can be seen in these two months. The only month with intense solar radiation anomaly which did not present an increase in ozone anomaly was March 1997, which, for some reason, presented a decrease in ozone anomalies compared to the previous month. The months highlighted in dark green (January 1997,

226 February 1998 and October 2005) correspond to months with intense solar radiation negative anomalies (lesser than –30W/m2), but did not present an ozone anomaly of -2 category. Initially, it was not possible to detect a possible reason for these month’s anomalies not being as low as the ones observed in table 21, and so, a deeper look on these matters is regarded as object of possible future studies. Possible reasons for relatively mild ozone anomalies in these months can reside on the behaviour of other atmospheric variables, specially precipitation and relative air humidity, and on the complex atmospheric chemistry reactions that are part of this pollutant’s cycle of production and destruction.

227 3.4 Atmosphere’s interference on tropospheric ozone

In this section, daily variability from the atmosphere and ozone are analysed, in the months with O3 intense negative and positive anomalies, representatives of categories -2 and 2 (indicated in tables 19 and 22), respectively. A comparative analysis is made based on the daily evolution of O3 concentration values and of the considered atmospheric variables, building daily temporal series for the months of interest. Daily variability study is important to allow a more detailed analysis of the atmospheric influence on tropospheric ozone’s variability in the synoptic scale, deepening the analysis performed in the previous section (analysis of atmospheric fields). Firstly, months of –2 category will be evaluated, followed by category +2 ones.

228 3.4.1 Months of category –2

April 1998

This month was featured by low concentrations, much lower than the average for the considered period, classified as category –2, as discussed in section 3.2 and indicated in table 22. Analysed atmospheric fields (figures 61a and 61e, section 3.3) indicate atmospheric dynamics which reflect the influence of cold fronts with considerable intensity in the South and Southeast regions of Brazil, incurring in negative OLR and surface pressure anomalies throughout the whole of these regions, mainly over the Paraná state. According to the Climanálise bulletin for this month, these systems were intensified by the presence of troughs in all atmospheric levels and upper level cyclonic vortexes.

In an initial analysis of the figure 92a, it is possible to see that although in eight days of the month O3 daily mean concentration surpassed the month’s average (23,36 μg/m3), in most days concentration was low, even reaching values close to 4,0 μg/m3 around the 29th and 30th of April. During this month, O3 concentration presented small peaks on the 06th, 12th and 21st, which, by its turn, did not contribute significantly in the monthly mean calculation, and it remained low. Based on the monthly analysis of frontal systems’ incursion in the Southeast region of Brazil, information obtained in the Climanálise bulletin (figure 60 – Passing of frontal systems on the Brazilian coast, April 1998), for this month, one can see that four frontal systems passed through the city of Santos’ latitude, on the 8th, 18th, 24th and 29th that probably interfered with atmospheric conditions in the SPMA. Despite in a smaller number than the climatological mean (six systems), these systems influenced the South and Southeast regions and the state of Mato Grosso do Sul with greater intensity. By daily precipitation data (figure 92d) it is evident that precipitation events in the SPMA occurred close to the frontal system’s passing day. Besides that, in the same days, the lowest ozone concentrations were registered, as shown in figure 92a. OLR daily values (figure 92b) allow verifying that the periods (from three to six days) that preceded both frontal systems passing in middle April 1998, on the 18th and 24th, presented the highest O3 concentrations (with peaks on the 12th and

229 21st, figure 92a). Due to lesser nebulosity in these periods (eight days with OLR higher than 250 W/m2 in the period preceding the second frontal system passing and six days with OLR higher than 250 W/m2 in the period preceding the third frontal system passing), it is strongly suggested that O3 formation in these periods was linked to the greater sunlight availability. The period which preceded the first frontal passing of the month (08/04) was featured by variable nebulosity, which may have hindered O3 production and concentration, as shown by the concentration values reached in these days, with a maximum on the 6th. On the other hand, form the 11th to the 15th, there was an increase on surface pressure and OLR averages remained high and stable, indicating several consecutive days with significant incoming solar radiation. It was precisely when the highest O3 concentration average in the month occurred, on the 12th. The month’s last O3 peak, on the 21st/04, was also inside a period characterized by low nebulosity, in which OLR averages remained high and stable compared to the rest of the month (19th to the 22nd/04). The month’s last frontal system passing (29/04) was preceded by a period of high nebulosity, lowering O3 production. Frontal systems’ passing’s can also be identified through air temperature and surface pressure data, as indicated by the arrows in the figures 92b and 92c. The linear correlation coefficient between O3 and OLR daily values in April 1998 was 0.48.

In this months, all periods featured by decrease in O3 concentrations were also associated to a decrease in surface pressure values, which was associated to a greater atmospheric dynamic instability, and so, to more favourable situations for ozone dispersion. Additionally, situations of greater nebulosity in periods featured by frontal activity hindered solar radiation income, also contributing to lesser ozone production. Through precipitation data (figure 92d) it is possible to state that on the last day of the previous month, in March 1998, there was a great precipitation event, which might have had an influence on April’s starting low concentrations, and so, for the rest of the month as well.

230

Figure 92a: Tropospheric ozone and relative air humidity daily averages in April 1998. Frontal systems’ passing days are indicated by the arrow.

231

Figure 92b: OLR and air temperature daily averages in April 1998. Frontal systems’ passing days are indicated by the arrow.

232

Figure 92c: Surface pressure and wind speed daily averages in April 1998. Frontal system passing days are indicated by the arrow.

233

Figure 92d: Solar radiation and precipitation daily averages in April 1998. Frontal system passing days are indicated by the arrow.

234

Figure 92e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in April 1998.

235 July 2005 July 2005 was also considered as category –2 due to its low concentrations (table 18). During this month, there were positive anomalies of surface pressure, OLR, solar radiation and wind speed, as well as negative anomalies of air temperature and relative air humidity, suggesting significant influence of higher latitudes air masses associated to the four frontal systems which reached SPMA’s latitude, according to the Climanálise bulletin for this month. Supporting these system’s intensity, this same bulletin associates them to positive precipitation anomalies in the west of the São Paulo state. Concentrations above July’s average (19,5 μg/m 3) were registered on only four days of the month, indicating that atmospheric conditions probably were not favourable for ozone formation and concentration during this month (figure 93a). South and southeast wind directions stood out (figures 94a to 94c), as well as frequent frontal systems’ incursion, well distributed over the period. Data from figure 93a to 93e reveal frontal systems’ passing on the 05th, 17th, 24th, and 28th, indicated by an arrow. On the 5th, the frontal system’s incursion is brought to attention through changes in wind direction (figures 94a to 94c – predominant north winds, which, after the 5th/07, become SE and S), decrease in solar radiation, OLR and temperature values and increase in relative air humidity values (figures 93a to 93d). O3 concentration in the SPMA also decreased on this day. From this day on, atmospheric pressure rose, indicating the displacement of the typically-observed high-pressure region located on the back of frontal systems (GALVANI and AZEVEDO, 2003). A steady O3 concentration increase is observed in this period, between the first and second frontal systems’ passing, simultaneous to high OLR values. However, it can be suggested that the strong winds and continuous temperature decrease also contributed in a way to not increase ozone concentrations. An ozone maximum is observed in the day that preceded the passing of the second frontal system (17th). From this day, atmospheric variables indicated conditions featured by frontal system’s influence, without much solar radiation availability, which again is probably associated to O3 concentrations decrease. This pattern was repeated when the new frontal system arrived, on the 24th: concentration

236 increase in the pre-frontal period, and their reduction and stability after its passing, which is associated to the prevailing of high nebulosity, little incoming solar radiation and low OLR and temperature values. In contrast, figure 62 shows that the last frontal system of the month probably did not contribute to change tropospheric O3 concentrations in the SPMA. This system had a different trajectory from the other systems of the month, and a different intensity as well, as seen in figures 93a to 93e and little shift in wind direction (figures 94a to 94c). In spite of incurring in little atmospheric pressure decrease, this system lead to latter relative air humidity decrease, associated to relatively high solar radiation and OLR values. Thus, the continuation of high solar radiation and OLR values reinforce the fact that these variables are important in O3 production. Besides that, there were no major changes in wind speed and direction neither temperature decreases, as seen in previous frontal system’s passing’s in this month. This situation caused an increase in O3 concentration values in the end of the month, leading to the highest daily average in the month on the 31st, 25.83 μg/m3. In the beginning of the month, prevailing NE wind directions are seen on most CETESB stations. Figure 93a shows that O3 concentrations remained stable, with slight increasing tendency, and decrease during the first frontal system passing, on the 05th, when S winds were registered in all stations. In the following week, prevailing wind directions remained the same, but a gradual shift towards SW and W is noticed, especially from the 11th on. O3 concentrations in this period did not present defined tendency; nevertheless, on the 16th e 17th, they increase and reach the month’s maximum, and wind direction shifted to N/NE on these two days. On the 17th, the second frontal system passing of the month is seen; O3 concentrations show a decreasing tendency from the 17th to the 21st, while wind directions remained from SW to NE in all CETESB stations. There was no predominant wind direction during the rest of the month, but a slight increase on O3 concentrations is noticed after the 27th.

237

Figure 93a: Tropospheric ozone and relative air humidity daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow.

238

Figure 93b: OLR and air temperature daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow.

239

Figure 93c: Surface pressure and Wind speed daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow.

240

Figure 93d: Solar radiation and precipitation daily averages in July 2005. Frontal systems’ passing days are indicated by the arrow.

241

Figure 93e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in July 2005.

242

Figure 94a: Monthly evolution of wind direction and speed in Ibirapuera and Mooca stations in July 2005. The dotted line marks the days of frontal systems’ passing.

243

Figure 94b: Monthly evolution of wind direction and speed in Pinheiros, Santana and Santo Amaro stations in July 2005. The dotted line marks the days of frontal systems’ passing.

244

Figure 94c: Monthly evolution of wind direction and speed in São Caetano do Sul station in July 2005. The dotted line marks the days of frontal systems’ passing.

245 3.4.2 Months of category 2 August 1999

August 1999 was classified as category +2 regarding O3 concentrations, as seen in table 18. The analysis of this month’s atmospheric patterns in the previous section, pointed out to the presence of a high atmospheric pressure system located in the centre of the continent, responsible for positive anomalies of temperature, solar radiation, OLR, surface pressure and negative anomalies of specific and relative humidity. There were also negative precipitation deviations in several Brazilian areas, with strengthening of the South Atlantic Anticyclone (figures 72a to 72e).

During this month, three frontal systems passed over the Southeast of Brazil, as shown in figure 71 (frontal systems passing), on the 2nd, 7th and 14th, lesser than the climatological average for this latitude (seven systems). The remaining systems which reached the South of Brazil moved to the Atlantic Ocean without reaching the Southeast Region. Based on pressure, temperature, relative air humidity and solar radiation data it is possible to identify the passing of each frontal system, and that the passing of the 14th was the most intense of the month, with a strong decrease on atmospheric pressure (with the lowest value close to 955 hPa) and incursion of an air mass of higher latitudes with higher pressure (> 970 hPa) and lower temperatures in the SPMA (< 8ºC), in comparison to values observed in the rest of the month. On the 2nd, 3rd and 7th days of August 1999, relative minimums of surface pressure were observed, associated to two frontal systems passing of lesser intensity. Air temperature data show a sharp decrease on the 15th and relative decreases on the 3rd, 4th, 8th and 9th, associated to cold air masses with lesser intensity compared to the one on the 14th. Between the 13th and the 18th, a sharp decrease in ORL data is verified, associated to higher nebulosity due to the cold front passing. In the previous frontal passing, it is possible to notice only a slight decrease in OLR values on the 7th. Relative air humidity values show frontal systems passing with high contrast between values seen on the 7th and 9th (2nd frontal passing) and the 13th and the 15th (3rd frontal passing).

246 Because the beginning of the month was featured by high atmospheric instability (three frontal systems passed through the area), after the third frontal passing, the Southeast region’s atmosphere became, in average, more instable. Pollutants’ concentrations were expected to rise, particularly O3’s, from the middle to the end of the month, as seen in figure 95a, in which this tendency is verified. O3 concentration in the first two days of the month was low, with the minimum value (20 μg/m3) on the 2nd. On the second half of the month, from the 18th on, concentrations rose sharply, ranging from 20 to 50 μg/m3 and reached the month’s highest daily average on the 30th, of 57 μg/m3, while on the first half of the month O3 values oscillated between 20 and 40 μg/m3. It is possible to associate O3 concentration and atmospheric variable’s variability on the first period. Based on O3 concentration values, it is possible to establish a connection between maximum ozone values and the decrease in nebulosity. O3’s maximum concentration occurred in the first half of the month, on the 11th and the 12th, agreeing with high OLR values. Between the 13th and the 17th, a decrease in ozone concentrations is seen, associated to the first frontal system passing that brought lower OLR values (higher cloudiness), as seen in figures 95a to 95e. Temperature decreased considerably from the 15th to the 17th and relative air humidity increased as much, after the 13th, due to this temperature (and surface pressure) decrease. A decrease in OLR values is verified after the 12th, showing high cloudiness. After the frontal passing, winds shifted to SE, in Ibirapuera station, and to SW, in Osasco and Parque D. Pedro II (figures 96a to 96d), with the incursion of the air mass with anticyclonic circulation.

In this month, the division of the wind direction time series in two parts was necessary to allow a clearer view of the mentioned differences in this variable. In the beginning of the month, there were prevailing S/SW winds until the 6th (figures 96a to 96d). O3 concentration (figure 95a) rose after the passing of the first frontal system, probably due to the prevailing of high solar radiation income and low wind speed conditions in these days. During the following week, it is not possible to detect a predominant wind direction, and O3 concentrations remained stable, not decreasing even with the passing of the second frontal system (07th/08). Nonetheless, a secondary maximum is seen on the 11th and 12th, which relates to high OLR values

247 and NE wind directions in Osasco station. In the other stations, there is no representative wind data for these two days. After the last frontal system passing (on the 14th), prevailing wind direction became S, progressively shifting westward after the 17th. An increase on ozone concentrations is perceived on its time series in this period. In the last week of the month, ozone concentrations did not decrease; they remained high and stable, and there was no predominant wind direction.

248

Figure 95a: Tropospheric ozone and relative air humidity daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow.

249

Figure 95b: OLR and air temperature daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow.

250

Figure 95c: Surface pressure and wind speed daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow.

251

Figure 95d: Solar radiation and precipitation daily averages in August 1999. Frontal systems’ passing days are indicated by the arrow.

252

Figure 95e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in August 1999.

253

Figure 96a: Monthly evolution of wind direction and speed in Ibirapuera and Osasco stations from the 1st to the 16th of August 1999. The dotted line marks the days of frontal systems’ passing.

254

Figure 96b: Monthly evolution of wind direction and speed in Parque D. Pedro II station from the 1st to the 16th of August 1999. The dotted line marks the days of frontal systems’ passing.

255

Figure 96c: Monthly evolution of wind direction and speed in Ibirapuera and Osasco stations from the 17th of August to the 1st of September 1999. The dotted line marks the days of frontal systems’ passing.

256

Figure 96d: Monthly evolution of wind direction and speed in Parque D. Pedro II station from the 17th of August to the 1st of September 1999. The dotted line marks the days of frontal systems’ passing.

257 March 2002 March 2002 was considered a category +2 month, with +10μg/m3 ozone concentration anomaly. Although seven frontal systems influenced Southern Brazil in this month (figure 73), only one of them influenced atmospheric conditions in the SPMA, on the 20th. This means that, for SPMA’s latitude, there was a great negative deviation regarding the number of active frontal systems in this month, because the average is seven systems – exactly what was verified in the South. In the synoptic scale, the SPMA’s region presented surface pressure, solar radiation, OLR and temperature positive anomalies (figures 74a to 74c). As for air motion, anomalous convergent circulation was observed in upper levels and anomalous divergent circulation in lower levels over Brazil’s Southeast region, hindering the precipitation systems’ strengthening (74d e 74e). Despite the fact that only one cold front was active in the SPMA, on the 20th, several precipitation events were observed in the beginning of the month (figure 97d) and low solar radiation and OLR values with minimum values on the 8th and 14th showing atmospheric instability. On the 24th the lowest OLR value of the month was observed, indicating significant nebulosity (97b). Because March is a summer month in the region, when higher-latitude air masses reach the region, they do not lead to in great temperature decrease, as was seen in this month (figure 97b), in spite of having slightly increased surface pressure on the following days (figure 97c). In figures 98a to 98c, a shift in wind direction from N/NW to S/SW in the frontal system passing period (on the 20th) took place. From the 20th to the 24th, there was also a decrease in OLR and shortwave radiation values (figures 97b and 97d), which happened simultaneously, one more time reinforcing the link between ozone production and solar radiation and OLR variability. The most intense precipitation event took place on the 24th. In the rest of the month, no other frontal systems influenced the region. Figure 97d shows that there were several precipitation events in the beginning of the month, on the 2nd, 3rd and 4th, and it is evident that OLR values were lower in these days than in the following days, when there was no precipitation, and higher and more stable OLR values. Air temperature and air relative humidity behaved in the

258 same way. On the 7th and 8th, there were new precipitation events, along with a low OLR and solar radiation. OLR values oscillated a lot during the month and present a minimum (around 170 W/m2) on the 24th, when there was persistent precipitation during the whole day, associated to the frontal system activity. Ozone concentration was high during this month, but do wave considerably compared to other category +2 months. Maximum concentration values (59 and 66 μg/m3) happen on the 09th and 10th, when OLR and solar radiation values increase and there is no precipitation. In contrast of what was observed for other months of category +2 and -2, O3 concentration do not increase before the frontal system passing by the SPMA (20th); but in fact, decrease, along with OLR values, as aforementioned. The lowest O3 concentrations in the month happened on the 24th (daily average of 9 μg/m3), during a precipitation event that accompanied by great nebulosity; and on the 8th and e 14th, associated to a decrease in solar radiation and OLR values. The linear correlation coefficient between O3 and OLR in March 2002 was 0.45. In the CETESB’s stations wind direction and speed charts, a predominant N, W and NW component is evident in the first three weeks. Station Parque D. Pedro II presents prevailing N/NW winds, while São Caetano do Sul and Santo André, NW. Santana station has predominant W winds, and São Miguel Paulista, SW. Mooca station between NW and NE. Anyway, this wind configuration suggests that there is air divergence over the SPMA’s surface in this month. After the frontal system’s passing (20th), O3 concentrations decrease and winds become S. O3’s secondary maximum happen in the end of the month (27/03), and is again coexistent with wind directions that suggest air divergence over surface, according to CETESB’s stations. On the last days of the month, when concentrations become more stable (after the 28th), winds return to S, SE and SW directions.

259

Figure 97a: Tropospheric ozone and relative air humidity daily averages in March 2002. Frontal systems’ passing days are indicated by the arrow.

260

Figure 97b: OLR and air temperature daily averages in March. Frontal systems’ passing days are indicated by the arrow.

261

Figure 97c: Surface pressure and wind speed daily averages in March 2002. Frontal systems’ passing days are indicated by the arrow.

262

Figure 97d: Solar radiation and precipitation daily averages in March 2002. Frontal systems’ passing days are indicated by the arrow.

263

Figure 97e: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in March 2002.

264

Figure 98a: Monthly evolution of wind direction and speed in Mooca and Parque D. Pedro II stations in March 2002. The dotted line marks the days of frontal systems’ passing.

265

Figure 98b: Monthly evolution of wind direction and speed in Pinheiros, Santana, São Caetano do Sul and São Miguel Paulista stations in March 2002. The dotted line marks the days of frontal systems’ passing.

266

Figure 98c: Monthly evolution of wind direction and speed in Santo André - Capuava station in March 2002. The dotted line marks the days of frontal systems’ passing.

267 February 2003 This month was also classified as category +2. Only two cold fronts were able to influence the SPMA’s region in this month, although between 35ºS and 25ºS latitudes there were eight active cold fronts, two more than February’s climatologic average. Similar anomalies to March 2002’s were observed – temperature, surface pressure, solar radiation, OLR positive anomalies and air relative and specific humidity negative anomalies, and air divergence on surface. According to the Climanálise bulletin for this month, the influence of some ULCs in this month over diverse areas of the Brazilian territory hindered the incursion of frontal systems, and negative precipitation anomalies were observed. According to figure 99a, O3 concentration varied significantly during February 2003, comparing to the -2 category months, and even to +2 ones. O3 concentration somewhat follows OLR and solar radiation variability; Pearson’s correlation coefficient between ozone and OLR was of 0.44 and for ozone and solar radiation was 0.58. Low surface pressures were observed in this month on the 6th, 11th and 17th (minimum value), along with low OLR values on the 6th, 12th, 14th, 17th and 22nd. O3 minimum concentration values were observed on the 7 th, 14th, and 17th (minimum of 14 μg/m3). Despite the fact that only two frontal systems passing’s were observed in this month according to the Climanálise bulletin, low surface pressure events were observed, associated to turbulence and cloudiness, in some periods of the month (figures 99b, 99c and 99e). It can be seen that the pressure minimum on the 17 th is somewhat accompanied by minimum ozone values, suggesting the gradual decrease of solar radiation in the first half of the month. In the second half, in contrast, a decreasing tendency is seen for nebulosity (figure 99b) and an increasing tendency for both solar radiation (figure 99e) and the pollutant’s concentration (figure 99a). In this month, ozone concentrations remain generally high and occasional decreases are associated to cloudiness from precipitation events, apart from the ozone chemical removal mechanisms. In the beginning of the month, there is a continuous increase on nebulosity in the SPMA, on the 3rd and the 6th, associated to a ULC centred over the Bahia state. According to the study of Silva (2005), regions up to 1000 or 2000 km distant from the ULCs centre to the northeast, east and southeast might observe an increase in precipitation frequency, due to the convective

268 nebulosity band which is usually located west of the system. Therefore, it is logical to suppose this hypothesis for the SPMA on these days, for in this period, O3 concentration decreased and precipitation frequency increased (figure 99e). Although, by figure 99b there is a decrease in cloudiness from 19/02, followed by an increase on ozone concentrations. According to this month’s Climanálise bulletin, a ULC formed over the Brazilian coast on the 20 th, which moves through the Brazilian countryside, reaching northern Argentina on the 28th (figure 99d – ULCs, February/03), which hindered rainfall distribution specially in Minas Gerais and São Paulo. The different ULC configurations are seen in figures 99d and 76 (satellite images of February/03). There is no evident wind direction in the first half of the month and O3 concentrations fluctuate considerably during this period, comparing to other months of category +2, even reaching a maximum on the 10/02. However, from the 14th on, O3 concentrations decrease significantly, at the same time when N winds are observed in all measurement stations. This decrease in ozone concentrations did not happen due to a frontal system incursion, but may have been influenced by surface pressure decrease after the 8th of February (figure 97c) and air relative humidity after the 12th (figure 97a), with better pollutant dispersion conditions. In these days, there is also an increase in nebulosity (figure 97b) and precipitation events (figure 97d), which might be linked to a rather remote influence, that reached Iguape on the 12th. From the 14th to the 21st, O3 concentrations present a decreasing tendency until the passing of the first cold front of the month, on the 17 th. They continue to increase progressively until the 24th. On this day there is a frontal system passing in the SPMA on the 22nd, shifting wind direction (which becomes SW, and SE in the Santo Amaro station) on the CETESB station (figure 100a to 100c), until the end of the month. In this period, ozone concentrations raised again, although remain high comparing to the previous month. As a conclusion for this section’s analysis, it was possible to perceive a greater influence of the atmosphere on tropospheric ozone daily variability. Frontal system’s influence is perceived in different ways on ozone concentrations. When this systems present significant intensity and frequency, as in April 1998 and July 2005, they bring favourable conditions for ozone dispersion. On the other hand, the strong

269 frontal system observed in August 1999, responsible for a decrease in ozone concentrations when it passed through the SPMA, had an isolated influence. Therefore, under the influence of a high pressure system located in the centre of the country, the highest concentration levels of the month were observed in the rest of the month, specially compared to what was expected for the month of August. February 2003 and March 2002 show few frontal systems, of weak intensity, due to the atmospheric patterns observed in summer and UCL’s influence in February 2003. In these months, in spite of having relatively frequent precipitation events, there were negative anomalies of this variable, as well as of nebulosity. This conditions were favourable for ozone production and accumulation over the SPMA’s surface, given the high solar radiation intensity observed in summer.

270

Figure 99a: Tropospheric ozone and relative air humidity daily averages in February 2003. Frontal systems’ passing days are indicated by the arrow.

271

Figure 99b: OLR and air temperature daily averages in February 2003. Frontal systems’ passing days are indicated by the arrow.

272

Figure 99c: Surface pressure and wind speed daily averages in February 2003. Frontal systems’ passing days are indicated by the arrow.

273

Figure 99d: ULC’s formation and paths in South America in February 2003. Source: http://climanalise.cptec.inpe.br/~rclimanl/boletim/0203/vortice.html

274

Figure 99e: Solar radiation and precipitation daily averages in February. Frontal systems’ passing days are indicated by the arrow.

275

Figure 99f: Tropospheric ozone, solar radiation, OLR and atmospheric pressure daily averages in February 2003.

276

Figure 100a: Monthly evolution of wind direction and speed in Mooca and Parque D. Pedro II stations in February 2003. The dotted line marks the days of frontal systems’ passing.

277

Figure 100b: Monthly evolution of wind direction and speed in Santana, Santo Amaro and São Caetano do Sul stations in February 2003. The dotted line marks the days of frontal systems’ passing.

278

Figure 100c: Monthly evolution of wind direction and speed in São Miguel Paulista station in February 2003. The dotted line marks the days of frontal systems’ passing.

279 4. Conclusions and Final Considerations While atmospheric pollution is understood as being originated from diverse human activities inserted in the daily productive life of great cities, and able to influence in a negative manner the population’s health, the environment and visibility, it is vital to comprehend its different control mechanisms in order to minimize these hazardous aspects. Thus, atmospheric control over air pollution in the SPMA is of paramount importance for the daily life of 20 million people.

In this work, a study of the ozone’s average behaviour in the SPMA was made, by the means of the pollutant’s daily, monthly and yearly time series, besides a brief analysis of its spatial distribution. Regarding ozone means in these temporal scales (from hourly data of CETESB’s stations network), it was possible to established classification criteria among the analysed stations, based on the difference among their observed concentrations, on the temporal series’ tendency, on definition of their seasonal cycles and on their daily variability. Results show that stations located directly under the influence of vehicular emissions in areas of intense urban activity (ex: Lapa, Congonhas and Osasco) present lesser variability (in the daily scale), a less defined seasonal cycle and lower ozone concentrations compared to other stations located not only in residential areas; but specially to those stations away from those emission sources and from the city centre, located near green areas, in urban parks, schools and educational and sportive centres (ex: Santana, Santo Amaro and Pico do Jaraguá). It is then logical to suggest that the spatial distribution of the pollutant in the SPMA may be associated to the soil use in each site, due to the pollutants consume by the NOx emitted by vehicles, similar results to those obtained by NARSTO (2000), Azevedo (2002) and CETESB (2004). These stations also present a better defined average seasonal cycle, with a maximum October and a minimum in June, besides a secondary maximum in February, strongly associated to solar radiation availability and cloud covering through the year, com linear correlation coefficient of 0.98 for the average yearly cycles of ozone and solar radiation.

280 It was confirmed that the highest pollutant concentration on the daily scale occurs in the middle afternoon, according to results obtained from other works (CETESB, 2008; ANDRADE et al, 2004; MARTINS, 2006).

The station with highest increasing tendency was Pico do Jaraguá (angular coefficient of 0.3) and the station with the most significant decreasing tendency was Horto Florestal (angular coefficient of -0.4). When analysing the 17 stations average temporal series, an increasing tendency was found; however, it should not be taken as an indication of increase in the absolute pollutant concentrations in the SPMA, because the stations possess different working periods. What can be inferred from this data is that ozone concentration in the SPMA was also high in previously to 1996, when measurement began in most stations. The station with the lowest daily data variability was Congonhas (σ=8.01 μg/m3) and the highest was Santo Amaro (σ=19 μg/m3). It was possible to check that Congonhas and Lapa stations had the highest invalid data percentage (16.7% e 10.3%, respectively), while Santo Amaro, Pico do Jaraguá and Horto Florestal did not have any invalid data.

Analysis of the relations between monthly atmospheric behaviour and ozone variability was performed for periods of anomalous concentration, without the influence of the mean seasonal cycle in the stations’ average. From this analysis, it was possible to conclude that ozone concentration positive deviations (>+1) occur more frequently (85%) in situations of lesser nebulosity and air relative humidity, and, consequently, higher solar radiation income and air temperature and atmospheric pressure values. On the other hand, it was not possible to establish such strong connections between atmospheric variables and negative ozone concentration anomalies ( Accessed on: many days from 07/2006 to 10/2008

DOTY, B; The Grid Analysis and Display System, Edição revisada por Tom Holt da University of East Anglia e Mike Fiorino do Lawrence Livermore National Laboratory, 1995. Available at: < http://www.master.iag.usp.br/ind.php?inic=00&prod=mapa > Accessed on 11/2006

HERRMANN, V, I., FREITAS, S. R. Um estudo do transporte vertical de CO2 na atmosfera da Bacia Amazônica através dos sistemas convectivos, II Congresso de Estudantes e Bolsistas do Experimento LBA, Manaus, 2005. Available at: Accessed on 01/2007

LANDIM et al., Introdução à confecção de mapas pelo software SURFER,®. Universidade Estadual Paulista – UNESP,

Departamento de Geologia Aplicada

IGCE, Laboratório de Geomatemática, texto didático 08. Rio Claro, 2002 Available at: < http://omega.rc.unesp.br/mauricio/curso/bibliografia/8/216/Surfer.pdf >

RUBIN, M. B., THE HISTORY OF OZONE. THE SCHÖNBEIN PERIOD, 1839-1868, Bulletim of Historical Chemistry, Volume 26, No. 01, pg. 40-56, 2001. Available at: < http://www.scs.uiuc.edu/~mainzv/HIST/awards/OPA%20Papers/2001-Rubin.pdf > Accessed Websites:

293 Companhia de Tecnologia de Saneamento Ambiental < www.cetesb.sp.gov.br > Accessed on: many days in 2006, 2007 and 2008. Empresa Paulista de Planejamento Metropolitano SA – EMPLASA < www.emplasa.sp.gov.br > Accessed on 09/2006

Grupo de Estudos em Multiescalas: < http://www.icess.ucsb.edu/gem/index.htm > Accessed on: many days in 2006 and 2007

Instituto Brasileiro de Geografia e Estatística: < www.ibge.gov.br/home > Accessed on 03/2009

Instituto Nacional de Meteorologia < www.inmet.gov.br > Accessed on 01/2007 and 03/2009 LBA – Large Scale Biosphere-Atmosphere Experiment in Amazonia. < http://lba.cptec.inpe.br/lba/site/ > Accessed on: 11/2006

Meteorologia Aplicada a Sistemas de Tempo Regionais < http://www.master.iag.usp.br > Accessed on 01/2007

National Oceanic & Atmospheric Administration < http://www.cdc.noaa.gov/cgi-bin/db_search/SearchMenus.pl > < http://www.cpc.ncep.noaa.gov/products/wesley/reanalysis2/kana/reanl2-1.htm > Accessed on: many days from 2006 to 2008

ONU, Social Indicators, 2006. Available at:

294 < http://unstats.un.org/unsd/demographic/products/socind/hum-sets.htm > Accessed on: 10/2006

295

ANNEXES

296

Annex 1: Monthly means and linear tendency for each station. Annex 2: Atmospheric patterns observed in September 2004. Annex 3: Tropospheric ozone average spatial distribution in the SPMA (interpolating in Grids 0,02 and 0,03). Annex 4: Daily evolution of ozone production limits by VOC or NOx, according to the time of the day and vertical mixing conditions (on the top – moderate mixing, in the centre – weak mixing, on the bottom – stagnation). Annex 5: Precipitation, air temperature, air relative humidity and atmospheric pressure climatologies in the SPMA, and location of CETESB stations, hidrography and relief in the SPMA.

297

ANNEX 1 MONTHLY MEANS FOR EACH STATION AND LINEAR TENDENCY

298

299

300

301

302

303

304

ANNEX 2 ATMOSPHERIC PATTERNS OBSERVED IN SEPTEMBER 2004

305

306

307

308

ANNEX 3 TROPOSPHERIC OZONE AVERAGE SPATIAL DISTRIBUTION IN THE SPMA (INTERPOLATING IN GRIDS 0.02 AND 0.03)

309

ANNEX 4 NARSTO, 2000

310

ANNEX 5 City of Sao Paulo climatology (INMET) and location of CETESB stations in the SPMA (Created by Machado, 2008).