Spatial interpretation of ambient air quality for the

Environmental Pollution 112 (2001) 107±119

www.elsevier.com/locate/envpol

Spatial interpretation of ambient air quality for the territory of the Czech Republic I. Hunova Czech Hydrometeorological Institute, Prague, Czech Republic Received 30 November 1999; accepted 21 March 2000

``Capsule'': A method of spatial interpretation and visualisation of air quality data allows comparison of di€erent areas. Abstract A method for spatial interpretation and visualisation of measured air quality data is presented that may serve as a tool for ambient air quality characterisation using the least possible number of factors. This method enables comparison of di€erent areas in relative terms and has practical consequences in decision making and public information. The 1996 data for the Czech Republic was used as a database. According to my results, not a single universal indicator can fully describe the ambient air quality albeit the three factors identi®ed as ``ambient air pollution'', ``ground-level ozone'' and ``wet atmospheric deposition'' which are recommended. These selected factors represent three di€erent aspects of ambient air quality and its impact on receptors. For the above factors black and white charts are presented classifying the Czech Republic territory into ®ve categories as to relative ambient air quality. The air quality picture di€ers for the respective factors considerably. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Ambient air pollution; Ground-level ozone; Atmospheric deposition; The Czech Republic; 1996

1. Introduction Ambient air pollution in the Czech Republic has been for a long time considered as one of the principal environmental problems. The ®rst information on measured ambient air pollutant concentrations addressed sulphur dioxide (SO2) and total suspended particles (TSPs) and appeared as early as in the 1950s. The monitoring activities then focused on industrial areas and ambient air quality levels were observed regarding their negative impact on human health (Krasna, 1960; Symon et al., 1960). Regular ambient air quality monitoring has been in operation since the 1960s. At present the Czech Republic belongs to the countries with the most dense monitoring networks. The principal air pollutants recorded are SO2, nitrogen oxides (NOx), particles (both TSP and PM10), ozone (O3), carbon monoxide (CO) and heavy metals in aerosol. Chemical composition of precipitation is also monitored. All information on ambient air quality in the Czech Republic (including emission sources and atmospheric deposition) is summarised annually in tabular overviews and yearbooks (available also in English) published by the Czech Hydrometeorological Institute, CHMI (Fiala

et al., 1995, 1996, 1997, 1998). The impact of ambient air pollution on human health is regularly assessed in yearbooks published by the Public Health Institute, SZU (SZU, 1997, 1998) and ambient air pollution as one of the environmental spheres of the capital city of Prague is dealt with in periodically published yearbooks (available also in English) ``The Prague Environment'' (Solc, 1995, 1996, 1997, 1998). All of the ambient air quality data recorded over the territory of the Czech Republic is stored in one central database ISKO (Air Quality Information System) and regularly used for ambient air quality assessment. The air pollutant concentrations observed are related to ambient air quality standards (being either legislatively set up limit values, air quality guidelines, critical levels or critical loads). The advantage of this method is that it gives a rather detailed overview of the real situation as for the contents of the individual pollutants themselves and the variability of their concentrations re¯ected in daily and annual courses. The disadvantage is that it does not re¯ect reality in the sense of impact on receptors since the pollutants are considered as independent individuals. The fact that pollutants act in real atmosphere as a mixture (manifesting synergism, antagonism,

0269-7491/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0269-7491(00)00126-3

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I. Hunova / Environmental Pollution 112 (2001) 107±119

possibly potentiation) is not taken into account. Another disadvantage of this method is that such an assessment is fully understood by air quality experts whereas non-professionals, being either public, politicians or decision makers, might ®nd this method rather complicated: not entirely transparent but still not providing a simple and comprehensive answer to a commonly raised question of which areas are cleaner as for ambient air and which are more impacted. This reasoning lead in many countries to attempts to construct different air quality indices (Babcock, 1970; Thomas, 1972; Ott and Thom, 1976; Ott, 1978; Bello and Galatola, 1990; Hunt, 1991; Bezuglaya et al., 1993; Bakkes et al., 1994) and also to an intense debate on the question of if the air quality indices are sensible and for what purpose they should be used. For practical reasons we are interested in obtaining information on relative ambient air quality within the territory of the country with the aim of making clear which areas should receive more attention. Among these reasons can, for example, be reckoned: 1. indication of more impacted areas required by the EC ``framework directive'' (EC, 1996); 2. indication of areas towards which the ®nancial means should be directed in order to improve their ambient air quality; 3. indication of di€erences among regions as for the ambient air quality; and 4. indication of di€erences in temporal aspects among regions as for the ambient air quality, if it improves or deteriorates in time. Considering the above issues we present a method of spatial interpretation and visualisation of measured air quality data that would enable us to characterise the air quality data situation most accurately using the least possible number of describing factors. The paper examines a generally comprehensible and scienti®cally correct way of the ambient air quality characterisation, the goal of which is to enable the comparison of the di€erent sites and areas in relative terms. The presented method is applied to the ambient air quality data ®les for the territory of the Czech Republic for 1996. 2. Materials and methods The ambient air pollutants suitable for the purposes of this work have been chosen, out of the whole range of pollutants monitored in the Czech Republic in the long run, according to the following criteria: 1. their harmful e€ects on receptors are known (either in terms of toxicity for public health and vegetation, or negative impacts on ecosystems, e.g. eutrophication and acidi®cation);

2. information on their levels (concentrations or atmospheric deposition) are at disposal in the central database ISKO of the CHMI; 3. the measuring methods for these pollutants guaranteeing reliable results do exist; 4. reasonable data sets as for spatial station representativeness are at disposal; and 5. measurements are performed regularly and with sucient frequency, the measurements are continuous or performed in reasonably short time intervals. The analysis is carried out for the year of 1996. The reason is that at the time of data processing the latest veri®ed data ®les at my disposal were those for the year 1996. For identi®cation of mutual relations between the air pollutants under consideration the Pearson's correlations were calculated according to equation: n P

…Xi ÿ X†  …Yi ÿ Y† iˆ1 ; r ˆ s n n P P 2 2 …Xi ÿ X†  …Yi ÿ Y† iˆ1

…1†

iˆ1

where Xi, Yi are daily mean concentrations for examined variables, X; Y are averages of daily mean concentrations for examined variables, n is number of days. As the ambient air quality monitoring stations are numerous over the territory of the Czech Republic (Table 1), the selection has been done regarding to the principal station types operating in the monitoring network which are of distinctly di€erent nature and at which, consequently, correlations might di€er. The calculations have been carried out for: 1. mountain monitoring stations Ð at elevation above 800 m (Sneznik, Sous, Churanov, Hojna Voda, Rychory, Serlich, Bily Kriz and Rudolice); 2. rural stations representing the areas without close emission sources at elevation below 800 m (Kosetice, Svratouch and Ondrejov); and 3. city stations (Praha 1 Ð namesti Republiky, Praha 4 Ð Libus, Praha 5 Ð Mlynarka, Ostrava-Fifejdy and Plzen-Slovany). All the above stations are automated. They measure continuously giving the records in 30-min intervals. The Table 1 The numbers of ambient air quality monitoring stations in the Czech Republic, principal pollutants, 1996 Stations

SO2

TSP

PM10

NO

NO2

NOx

CO

O3

Automated Manual

187 356

24 158

115 ±

133 6

133 7

150 177

59 6

62 5

I. Hunova / Environmental Pollution 112 (2001) 107±119

methods used for chemical analysis are as follows: SO2 Ð ultraviolet ¯uorescence, NOx Ð chemiluminiscence, CO Ð infra-red correlation absorption spectrometry, O3 Ð ultraviolet absorption photometry and PM10 Ð radiometric method. Apart from these automated stations the calculation has been carried out for a selection of manually operated stations (Svratouch, Kosetice, Upice, Kolin, Benesov and Ostrava-Poruba). These stations measure discontinuously. The 24-h samples are taken and after transportation to the laboratory are analysed by spectrometry (SO2 and NOx) and gravimetry (TSP). The basic data from manually operated stations are 24-h means. The concentration of heavy metals is analysed by atomic absorption spectrometry (AAS). Twenty-four-hour concentrations of ambient air pollutants considered were used as input values for computing correlations. The systematic choice of every consecutive ®fth day resulted in the data set of 73 values for each station and air pollutant. The correlations for all possible pair combinations were calculated. 2.1. Classi®cation of respective factors Based on the present knowledge on ambient air pollutants resulting from their monitoring over the long term and the analysis of their mutual relations using the Pearson's correlations, three basic factors for description of the ambient air quality were identi®ed. These factors are as follows: 1. the ``ambient air pollution'' factor, 2. the ``ground-level ozone'' factor; and 3. the ``wet atmospheric deposition'' factor. For each of them ®ve quality categories are determined by criteria speci®ed in Table 2. The ``ambient air pollution'' factor contains the information on SO2, NOx including nitrogen oxide (NO) and nitrogen dioxide (NO2), CO, suspended particles, measured either as TSP (suspended particles without fraction resolution) or PM10 fraction (de®ned as particulate matter which passes through a size-selective inlet with a 50% eciency cut-o€ at 10 mm aerodynamic diameter), Table 2 Classi®cation of ambient air quality Category

Verbal classi®cation

Colours used in original mapsa Ð shades of grey

1 2 3 4 5

Very good Good Fair Deteriorating Hazardous

Yellow Ð 10% of black Orange Ð 30% of black Red Ð 50% of black Brown Ð 70% of black Black Ð black

a

Original charts are in colour. Colours for paper presentation in this journal were transformed into shades of grey.

109

and selected heavy metals Ð lead (Pb), cadmium (Cd) and arsenic (As) in TSP. Relative ambient air quality speci®cation has been carried out separately for each of the three basic factors. For the ``ambient air pollution'' factor it has been done on the basis of the annual medians calculated from 24-h concentration range for all single pollutants included in the factor. The reason for selecting a median as a classi®cation criterion is that this statistical characteristic is a representative for data ®les with asymmetrical frequency distribution, which is the common case of ambient air concentration data ®les. The 24-h concentrations have been chosen as input data for annual median concentrations in order to get comparable results both from automated and manually operated monitoring stations, giving records as 30 min and 24-h averages, respectively. The ``ambient air pollution'' factor was derived as follows: for each component considered (SO2, NOx, CO, TSP or PM10, Pb, Cd and As in TSP) the range of annual median of concentrations was found which was used as a criterion for sorting all measuring stations out into ®ve evenly distributed categories. Each monitoring station was classi®ed analogously for each pollutant measured and the ®nal category for a station was computed as a mean from all categories for the di€erent pollutants measured. For computing the categories for ``ambient air pollution'' factor the following equations are used:  kÿ1 Fmax ÿ Fmin †; C…Fi † ˆk when Fi 2 Fmin ‡ 5  …2† k Fmin ‡ …Fmax ÿ Fmin † ; k ˆ 1; . . . ; 5 5 where C(Fi) is a category for measuring station i for an air pollutant concentration considered (SO2, NOx, CO, TSP or PM10, Cd and Pb). Fi ˆ

TSP 2 x C…RSO † ‡ C…RNO † ‡ C…RCO † i i i † ‡ C…Ri Pb ‡C…RCd i † ‡ C…Ri † ; n

…3†

where Fi is a ®nal category for a measuring station calculated from all categories for air pollutants measured at the measuring station i; C(RY i ) is a category for measuring station i for an air pollutant Y, calculated Y similarly as C(Fi); RY i is a rank of values of Mi among Y all measuring stations; Mi is an annual median of 24-h average concentrations for an air pollutant Y at the measuring station i; and n is number of air pollutants measured at the measuring station i. The ``ground-level ozone'' factor includes the information on concentrations of that part of the tropospheric

110

I. Hunova / Environmental Pollution 112 (2001) 107±119

O3 formed as a secondary pollutant through photochemical reactions from precursors which are NOx and volatile organic compounds (VOCs). For the ``ground-level ozone'' factor the relative ambient air quality has been assessed apart from the annual median concentration range also for exposure indices range, to be speci®c for AOT40 (accumulated exposure over threshold of 40 ppb) for crops (AOT40C) and forests (AOT40F) and for AOT60 (accumulated exposure over threshold of 60 ppb) for human health. The exposure indices for crops and forests are calculated according to a recently speci®ed method (Werner and Spranger, 1996). The following equations are used: n X XX …Cijk ÿ p†; AOT40C ˆ

…4†

i2V jˆ1 k2D

where cijk means ground-level O3 concentration measured in the ith month, jth day and kth hour; p means threshold concentration 40 ppb; V is a set of months of vegetation season (May±July); D is a set of daylight hours, de®ned as those hours with a mean global radiation of 50 Wmÿ2 or greater; and n is number of days in a month. AOT40F ˆ

n X XX

…Cijk ÿ p†:

…5†

i2V jˆ1 k2C

where cijk means ground-level O3 concentration measured in the ith month, jth day and kth hour; p means threshold concentration 40 ppb; V is a set of months of vegetation season (April±September); D is a set of daylight hours, de®ned as those hours with a mean global radiation of 50 Wmÿ2 or greater; and n is number of days in a month. AOT60 ˆ

n X XX …Cijk ÿ p†;

…6†

i2R jˆ1 k2C

where cijk means ground-level O3 concentration measured in the ith month, jth day and kth hour; p means threshold concentration 60 ppb; R is a set of months in a calendar year; C is a set of all hours of a day; and n is number of days in a month. The ``ground-level ozone'' factor was derived as follows: each monitoring station was classi®ed by the annual median value computed from 24-h concentrations of ground-level O3 and by exposure index values (Eqs. (4), (5) and (6)). For construction of a map presented in this paper (Fig. 5) only the annual median value classi®cation was used. For this purpose the following equation is used:

 C…Mi † ˆ k when Mi 2 Mmin kÿ1 …Mmax ÿ Mmin †; Mmin 5  k ‡ …Mmax ÿ Mmin † ; k ˆ 1; . . . ; 5 5 ‡

…7†

where C(Mi) is a category for a measuring station i for a ground-level O3 concentration; and Mi is an annual median of 24-h average concentrations. The wet atmospheric deposition for the ``wet atmospheric deposition'' factor is calculated using the equation: Dˆ

n X c i  pi ;

…8†

iˆ1

where ci is the ion concentration in monthly precipitation sample; pi is the monthly precipitation amount measured at the relevant monitoring site; and n are months in a year. It includes the information on wet ÿ + + deposition of SO2ÿ 4 , NO3 , H , NH4 , Pb and Cd linked to episodes of so-called vertical precipitation, rain and snow. For the ``wet atmospheric deposition'' the relative ambient air quality has been classi®ed on the basis of the range of annual deposition values. The `wet atmospheric deposition' factor was derived as follows: for ÿ + + each air pollutant considered (SO2ÿ 4 , NO3 , H , NH4 , Pb and Cd) all measuring stations were classi®ed into ®ve categories by the annual wet atmospheric deposition value. The ®nal category for a station was computed as a mean from all categories for di€erent pollutants measured. For the purpose of categorisation of measuring stations for the `wet atmospheric deposition' factor the following equations are used:  kÿ1 …Fmax ÿ Fmin †; C…Fi † ˆ k when Fi 2 Fmin ‡ 5  …9† k Fmin ‡ …Fmax ÿ Fmin † ; k ˆ 1; . . . ; 5 5 where C(Fi) is a category for a measuring station i for an annual wet deposition of an air pollutant considered ÿ + + (SO2ÿ 4 , NO3 , H , NH4 , Pb and Cd); Fi ˆ

SO2ÿ 4

C…Ri

NOÿ 3

† ‡ C…Ri

‡

† ‡ C…RH i †

Pb ‡C…RCd i † ‡ C…Ri † ; n

…10†

where Fi is a ®nal category for a measuring station calculated from all categories for air pollutants measured at the measuring station i; C(RY i ) is a category for

I. Hunova / Environmental Pollution 112 (2001) 107±119

measuring station i for an air pollutant Y, calculated Y similarly as C(Fi); RY i is a rank of values of Di among Y all measuring stations; Di is an annual wet deposition value for an air pollutant Y at the measuring station i; and n is number of air pollutants measured at the measuring station i. The results of categorisation of measuring sites are presented in maps produced using the ARC/INFO system (ESRI, 1994) and its module GRID. For interpolation between the values at monitoring stations the IDW (Inverse Distance Weighted) interpolation method (Isaaks and Srivastava, 1989) has been employed. The interpolated values have been calculated from the values recorded at the 12 closest neighbouring stations according to the following equation: n Z…s † P i

Z…S0 † ˆ

h ij n 1 ; P

iˆ1

…11†

iˆ1 hij

where Z(s0) is the interpolated grid value; Z(si) is the neighbouring data point; hij is the distance between the grid node and the data point; is the weighting power (the Power parameter); and n is the number of measuring points. 3. Results The Pearson's correlations are summarised in Tables 3±9. Values signi®cant for a signi®cance level of 0.01 (its table value for n=73 equals 0.304) are marked with two asterisks and those signi®cant for a signi®cance level of 0.05 (its table value for n=73 equals 0.2335) are marked with one asterisk. Elevated CO concentrations are expected to be monitored only at the urban stations while rural or background sites monitor CO only rarely. Correlation coecient values for CO and other pollutants are presented hence only for some instances.

111

The results reveal that SO2, NO and NO2 as well as NOx as a group, particulate matter and CO concentrations monitored in ambient air are well correlated together in most cases. In many cases a relatively strong positive correlation is observed and therefore it seems that selecting a single independent ``ambient air pollution'' factor incorporating SO2, NOx, CO and particulate matter is well founded. At mountain and background stations extremely strong positive correlation (0.91±1.00) appears between NO2 and NOx proving the presumption that at these types of monitoring sites remote from major emission sources, the recorded concentrations of NOx group are determined predominantly by NO2 concentrations while contribution of NO on total concentration of NOx is negligible. Ambient air pollutant long-range transport appears logically as the basic factor for observed NOx levels at these sites. At urban sites on the contrary it is NO which contributes signi®cantly to NOx concentrations (increasing even at trac stations), which corresponds with calculated correlation coecient values (0.90±0.98). Insigni®cant correlation is observed at most mountain and background stations between PM10 and NO. At urban stations on the contrary the correlation between these two air pollutants is signi®cant. Low and rather negative correlation for ground-level O3 and other ambient air pollutants indicates only a weak link between them. Statistically signi®cant values for a signi®cance level of 0.01 are observed at most mountain and urban sites for ground-level O3 and PM10. This might be due to the fact that in chemical reactions leading to ground-level O3 formation ®ne aerosols are also formed. Statistically signi®cant negative correlation between ground-level O3 and CO, ground-level O3 and NOx and ground-level O3 and NO are observed at urban sites. This corresponds with the fact that ground-level O3 concentrations in cities are lower in the vicinity of emission sources whereas higher ground-level O3 concentrations are recorded in suburban areas and especially downwind of big cities. CO and NO concentrations decrease with increasing distance from emission sources.

Table 3 Correlations within the ``ambient air pollution'' factor, mountain sites, automated stations (AIM) Site

SO2-NO

SO2-NO2

SO2-NOx

NO-NO2

NO-NOx

NO2-NOx

PM10-NO

PM10-NO2

PM10-NOx

PM10-SO2

Sneznik Sous Churanov Hojna Voda Rychory Serlich Bily Kriz Rudolice

0.76** 0.43** 0.54** 0.03 0.37** 0.14 0.38** 0.33**

0.88** 0.72** 0.75** 0.73** 0.57** 0.48** 0.72** 0.72**

0.90** 0.67** 0.76** 0.64** 0.55** 0.52** 0.63** 0.65**

0.74** 0.61** 0.60** 0.31** 0.75** 0.17 0.60** 0.52**

0.88** 0.85** 0.67** 0.55** 0.83** 0.21 0.88** 0.81**

0.97** 0.93** 1.00** 0.96** 0.99** 0.99** 0.91** 0.92**

0.04 0.41** 0.23 ÿ0.04 0.13 0.14 0.02 0.31**

0.40** 0.41** 0.70** 0.62** 0.33** 0.72** 0.35** 0.57**

0.26* 0.46** 0.68** 0.54** 0.31** 0.75** 0.22 0.53**

0.36** 0.45** 0.67** 0.73** 0.45** 0.57** 0.52** 0.19

**P=0.01.

112

Table 4 Correlations within the ``ambient air pollution'' factor, background sites, automated stations (AIM) Site

SO2-NO

SO2-NO2

SO2-NOx

NO-NO2

NO-NOx

NO2-NOx

PM10-NO

PM10-NO2

PM10-NOx

PM10-SO2

CO-SO2

CO-NO

CO-NO2

CO-NOx

CO-PM10

Kosetice Ondrejov Svratouch

0.44** 0.34** 0.64**

0.62** 0.69** 0.62**

0.61** 0.68** 0.67**

0.73** 0.51** 0.58**

0.78** 0.59** 0.71**

1.00** 0.99** 0.99**

0.14 0.30* 0.20

0.31** 0.63** 0.53**

0.30* 0.61** 0.51**

0.51** 0.72** 0.57**

0.57** 0.16

0.42** 0.23

0.67** 0.46**

0.66** 0.46**

0.36** 0.32**

I. Hunova / Environmental Pollution 112 (2001) 107±119

*P=0.05. **P=0.01.

Table 5 Correlations within the ``ambient air pollution'' factor, urban sites, automated stations (AIM) Site

SO2-NO

SO2-NO2

SO2-NOx

NO-NO2

NO-NOx

NO2-NOx

PM10-NO

PM10-NO2

PM10-NOx

PM10-SO2

CO-SO2

CO-NO

CO-NO2

CO-NOx

CO-PM10

Ostrava-Fifejdy P1-n. Republiky P4-Libus P5-Mlynarka Plzen-Slovany

0.53** 0.38** 0.31** 0.31** 0.34**

0.62** 0.58** 0.68** 0.60** 0.73**

0.59** 0.51** 0.53** 0.39** 0.59**

0.84** 0.51** 0.62** 0.64** 0.57**

0.97** 0.95** 0.92** 0.98** 0.90**

0.95** 0.75** 0.87** 0.76** 0.87**

0.71** 0.20** 0.33** 0.48** 0.28**

0.79** 0.55** 0.60** 0.72** 0.58**

0.77** 0.36** 0.50** 0.57** 0.49**

0.70** 0.53** 0.68** 0.59** 0.37**

0.64** 0.46** 0.48** 0.53** 0.53**

0.84** 0.57** 0.78** 0.89** 0.72**

0.89** 0.66** 0.66** 0.77** 0.83**

0.90** 0.69** 0.81** 0.93** 0.88**

0.88** 0.53** 0.52** 0.66** 0.69**

**P=0.01.

I. Hunova / Environmental Pollution 112 (2001) 107±119 Table 6 Correlations within the ``ambient air pollution'' factor, manually operated sites Site

SO2-NO2

Svratouch Kosetice Upice Kolin Benesov Ostrava-Poruba

0.49**

SO2-NOx

TSP-NOx

TSP-SO2

0.69** 0.34** 0.66**

0.06 0.53** 0.07 0.72**

0.29* 0.35** 0.33** 0.70**

*P=0.01. **P=0.05. Table 7 Correlations between ground-level ozone and other air pollutants included in ``ambient air pollution'' factor, mountain sites, automated stations (AIM) Site

O3-SO2 O3-NO

O3-NO2 O3-NOx O3-PM10 O3-CO

Sneznik Sous Churanov Hojna Voda Rychory Serlich Bily Kriz Rudolice

ÿ0.41** ÿ0.01 ÿ0.14 ÿ0.08 0.07 0.10 ÿ0.27 ÿ0.11

ÿ0.53** ÿ0.24 ÿ0.12 ÿ0.06 0.16 0.31** ÿ0.17 ÿ0.09

ÿ0.53** ÿ0.14 ÿ0.38** ÿ0.01 ÿ0.02 0.19 ÿ0.43** ÿ0.24

ÿ0.57** 0.23 ÿ0.23 0.41** ÿ0.15 0.35** ÿ0.06 0.31** 0.14 0.62** 0.27 0.20 ÿ0.21 ÿ0.02 ÿ0.16 0.34**

**P=0.01.

Low correlation values or statistically signi®cant negative correlation values indicate that the ground-level O3 behaviour di€ers from other monitored air pollutants (SO2, NOx, particulate matter and CO). This acknowledges the preliminary assumption that it is reasonable to detach the ground-level O3 from the ``ambient air pollution'' factor and consider it as an independent factor. The monitoring site distribution into ®ve categories according to the annual median concentrations for the ``ambient air pollution'' factor is presented in Fig. 1. The monitoring site distribution into ®ve categories according to the annual median concentrations, as well as according to exposure indices (AOT40C, AOT40F and AOT60), for the ``ground-level ozone'' factor is presented in Fig. 2. Finally, the monitoring site distribution into ®ve categories according to the annual atmospheric wet-only deposition for the ``atmospheric wet deposition'' factor is presented in Fig. 3. Spatial categorisation for the entire territory of the Czech Republic is carried out separately for individual factors. Spatial categorisation of the Czech Republic for ``ambient air pollution'' factor is presented in Fig. 4, spatial categorisation of the Czech Republic for ``groundlevel ozone'' factor is presented in Fig. 5 and spatial categorisation of the Czech Republic for ``wet atmospheric deposition'' factor is presented in Fig. 6. The relative area (in %) of the entire country territory corresponding with ®ve categories is presented in Table 10.

113

Table 8 Correlations between ground-level ozone and other air pollutants included in ``ambient air pollution'' factor, background sites, automated stations (AIM) Site

O3-SO2 O3-NO

Ondrejov 0.07 Kosetice 0.03 Svratouch ÿ0.17

O3-NO2 O3-NOx O3-PM10 O3-CO

ÿ0.06 ÿ0.17 ÿ0.21 ÿ0.10 ÿ0.38** ÿ0.23

ÿ0.17 ÿ0.11 ÿ0.27

0.31** ÿ0.02 0.31**

ÿ0.04

**P=0.01. Table 9 Correlations between ground-level ozone and other air pollutants included in ``ambient air pollution'' factor, urban sites, automated stations (AIM) Site

O3-SO2 O3-NO O3-NO2 O3-NOx O3-PM10 O3-CO

P4-Libus ÿ0.27 ÿ0.52** ÿ0.29 ÿ0.47** ÿ0.09 Ostrava-Fifejdy ÿ0.28 ÿ0.29 ÿ0.23 ÿ0.28 ÿ0.13 Prachatice ÿ0.24 ÿ0.36** ÿ0.33** ÿ0.39** ÿ0.082 Plzen-Slovany ÿ0.38** ÿ0.32** ÿ0.28 ÿ0.36** ÿ0.29

ÿ0.44** ÿ0.22 ÿ0.38** ÿ0.32**

**P=0.01.

The ambient air quality image di€ers for the respective factors considerably. The worst category 5 for the ``ambient air pollution'' factor appears in Prague and adjacent areas, in the north-west region of Bohemia and in the Ostrava region. Substantial ``colour'' fragmentation in view of this factor compared to other factors, especially in the adjacent areas of the capital city of Prague, north-west region of Bohemia and the Jizerske Mts. region is caused by the fact that in all mentioned areas considerable concentration gradients between relatively close stations do exist. Particularly in Prague the fragmentation is caused by including monitoring stations of all types into analysis, i.e. those exposed to emission sources as well as those representing the suburban areas, where concentrations di€er substantially. The highest values of ground-level O3 are recorded in areas classi®ed as very clean by the ``ambient air pollution'' factor. These are the rural areas situated some distance from emission sources and in areas with higher elevation. The highest levels of ground-level O3 are measured in borderline mountain ranges, in the Jizerske Mts., the Giant Mts., the Orlicke Mts. and the Jeseniky Mts. in the northern part of the country, the Sumava Mts. and the Novohradske Mts. in the southern, the Beskydy Mts. in the eastern and central parts of the Czech-Moravian Highlands. The continuous wide belt of elevated though not maximal O3 concentrations extends from the submontane area of the Sumava Mts. across the Czech-Moravian Highlands, approximately from the south-west to the north-east. Low concentrations, in contrast, are measured at urban stations, particularly at those located in close vicinity of motorways with heavy trac. The ambient air of very

114

I. Hunova / Environmental Pollution 112 (2001) 107±119

Fig. 1. Monitoring site distribution into categories for ``ambient air pollution'' factor as to annual median of air pollutant concentrations included.

Fig. 2. Monitoring site distribution into categories for ``ground-level ozone'' factor as to annual median (O3 column) and exposure indices.

good quality from this point of view is observed in the area of Prague and Teplice. The ``wet atmospheric deposition'' factor classi®es most of the mountain measuring sites (not all of them, however, as it was hypothesised) in the Krusne Mts., the Sumava Mts., the Orlicke Mts., the Beskydy Mts. and the Jizerske Mts. as the most loaded ones. The loads at mountain sites are linked to the precipitation amounts as these are much more variable than relevant ion concentrations in precipitation within the country.

4. Discussion The results for only one model year (calendar year of 1996) are presented. We o€er a scenario of a possible way of ambient air quality data interpretation, which of course could be carried out for other years for which data is available. Comparison of such an analysis for subsequent years would indicate variations or trends in monitoring site categorisation and thus changes in the size of areas impacted by ambient air pollution. Rather

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Fig. 3. Monitoring site distribution into categories for ``wet atmospheric deposition'' factor and its components as to annual atmospheric wet deposition values.

Fig. 4. Spatial categorisation of the Czech Republic for ``ambient air pollution'' factor, 1996.

interesting would also be to check the seasonal aspects carrying out this analysis separately for winter and summer part of the year regarding remarkable seasonal variability of some air pollutants (e.g. SO2, O3) or to make an analysis for longer periods (e.g. 2±5 years), which appears to be especially relevant for atmospheric wet deposition. The picture of this factor presented in

this paper is not in accordance with presumptions that the wet deposition is highest in the mountain areas. This is caused by a spring anomaly which occurred in the year under review. During that anomaly, extreme ion concentrations were recorded at some sampling points in March and April. The atypical picture of this factor proves that 1 year is not sucient for an accurate

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Fig. 5. Spatial categorisation of the Czech Republic for ``ground-level ozone'' factor, 1996.

Fig. 6. Spatial categorisation of the Czech Republic for ``wet atmospheric deposition'' factor, 1996.

representation of wet deposition as well as deriving its impact on receptors. Wet deposition data for a longer period should be analysed. Horizontal deposition (fog, rime) was not considered in the data analysis. This type of deposition is not routinely monitored in the Czech Republic and the input

data for analysis does not exist. The author, however, is aware of the fact that horizontal deposition can take substantial part in total atmospheric deposition. It applies especially for the higher elevations, roughly above 800 m above sea level, where horizontal deposition due to frequent and long-term occurrence can play

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Table 10 The relative area (in %) of the entire country territory corresponding with ambient air quality categories 1±5 Factor

Category 1

Category 2

Category 3

Category 4

Category 5

Ambient air pollution Ground-level ozone Wet atmospheric deposition

43.1 3.0 9.5

46.4 16.5 39.5

5.8 37.3 39.1

3.8 29.9 9.6

0.9 13.3 2.3

an important role (Moldan, 1992). Experiment showed that fog and rime are more mineralised (have higher ion concentrations) than rain and snow, if sampled at the same site; e.g. sulphate and nitrate concentrations in horizontal precipitation sampled at some Czech sites were about 10 times higher that those ones in rain and snow (Moldan, 1989). The detailed ambient air quality analysis according to individual air pollutant concentrations related to air quality standards (being air quality limit values, critical loads or critical levels or air quality guideline values), as done in most cases (e.g. Broughton et al., 1997; Fiala et al., 1997, 1998), poses the most comprehensive air quality description. The way of interpretation presented in this paper implies a certain simpli®cation which has both advantages and disadvantages. The transformation of a considerable amount of measured data into an easy ®ve-scale range categorisation gives information of ambient air quality at a measuring site or in a certain area. It provides comprehensible and visual information for the public and all those who are not acquainted with the complicated air quality issues. This is an unquestionable advantage. The disadvantage might be an oversimpli®cation of the whole problem resulting in the hiding of time trends and variability of individual air pollutants. The oversimpli®cation is also apparent in that all air pollutants considered are given the same weight as to their impact, though actually the impacts of di€erent pollutants di€er. An idea for continuation of the work might be an attempt to give weight to di€erent air pollutants according to their impacts on receptors. As to health impacts it seems appropriate to classify the considered pollutants basically into substances of carcinogenic or non-carcinogenic character. The carcinogens might be possibly weighted due to their risk units de®ned by US Environmental Protection Agency (US EPA, 1990). Non-carcinogenic substances might be weighted both due to the mortality increase and due to an increase of undesirable e€ects (e.g. cardiovascular diseases). It will be dicult to apply air pollutant weighting due to its impact on vegetation, because ambient dose response is scarce and unambiguous scienti®c information is lacking. Presented spatial interpretation of ambient air quality does not take into account the air quality standards and always separates distinctly the sites belonging in di€erent categories. Certain hazards can represent these cases

when a monitoring site annual median concentration is an outlier as compared to the rest of the stations. It results in having one or very few stations in category 5 and in ranking other stations into categories indicating very good air quality. As, Cd, CO and NO charts are examples of such rather uneven annual mean distribution. Since only reliable monitoring stations and veri®ed data were included in analysis, the cause of outlying annual mean values at some stations might possibly be the low representativeness of these stations; the records of which represent only the close vicinity, and/or possibly exclusively themselves. To make the measuring site distribution into categories more even, these stations should be excluded from the analysis. Such an exclusion, however, is uncorrect as afterwards in the data set analysed, many stations with similarly poor representativity would remain, the records of which would not at ®rst sight appear non-representative. That is the main reason why ®nally all stations giving reliable results were used for analysis. The results show that one single indicator, including all measured pollutants and taking into account their impacts on di€erent receptors, cannot be recommended for a full description of the ambient air quality. It is in accordance with the opinion of Norwegian experts (Sivertsen, 1991). The combination of the air pollutants would result in ineligible and undesired ``averaging'' not re¯ecting the real situation. For full ambient air quality description three indicators are needed. The selected factors represent three di€erent aspects of ambient air quality and their impact on receptors. The ``ambient air pollution'' factor describes the ambient air quality mainly from the direct impact on human health point of view, the ``groundlevel ozone'' factor from the impact on ecosystem point of view and the ``wet atmospheric deposition'' factor from the impact on soils and ecosystem point of view. Other impacts, as direct in¯uence of higher ground-level O3 concentrations and indirect in¯uence of atmospheric deposition on human health as well as both direct and indirect in¯uence of ``ambient air pollution'' factor on vegetation, however, cannot be neglected. The estimation of the impact of air quality on human health using the measured concentrations recorded by the present monitoring network in the Czech Republic appears limited. The majority of stations are located in impacted areas, in cities and industrial agglomerations. The present monitoring network does not record the air

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quality of minor towns and villages, where concentrations of some air pollutants are expected to be as high as in areas traditionally considered to be endangered. This is principally the case for SO2 from local heating systems, which in areas with low buildings typical for minor towns and villages is emitted directly into the breathing zone of population. The same is true for NOx due to trac travelling through community centres and for particulate matter from di€erent sources. Another major problem is also common waste incineration (especially plastic) in households resulting in an increase in harmful organic compounds emissions which so far have not been monitored. Due to the fact that a considerable share of the Czech population lives in minor town and communities, it can be expected that much more of the population is exposed to ambient air pollution than usually stated. For the simpli®cation and summarisation of air quality interpretation the three describing factors have been chosen. The factors and their constituents, however, do not act independently. Photochemical oxidants, for instance, do not directly interact with ecosystem acidi®cation. The oxidants impact on vegetation parts above ground while acidifying compounds in¯uence vegetation mostly via the soil. Some works, however, have suggested that there exists a synergy between ground-level O3 and acidifying substance impacts (Stanners and Bourdeau, 1995). The stress by acid deposition combined with photochemical oxidants is hypothesised to increase forest dieback. This hypothesis is alarming since higher ground-level O3 concentrations are likely to occur in areas with higher atmospheric deposition values. 5. Conclusions The paper presents a suitable, uncomplicated and transparent way of ambient air quality data spatial interpretation which can become an instrument of ambient air quality characterisation by using the least possible number of factors. A new approach to air quality measured data interpretation, corresponding with the Europe-wide discussion on data interpretation using the air quality indices or indicators, is o€ered. The method used aggregates a huge volume of data giving information on relative ambient air quality using the least number of explaining factors. This way of data interpretation o€ers certain simpli®cation and summarisation of the issue. In this paper spatially related data are used enabling the direct comparison of air quality in di€erent sites and areas. The data measured are interpreted with regard to the air pollutant impacts on receptors. The results show that one single indicator cannot be recommended for full description of the ambient air quality, three indicators are needed. The selected factors

represent three di€erent aspects of ambient air quality and their impact on receptors' evaluation. The ``ambient air pollution'' factor describes the ambient air quality mainly from the direct impact on human health point of view, the ``ground-level ozone'' factor from the impact on ecosystem point of view and the ``wet atmospheric deposition'' factor from the impact on soils and ecosystem point of view. Ground-level O3 can be utilised as a photochemical air pollution indicator for photochemical smog which contains a mixture of other air pollutants originating together with O3 via photochemical reactions. Some of them, such as peroxyacetylnitrate (PAN), are suspected to have more serious health impacts than the ground-level O3 itself. The data interpretation method presented is based on data measured and available at present. It can be anticipated that the air quality control concept will change in the future due to the changing share of emission sources, introduction of new monitoring methods and the changing of state of the art of air pollutant impacts on receptors. In EU accession process more attention will be paid to ``new'' air pollutants, such as persistent organic pollutants, ®ne particles (PM 2.5), etc. These pollutants are known to have pronounced negative impact on human health. They have not been measured so far in the Czech Republic and so they could not be included into analysis. The method presented is useful for area classi®cation in decision-making processes. This method could be useful as a supporting tool for determining which area or areas should be given the priority in providing ®nancial means for environmental protection. For spatial data analysis the monitoring site categorisation is more correct than area categorisation. Area categorisation using the colour charts (for purposes of publication in this journal the originally coloured maps were transformed into black and white version) has a high expressive value nevertheless, we have to be aware of the fact that a certain error, originating in process of interpolation between values at single monitoring sites, is introduced into data interpretation and presentation. This error cannot be speci®ed using the IDW interpolation method. In spatial categorisation the maximal attention should be paid to single input thematic charts used for making ®nal charts for each of the factors. The output map cannot be better than single input maps. It is necessary to know the limitation of methods used and to interpret results in this sense. An essential prerequisite for correct charts construction, as close to reality as possible, is undoubtedly objective classi®cation of monitoring stations according to their type regarding the consequent use of measured data for assessment and interpretation (e.g. air quality impact on ecosystems, air quality impact on human health). Such an objective classi®cation has not been

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done for stations in the Czech Republic so far. It is a highly demanding procedure requiring an expert team and it seems to be suitable to build on the classi®cation of European monitoring network EUROAIRNET which has started to be implemented recently. Acknowledgements This paper summarises work submitted in 1998 and defended in 1999 as a PhD thesis. The data used for analysis were provided by the CHMI in Prague. The author would like to thank Prof. Moldan, the supervisor of the PhD thesis, for helpful comments on the work itself as well as the manuscript. The author would also like to thank her colleagues for fruitful debates on ambient air quality issues. References Babcock Jr., L.R., 1970. A combined pollution index for measurement of total air pollution. JAPCA 20 (10), 653±659. Bakkes, J.A., van den Born, G.J., Helder, J.C., Swart, R.J., Hope, C.W., Parker, J.D.E., 1994. An Overview of Environmental Indicators: State of the Art and Perspectives. UNEP/RIVM, Nairobi. Bello, G.C., Galatola, E., 1990. Relation between physico-chemical and biotic indices of water and air quality. In: Colombo, A.G., Premazzi, G. (Eds.), Workshop on Indicators and Indices for Environmental Impact Assessment and Risk Analysis Proceedings. CEC, Luxembourg, pp. 175±189. Bezuglaya, E.Y., Shchutskaya, A.B., Smirnova, I.V., 1993. Air pollution index and interpretation of measurements of toxic pollutant concentrations. Atmospheric Environment 27A (5), 773±779. Broughton, G.F.J., Bower, J.S., Willis, P.G., Clark, H. (Eds.), 1997. Air Pollution in the UK: 1995. AEAT Culham. EC, 1996. Council Directive 96/62/EC of 27 September 1996 on Ambient Air Quality Assessment and Management. Ocial Journal of the European Communities, No. L 296/55. ESRI, 1994. ARC/INFO GIS. Manual. ESRI, Redlands, CA. Fiala, J. et al., 1995. In: Fiala, J., Ostatnicka, J. (Eds.), Znecisteni ovzdusi na uzemi Ceske republiky v roce 1994. [Air Pollution in the Czech Republic in 1994]. Gra®cka rocenka. CHMU, Praha (in Czech). Fiala, J. et al., 1996. In: Fiala, J., Ostatnicka, J. (Eds.), Znecisteni ovzdusi na uzemi Ceske republiky v roce 1995. [Air Pollution in the Czech Republic in 1995]. Gra®cka rocenka. CHMU, Praha (in Czech). Fiala, J. et al., 1997. In: Fiala, J., Ostatnicka, J. (Eds.), Znecisteni ovzdusi na uzemi Ceske republiky v roce 1996. [Air Pollution in the Czech Republic in 1996]. Gra®cka rocenka. CHMU, Praha (in Czech).

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