Mapping of Dispersion of Urban Air Pollution Using Remote ... - ITC

Mapping of Dispersion of Urban Air Pollution Using Remote Sensing Techniques and Ground Station Data

I. K. Wijeratne February, 2003

Mapping of Dispersion of Urban Air Pollution Using Remote Sensing Techniques and Ground Station Data by I. K. Wijeratne

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in GeoInformatics.

Degree Assessment Board Prof. Dr. A. Stein (Chairman of board of examiners) Dr. Ir. B.G.H. Gorte (External examiner) Dr. W. Bijker (Supervisor) Dr. N. Kerle (Supervisor)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

Abstract Mapping of dispersion of air pollution is complex because it is depends upon various factors including weather conditions, topographical situation of the area and terrain of the area. Even in a small area, air pollution widely varies from place to place according to the local situation of the area such as situation of the building and ventilation condition in traffic corridors. Mathematical models and interpolation methods are widely used to map the dispersion of air pollution. These methods are hampered by the limited number of ground station data and difficulty of considering all the factors that cause dispersion of air pollution. To overcome this problem remote sensing techniques are used. Urban air pollution is studied less extensively using remote sensing techniques, because lack of sensors for detect urban air pollution. Therefore aerosol optical thickness is considered. Aerosol optical thickness indicates how much radiance is disturbed on its way from the earth objects to the sensors. In this study, through establishing the relationship among optical thickness and ground station data, dispersion of urban air pollution is mapped. For this study visible, near infrared and thermal infrared band of the Landsat-7/ETM+ are used. Linear regression analysis is used to establish the relationship between processes images and ground station data. Optical thickness of green and red bands and difference of at-satellite temperature of thermal infrared band show statistically significant relationships with, particulate matter, black particles and carbon monoxide respectively.

Acknowledgement It is my pleasure to express sincere thanks and gratefulness to all those who render their valuable encouragement, guidance and necessary help. I would like to give my utmost thanks and appreciation for the following persons and institutes. Dr. W. Bijker, my supervisor, I do have enough words to thank for your valuable guidance, support with so much understanding and consideration, providing necessary data, reports, documents, bringing me to the most problematic ground data collection stations, lengthy discussions; without Dr. Bijker’s support it may not come to an end of this study. Dr. N. Kerle, for taking the responsibility as my second supervisor. Mr. G.C. Huurneman, for valuable advisories and encouraging me for this study by providing web sites. Mr. W. Bakker valuable discussions and proving me books, documents and web sites specially related to the remote sensing. Ir. R.L.G. Lemmens, providing me digital data of the Netherlands. Drs B. van Leeuwen, Mr. G. Reinink, Mr. J. Hendrikse and Drs. W. Bakx helping me lot in working on ILWIS software and proving me relevant web sites. Prof. Dr. A. Stein, wake-up my mind by questioning on my regression models, during the mid term presentation. Dr. D. Rossitor, helping me to see the data in different angles and correct me in most possible errors. All GFM staff, including Mrs. E. Augustijn, Dr. R.A. de By and late Mr. A. Brown, for their understanding student well, helping to over come most difficulties in thousands of kilo meters away from home country and helping to archive targets in short time period. All the staff, working in library, help desk and cluster manager Mr. A. Blenke for their friendly help. All friends in the ITC, classmates and others, specially Jayamali and other Sri Lanken friends for the wonderful time we spent together, various help in studies and moral support. All other friends including Yann Chemin for their encouragements and valuable advises. Mr. Banduseela and Mr. Indrasiri selecting me as a candidate for this scholarship from Urban Development Authority and Netherlands Fellowship Program for offering me this scholarship. The National Institute of Public Health and the Environment of the Netherlands (RIVM) for making the ground truth data available as well as additional data, reports and documents which help the study lot. Special thanks for Drs Daan P. J. Swart and Mr. R. Koelemeijer working at RIVM sending me coordinates of ground data and reports through e-mails. Royal Netherlands Meteorological Institute (KNMI), for their data available on web. My loving husband Lal, kid Thenuka, mother and mother-in-low for taking care of our kid, the responsibility and hard time they had to pass due to my absence at home during study period at ITC.

Contents

1.

2.

3.

4.

5.

6.

7.

Introduction 1

1.1. General Introduction 1.2. Research Objective 1.2.1. Research Questions 1.3. Methodology in General 1.4. Data Requirement in General 1.5. Structure of the Thesis

1 2 2 3 3 3

Urban Air Pollution and Remotely Sensed Images

2.1. 2.2. 2.3.

Method, Data and Study Area

9

3.1. Introduction 3.2. Method 3.2.1. Method in Detail 3.3. Data requrement and availability 3.3.1. Data requirement 3.3.2. Data availability 3.4. Data selection and Standards 3.5. Study Area Introduction Calculation of aerosol optical thickness Difference of at-satellite temperature

Regression Analysis

5.1. 5.2.

28

Regression analysis under arbitrary grid situation Regression analysis under ideal grid situation

Results and Discussion 52

6.1. 6.2.

Mapping of dispersion of air pollution Discussion

Conclusion and Recommendation

7.1. 7.2.

Conclusion Recommendation

5 5 6 9 9 10 13 13 14 15 18

Image Analysis 20

4.1. 4.2. 4.3.

5

Introduction Aerosol optical thickness and related air pollution gasses Studies of Urban Air Pollution Using Remote Sensing Images

20 20 25 28 46 52 55

59

59 60

Contents of Figures Figure 3.1: Method.................................................................................................................................9 Figure 3.2: Various components of total transmission of downwelling irradiance and upwelling radiance ........................................................................................................................................10 Figure 3.3: Ground stations..................................................................................................................14 Figure 3.4: Ground stations, Utrecht-Rotterdam..................................................................................14 Figure 3.5: Study area ..........................................................................................................................18 Figure 3.6: Ground stations..................................................................................................................18 Figure 3.7: Major road and rail network over the area ........................................................................19 Figure 3.8: Urban areas ........................................................................................................................19 Figure 4.1: Calculation steps of Aerosol Optical Thickness................................................................21 Figure 4.2: Viewing angle....................................................................................................................24 Figure 4.3: Water bodies and land, by maximum likely hood method ................................................24 Figure 4.4: Cloud covered area ............................................................................................................25 Figure 4.5: Calculation steps of observed radiative temperature (at-satellite temperature) ................26 Figure 5.1: Relationship between CO and AT2 ...................................................................................33 Figure 5.2: Relationship between BP and AOT3.................................................................................34 Figure 5.3: Relationship between PM10 and AOT2............................................................................37 Figure 5.4: Relationship between NO and AOT3................................................................................39 Figure 5.5: Relationship between NO and AT2...................................................................................40 Figure 5.6: Relationship between NO and AOT/AT ...........................................................................41 Figure 5.7: Relationship between SO2 and AOT3 ...............................................................................42 Figure 5.8: Relationship between NO2 and AOT3...............................................................................43 Figure 5.9: Relationship between NO2 and AT2 .................................................................................44 Figure 5.10: Relationship between O3 and AOT3 ...............................................................................45 Figure 5.11: Relationship between O3 and AT2 ..................................................................................46 Figure 5.12: Comparison of arbitrary and ideal grid situation.............................................................47 Figure 6.1: Concentration of Black Particles and Particulate matter over the study area ...................53 Figure 6.2: Concentration of CO over the Study Area.........................................................................54 Figure 6.3: Shift of DN values of band 4 to the direction of higher values compared to the band 7. .55 Figure 6.4: Scatter plot of DN values in band 4 versus band 7............................................................55 Figure 6.5: Spread of daily and hourly average of SO2 concentration with AOT2 ideal grid situation57 Figure 6.6: Effect of changing grid cell size on at-satellite temperature .............................................57 Figure 6.7: Effect of changing grid cell size on AOT..........................................................................58

Contents of Tables Table 3.1: Data Availability in Utrecht-Rotterdam area......................................................................15 Table 3.2: Days with high pollution levels ..........................................................................................16 Table 3.3: Days with low pollution levels ...........................................................................................16 Table 3.4: Pollution threshold values for the Netherlands - 2002 .......................................................17 Table 3.5: Amount of pollution (µm/m3) for selected days .................................................................17 Table 4.1: Spatial and spectral resolution of bands of Landsat-7/ETM + sensor................................20 Table 4.2: Gain and Offset Values for the Reference and Polluted days ............................................22 Table 4.3: E0λ extraterrestrial solar irradiance incident at the horizontal plane (W/(m2 *µm))..........23 Table 4.4: Atmospheric pressure in the study area for the two examined days...................................27 Table 5.1: Data availability at ground stations ....................................................................................30 Table 5.2: Regression outputs for AOT3 and ground data ..................................................................31 Table 5.3: Other forms of relationships ...............................................................................................32 Table 5.4: Relationship between HA_CO and AT2 ............................................................................32 Table 5.5: Relationship between DA_BP and AOT3 ..........................................................................34 Table 5.6: New values for location 131 and 133. ................................................................................36 Table 5.7: Relationship between DA_BP and AT2 .............................................................................36 Table 5.8: Relationship between HA_PM10 and AOT2 .....................................................................38 Table 5.9: Relationship between HA_NO and AOT3 .........................................................................39 Table 5.10: Relationship between HA_NO and AT2 ..........................................................................40 Table 5.11: Relationship between HA_NO and AOT3 and AT2 ........................................................41 Table 5.12: Relationship between HA_ SO2 and AOT3......................................................................42 Table 5.13: Relationship between DA_ NO2 and AOT3 .....................................................................43 Table 5.14: Relationship between DA_ NO2 and AT2 ........................................................................44 Table 5.15: Relationship between HA_ O3 and AOT3 ........................................................................45 Table 5.16: Relationship between DA_O3 and AT2............................................................................46 Table 5.17: Relations of hourly average of CO with AT2...................................................................48 Table 5.18: Relations of daily average of BP with AOT3 ...................................................................48 Table 5.19: Relations of hourly average of PM10 with AOT2............................................................48 Table 5.20: Relations of hourly average of NO with AOT3................................................................49 Table 5.21: Relations of daily average of SO2 with AOT2 ..................................................................49 Table 5.22: Relations of daily average of NO2 with AOT3 .................................................................50 Table 5.23: Relations of daily average of NO2 with AT2 ....................................................................50 Table 5.24: Relationship of hourly average of O3 with AOT3.............................................................50 Table 5.25: Regression Summary ........................................................................................................51 Table 6.1: Concentration range of BP, PM10 and CO over the Study Area (µg/m3) ..........................52

Appendix 1: Tables and figures related to selection of polluted and reference day Figure A1. 1: Image availability Landsat-7/ETM+................................................................................ I Figure A1. 2: Hourly average of polluting components for Rotterdam area ........................................ II Figure A1. 3: Comparison of hourly average (12hrs) values of pollutant in reference (clear) and polluted days ................................................................................................................................VI Table A1. 1: Daily average of air polluted components around Rotterdam area................................. III Table A1. 2: Daily average of air polluted components around Utrecht area ......................................V Appendix 2: Tables and figures related to images analysis Figure A2. 1: AOT/AT over study area and relevant histograms ......................................................... II Figure A2. 2: Histograms of estimated pollutants ............................................................................... III Table A2. 1: Summary of methods considered to distinguish water and land....................................... I Appendix 3: Data and Correlation Tables Table A3. 1: Daily and hourly average concentration of air polluted components at ground level (µg/m3) and AOT (unit less)/ AT (K) ............................................................................................ I Table A3. 2: Linear Correlations of ground station data, AOT and AT............................................... II Table A3. 3: Correlations of ground station data, AOT and AT – Converted hyperbolic relationship to linear relationship......................................................................................................................... III Table A3. 4: Correlations of ground station data, AOT and AT – Converted Exponential relationship to linear relationship......................................................................................................................V Table A3. 5: Correlations of ground station data, AOT and AT – Converted Geometric relationship to linear relationship....................................................................................................................... VII Table A3. 6: Correlations of CO with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. ................................................................................ VIII Table A3. 7: Correlations of BP with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. ...................................................................................IX Table A3. 8: Correlations of PM10 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. ......................................................................X Table A3. 9: Correlations of NO with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. ...................................................................................XI Table A3. 10: Correlations of SO2 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. ................................................................... XII Table A3. 11: Correlations of NO2 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. .................................................................. XIII Table A3. 12: Correlations of O3 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. ................................................................................XIV Appendix 4: Tables and Figures related to regression analysis and results and discussion Figure A4. 1: Shrinkage of histograms of polluted day compared to the clear day............................... I Figure A4. 2: Regression results of CO with AT2 considering ideal grid situation ............................. II Figure A4. 3: Regression results of BP with AT2 considering ideal grid situation............................. III

Figure A4. 4: Regression results of PM10 with AOT3 considering ideal grid situation.....................IV Figure A4. 5: Regression results of NO with AOT3 considering ideal grid situation.........................IV Figure A4. 6: Regression results of SO2 with AOT2 considering ideal grid situation .........................V Figure A4. 7: Regression results of NO2 with AOT3 considering ideal grid situation........................VI Figure A4. 8: Regression results of O3 with AOT3 considering ideal grid situation ..........................VI Table A4. 1: AOT/AT values for arbitrary and ideal grid cells and distance to arbitrary grid cell boundary......................................................................................................................................... I Table A4. 2: Comparison of Air Temperature in Study Area............................................................ VII List of Abbreviation AOD – aerosol optical depth AOT – aerosol optical thickness AOT1 – aerosol optical thickness for band 1 of Landsat-7/ETM+ AOT2 – aerosol optical thickness for band 2 of Landsat-7/ETM+ AOT3 – aerosol optical thickness for band 3 of Landsat-7/ETM+ AOT4 – aerosol optical thickness for band 4 of Landsat-7/ETM+ AT - at-satellite temperature AT1 – at-satellite temperature of low gain thermal infrared band of Landsat-7/ETM+ AT2 – at-satellite temperature of high gain thermal infrared band of Landsat-7/ETM+ DN – digital number GMT – Greenwich Mean Time GPS – Ground Positioning System NDVI – Normalized Difference Vegetation Index RIVM - The National Institute of Public Health and the Environment of the Netherlands RT – Rotterdam TOA - top of the atmosphere UT – Utrecht

MAPPING OF DISPERSION OF URBAN AIR POLLUTION USING REMOTE SENSING TECHNIQUES AND GROUND STATION DATA

1. Introduction 1.1.

General Introduction

Air pollution is currently one of the major problems in developed countries as well as in developing countries. It has bad effects on human life causing diseases in respiratory systems and chronic illness (McCubbin and Delucchi 1999), on soil and plants (El Desouky, Moussa et al. 1998) and on the forests (Zhang, Pouyat et al. 2000). Sources of air pollution are twofold: human activities and natural environmental processes. Human activities causing air pollution are industry, use of motorized vehicles and low quality fuel for food preparation and heating purposes (Boyazi 1998). Natural sources are volcanoes, dust storms, forest - and grassland fires etc. Seasonal changes (Cheng and Lam 1997) and chemical reactions contribute to the concentration of the polluted air. There are many factors that cause dispersion of air pollution, including weather conditions such as temperature, wind speed and direction, humidity, topography of the area, relief of the area such as flat or hilly, or the local situation of the area such as whether the area is covered by buildings or whether there is ventilation in traffic corridors. Air quality standards are defined by international organizations (eg. World Health Organization - WHO, European Environmental Agency - EEA) or by local governments (eg. US Environmental Protection Agency - EPA, The National Institute of Public Health and the Environment of the Netherlands RIVM). When concentration of gasses like ground ozone (O3), nitrogen oxides (NOx) carbon monoxide (CO), carbon dioxide (CO2), sulphur dioxide (SO2), methane (CH4), particulate matter (PM) exceed the defined standards, this is considered air pollution. Generally, the amounts of NOx, CO, SO2, particulate matter (PM), temperature, humidity, wind direction and speed are measured at ground stations. After that, dispersion models or interpolation methods are used to visualize the spatial distribution of air pollution. As an example CAL3QHCR is an air pollution dispersion model developed by the California Department of Transportation. In some cases, the number of vehicles is taken into account where ground station data is not available. Then prior knowledge is used to calculate the amount of air pollution. The air pollution problem is also studied using spatial analysis methods such as buffering and overlay operations. Mathematical models are the most used methods to calculate magnitude and dispersion of urban air pollution (Boyazi 1998; Chakraborty, Forkenbrock et al. 1999). There is a large spatial variability associated with air pollution. Therefore even in a small area, air pollution varies widely from place to place. In general, a limited number of ground data collection locations are available, because ground data collection is expensive (Builtjes, H.M. et al. 2001; Ung, Wald et al. 2001). With limited number of data collection points, use of mathematical models and interpolation methods only does not give a correct picture of the air pollution for any given area. Another drawback of these methods is the difficulty to considering all the factors simultaneously, which cause the dispersion of air pollution. To overcome this problem, remote-sensing techniques can be used.

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Urban air pollution mostly occurs in the lower troposphere and is less extensively studied using remote sensing images, because of lack of sensors in detecting urban air pollution. In the ENVISAT satellite, there is an instrument called SCIAMACHY to detect large amounts of polluted air, such as identifying industrial areas, in small-scale mapping. One of the sensors used to monitor aerosols over Europe is the Along Track Scanning Radiometer (ATSR), which is on board European Remote Sensing Satellite (ERS-2), and provides column-integrated data at coarse resolution (Builtjes, H.M. et al. 2001). Moderate Resolution Imaging Spectroradiometer MODIS and Multi-angle Imaging SpectroRadiometer (MISR) are sensors at Terra satellite used to detect climate change by aerosols (w3 2002). The recent air pollution researches are in the direction of detecting air pollution using aerosol optical thickness. Tiny particles called aerosols and gasses at the atmosphere disturb the radiance reaching to the sensor by scattering and absorption. This reduces the contrast of the remotely sensed images (Sifakis and Paronis 1998). Optical thickness indicates the amount of scattering and absorption by particles and gasses. Many studies have been carried out in the field of air pollution. However there remain some questions to be studied. For example: How can the air pollution be visualized in a map using a limited number of ground data collected stations? Can mathematical models be built using only ground data to calculate the magnitude and dispersion of air pollution correctly? How far can direct and indirect factors related to the air pollution be modelled? Is it possible to use the models that are calibrated for one country, for another country? Is it possible to use the remotely sensed images to map dispersion of air pollution? This study focused on the mapping of dispersion of air pollution using remote sensing techniques and ground station data.

1.2.

Research Objective

Finding a suitable method to map urban air pollution using remote sensing techniques and ground data. 1.2.1. Research Questions 1. What is a suitable method for detecting aerosol optical thickness, using remotely sensed images? 2. What are the relationships between remote sensing images and air pollution data collected at ground stations? 3. How can selected components of air pollution be mapped in an urban area? The basic method used was a radiometric comparison of a satellite image under polluted condition, with a reference image acquired under unpolluted condition. Prior knowledge of ground data was used to select a pollution free reference image. In remote sensing techniques, different algorithms were used to detect aerosol optical thickness according to the different sensors. Even with the same sensor, methods of detecting aerosols differ according to the chosen spectral band (Sifakis and Deschamps 1992; Sifakis and Paronis 1998; Retalis, Caralis et al. 1999; Wald and Baleynaud 1999; Ung, Wald et al. 2001).

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Sifakis and Deschamps (1998) state that “Various methods are available to deal with the task of isolating the aerosol optical thickness by means of high spatial resolution (HSR) sensors, namely: `the ocean method’ applied over clear water using visible data or infrared data (e.g. Griggs 1975 ); the `brightness method’ applied over land using data in the visible spectrum (Fraser et al. 1984 ); the `contrastreduction method’ applicable over land (Tanre et al. 1988) or a mixture of land and water (Kergomard and Tanre 1989 );and the `dark vegetation method’ using long-wavelength visible data (Kaufman and Sendra 1988 ).” Holben, Vermote et al. (1992), have discussed these methods briefly. In this study, the method developed by Sifakis and Paronis (1998) is selected to detect urban air pollution using remote sensing techniques. In Sifakis and Paronis (1998) method, aerosol optical thickness (AOT) and difference of at-satellite temperature were calculated using remotely sensed images to detect urban air pollution.

1.3.

Methodology in General

To achieve the research objective, the methodology of this study is mainly split into three parts:

1. Detect aerosol optical thickness and at-satellite temperature using Remote Sensing images. 2. Find relationships between aerosol optical thickness and air pollution data from ground stations.

3. Map the urban air pollution for selected pollutants, using aerosol optical thickness and relationships derived in step 2.

1.4.

Data Requirement in General

To follow the methodology mentioned in section 1.3, there should be remotely sensed images as well as ground data for the corresponding time at satellite overpass. As further explained in the description of the methodology in Chapter 3, spatial and spectral resolutions are important. To meet the requirement of the methodology, only the green band and the thermal infrared band of Landsat satellite images are needed. Because of data availability this method is tested for blue, green, red, near infrared and thermal infrared bands of Landsat-7/ETM+. Hourly-recorded ground station data of particulate matter (PM10), Carbon monoxide (CO), Nitrogen oxide (NO), Nitrogen dioxide (NO2) Sulphur dioxide (SO2), Ammonia (NH3), Ozone (O3) and the daily average of black particles (BP) are considered according to the availability of data.

1.5.

Structure of the Thesis

Chapter one, Introduction, includes the general introduction, research objective and research questions, the methodology and the data requirement in general. General introduction explains why mapping of dispersion of air pollution is complex. Chapter two explains how urban air pollution affects remotely sensed images. In the same chapter other studies that have been carried out for urban air pollution using remote-sensing techniques are discussed briefly.

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In Chapter three, method, data and study area are discussed. The data is explained under three sections, data requirements, data availability and data selection. The study area is selected according to data availability. Air pollution standards that are considered for data selection are also included in the same chapter. Chapter four, image analysis, describes calculation of the aerosol optical thickness using visible bands and difference of at-satellite temperature using the thermal infrared band. Chapter five is regression analysis. This chapter describes establishing relationships of aerosol optical thickness and difference of at-satellite temperature with air pollutant data collected at ground level. Chapter six is results and discussion. This chapter includes the results and discussion of the overall study. Chapter seven is conclusion and recommendation.

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2. Urban Air Pollution and Remotely Sensed Images 2.1.

Introduction

In this chapter Section 2.2 describes how urban air pollution affects remote sensing images. Section 2.3 briefly summarises other studies on urban air pollution using remote sensing images.

2.2.

Aerosol optical thickness and related air pollution gasses

Most of the studies have used aerosol optical thickness to study urban air pollution as explained in Section 2.3. In some articles aerosol optical thickness is described as aerosol optical depth (AOD) as well. Optical depth is a measure of the transmittance of a vertical atmospheric column of unit cross-sectional area. A large optical depth implies less atmospheric transmittance. The transmissivity of the atmosphere has a value between 0 and 1, where 0 corresponds to a perfectly opaque atmosphere and 1 corresponds to a perfectly transparent atmosphere. Optical thickness also called “turbidity” and is a dimensionless, positive number (DuBois 1998). The optical depth is a result of the combined effect of scattering and absorption in a vertical column. Major contributors to this extinction in the atmosphere are aerosols and air molecules. The optical depth due to aerosols only is called aerosol optical depth (Satheesh 2002). Aerosols are tiny particles varying from 10–3 to 102 µm in size. Aerosols in 0.1 µm to 1µm significantly influence the visible solar radiation (Satheesh 2002). These are added to the atmosphere because of human activities, mainly industries and fuel burning and natural processes such as volcanoes, dust storms, forest - and grassland fires and sea spray. Except to these direct methods, different chemical reactions generate aerosols, these reactions are called “gas to particle conversion” processes. Lifetime of the aerosols in the troposphere mainly depends upon the particle size. It’s normally from a few hours to a few days. Large particles are falling because of gravity, which is called “dry deposition” or “sedimentation”. Small particles are removed by rainfall, which is called “wet removal” or “rain out”. Aerosol particles may be solid or liquid. Aerosols consist of sulphates and nitrates mostly due to industrial activities and volcano eruptions, mineral dust due to surface wind (dust storms), organic aerosols due to gas to particle conversion, carbonaceous aerosols (soot) due to fuel burning and sea salt due to sea spray. Aerosol particles larger than 1µm in size are produced by windblown dust and sea salt from sea spray and busting bubbles. Aerosols smaller than 1µm are mostly formed by condensation processes such as conversion of SO2 gas to sulphate particles and by formation of soot and smoke during burning processes (w2 2002).

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Scattering and absorption effects of the aerosols differ with the method of generation and chemical properties of aerosols. Particles originating from combustion processes usually have high absorption properties, eg. soot. Sulphate and nitrate aerosols have scattering effects hence solar radiation is reflected back to space. This increases the outgoing radiation to the sensors. In general scattering and absorption reduce the solar radiation reaching the earth’s surface and increase the outgoing radiation due to backscatter (Satheesh 2002). Sources of carbon monoxide (CO) are fossil fuels and biomass burning. Its lifetime in the troposphere is several months. CO has a large influence on methane oxidation. Increase of methane is related to increases in CO. Sulphur dioxide (SO2), enters to the atmosphere as a result of both natural phenomena like volcanic eruptions and anthropogenic activities like combustion of fossil fuels and biomass burning. Sulphur dioxide reacts on the surface of a variety of airborne solid particles (aerosols), is soluble in water and can be oxidised within airborne water droplets, producing sulphuric acid. The lifetime of sulphur dioxide molecules in the troposphere is a few days. The amount is highly variable, in gas phase by formation of sulphuric acid, and directly forming aerosols and clouds. The lifetime of sulphur dioxide molecules in the stratosphere due to volcano eruption, on the other hand, is several weeks. During this time it produces sulphate aerosols. NO and NO2 together are called NOx. Human activities such as traffic and industry are the main sources of NOx. The amount of ozone in the troposphere is largely determined by the concentration of NOx. Lifetime of NOx in the troposphere is short (w1 2002). In ground stations common pollutant measurements are NOx, CO, SO2, particulate matter (PM). When the particle size is less than 10µm it is denoted as PM10. Even though aerosols consist of a lot of components, this study only considers the commonly available ground measurements data for air pollution. Those are PM10, CO, NO, NO2, SO2, NH3, O3 and BP. It is clear that there are lots of sources and components causing generation of aerosols. In the images, there are no methods to separate aerosol optical thickness according to components or to source. For this reason, aerosol optical depth would not give a good relationship with pollutant components collected at ground level, when components would be considered separately.

2.3.

Studies of Urban Air Pollution Using Remote Sensing Images

Use of satellite images to detect urban air pollution is not very common. Images are used to detect air pollution at regional level, in coarser resolution. Some of the studies that have been carried out are summarized below. Crist (1984), describes the method to normalize Landsat data affected by haze, using the third feature of the Tasseled Cap transformation. He stated that the “Analysis revealed that changes in the amount of aerosol scattering caused a predictable shift in the MSS data plane in the Yellowness direction”. Further he explains that “Since atmospheric scattering decreases in severity with increasing wave length, and since the visible bands of the Landsat MSS sensor (band 1 and 2) are highly correlated in their response to surface features, a contrast of these two bands, as represented in Yellowness, could be expected to provide atmospheric scattering information. After elimination of residual scene effects and noise by means of a moving window low pass spatial filter, Yellowness proved to be a reliable indica-

6


tor of the level of atmospheric haze”. He has used both laboratory data and actual Landsat data in his study. Tanre, Deschamps et al. (1988) present a method which allows to derive aerosol optical thickness over land surfaces from satellite data by using the blurring effect due to scattering. They have summarised their work as “By assuming the ground reflectance to be constant, variations of the satellite signal may be attributed to variations of the atmospheric optical properties. The time evolution of the structure function then allows to infer the aerosol content over land surfaces. The method was applied to Saharan aerosols, which represent the most important contribution to the atmospheric aerosol loading. The result derived from Thematic Mapper data proved to be in good agreement with simultaneous groundbased measurements. Therefore the method seems able to provide the aerosol contents during Saharan dust events”. Sifakis and Deschamps (1992), used SPOT XS1 band to map the horizontal dispersion of airborne particles over urban polluted areas. In this study two satellite images were radiometrically compared. One image under polluted condition and an other image called reference image acquired under clear atmospheric condition. By this method they have approximated the AOD (AOT). In this study they have assumed that the intrinsic surface radiance has remained unchanged. Poli, Pignataro et al. (1994) have studied the relationship between an apparent temperature of Rome (Italy) and the total particulate matter suspended in the air (TPM). The TPM as well as sulphur dioxide were measured at five locations and were summed daily. The particulate matter is assumed to be a significant tracer of the atmospheric pollution as well as a good indicator of the air quality. They have found a strong negative correlation (-0.97) between the satellite derived temperature and the TPM. Confidence level was above 95%. On the contrary, the correlation between the SO2 and satellite derived temperature was weak and there was no significant relationship. Sifakis and Paronis (1998) present a further developed method than that presented by Sifakis and Deschamps in 1992. They have pointed out that the method presented in 1992 can misclassify the AOT due to ground reflectance temporal variation. To prevent from this misclassification they have introduced a method called ‘temperature attenuation procedure’ which uses the thermal bands. Here they have used the visible bands as well as the thermal bands of Landsat-5 / TM. Visible bands were processed according to the method developed in 1992 and thermal bands were processed separately. Finally they have taken the common area of both procedures as polluted area. Retalis, Caralis et al. (1999) have used basically the same method as that discussed in Sifakis and Deschamps (1992). They have used spectral region 0.45-0.52µm i.e. band 1 of Landsat-5/TM. In this study, they have assumed that the atmospheric thickness of the reference day as zero (τ1 = 0). This result was called ‘optical density’. They have graphically shown that SO2 and smoke have a positive relationship with ‘optical density’, using only three ground station data. In this study, spatial distribution of aerosols was assessed qualitatively. Wald and Baleynaud (1999) investigated the potentials of satellite-made observations for the mapping of air quality parameters. For this study Landsat TM6 thermal infrared band was used. It was found 7


that the amount of black particulates is highly correlated to the apparent temperature observed by the satellite. They have concluded that the mapping of the spatial distribution of the black particulates is possible from remotely sensed data even though it is not very accurate and discussed possible improvements. They have stated that “Analysis of the TM6 image reveals that the change in temperature from a polluted area to an unpolluted, one of similar type of land use, may be several degrees Celsius.” The paper has reviewed other studies related to ground station data and the thermal band of remotely sensed images. Ung, Wald et al. (2001) discuss the importance of studying urban air pollutions, drawbacks of existing methods and proposed a method using virtual stations. In their method they have defined pseudo stations, which have the same environmental, morphological and pollution sources as real measuring stations, using existing measuring stations and other data. By establishing the relationship between DN numbers of Landsat bands and pollutants from real measuring stations, virtual stations are defined out of pseudo stations, where the pollutants can be predicted by the derived relationship. They have used the thin plate interpolation method to map black particles using real measuring and virtual station data. They state that “Actually, no accurate knowledge of the spatial distribution of atmospheric pollutants, over a city is currently available.” They conclude the study by saying “It shows that the mapping of concentration of black particles is possible using the thermal band TM6 image of the Landsat satellite. The use of remotely sensed data for the mapping of pollutants over a city brings a better spatialisation of the phenomena under study”.

8


3. Method, Data and Study Area 3.1.

Introduction

Methodology, data requirements, data availability and data selection are described in this chapter. In Section 3.2 the method that is used to achieve the objective of the study is discussed. Details and background of the method discussed under Section 3.2.1. In Section 3.3, Section 3.3.1 and 3.3.2 contain the data requirement and availability respectively, which are necessary to follow the method discussed in Section 3.2. Data selection and air pollution standards that are considered for data selection are included in Section 3.4. The selection of the study area is based on data availability. The selected study area is described in Section 3.5

3.2.

Method

To map urban air pollution using remote sensing techniques and ground data, it is important in the first place to select a method for detecting urban air pollution using remote sensing techniques. The method developed by Sifakis and Paronis (1998) is selected to detect urban air pollution using remote sensing techniques. In this method aerosol optical thickness (AOT) was calculated using remotely sensed images to detect urban air pollution. The absorption effect of the polluted components was estimated using the difference of at-satellite temperature (AT). Regression analysis is used to establish a relationship between air pollution data collected at ground stations and the AOT or AT from processed images corresponding to ground data collection stations. To do the regression analysis, each of the airpollution components, which are collected at ground stations, are considered separately. Final step of the method is mapping of air pollution for separate components over the study area using AOT or AT and the relationships from the regression analysis. This method is illustrated in Figure 3.1.

RS images

Air pollution data from ground monitoring stations

Detect AOT / AT using RS images

Find relationship(s) with AOT / AT and air pollution data from ground monitoring stations

Plot the air pollution over the study area using relationship(s) and AOT or AT

Figure 3.1: Method

9


3.2.1. Method in Detail A combination of two independent methods is used to evaluate the optical thickness of aerosol scattering and absorption in the visible (τascat at 0.55 µm) and thermal parts of the spectrum, namely ‘Blurring effect’ and ‘Screening effect’. Blurring effect By `Blurring effect’ the spectral response patterns in the images are distorted due to the contrast reduction caused by ‘scattering-efficient airborne particles’ at short wavelengths. In this study, the equation derived by Sifakis and Deschamps (1992) is used to calculate the aerosol optical thickness. This method consideres the total transmission of down-welling irradiance and up-welling radiance as described in Figure 3.2.

Figure 3.2: Various components of total transmission of downwelling irradiance and upwelling radiance

The basic equation that was used to calculate apparent reflectance at satellite is Equation 3.1 (Sifakis and Deschamps 1992). Three stages of this equation were discussed according to the target diameter. For large dimension targets (such as > 1km), Equation 3.1 was used. Once the diameter is less than 100m, the adjacency effect is also introduced as in Equation 3.2. It was assumed that “the standard deviation expresses the analogical contrast in images”. Then the relationship between the standard deviation of apparent reflectance σ(ρ*) and the standard deviation of real reflectance σ(ρ) was derived as shown in Equation 3.3, assuming uniform background contribution to all the surrounding pixels. Only all the surrounding pixels. Another reason to select this Equation (3.3) was, that urban areas compose of small targets.

ρ* = ρ

T (θ s )T (θ v ) + ρa 1 − ρS

where ρ* ρ ρa S

-

(3.1)

apparent reflectance intrinsic surface reflectance atmospheric or sky reflectance spherical albedo of the atmosphere Define as a ratio of scattering to total attenuation radiation (i.e. scattering + absorbed) 10


- solar zenithal angle θs - observation zenithal angle θv T(θs) - total transmission function on the downwelling path It can be analysed as the sum of tdir(θs) and tdiff(θs) which are direct and diffused transmission functions. T(θv) - total transmission function on the upwelling path It can be analysed as the sum of tdir(θv) and tdiff(θv) which are direct and diffused transmission functions

ρ* = ρ

T ( θ s ) t dir ( θ v ) T ( θ s ) t diff ( θ v ) + ρe + ρa 1 − ρ eS 1 − ρ eS

where ρe

(3.2)

- reflectance from the adjacent objects

σ(ρ *) = σ(ρ )

T(θs ) t dir (θ v ) 1 − ρS

(3.3)

where σ(ρ*) and σ(ρ) are standard deviation of apparent and surface reflectance respectively. Using Lambert-Bouguer’s transmission law as shown in Equation 3.4, Equation 3.3 was converted to the Equation 3.5.

t dir (ϑ v ) = exp(−k λ m)

(3.4)

Where -kλm is aerosol optical thickness (AOT, dimensionless) and measured normal to the rays (Iqbal 1983). When viewing angle (observation zenith angle) is θv and the AOT is denoted as τ then AOT along the path is τ/cosθv. Then Equation 3.3 can be represented as 3.5.

σ(ρ *) = σ(ρ )

T (θs ) exp(−τ / cos θ v ) 1 − ρS

(3.5)

Equation 3.5 can be applied to reference (clear) day and to a polluted day respectively as follows (3.6 and 3.7). The factor T(θs) may be taken as constant independently of any temporal variation in AOT, because variation of its two additionals (tdir(θs) and tdiff(θs)) cancel out each other i.e. tdir(θs) diminishes at the same time as tdiff(θs) increases when τ increases (Sifakis and Deschamps 1992). Ratio of Equation 3.6 over 3.7 can be written as a 3.8.

σ1 (ρ *) = σ(ρ )

T(θs ) exp(−τ1 / cos θ v1 ) 1 − ρS

for reference day

(3.6)

σ 2 (ρ *) = σ(ρ)

T (θs ) exp(−τ 2 / cos θ v 2 ) 1 − ρS

for polluted day

(3.7)

11


σ1 (ρ) = exp((−τ1 / cos θ v1 ) + ( τ 2 / cos θ v 2 ) ) σ 2 (ρ)

(3.8)

This Equation (3.8) is used when AOT is calculated due to scattering effect at Section 4.2. Optical thickness can include scattering effect as well as absorption effect due to molecules and particles. This can be written down as Equation 3.9.

τ = τ am + τ ap + τsm + τsp

(3.9)

where τ represent optical thickness, superscripts ‘a’ and ‘s’ are represent absorption and scattering respectively and subscript ‘m’ and ‘p’ represent molecules and particles respectively. Absorption due to molecules is minimal in remotely sensed images because sensors use atmospheric windows to produce images. In small wavelengths absorption by particles is negligible and to a lesser degree also in the near-infrared. This is true when the aerosols contain no or few black particles. Rayleigh type scattering can be expected due to gasses (molecules), such as N2, O2, O3 and CO2. Molecular scattering can be considered as constant in similar atmospheric pressure conditions. Mie type scattering can be caused by particles with a size around 0.08 to 2.5 µm. With much bigger particles, non-selective type scattering can be expected (Sifakis and Deschamps 1992). According to these factors, optical thickness that is calculated using Equation 3.8 can be considered due to particle scattering when considering small wavelengths. Screening effect The `Screening effect’ results in a veiling of the images at longer wavelengths due to the radiation attenuation of particles that engender predominantly through absorption (Sifakis and Paronis 1998). This was calculated using thermal infrared bands. Absorption results in a reduction of the incoming solar radiation to the ground as well as the radiative temperature at-satellite. Equation 3.10 was used to calculate screening effect using two images, i.e. those of polluted and reference days. ∆T* = T1*- T2* = Te1 - Te2 + ∆Ta1- ∆Ta2 + ∆Tg1- ∆Tg2

(3.10)

where T1*, T2*

- observed radiative temperatures measured in the image Te1, Te2 - target’s radiative temperatures at the moment of image acquisition ∆Ta1, ∆Ta2 - local variations of the radiative temperature introduced by the presence of aerosols ∆Tg1, ∆Tg2 - variations of the radiative temperature due to changes in gas concentration Note: 1 and 2 are images for reference and polluted days respectively.

If there is no noticeable difference in atmospheric pressure between two days on which the images are taken, then Tg1 = Tg2. Intrinsic radiative temperature of the targets (Te1 and T e2) are subjected to variation in solar angle and air temperature. However, assuming that (Te1-Te2) is constant throughout the land area covered by the scene, any remaining decrease in the observed radiative temperature (atsatellite temperature) at pixel level can be attributed to attenuation engendered by aerosols (Sifakis and Paronis 1998). 12


Relationship between AOT or AT and air pollution ground station data In this study the known variable over the study area is AOT or AT, therefore it will become an independent variable. Air pollution components such as PM10, CO, NO, NO2, SO2, NH3, O3 and BP are known only at ground collection stations (i.e. unknowns over the study area). Therefore they will become a dependent variable, one at a time. Regression analysis is used to build a relationship between dependant and independent variables. In regression analysis R statistics, multiple R, R2, adjusted R2, standard error of the estimate, f-statistics, t-significance, Regression coefficient B and standard error of B can be used to check the goodness of fit. To do this analysis a statistical package called “Statistical Package for Social Sciences (SPSS)” is used.

3.3.

Data requrement and availability

3.3.1. Data requirement To follow the method discussed in Section 3.2, two types of basic data are required, namely remotely sensed data and air pollution data collected at ground level. The method is based on radiometric comparison; hence data is needed for two days, one under clear atmospheric condition called reference data and one of a polluted day. Remotely sensed images In case of images two spectral bands are required, i.e. bands at visible and thermal infrared. Sifakis and Deschamps and Sifakis and Paronis (1992; 1998) mentioned that AOT can be detected successfully in the green band. Two images are required, one for pollution free (less polluted) and one under polluted condition. The method discussed in Section 3.2 is valid only over land areas. Therefore having cloud free images is important. Atmospherically corrected data cannot be used with the above-mentioned method. Stretch or other contrast enhancement technique should not be applied to the images. Ground data The study requires data from several ground stations, spread over the study area, which measure different air polluting components separately. Ground data should be collected at same day as the images are acquired. If ground data is available at ‘the time’ that the satellite over passed, then a good relationship between images and ground data can be expected. Weather condition at the ground stations is also important in this kind of analysis. In calculation of the screening effect it is assumed that the absorption by gasses is equal when the pressure is similar at two selected days. Therefore to check the validity of this assumption, there should be at least atmospheric pressure data corresponding to selected days. If an air pollution free day is selected using ground data, data should be available for a considerable time period, at least for two to three years. It is not easy to find a less air-polluted day, which coincides with a cloud free image.

13


Other data that can be use for the study If the images need to be geo-referenced, it is useful to have map sheets in required scale and coordinate system, or one needs GPS coordinates at several places that can be identified on the images. Weather conditions like wind speed and direction can be helpful to explain the results of the study. Air pollution related facts like traffic volumes, road network, urban areas, industries or industrial areas are also useful to explain the outputs. 3.3.2. Data availability Ground data availability The Netherlands is selected for the study because of easy access to ground data. The National Institute of Public Health and the Environment of the Netherlands (RIJKSINSTITUUT VOOR VOLKSGEZONDHEID EN MILIEU – RIVM) are collecting data on an hourly basis for PM10, CO, NO, NO2, SO2, NH3, O3 and daily average of black particles. Data is available from January 2000 to March 2002 in RIVM web site http://www.lml.rivm.nl/. RIVM report 723101 055 (van Elzakker 2001) explains about the measuring instruments in the Dutch air quality network. Same report mention that the PM10 values are multiplied by 1.33 to correct the systematic errors. Initially Utrecht and Rotterdam area were selected as study area, after a quick look to the ground data. In this area pollution can be expected, since it is urban, densely populated with housing, industries and traffic. Another fact that has been considered selecting Utrecht and Rotterdam area is Figure 3.3: Ground stations that the two areas are situated considerably close and a large number of ground stations are situated in the area (Figures 3.3 and 3.4). To select a polluted and a reference day eight and nine locations are selected around Rotterdam and Utrecht area respectively. Polluting components that considered are PM10, CO, NO, NO2, SO2, and O3. Table 3.1 shows the data availability for selected components in Utrecht and Rotterdam area. Ground stations at Utrecht-Rotterdam area are shown in Figure 3.4. Figure 3.4: Ground stations, Utrecht-Rotterdam

14


Table 3.1: Data Availability in Utrecht-Rotterdam area Ground Stations

ID no

Station No.

PM10

SO2

NO

NO2

CO

O3

Rotterdam area Den Haag - Rebecquestraat Schipluiden - Groenveld Maassluis - Vlaardingsedijk Vlaardingen - Lyceumlaan Rotterdam - Schiedamsevest Vlaardingen - Floreslaan Westmass - Groeneweg Dordrecht - Frisostraat

1

404

X

X

X

X

-

X

2

411

-

X

X

X

X

X

3

415

-

X

-

-

-

-

4

416

-

X

-

-

-

-

5

418

X

X

X

X

X

-

6

433

X

X

X

X

-

X

7

437

X

X

X

X

-

X

8

441

X

-

X

X

X

X X

Utrecht area Cabauw - Zijdeweg Bilthoven - van Leeuwenhoeklaan Zegveld - Oude Meije Utrecht - de Jongweg Utrecht - Wittevrouwenstraat Utrecht - Vleutenseweg Utrecht - Erzeijstraat Utrecht - Universiteitsbibliotheek Breukelen - Snelweg

1

620

-

X

X

X

-

2

627

-

X

-

-

-

-

3

633

-

X

X

X

X

X

4

636

-

-

X

X

X

X

5

637

-

-

X

X

X

-

6

638

-

X

X

X

X

X

7

639

X

-

X

X

X

X

8

640

-

-

X

X

X

X

9

641

X

X

X

X

X

X

Image availability With respect to images Landsat-7/ETM+ is selected because it has visible bands as well as thermal bands in high spatial resolution. Landsat image availability is searched in the “EOS Data Gateway” web site (w6). Ground data availability, that is time period from 1st January 2000 to 31st March 2002, is considered to search the image availability. Rotterdam area is searched under latitude – longitude N51.49 - 52.1 and E3.84 - 4.71 and 44 images are found. Utrecht area is searched under latitude – longitude N51.8 – 52.3 and E 5.1 – 5.75 and 98 images are found. Landsat has sidelap approximately 7% at the equator to nearly 84% at 810 north or south latitude. Path 199 and row 24 covers only the Rotterdam area. Path 198 and row 24 covers both areas Rotterdam and Utrecht because of the sidelap. All the images over the selected area were taken around 10.25am to 10.35am in GMT time. 3.4.

Data selection and Standards

Not all the search outputs can be used because of cloud cover. Seven images in Utrecht area (UT) and fourteen in Rotterdam area (RT) are found under conditions with few clouds. Four images are common for both areas. Figure A1.1 in Appendix 1 shows image availability. Date, area covered by the particular image (UT or RT or both) and cloud cover of the particular area is mentioned below each image. Ground data corresponding to image available days are analysed. Ground stations and pollutant components mentioned in Table 3.1 are considered in this analysis. Minimum, maximum, daily average and hourly average are taken into account to find out a clear day and a polluted day. Analysis is separately done for Rotterdam and Utrecht areas using Excel software. Outputs showed similar pattern of polluting components in hourly average and daily average even though the magnitudes are different. The main difficulty that faced is, different components show different high and low days of pollution and this also changes with locations as shown in Figure A1.2 in Appendix 1. 15


As an example, PM10, CO and NO in Rotterdam area show that the 23rd of December 2000 has highest concentrations while SO2 and NO2 show that the 15th of November 2001 has the highest concentrations. O3 shows highest concentrations on 26th of July 2001. This is also valid for the Utrecht area. To overcome this problem more than one higher and lower days are selected for each component, from all the considered ground stations. This analysis is carried out using daily averages, rather than looking on an hourly basis. Daily average of pollutant concentrations for the days that the images are available, are tabulated in Appendix 1 Table A1.1. Summaries are shown in Tables 3.2 and 3.3. Table 3.2: Days with high pollution levels Around Rotterdam area Component PM10 SO2 NO NO2 CO O3

Date1 Amount1 23-Dec-00 92.45 07-Mar-02 25.75 15-Nov-01 241.79 15-Nov-01 82.67 23-Dec-00 155.75 26-Jul-01 80.83

Amount around Utrecht area Date2 Amount2 30-Oct-01 69.56 15-Nov-01 22.88 23-Dec-00 228.33 23-Dec-00 71.25 15-Nov-01 131.92 23-May-01 67.04

Component PM10 SO2 NO NO2 CO O3

Date1 Amount1 23-Feb-00 86.03 23-Feb-00 16.38 23-Feb-00 231.38 23-Feb-00 93.04 23-Dec-00 204.67 13-May-00 78.79

Date2 Amount2 23-Dec-00 84.42 01-Aug-00 10.96 23-Dec-00 173.17 11-Apr-00 86.46 23-Feb-00 172.88 01-Aug-00 63.52

Table 3.3: Days with low pollution levels Around Rotterdam area Component Date1 PM10 23-Jul-00 SO2 23-Jul-00 NO 23-Jul-00 NO2 23-Jul-00 CO 24-Aug-00 O3 15-Nov-01

Amount1 15.57 -0.25 0.33 7.21 20.33 0.96

Date2 Amount2 07-May-01 16.73 07-May-01 0.42 07-Mar-02 0.48 07-Mar-02 13.39 07-May-01 20.46 23-Dec-00 2.30

Date3 Amount3 24-Aug-00 20.75 23-May-01 0.67 3-Jul-01 1.08 07-May-01 19.17 03-Jul-01 21.04 15-Jan-01 6.30

Around Utrecht area Component Date1 Amount1 Date2 Amount2 Date3 Amount3 PM10 03-Jul-01 13.06 05-Nov-00 23.41 13-May-00 40.31 SO2 03-Jul-01 1.43 23-Dec-00 2.58 13-May-00 3.21 NO 13-May-00 0.46 5-Nov-00 1.29 3-Jul-01 2.08 NO2 03-Jul-01 17.63 13-May-00 19.17 05-Nov-00 20.25 CO 03-Jul-01 22.96 11-Apr-00 29.04 05-Nov-00 29.33 O3 23-Feb-00 2.04 23-Dec-00 3.63 05-Nov-00 16.83 Note: Negative values are due to the instrumental errors, because measuring instruments are very sensitive even for small changes. Source: http://www.lml.rivm.nl/

Next the image availability (Appendix 1, Figure A1.1) is considered to select maximum and minimum polluted days from above tables. As an example 23rd of July 2000 is the minimum polluted day for Rotterdam area and for this day an image is not available for Utrecht area. In case of Utrecht most polluted day is 23rd of February 2000 and it is not the case for Rotterdam area, then the second highest is considered. Likewise 3rd of July 2001 is selected as day with lowest pollution and as day with highest pollution day 23rd of December 2000 is selected. CO and PM10 are also considered in selecting these dates, because it is expected that the aerosols are more related with CO and PM10. Data of these two days are compared with pollution standards of the Netherlands. Standards are taken from RIVM web. RIVM use standard values based on European Union (EU). Only for the O3 this values will be changed in 2003/2004. 16


Table 3.4: Pollution threshold values for the Netherlands - 2002 Threshold (µg/m3) SO2 Hourly average concentration, may be trespassed 24 hrs per year 350 Daily average concentration, may be trespassed 3 days per year 125 Yearly average concentration1 20 Winter half of the year, average concentration1 20 NO2 Hourly average concentration, may be surpassed 18 hrs per year 200 Yearly average concentration 40 NOx Yearly average concentration1 30 PM10 Daily average concentration, may be surpassed 35 days per year 50 Yearly average concentration 40 CO 8 hour average concentration 10,000 O3 Hourly average concentration 240 8 hour average concentration 110 Daily average concentration1 65 Average concentration over the growing season1,2 100 Note:1 and 2 denotes ‘Threshold values to protect ecosystems’ and ‘Average over period of May to September for daily period 9:00 – 17:00 Description of pollutant and average values

hrs.’ respectively. Source: http://www.lml.rivm.nl/info/normen.html

Table 3.5: Amount of pollution (µ µm/m3) for selected days Ground Stations Den Haag – Rebecquestraat Schipluiden - Groenveld Maassluis - Vlaardingsedijk Vlaardingen - Lyceumlaan Rotterdam - Schiedamsevest Vlaardingen - Floreslaan Westmass - Groeneweg Dordrecht - Frisostraat

Cabauw – Zijdeweg Bilthoven - van Leeuwenhoeklaan Zegveld - Oude Meije Utrecht - de Jongweg Utrecht - Wittevrouwenstraat Utrecht - Vleutenseweg Utrecht –Erzeijstraat Utrecht - Universiteitsbibliotheek Breukelen – Snelweg

ID no max min max min max min max min max min max min max min max min max min max min max min max min max min max min max min max min max min

1 2

Station No. PM10 Rotterdam area 404 92.45 31.44 411 -

3

415

-

4

416

-

5

418

SO2

NO

8.79 2.21 11.71 2.17 13.25 7.75 17.5 4.0 13.71 2.96 21.04 4.79 5.58 1.33 -

180.75 7.33 164.26 3.21 -

99.0 26.0 80.0 18.0 -

124.0 21.04 -

3.04 43.29 2.3 44.04 -

-

-

-

-

137.0 6.26 228.33 n/a 99.42 3.13 121.63 1.08

68.0 28.0 83.0 n/a 39.0 n/a 49.0 15.0

136.21 43.13 -

-

79.54 2.08 -

42.0 11.0 -

-

74.38 3.13 108.92 13.75 171.58 13.75 153.96 23.50 173.17 31.96 76.96 3.17 121.50 2.33

48.0 10.0 53.0 26.0 75.0 28.0 65.0 33.0 50.0 31.0 48.0 12.0 64.0 10.0

85.21 25.67 130.75 31.96 175.04 41.04 204.67 60.33 186.75 46.13 101.96 24.88 94.42 22.96

1

91.05 23.79 433 84.26 28.69 437 85.71 31.97 441 65.88 22.88 Utrecht area 620 -

2

627

-

3

633

-

4

636

-

2.58 2.00 5.54 1.96 3.08 1.58 -

5

637

-

-

6

638

-

7

639

8

640

84.42 27.37 -

7.92 1.43 -

9

641

6 7 8

65.76 13.06

17

6.63 n/a

NO2 hr av

CO

O3 -

155.75 n/a

-

4.96 41.08 2.33 n/a 9.33 54.67 n/a 52.83 3.63 46.58 4.42 41.38 5.39 38.75 4.13 37.88 3.75 45.29 4.63 49.75


Threshold values, which are tabulated in Table 3.4, are compared with pollution concentrations of selected days (Table 3.5). In Table 3.5 maximum value is with respect to the 23rd of December 2000 and minimum is with respect to the 3rd of July 2001. O3 shows low concentration at 23rd December and high value on 3rd July, different to the other pollution components. Daily average values are used for comparison, except for NO2. In NO2 hourly average corresponding to the time of satellite overpass, is used. Even though NOx contain both NO and NO2, NO couldn’t be compared because only annual average threshold exists. CO couldn’t be compared because standards are given for 8-hour averages.

3.5.

Study Area

According to the standards the 23rd of December 2000 and the 3rd of July 2001 images are selected as polluted and clear days respectively. In cloud free condition, these images covered a larger area than Rotterdam and Utrecht area. Therefore this study can be carried out in five provinces out of the twelve provinces of the Netherlands. Those provinces are Zeeland, Noord-Brabant, Limburg, Zuid-Holland and Utrecht. Cloud cover of the 23rd of December image mainly covers the northFigure 3.5: Study area west and the 3rd of July image covers the northeast of the Netherlands as shown in Figure A1.1 (Appendix 1). Especially the Utrecht area is partly covered by clouds on both images. North of Zuid-Holland, northeast of Noord-Brabant and north of Limburg are also affected by cloud cover (Figure 3.5). Once the area covered by cloud is carefully excluded, more ground stations than those that were considered in Section 3.4 can be included in the study. That is 28 out of 48 ground data collection stations can be considered in this study as shown in Figure 3.6. Comparison of pollutants at each location for reference and pollutant days can be represented graphically as Figure A1.3 in Appendix 1. The locations given in the x-axis are the same as the GS_ID in Table 5.1. Once the cloud cover and water bodies are removed (Section 4.2), the area that is considered for the study is about 11,000 km2. The width of the study area is about 180km and the length is about 100km. Major road and rail network is spread over the area as show in Figure 3.7. There are nine airports in the study area. Two of them i.e. Rotterdam and Maastricht are categorized as active civil and others as active military in ESRI “The Digital Chart of the World for use with ARC/INFO”, November 1993 digital data. Figure 3.6: Ground stations

Urban areas from the ESRI Europe digital data cover18


age show that the Rotterdam area is more urbanized than the other areas (Figure 3.8). Urban areas over southwest of the study area are very low. One of the biggest harbours in the world is situated at Rotterdam. All the images and other spatial data that have been used for this study are in UTM projection, Datum WGS 1984, Ellipsoid WGS84, Northern hemisphere, Zone 31.

Figure 3.7: Major road and rail network over the area

Figure 3.8: Urban areas 19


4. Image Analysis 4.1.

Introduction

This chapter describes calculation of aerosol optical thickness using visible bands in Section 4.2 and calculation of difference of at-satellite temperature using the thermal infrared band in Section 4.3. For this analysis visible bands 1, 2, 3, near infrared band (band 4), and thermal infrared band (band 6) are used. Middle infrared band (band 7) is used for qualitative checking of presence of aerosols at the atmosphere. Spatial and spectral resolution of theses bands can be tabulated as Table 4.1 for Landsat7/ETM+. Table 4.1: Spatial and spectral resolution of bands of Landsat-7/ETM + sensor Resolution Spectral (µm) Spatial (m)

Band 1 0.45-0.52

Band 2 0.53-0.61

Band 3 0.63-0.69

Band 4 0.78-0.90

Band 7 2.09-2.35

Band 6 10.4-12.5

30

30

30

30

30

60

Source: Landsat Processes Distributed Active Archive Centre - (w7 2002)

4.2.

Calculation of aerosol optical thickness

The process of calculation of aerosol optical thickness using visible bands and NIR band of Landsat-7/ ETM+ can be shown as in Figure 4.1. December 23rd image is used as a reference image in the image registration. To calculate the reflectance values at satellite, digital numbers (DN) of images are first converted to the radiance values at satellite (Lλ). This is calculated by Equation 4.1 (w3 2002) for the first four bands of Landsat 7. A and B of the equation are called ‘gain’ and ‘offset’. Minimum radiance for each band was taken as offset value. Gain calculation is done as in Equation 4.2. In this equation Lmin and Lmax are minimum and maximum radiance values, which are taken from the header file of an image. Qmin and Qmax are minimum and maximum pixel values, also taken from the header files. This equation varies according to the source of image acquisition. This was explained in the Landsat-7 Science Data Users Handbook (w3 2002) as “LPGS (EOS Data Gateway) uses 1 for Qmin while NLAPS (EarthExplorer) uses 0 for Qmin. Other product differences exist as well”. For this study images are acquired through EOS Data Gateway. Therefore, Qmin is considered as 1. Calculated gain and offset values are shown in Table 4.2. These calculated gain and offset values are compared with USGS document titled “MRTC 2000 Image Processing Procedure” (w4 2002) and values are compatible up to two decimals.

20


Figure 4.1: Calculation steps of Aerosol Optical Thickness 21


Radiance values at satellite (Lλ)

L λ = A ∗ (DN − Q min ) + B

where Lλ A B Qmin

-

(W/(m2 * ster * µm))

(4.1)

radiance values at satellite Gain Offset = Lmin = minimum radiance values minimum pixel values

A = (L max − L min ) / (Q max − Q min )

(4.2)

Where Lmax and Lmin are maximum and minimum radiance Qmin and Qmax are minimum and maximum pixel values Table 4.2: Gain and Offset Values for the Reference and Polluted days Band / Date

B1 - 0.45 – 0.52 µm

23 December 2000 (Julian day = 358) Polluted day Gain Offset 0.7787402 -6.2

3 July 2001 (Julian day = 184) Reference day Gain Offset 0.7787402 -6.2

B2 - 0.53 – 0.61 µm

0.7988189

-6.4

0.7988189

-6.4

B3 - 0.63 – 0.69 µm

0.6216535

-5.0

0.6216535

-5.0

B4 – 0.78 – 0.90 µm

0.6397638

-5.1

0.9692913

-5.1

After that radiance values are converted to the reflectance values at top of the atmosphere (TOA) using Equation 4.3 (w3 2002; w4 2002). In some documents reflectance at TOA (ρλ) is called reflectance at satellite (w4 2002) and in some documents it is called planetary reflectance or albedo (w3 2002). This is called as apparent reflectance by Sifakis and Deschamps (1992). Equation 4.4 is used to calculate the earth sun distance in astronomical units (Bandara 1998). These values are cross-checked with Landsat 7 Science Data Users Handbook (w3 2002). Equation 4 gives compatible results for Julian days 1, 182 and 365 with the Landsat 7 Science Data Users Handbook (w3 2002) document. Sun earth distances are compatible up to 3 decimal places for the particular days that the images are acquired for the study, compared to Landsat 7 Science Data Users Handbook. E0λ, extraterrestrial solar irradiance incident at the horizontal plane in Watts/(m2 * µm) is taken from the Landsat 7 Science Data Users Handbook (w3 2002). This is a band specific constant value, shown in Table 4.3. Sun zenith angle is taken from the header file for each image. Erdas Imagine 8.5 and Excel are used for these calculations. Reflectance values at the top of the atmosphere (ρλ)

π ∗ Lλ ∗ d 2 ρλ = E0 λ ∗ sin (θ) where ρλ Lλ E0λ d -

(unitless)

(4.3)

reflectance values at the top of the atmosphere radiance values at satellite (W/(m2 * ster * µm)) extraterrestrial solar irradiance incident at the horizontal plan (W/(m2 * µm)) Earth sun distance (astronomical units) and θ - solar zenithal angle (degrees) 22


Earth sun distance

d = 1 + 0.01672 ∗ sin

2π(J − 93.5) 365

(Astronomical units AU)

(4.4)

where d - sun-earth distance J - Julian day, day of the year starts January 1st as 1 Note : The absolute value of 1 Astronomical units (AU) is 1.496 * 108 km (Bandara, 1998) Table 4.3: E0λ extraterrestrial solar irradiance incident at the horizontal plane (W/(m2 *µ µm)) Band 1 2 3 4

E0λ (W/(m2 *µm)) 1969 1840 1551 1044

Source: Landsat 7 Science Data Users Handbook (w3 2002)

Aerosol optical thickness is calculated using Equation 4.5, which is derived from Equation 3.8 (Sifakis and Deschamps 1992). Water bodies and clouds are to be removed because this equation gives correct values of aerosol optical thickness only over the land. In Equation 4.5, standard deviations of the apparent reflectance are used to calculate AOT. To calculate the standard deviation of apparent reflectance, images are divided into 20 x 20 pixel grid cells (in 30 m resolution). 20 X 20 pixel grid cells To find local standard deviations 20 X 20 pixel grid cells are used (in 30m resolusion). Then one grid cell represents 600m X 600m on the ground. Sifakis and Deschamps (1992) called these grid cells as ‘arrays’. To select the grid cells the considerations of Sifakis and Deschamps (1992) are the following: The spectral response of the ground within each array (grid cells) will be assumed as variable in • space but not in time. Atmospheric composition within each array (grid cell) will be considered as variable in time but • not in space, so that σ(ρ) will be attributed to ground spectral variations exclusively. Sifakis and Paronis (1998) states that requirements of selecting grid cells as follows: It is large enough to include some visible ground structure • Sufficiently small to allow consideration of a homogeneous atmosphere inside the grid cells. •

σ1 (ρ) = exp((−τ1 / cos θ v1 ) + ( τ 2 / cos θ v 2 ) ) σ 2 (ρ)

(3.8)

where σ1(ρ) and σ2(ρ) - standard deviation of apparent reflectance in clear day and polluted day respectively. - optical depth in clear day and polluted day respectively τ1 and τ2 - viewing angle (observed zenith angle) in clear day and polluted day respecθv1 and θv2 tively 23


Viewing angle (Observation zenith angle) Landsat-7/ETM+ is nadir looking satellite. Therefore the viewing angle or observation zenith angle is zero in the scene centre. Away from the centre of scene, the viewing angle can be larger than zero. The maximum value of the viewing angle, at the edges of the scene, can be calculated as 7.3950, since the altitude of Landsat 7 is 705 km and the swath width is 183km. This means that the viewing angle ranges from 0 to 7.3950 for any Landsat image, as shown in Figure 4.2. To generalize the viewing angle, 5.230 can be selected which is in between 00 and 7.3950 with +/- 0.417% error, or since the angle is very small, observation zenith can be assumed 00 with 0.83% error at scene end and with zero error at nadir. For this study observation zenith is assumed 00, so that Equation 3.8 can be simplified as 4.5.

∆τ = τ 2 − τ1 = ln

σ1 (ρ ) σ 2 (ρ )

θ 705km

183/2 km

Figure 4.2: Viewing angle

(4.5)

where ∆τ = (τ1- τ2) - Aerosol optical depth (unitless) σ1(ρ) and σ2(ρ) - standard deviation of apparent reflectance in a clear day and in a polluted day respectively. Water bodies The December 23rd image is used to remove the water bodies, because in summer, on the 3rd July image, less water bodies may be visible. In case of removing water bodies, NDVI (Normalized Difference Vegetation Index), density slicing, unsupervised classification with 20 and 30 classes, supervised classification and digitized water boundary map were considered. In density slicing, a histogram of DN values of band four is considered. Even though water and land are very slightly mixed up at 18-20, 20 is used as cut off value for land and water, as two peaks are clearly separated at DN values around 1820. Under supervised classification, box, minimum distances to mean, minimum Mahalanobis distance and maximum likelihood classification methods are considered. In NDVI all negative val- Figure 4.3: Water bodies and land, ues are considered as water bodies. All the methods in super- by maximum likely hood method vised classification except for the box method, clearly distinguish main water bodies from the land. The box classifier shows more unknowns and with increase of the threshold value, it shows more inland water areas. Density slicing and maximum likelihood methods show less inland water bodies than NDVI. When considering the inland minor water bodies, NDVI, minimum distance to the mean and unsupervised classification show good results when compared with digitized data. These methods show more water bodies than the digitized map. Unsupervised classification with 20 and 30 classes shows water bodies up to four and three classes respectively in two methods. From these two methods unsupervised classification with 20 classes gives clear distinction between water and land. Minimum distance to mean method and unsupervised classification with 20 classes show continuity of inland water bodies like canals rather than NDVI. 24


The drawback of the NDVI, minimum distance to the mean, unsupervised classification and density slicing is, that these methods misclassify urban areas as water. Urban areas have mixed textures and therefore the DN values of such areas have large standard deviations. In density slicing, it is difficult to fix the cut off value for the DN to separate water and land. Maximum likelihood method is selected to distinguish water bodies and land, even though this method show less amount of water bodies; it does not misclassify the urban areas. Majority filters are not used to recalculate the most frequently occurring values because small water features would disappear. A summary of these methods is given in Appendix 2 Table A2.1. Figure 4.3 shows the water bodies and land by maximum likelihood method. Top left hand side of the figure is area of sea covered by the cloud. Cloud cover Cloud areas are excluded using province boundaries and by manual digitising, trying to keep survey locations as much as possible. Both images, that is, of the clear day and of the polluted day, are used to prepare cloud cover as shown in Figure 4.4.

3rd July 2001 image

23rd December 2000 image

Cloud covered area and land area

Figure 4.4: Cloud covered area

4.3.

Difference of at-satellite temperature

The screening effect is calculated using Equation 3.10 (Sifakis and Paronis 1998), for thermal infrared band 6 of the Landsat-7/ETM+. In band 6, two gains are available in Landsat-7/ETM+, one for low gain often referred as band 6L and one for high gain referred as 6H. Band 6L saturates at 347.5K and 6H saturates at 322K. Band 6H is more sensitive to most land targets, while 6L is used when the temperature of some land surfaces like desert, sand beach and impervious surfaces is higher than 322K (w4 2002). Therefore in this study thermal infrared bad 6H is used. Between the two images difference in radiative temperature due to change in gases concentration are assumed as negligible (section 3.2.1 method in detail, screening effect), because there is no noticeable difference in atmospheric pressure between two examined days as shown in Table 4.4. Target’s radiative temperature is assumed constant as explained in Section 3.2.1. This constant is eliminated from the equation using a cut-off value as further explained in Section 6.2. The observed radiative temperature (at-satellite temperature) is calculated using Equation 4.6. In some documents this is called “effective at-satellite temperature” (w3 2002). Calculation steps of at-satellite temperature (observed radiative temperature) are shown in Figure 4.5. 25


Figure 4.5: Calculation steps of observed radiative temperature (at-satellite temperature) 26


∆T* = T1*- T2* = Te1 - Te2 + ∆Ta1- ∆Ta2 + ∆Tg1- ∆Tg2

(3.10)

where T1*, T2*

- observed radiative temperatures measured on image Te1, Te2 - target’s radiative temperatures at the moment of image acquisition ∆Ta1, ∆Ta2 - local variations of the radiative temperature introduced by the presence of aerosols ∆Tg1, ∆Tg2 - variations of the radiative temperature due to changes in gas concentration Note: 1 and 2 are images for reference and polluted days respectively.

Table 4.4: Atmospheric pressure in the study area for the two examined days Name of the City

Inch De Bilt (near Utrecht) Eindhoven Maastricht Rotterdam

Atmospheric pressure on 3rd June 2001 Standard Sea Level

Atmospheric pressure on 23rd December 2000 Standard Sea Level

29.9 -

hPa 1011.3 -

Inch 29.9 29.9 -

hPa 1011.7 1012.9 -

Inch 30.1 -

hPa 1020.1 -

Inch 30.1 30.1 30.2 30.2

hPa 1020.5 1020.0 1021.9 1022.2

Source: Weather Underground: History (w5 2002)

T=

K2 K1 ln +1 Lλ

where T K2 K1 Lλ

(4.6)

- Effective at-satellite temperature in Kelvin - Calibration constant 2 = 1282.71 in Kelvin - Calibration constant 1 = 666.09 in W/(m2 * ster *µm) - Spectral radiance in W/(m2 * ster *µm)

Equations 4.1 and 4.2 are used to calculate the spectral radiance (Lλ). At-satellite temperature is calculated per pixel. Equation 4.6 is applied to calculate “difference in radiative temperature”; it is not calculated at pixel level but average value over 10 by 10 pixel grid cells (for thermal bands of Landsat7/ETM+ resolution is 60m). That is average radiative temperature value over 600x600 m2 grid area. Then the differences are calculated by subtracting polluted day temperature values from those of the reference day. AOT for band one to four, difference of at-satellite temperature (AT2) and histograms for corresponding images are shown in Appendix 2, Figure A2.1. Minimum and maximum values of AOT/AT2 are given below the relevant figure. Corresponding grid cell values of aerosol optical thickness to the data collection station at ground is considered to carry out the regression analysis.

27


5. Regression Analysis This chapter describes establishing relationships of aerosol optical thickness and difference of atsatellite temperature with air pollutant data collected at ground level. The method used to establish the relationships is regression analysis. From this study it was understood that the selection of grid in Chapter 4 has a large influence on the regression results. Grids can be mainly divided in to two categories. They are arbitrary grids and ideal grids. Section 5.2 describes the regression analysis under arbitrary grid situation and Section 5.3 describes the regression analysis under ideal grid situation. Results and the discussion of the regression analysis are included in each step where it is possible.

5.1.

Regression analysis under arbitrary grid situation

The first grid that was selected, with grid cells of 600 by 600 m2 area (20 by 20 pixels together in 30m resolution in visible bands and 10 by 10 pixels in 60m resolution of thermal infrared band) is called the arbitrary grid in this study. In the arbitrary grid situation, a ground location can be situated whereever inside the particular grid cells. In regression analysis it is clear that the pollutant data collected at ground level and processed image outputs (AOT and AT2) do not show a good relation and ‘outliers’ popup in the analysis under arbitrary grid situation. The regression analysis done with AOT and AT2 from arbitrary grid cells corresponding to ground locations is discussed in this section. Linear regression analysis is carried out to find the relationship between ground data and AOT and/or AT2. Air pollution components collected at ground stations are PM10, CO, NO, NO2, SO2, NH3, BP and O3. Table 5.1 shows the data availability. These components are considered together as independent variables in the first part of the analysis and considered separately as dependant variables in a later stage. Daily averages and hourly averages of ground data corresponding to the polluted day, that is 23rd December 2000, and corresponding AOT and AT for ground locations which are used in regression analysis are tabulated in Appendix 3, Table A3.1. GS_ID in Table A3.1 is ‘ground station id’, the same as in Table 5.1. The second column of Table A3.1 is case number, from 1 to 28. The main consideration of this study is the fact that the radiation that reaches the sensors is disturbed by scattering and absorption effect due to polluted air and particles. To verify this effect, the correlation is checked between the air polluting components collected at ground level and corresponding values of processed images. To check the correlation ‘Bivariate Correlation’ is used in SPSS software. In this procedure Pearson’s correlation coefficient gives the measure of linear association and the two tailed significance level. Scatter plots indicate the relationships between two variables. Sometimes scatter plots do not show linear relationship but some other curve, i.e. y = f(x) which can approximate the linear function as follows.

28


1) Hyperbolic curve: y = 1/(a+bx) or 1/y = a+bx 2) Exponential curve: y = a Exp(bx) or ln(y) = a0 + bx 3) Geometric or power curve: y = a xb or ln(y) = a0 + b ln(x) Using relationships at 1,2 and 3, hyperbolic, exponential and geometric (power) curves can be converted to linear relationship considering 1/y versus x for hyperbolic; ln(y) versus x for exponential and ln(y) and ln(x) for geometric curves (Lipschutz and Schiller 1998). For these linear relationships, bivariate correlation coefficients between AOT, AT2 and air polluting components collected at ground level are tabulated in Appendix 3. Table A3.2 shows correlation for y = a + bx type relationships. Table A3.3, A3.4 and A3.5 are correlation coefficient for hyperbolic, exponential and geometric (power) curves respectively. In these tables DA and HA prefixes are for ‘daily average’ and ‘hourly average’ respectively. LN is for natural logarithmic and prefix ‘o’ for inverse (reciprocal) values (Table A3.3). As example HA_BP reads as: hourly average of black particles, while LNDAPM10 reads as: natural logarithm of daily average of particulate matter. From Table A3.2 hourly averages of NO and NO2 show significant positive correlation with AOT band 2 and 3 while O3 has a significant negative correlation. Hourly average of CO shows significant positive relationship with AT2. This can be explained by the absorption property of CO. For black particles, only daily average concentrations are available. Absorption property can be expected from black particles, however this is not reflected in the available data. Particular matter shows a positive correlation with AOT2, but with low significance (0.091). Polluting components collected at ground level are significantly correlated each other: Daily average of BP is significantly positively correlated with daily averages of NO, NO2 and CO. Linear relationship(s) with AOT or AT2 as dependant and data collected at ground level as independents Relationship between pollution data at ground level and AOT were examined. NO, NO2 and O3 together show significant relationship with AOT3. When R (multiple R) values are between 70 and 80, the same relationships gave low adjusted R2. This implies the relationships give good fit for this data set (sample data) but not for the whole population. Another important point is the data availability of selected study area. Once the different polluting components are introduced to the regression, the number of cases reduces because regression analysis considers the common cases together. As an example when NO, NO2 and O3 are considered together, the total number of cases will be 22; if CO is introduced, then the total number of cases will become 11; if PM10 is introduced too, then number of cases will be 10, and so on and so forth. Several regressions were carried out to find the best fit of air-polluting components together with AOT and AT2. The stepwise method is used to check the validity of independent variables together in an equation. In first step NO, NO2, NOX, O3, SO2 are considered together. Once it is clear that the SO2 does not show any effect to the equations it is removed and the same steps are done again. The number of cases is then larger and it shows a decrease of R2 and adjusted R2. This shows that data is more case specific, rather than giving good output for whole population. Some of the results, with the AOT3 as dependant and NO, NO2, NOX, O3, SO2 as independents, are tabulated in Table 5.2. 29


Table 5.1: Data availability at ground stations Ground stations

ID

GS_ID

CO

NH3

NO

NO2

O3

Posterholt - Vlodropperweg

1

107

-

-

X

X

X

PM10 SO2 -

X

BP -

Vredepeel - Vredeweg

2

131

-

X

X

X

X

X

X

X X

Wijnandsrade - Opfergeltstraat

3

133

-

-

X

X

X

X

X

Budel - Toom

4

227

-

-

X

X

X

-

X

-

Biest Houtakker - Biestsestraat

5

230

X

-

X

X

X

X

X

X

Volke l -Heikantsepad

6

232

-

-

X

X

X

-

X

-

Huijbergen - Vennekenstraat

7

235

-

X

X

X

X

-

X

-

Eindhoven - Genovevalaan

8

236

X

-

X

X

X

-

-

-

Eindhoven - Noordbrabantlaan

9

237

X

-

X

-

-

-

X

-

Eindhoven - Piuslaan

10

238

X

-

X

X

X

-

-

-

Zierikzee - Lange Slikweg

11

301

-

-

X

X

X

-

X

-

Philippine - Stelleweg

12

318

-

-

X

X

X

X

X

X

Den Haag - Rebecquestraat

13

404

-

-

X

X

X

X

X

-

Schipluiden - Groenveld

14

411

X

-

X

X

X

-

X

-

Maassluis - Vlaardingsedijk

15

415

-

-

-

-

-

-

X

-

Vlaardingen - Lyceumlaan

16

416

-

-

-

-

-

-

X

-

Rotterdam - Schiedamsevest

17

418

X

-

X

X

-

X

X

-

Vlaardingen - Floreslaan

18

433

-

-

X

X

X

X

X

X

Westmass - Groeneweg

19

437

-

-

X

X

X

X

X

X

Dordrecht - Frisostraat

20

441

X

-

X

X

X

X

-

-

Cabauw - Zijdeweg

21

620

-

-

X

X

-

-

X

-

Zegveld - Oude Meije

22

633

X

X

X

X

X

-

X

-

Utrecht - de Jongweg

23

636

X

-

X

X

X

-

-

X

Utrecht - Wittevrouwenstraat

24

637

X

-

X

X

-

-

-

X

Utrecht - Vleutenseweg

25

638

X

-

X

X

X

-

X

-

Utrecht - Erzeijstraat

26

639

X

-

X

X

X

X

-

-

Utrecht - Universiteitsbibliotheek

27

640

X

-

X

X

X

-

-

X

Breukelen - Snelweg

28

641

Total Number of stations

X

-

X

X

X

X

X

-

14

3

26

25

22

11

21

9

The regression equation at step 18 (Table 5.2) can be selected as the best among those tested, with adjusted R2 of 0.51. The correlation between NO, NO2, O3 and AOT3 is 0.76 while these three components explain 58% of the variability of AOT3. This relationship can be written as: ln(AOT3) = -3.219 – 0.305ln(DA_O3)+1.304ln(DA_NO2)-0.326ln(DA_NO) or

AOT3 =

0.04 * DA_NO1.304 2 DA_O30.305 * DA_NO 0.326

As explained in Section 2.2 NO and NO2 together are called NOX and the amount of ozone in the troposphere is largely determined by the concentration of NOX. At the same time, Table A3.2 shows these three components are correlated.

30


Table 5.2: Regression outputs for AOT3 and ground data Test components (independent variables)

No of cases

R

1. NO,NO2,NOx,O3,SO2

15

.72

2. NO,NO2,NOx,O3,SO2

15

.79

3. NO,NO2,NOx, O3,SO2

15

.68

4. NO,NO2,NOx, O3,SO2

15

.79

5. NO,NO2,NOx, O3,SO2 Ln of ind. variables 6. NO,NO2,NOx, O3,SO2 Ln of ind. variables 7. NO,NO2,NOx, O3,SO2 Both ind. & dep are ln 8. NO,NO2,NOx, O3,SO2 Both ind. & dep are ln 9. NO,NO2,NOx, O3,SO2 Both ind. & dep are ln 10. NO,NO2,NOx, O3

15

.68

15

.77

15

.66

15

.67

15

.77

22

.62

11. NO,NO2,NOx, O3

22

.71

12. NO,NO2,NOx, O3 (only NO2 significant) 13. NO,NO2,NOx, O3 Ln of ind. var 14. NO,NO2,NOx, O3 Ln of ind. var 15. NO,NO2,NOx, O3 Both ind. & dep are ln 16. NO,NO2,NOx, O3 Both ind. & dep are ln 17. NO,NO2,NOx, O3 Both ind. & dep are ln 18. NO,NO2,NOx, O3 Both ind. & dep are ln

22

.66

22

.66

22

.73

21

.59

21

.65

21

.71

21

.76

R2 and Adj. R2 .52 .48 .62 .56 .46 .42 .63 .57 .46 .43 .59 .53 .44 .39 .42 .37 .60 .53 .39 .36 .50 .45 .43 .41 .43 .40 .53 .48 .35 .31 .37 .33 .50 .44 .58 .51

F and significance f 15.0 (0.002) 10.61 (0.002) 12.23 (0.004) 10.81 (0.002) 12.14 (0.004) 9.28 (0.003) 10.1 (0.007) 9.27 (0.009) 8.96 (0.004) 12.5 (0.002) 9.5 (0.001) 15.34 (0.001) 15.2 (0.001) 10.74 (0.001) 10.13 (0.005) 10.98 (0.004) 8.90 (0.002) 7.82 (0.002)

Std. error of estimate .28 .26 .29 .26 .29 .27 .30 .30 .26 .27 .25 .26 .26 .24 .27 .27 .25 .23

Coefficients and t-significance Constant 1.7 (0.000) 1.15 (0.007) 3.19E-2 (0.906) 0.654 (0.085) 2.23 (0.000) 0.12 (0.916) 1.196 (0.012) -3.4 (0.008) -2.395 (0.041) 0.32 (0.086) 0.851 (0.011) 2.86E-2 (0.903) -2.554 (0.010) -1.96 (0.038) -2.503 (0.004) -3.294 (0.003) -3.773 (0.001) -3.219 (0.003)

O3

NO2

-0.135 (0.002) -9.95E-2 (0.019) -0.135 (0.036) -0.77 (0.004) -0.512 (0.051) -0.758 (0.007) -0.54 (0.038) -6.45E-2 (0.050) -0.338 (0.060) -0305 (0.085)

Comment

NO -

-

6.88E-3 (0.083) 1.83E-2 (0.004) 1.69E-2 (0.003) -

-

.433 (0.070) -

-

0.864 (0.009) 0.789 (0.008) 1.17E-2 (0.002) 8.51E-3 (0.022) 1.78E-2 (0.001) 0.894 (0.001) 0.867 (0.001) 0.620 (0.005) 0.823 (0.004) 1.412 (0.001) 1.304 (0.001)

-

-

-

-0.389 (0.044) -0.326 (0.077)

Hourly avg. -doDaily avg. -doHourly avg. -doHourly avg. Daily avg. Daily avg. Hourly avg. -doDaily avg. Daily avg. -doHourly avg. Daily avg. -do-do-

Note: Significance levels are given in brackets.

Other form of relationship that has been tested is AOT3 = b*NOa1*NO2a2*Exp(a3O3). In this type of relationship, the natural logarithm should be calculated on both sides of the equation to convert the relationship into a linear relationship. Then the relationship will look like ln(AOT3) = b0 + a1 Ln(NO) + a2 Ln(NO2) + a3O3 where a1, a2, a3, b and b0 are constants. These results can be tabulated as Table 5.3. The 3rd relationship of the Table 5.3 is better than others with an adjusted R2 of 0.48. The tsignificance of coefficients is acceptable except for NO (0.06). Independent variables of this equation explain 56% of the variation of the dependent variable, AOT3. Significance of f is low, which indicates that the relationship is not due to a random chance. SO2, NO, NO2 and NOX together, also do not show a good relationship with AOT3. CO with NO, NO2, NOX and O3 also does not show a significant relationship with AOT3. 31


Table 5.3: Other forms of relationships Test components (indep. variables)

No of cases

R

1. NO,NO2,NOx, O3 Both ln, except O3 2. NO,NO2,NOx, O3 Both ln, except O3 3. NO,NO2,NOx, O3 Both ln, except O3 4. NO,NO2,NOx, O3 Both ln, except O3 5. NO,NO2,NOx, O3 Both ln, except O3

21

.59

21

.67

21

.75

21

.61

21

.71

R2 and Adj. R2 .35 .31 .45 .39 .56 .48 .37 .50 .50 .44

F and significance f 10.13 (0.005) 7.42 (0.004) 7.14 (0.003) 10.98 (0.004) 8.90 (0.002)

Std. error of estimate

Coefficients and t-significance Constant

.27 .26 .24 .27 .25

-2.503 (0.004) -1.481 (0.119) -2.169 (0.028) -3.294 (0.003) -3.773 (0.001)

O3

NO2 -

-6.34E-2 (0.081) -8.79E-2 (0.028) -

0.620 (0.005) 0.448 (0.043) 1.114 (0.010) 0.823 (0.004) 1.412 (0.001)

Comment

NO -0.376 (0.060) -0.389 (0.044)

Hourly avg. Hourly avg. Hourly avg. Daily avg. Daily avg.

Note: Significance levels are given in brackets.

Linear relationship between CO as dependant and AOT / AT2 as independent Daily and hourly averages of CO show negative relationship with AOT except with AOT2. These correlations are not significant. Hourly average of CO (HA_CO) is positively related with AT2, at tsignificance 0.074 (Appendix 3, Table A3.2). Scatter plot of AT2 versus HA_CO (Fig. 5.1a) illustrates the linear relationship and the regression results are summarised in Table 5.4. In regression analysis standardized predicted values versus observed CO (Fig. 5.1b) shows that the location 237 (case number 9) is not predicted properly by this regression results. In other words: if the model would fit each data values exactly, the observed and predicted values would coincide on a straight line extending from the lower left corner to the upper right. At the same time, the centered leverage value is above 0.2 at location 237 (Fig. 5.1c). In the next step this location is omitted from the regression and results are much improved (Table 5.4, step 2). In this step standardized predicted values versus studentized residual graph (Fig. 5.1d) shows that the errors fall between -/+ 2, but variance of residual is an increasing function with predicted value. Then a transformation such as the log of dependent or independent or both are needed. To improve the relationship, in the third step, the natural logarithm of the dependant variable is considered. As indicated in Figure 5.1e now the residuals fall between -/+ 2 band, and variance of the residual is constant. Figure 5.1f illustrated the plot of observed and predicted values. This model can be more improved by identifying and omitting outliers. But CO data available only at 14 ground locations and omitting a location will diminish the sample size. Table 5.4: Relationship between HA_CO and AT2 Comments

No of cases

STD of Y

R

R2

Adj. R2

F


1. All cases 14 53.87 .49 .24 .18 3.84 48.81 2. Omit case 9 13 56.05 .57 .33 .27 5.33 48.04 3. Ln(HA_CO), O9 13 .34 .59 .35 .29 5.98 0.29 Note: t-significance are placed in the brackets; O9 – Omitting case 9 (location 237) Significance levels are given in brackets.

32

Coefficients and t-significance Constant AT2 -175.920 (0.339) 12.668 (0.074) -289.597 (0.173) 16.666 (0.041) 2.196 (0.090) 0.105 (0.033)


Figure 5.1: Relationship between CO and AT2 Dependent Variable: HA_CO

300 1.5

639

Regression Standardized Predicted Value

236 637 638

636

200 237

418

411

238 640

633

HA_CO

100

441

641

230

0 23

24

25

26

27

28

29

30

27

24 8 23

-.5 28 -1.0

5

22

14

-1.5

9

-2.0 0

100

200

300

HA_CO

Fig. 5.1b: Observed and standardized predicted values Dependent Variable: HA_CO

2.0 236 637

1.5

2.0

237 1.0

Regression Studentized Residual

411638 641

0.0 418

633 441

-1.0

230

238 640

-1.5 -2.0 0.00

.05

.10

.15

8 24

1.5

639

.5 636

Studentized Residual

26

17

0.0

31

Fig. 5.1a: Relationship of CO and AT2

.20

1.0

14

22 5

10 27

-1.5 -2.0 -1.5

-1.0

-.5

0.0

.5

1.0

1.5

Dependent Variable: LNHA_CO 8

1.5

24


1.0 26

23

28

25

0.0

17

22 -.5

20 -1.0

10

5 -1.5

27

-2.0 -1.0

20

-1.0

Fig. 5.1d: Residual variance is an increasing function

14

-1.5

17

-.5

Dependent Variable: LNHA_CO 1.5

-2.0

25

0.0


Fig. 5.1c: Centered leverage values at each location

.5

23

28

-2.0

.25

26

.5

Centered Leverage Value


10

20

.5

AT2

-.5

25

1.0

-.5

0.0

.5

1.0

1.5

25

1.0

20

24 8 17

23

0.0 -.5 -1.0

28 5

22

14

-1.5 -2.0 4.4


26

10

27 .5

4.6

4.8

5.0

5.2

5.4

5.6

LNHA_CO

Fig. 5.1e: Studentized residual falling between -/+ 2

Fig. 5.1f: Observed and standardized predicted values

Linear relationship between black particles (BP) as dependant and AOT3 as independent Daily average of black particles shows the highest correlation with AOT3 (Appendix 3, Table A3.2). Distribution of AOT at band 3 versus daily average of black particles is shown in Figure 5.2a. Regression between BP and AOT3 is not significant (Table 5.5 step 1). Variance of studentized residuals increases with predicted value (Fig. 5.2b). Therefore, in the next step the natural logarithm of the dependent variable was considered. In this step P-P plot indicated that the residuals are not distributed normally (Fig. 5.2c). The predicted versus the observed plot (Fig. 5.2d) shows that the case number 5 (location 230) is not predicted correctly by this model. Omitting location 230 improve the model results. (Table 5.5, step 3 and 4). 33


Except case 12 (location 318), residuals of this model are normally distributed (Fig. 5.2e). Residuals fall in between -/+ 2 (Fig. 5.2f). But the leverage plot (Fig. 5.2g) shows independent value of location 131 is an outlier. DfBeta graph tells that the sample may not be homogeneous; the model seems to fit less well at peaks points (Fig. 5.2h). In same time DFFIT plot (Fig. 5.2i) says that the model is very sensitive to location 131. Standardized predicted versus observed plot make clear that the model is held at lower point by location 131 (Fig. 5.2j). Because of these reasons, predictor values (AOT3) and original data are checked, rather than omitting location 131 (case number 2). Table 5.5: Relationship between DA_BP and AOT3 Comments

No of cases

STD of Y

R2

R

Adj. R2

F

Std. error of estimate 1.All cases 9 18.41 .55 .30 .20 2.99 16.48 2.Ln(DA_BP) 9 0.34 .60 .36 .27 3.97 0.29 3. Omit loc. 230 (case5) 8 18.23 .74 .55 .48 7.41 13.18 4.Ln(DA_BP) O5 8 .33 .82 .68 .62 12.61 0.20 5.New AOT3 for 131 8 18.23 .65 .42 .33 4.41 14.96 6.change AOT3 131, 133 8 18.23 .69 .47 .38 5.34 14.32 7. Ln(BP) vs new 131, 133 8 .33 .68 .47 .38 5.28 0.26 Note: O5 – Omitted case 5 (location 230), Significance levels are given in brackets.

Coefficients and t-significance Constant AOT3 30.817 (0.075) 3.467 (0.000) 26.898 (0.065) 3.390 (0.000) -5.21 (-0.868) -21.095(0.558) 2.578 (0.006)

24.750(0.128) 0.502(0.087) 32.642(0.035) 0.658(0.012) 60.358(0.081) 74.803(0.060) 1.358(0.061)

Figure 5.2: Relationship between BP and AOT3 Dependent Variable: DA_ZR

90 433

2.0

637

80


70 318 60

636 437

50 640 133

DA_ZR

24

1.5

40

230 131

30 -.2

0.0

.2

.4

.6

.8

1.0

1.2

1.4

1.0 .5

12 23

2

0.0

3

19

-.5

27

-1.0 -1.5

5

-2.0 -2.5

AOT3

-2.0

-1.5

-1.0

-.5

0.0

.5

1.0


Fig. 5.2a change of black particles AOT3

Fig. 5.2b: Variance of studentized residuals is an increase function Dependent Variable: LNDA_BP

Normal P-P Plot of Regression Standardized Residual

1.0

Dependent Variable: LNDA_BP

18 .75


1.00 24

12 23

Expected Cum Prob

18

.50 19

3

2

27

.25

5 0.00 0.00

.25

.50

.75

1.00

Fig. 5.2c: P-P plot of standardized residuals

19 .5

27

24

23 12

0.0 -.5

3

-1.0

-1.5 -2.0 2 -2.5 3.4

Observed Cum Prob

18

5

3.6

3.8

4.0

4.2

4.4

4.6

LNDA_BP

Fig. 5.2d: Predictor versus observed, case 5 (loc 230) as outlier

34



Dependent Variable: LNDA_BP 2.0

Dependent Variable: LNDA_BP 1.00

24

24

1.5 18

18


1.0

.75 12

Expected Cum Prob

2 23

.50 3 .25

19 27

0.00 0.00

.25

.50

.75

1.00

2

.5

12 23

0.0 3 -.5

19

-1.0 27

-1.5 -2.0 -2.5

Observed Cum Prob

-2.0

-1.5

-1.0

-.5

0.0

.5

1.0


Fig. 5.2e: normal distribution of P-P plot of residuals 2.0

Fig. 5.2f: Residuals fall in between -/+ 2 .3

637

1.5

.2

433 1.0

.1

131

.5 318

-.0


0.0 636 133 -.5

-.1 437

-1.0

-.2

DFBETA Intercept

640

-1.5

-.3

-2.0 0.0

.1

.2

.3

.4

.5

.6

DFBETA NAOT3

107

.7

230 133


237 235

404 301

418 415

620 437

638 636

640

GS_ID

Fig. 5.2g: Leverage plot shows outlier at 131

Fig. 5.2h: peaks at location 131 Dependent Variable: LNDA_BP

.3


2.0

.2

.1

DFFIT

0.0

-.1 107 133

230

235 237

301 404 415 418

437 620

636 638

640

1.5 18

1.0

19 27

.5

23 12

0.0 3

-.5 -1.0 -1.5 -2.0

2

-2.5 3.4

GS_ID

3.6

3.8

4.0

4.2

4.4

4.6

LNDA_BP

Fig. 5.2i: Dffit models sensitive at peaks.

Fig. 5.2j: Model is hold by point 2 (loc.131)

2.0

2.0

637

1.5

637

1.5

433

1.0

1.0

133

.5

133

318 .5

433

636

318 0.0

636 0.0



24

-.5 640

-1.0

437 131

-1.5 -.1

0.0

.1

.2

.3

.4

.5

640

437

131

-1.5 -2.0 -.1

.6

0.0

.1

.2

.3

.4

.5



Fig. 5.2k: Outlier at 133 before change the value

-.5 -1.0

Fig. 5.2l: Outlier at 133 after change the AOT3 at 133

35


It is identified that the location 131 and 133 are situated closer to the boarder of the grid cell, which are considered at calculation of AOT (Section 4.2). Location 131, and 133 are about 34.6 m and 2.75m away from the border respectively. In Landsat data this is around one to one and half pixel size. This kind of shift can be expected in geo-referencing images and/or measuring the situation of ground locations. This situation is checked using nearest grid cell values to these locations (Table 5.6). Table 5.6: New values for location 131 and 133. Location /case no

Original AOT3 values

131 / case 2 133 / case 3

New AOT3 values

0.0000 0.6478

0.8957 0.7611

Distance to nearest grid cell (m) 34.6 2.75

Step 5 in Table 5.5 is with this new value for the independent variable at location 131. Then the leverage plot (Fig. 5.2k) shows the independent value of location 133 as outlier. In step 6 independent values are updated relevant to locations, 131 and 133. The natural logarithm of the dependant variable is considered in step 7. Statistics do not show improvement of the models. It still shows location 133 as an outlier (Fig. 5.2l). Linear relationship between black particles (BP) as dependant and AT2 as independent Black particles are generated as a result of incomplete burnings of fuel. An absorption effect can be expected from black particles. There is a correlation between daily average of black particle concentration and AT2 but this is not significant. Some of the regression results area tabulated in Table 5.7. Table 5.7: Relationship between DA_BP and AT2 Comments

No of cases

STD of Y

R

R2

Adj. R2

F


Coefficients and t-significance Constant AT2 1.All cases 9 18.41 .46 .21 .10 1.89 17.46 -58.005(.502) 4.327(.211) 2.ln(AT2) 9 18.41 .46 .22 .10 1.91 17.44 -314.605(.277) 113.373(.209) 3.Omit loc.640 (case 27) 8 19.31 .65 .42 .36 4.37 15.86 -120.295(.204) 6.853(.081) 4.ln(AT2) O27 8 19.31 .65 .42 .32 4.30 15.92 -518.625(.110) 177.067(.083) 5.ln(DA_BP) O27 8 0.36 .61 .37 .27 3.57 0.31 .894(.603) 0.120(.108) 6.Omited 131 and 640 7 17.87 .69 .48 .37 4.52 14.19 -102.613(.236) 6.282(.087) 7.ln(AT2) O27, 2 7 17.87 .69 .48 .37 4.57 14.16 -470.781(.116) 163.259(.086) 8.ln(DA_BP) O27, 2 7 0.31 .68 .46 .35 4.20 .25 1.288(.383) 0.107(.096) 9.ln(DA_BP) & ln(AT2) 7 0.31 .68 .46 .35 4.23 .25 -4.969(.308) 2.775(.095) Note: O27 – Omitted case 27 (location 640); O27, 2 – Omitted case 27 and 2 both (location 640 and 131) Significance levels are given in brackets.

Linear relationship between particulate matter as dependant and AOT2 as independent Particulate matter shows a significant correlation with AOT2. Figure 5.3a indicates the variation on hourly average concentration of PM10 with AOT2. It displays that station 131 is away from the linear relationship. In other words, to have clean air without aerosols, the amount of particles at the troposphere should be very low, around 30µg/m-3. But PM10 at location 131 is around 65µg/m-3. The leverage graph (Fig. 5.3b) shows that the error is at the independent variable. In the same way as for the case of BP, nearest grid cell to the location 131, which is situated around 34.6 m away from the location 131, shows the value 0.8804. In next step location 131 is omitted. The scatter plot of PM10 and 36


AOT2 shows that location 230 is away from the linear relationship (Fig. 5.3c). This location shows a high AOT value for a low amount of PM10. In case of black particles this location also shows up as an outlier (Fig. 5.2a and 5.2f). From the images and available thematic data location 230 was closely checked. It was identified that the water bodies situated in this grid cell were not properly removed. This can have an influence on the calculation of AOT: theory says that the algorithm used to calculate the AOT does not give correct results on water bodies (Section 4.2.). Omitting this location improves the results as well as the significance of the independent variable. Studentized residuals varied between -/+ 1.5 (Fig. 5.3d). Observed versus predicted shows almost a straight line going through the lower left to the upper right (Fig 5.3e). Location 404 (case number 13) is indicated as an outlier at leverage graph (Fig. 5.3f). This location is not removed because this point holds the upper corner of the regression line and because of the limited number of cases. The natural logarithm of the independent variable is checked, because the variance of the residuals shows a decreasing function with the predictor. The result is significant however it does not improve the statistic in step 3. Table 5.8 is a summary of linear relations of PM10 with AOT2. Figure 5.3: Relationship between PM10 and AOT2 100

1.5

404

90 318 418 80

437

639

28


641

60

133

50

441

40 -.5

0.0

.5

1.0

26

0.0 230

131

2

19 18

.5

433

70

HA_PM10

13 12 17

1.0

1.5

-.5

-1.0

5 20 3

-1.5 -2.0 -.1

2.0

0.0

.1

.3

.4

.5


AOT2

Fig. 5.3a: AOT2 versus HA_PM10

Fig. 5.3b: leverage plot shows case 2 as an outlier

100

Dependent Variable: HA_PM10

404

1.5 90

418 318

1.0 318 418

437

433

437


80

639

70

230 641

60

HA_PM10

.2

441

133

50

40 .4

.6

.8

1.0

1.2

1.4

1.6

1.8

641

433 404

0.0 639 -.5 -1.0 133 -1.5

441

-2.0 -2

AOT2

Fig. 5.3c: Location 230 is away from a linear relationship

.5

-1

0

1

2


Fig. 5.3d: Studentized results falls between +/- 1.5

37

3


Dependent Variable: HA_PM10

1.5 17 12 1.0

404

19

2

.5

28

18

13

0.0

1 639 0

433

437

441

26



3

318 418

133 -1

641

-2 40

50

60

70

80

90

-.5

-1.0 3 -1.5 20 -2.0 0.0

100

.1

.2

.3

.4

.5

.6

.7


HA_PM10

Fig. 5.3e: Observed versus predicted lay on a straight line

Fig. 5.3f: Case 13 show as an outlier in leverage plot

Table 5.8: Relationship between HA_PM10 and AOT2 Comments

No of cases

STD of Y

R

R2

Adj. R2

F

Std. error of estimate 1.All cases 11 14.55 .53 .28 .20 3.57 12.97 2. Omit loc.131 (case 2) 10 15.16 .59 .35 .26 4.21 13.01 3. Omit loc.131, 230 9 15.95 .72 .51 .45 7.40 11.89 4.Ln(AOT2) O2,5 9 15.95 .70 .50 .42 6.87 12.12 Note: O2, 5 - omitted case number 2 and 5, Significance levels are given in brackets.

Coefficients and t-significance Constant AOT2 55.541 (0.000) 46.724 (0.007) 40.314 (0.014) 76.147 (0.000)

17.356 (0.091) 25.372 (0.074) 34.125 (0.030) 32.798 (0.034)

Linear relationship between nitrogen monoxide as dependant and AOT3/AT2 as independent Daily average and hourly average of nitrogen monoxide (NO) are significantly correlated with AOT band 3. Furthermore hourly average of NO concentration is significantly correlated with AOT at band 2 and AT2. In this section linear relationships of hourly average of NO with AOT3 and with AT2 are considered. Furthermore scattering and absorption properties of NO are checked together, that is with AOT3 and AT2 both as independent variables. Scattering effect of NO For NO, data is available at 26 locations out of 28 locations. Figure 5.4a and 5.4b show that the NO is right skewed and AOT3 is almost normally distributed. Figure 5.4c shows that the location 131 is a strong holding point at lower corner of the linear relationship of AOT3 and NO. Variance of residuals shows the increase function with predicted values (Fig. 5.4d) and also according to the histogram of NO (Fig. 5.4a), NO data need a conversion. In the following step natural logarithm of NO is considered and statistics are much improved with respect to step 1 (Table 5.9). Predicted versus observed plot (Fig. 5.4e) shows that case 5 (location 230) is not predicted properly. By omitting case 5 the model can be improved (Table 5.9, step 3). But the residuals plot indicated that the case 7 and 8 (location 235 and 236) have high residuals (Fig. 5.4f).

38


Figure 5.4: Relationship between NO and AOT3 8

7 6

6

5 4

4 3 2

2 Std. Dev = 87.89 N = 26.00

0 25.0

75.0 50.0

125.0 100.0

175.0 150.0

225.0 200.0

Mean = .91 N = 28.00

0 0.00

275.0

250.0

Std. Dev = .30

1

Mean = 138.4

.25 .13

300.0

.50 .38

.75 .63

1.00

1.25

.88

1.13

1.38

AOT3

HA_NO

Fig. 5.4a: Distribution of NO concentration

Fig. 5.4b: Distribution of AOT3 Dependent Variable: HA_NO

400 3 2

404

637 641 300

638

636

133 633 237 441640 238 301 620 318

100 131

437 230

232 235

107

227


411 433 418639

200

HA_NO

13

2

236

0 -.2

0.0

.2

.4

.6

.8

1.0

1.2

17

1

0

11 21

4

14 18

23

3

1022

25

9 20

-1 1 -1

27

12

6 7

19

-2

5

-2 -3

-2

-1

0

1

2


AOT3

Fig. 5.4d: Variance of residuals are increasing function

Dependent Variable: LNHA_NO

Dependent Variable: LNHA_NO 2.5

2 25

5

19

1

6

7

10

1

21 11

-1

24 28

23 26 17

9 22

8

3

4 -2 2

-3 3.5

4.0

8

2.0

2824

1.5

27 20

12

1814

13



26

2

1

1.4

Fig 5.4c: Scatter plot of AOT3 versus NO

0

2824

8

4.5

5.0

5.5

17

1.0 .5

3 11 21

2

26

13 23

1022

0.0

20 -.5

4 12

-1.0 1

14 18

9

-1.5

6

-2.0

7

25 27 19

-2.5 -3

6.0

-2

-1

0

1

2


LNHA_NO

Fig. 5.4e: case 5 is not predict properly by the model

Fig. 5.4f: Case 7 and 8 shows high residuals

Table 5.9: Relationship between HA_NO and AOT3 Comments

No of cases

STD of Y

R

R2

Adj. R2

Coefficients and t-significance Constant AOT3 1.All cases 26 87.89 .49 .24 .20 7.4 78.46 15.883(0.742) 135.293(0.012) 2. Ln(Ha_NO) 26 .69 .52 .27 .24 8.8 .60 3.693(0.000) 1.132(0.007) 3. Ln(Ha_NO) O5 25 .68 .61 .37 .34 13.4 .56 3.565(0.000) 1.330(0.001) Note: O5 – by omitting case number 5 (location 230), Significance levels are given in brackets.

39

F



Absorption effect of NO Scatter plot of AT2 versus hourly average of NO shows that some cases like 641 are away from a linear relation (Fig. 5.5a). Variance of residuals increases with predicted values (Fig. 5.5b) and the residual is larger at case 28 (location 641). Natural logarithm of HA_NO does not improve the relation (Table 5.10, step 2), but shows more outliers like cases 1, 4 and 28 (location 107, 227 and 641 in Fig. 5.5c). Removing case 28 (location 641) improved the statistics (Table 5.10, step 3); but the outliers at cases 13, 14 and 24 (location 404, 411 and 637 in Fig. 5.5d) remain. Figure 5.5: Relationship between NO and AT2 Dependent Variable: HA_NO

400 3

28

404 637

641

2

236

411

433 418 636

200

133237 437

HA_NO

100


300

633 620301 318

639

638

640 238441

230 232 131 235

227

107

0 23

24

25

26

27

28

29

30

18

1 3 9 19

0

-1

Fig. 5.5a: Spread of NO concentration with AT2 2.0

2

7

27 10 20

6 1

-2 -1.5

-1.0

-.5

0.0

.5

1.0

1.5

2.0

Fig. 5.5b: Variance of residuals is increased Dependent Variable: HA_NO 2.5

28 14

1.0 3 9 19

1.5

8 17 23

22

26 25

21 11

0.0

12

27 10 20

-.5 5 -1.0

6 2 7

-1.5

4

1

18

-1.0

-.5

0.0

.5

1.0

1.5

2.0

8

1.0 3 9 19

.5 0.0

17 23

22 21 11

26 25

12

-.5

5 2

-1.0

27 10 20

6 7 4

-1.5

1

-2.0 -2.0

-2.0 -1.5

13 24

14

2.0

13 24

18


1.5


25

4

Dependent Variable: LNHA_NO

-2.0

26

12


AT2

.5

8 17 23

22 21 11 5

-2.0

31

13 24

14

-1.5

-1.0

-.5

0.0

.5

1.0

1.5

2.0



Fig. 5.5c: Spread of residuals in case of Ln(HA_NO)

Fig. 5.5d: Residuals lay between -/+2

Table 5.10: Relationship between HA_NO and AT2 Comments

No of cases

STD of Y

R

R2

Adj. R2

F


1.All cases 26 87.9 .35 .12 .08 3.3 84.1 2. Ln(Ha_NO) 26 0.69 .33 .11 .07 2.9 0.66 3. AT2; O28 25 82.8 .43 .19 .15 5.3 76.3 Note: O28 – by omitting case number 28, Significance levels are given in brackets.

Coefficients and t-significance Constant AT2 -265.329(0.245) 15.231(0.082) 1.716(0.339) 0.113(0.100) -336.917(0.112) 17.648(0.031)

Scattering and Absorption effect of NO Statistics of linear relationships improved when AT and AOT were both considered together with NO. Residuals vary as an increasing function (Fig. 5.6a) and case 5 and 28 have high residuals (location 230 and 641). Once case 5 was omitted, the model improves (Table 5.11, step 2). But the significance of AT as an independent variable is very low. Omitting case 28 with case 5 improves the significance 40


of AT from 0.259 to 0.114. Figure 5.6b shows most of the cases can be predicted by this relationship (Table 5.11, step3) except the cases like 25. Case 2 influences the model in the lower corner. If case 25 (location 638) is omitted, t-significance level of AT is increased to 0.084 (Table 5.11, step 4). Natural logarithm of dependant variable does not improve the relationship. Significance of AT is similar in step 4 and when considering natural logarithm of independent variable (step 6). Once the natural logarithm of independent variables is considered, location 131 is omitted by the system, because AOT3 value at location 131 is zero. Then the number of cases becomes 22. Table 5.11: Relationship between HA_NO and AOT3 and AT2 Comments

STD of Y

R2

R

Adj. R2

F

Std. Err.

Coefficients and t-significance Constant AOT3 AT2 -282.469(.177) 121.559(0.022) 11.725(0.144) -223.177(.274) 144.421(0.008) 8.896(0.259) -290.217(.127) 130.618(0.009) 11.631(0.114) -347.126(0.091) 139.160(0.008) 13.584(0.084) 1.436(0.373) 1.209(0.004) 8.327x10-2 (0.181) -1064.76(.129) 127.659(0.018) 372.47(0.085)

1.All cases -C26 87.9 .55 .30 .24 5(0.015) 76.4 2.O5 -C25 87.8 .60 .35 .30 6(0.008) 73.7 3.O5, 28 -C25 82.8 .64 .41 .35 7.1(0.004) 66.9 4.O5, 28, 25-C23 81.1 .65 .42 .36 7.2(0.005) 67.3 5.Ln(HA_NO) .67 .64 .41 .35 7.0(0.005) .54 O5, 28, 25 -C23 6.Ln(indep) 83.5 .60 .36 .30 5.4(0.014) 70.0 O5, 28, 25 -C22 Note: O5 - Omitting case 5; O5, 28: Omitting case 5 and 28; O5, 28, 25 - Omitting case 5, 28 and 25 C - Number of cases eg. C26; Ln(indep) - natural logarithm of independent variables, Significance levels are given in brackets.

Figure 5.6: Relationship between NO and AOT/AT Dependent Variable: HA_NO

Dependent Variable: HA_NO

28


2

24

8

13

14 1

17

3

2

11 21 4

12 6 71

-1

18

26 23

9 22

0

1910

25 20 27 5

-2 -3

-2

-1

0

1

-1

4

21 11

24 14

17 8

3

2 -3 100

21 11

3

-2 2 -3 100

200

300

2

-2

0

4

400

HA_NO

22 9 -1

8

Fig. 5.6b: Case 25 is away from the relationship


18 23 26

12

17

Dependent Variable: HA_NO

27 20

71 6

10 19

12

0

13

0

71 6

24 14

22 9

Dependent Variable: HA_NO 2

10 19

23 26

0


1

13 18

27 20

1

2

Fig. 5.6a: Variance of residuals increases


25

2


3

200

300

13 27 20

1

23 26

71 6

0

12

10 19

17

14 8

22 9 -1

21 11

3

4

-2

-3 0

400

24

18

100

200

300

HA_NO

HA_NO

Fig. 5.6c: Predicted versus observed for step 4

Fig. 5.6d: Predicted versus observed for step 6

41

400


Linear relationship between sulphur dioxide as dependant and AOT3/AT2 as independent SO2 shows a low correlation in less significant level with image outputs (Appendix 3, Table A3.2 to A3.5). Among them the best relation with highest significance is with AOT3. Hourly average of SO2 concentration is right skewed (Fig. 5.7a). To normalize the distribution of SO2, the natural logarithm of SO2 is considered. A graph of predicted versus observed (Fig. 5.7b) explains that most of the cases are not properly predicted by this relationship (Table 5.12, step2). Variance of residuals is still increasing function (Fig. 5.7c). The leverage graph shows that location 131 is an outlier among the values of independent or predictor variables (Fig. 5.7d). Omitting location 131 does not improve the relationship (Table 5.12, step3). Considering natural logarithm of both dependant and independent variables does not improve the statistics of relation either. Statistics of relation between SO2 and AOT3 are summarized in Table 5.12. Table 5.12: Relationship between HA_ SO2 and AOT3 Comments

No of cases

STD of Y

R2

R

Adj. R2

F


Coefficients and t-significance Constant AOT3 0.835(0.923) 16.250(0.080) 1.602(0.002) 0.923(0.066) 1.376(0.034) 1.142(0.075) 2.551(0.000) 0.886(0.098)

1.All cases 21 14.26 .39 .15 .11 3.43 13.46 2. Ln(Ha_SO2) 21 .78 .41 .17 .12 3.82 .73 3. Ln(Ha_SO2), O2 20 .79 .41 .17 .12 3.56 .74 4. ln of dep. and indep. 20 .79 .38 .15 .10 3.05 .75 Note: O2 – by omitting case number 2 (location 131) ln of dep. and indep. - Natural logarithm of dependent and independent variables, Significance levels are given in brackets.

Figure 5.7: Relationship between SO2 and AOT3 Dependent Variable: LNHA_SO2

12


2

10

8

6

4

2

Std. Dev = 14.26 Mean = 15.6 N = 21.00

0 0.0

10.0

20.0

30.0

40.0

50.0

418

633

415

107 301

620 133 -1 227 -2 131 -3 1.5

2.0

2.5

3.0

3.5

4.0

4.5

Fig. 5.7b: Observer versus predictor plot

Dependent Variable: LNHA_SO2

2.5

2.5

415

1.5

415 416 1.5

416

418 1.0

418 318

107

.5

411

227

404

641

0.0

.5


301

131

235 437

-.5

133

-1.0

620

-1.5

237 633

638 230

232

-2.0 -2

-1

0

433

2.0

433

2.0


416

318

235

237

433

411

437

LNHA_SO2

Fig. 5.7a: Distribution of SO2

-3

404

638

641 232

0

1.0

60.0

HA_SO2

1.0

230 1

1

2

318107

437 -.5 237 133 -1.0 633 232 -1.5

131 404

0.0 641 235

227

638

620 230

-2.0 0.0


301411

.1


Fig. 5.7c: Predictor versus residual plot

Fig. 5.7d: Leverage plot 42

.2

.3

.4


Linear relationship between nitrogen dioxide as dependant and AOT3/AT2 as independent variables Daily average of NO2 is correlated with AOT with a high significance level except with AOT of band 1. Daily average of NO2 is correlated with AT2 for 49 % at 0.013 significance level (Appendix 3, Table A3.2). Scattering effect of NO2 Distribution of daily average of NO2 is almost normal (Fig. 5.8a) and it seems that location 131 holds the regression line in the lower corner (Fig. 5.8b). Some locations, such as 230, 437 are not properly predicted by this relationship (Fig. 5.8c). Leverage graph shows location 131 as an outlier (Fig. 5.8d). Residuals fall between -/+2. Removing case 2 (location 131) does not improve the relationship. Results are tabulated in Table 5.13. Table 5.13: Relationship between DA_ NO2 and AOT3 Comments

No of cases

STD of Y

R2

R

Adj. R2

F


Coefficients and t-significance Constant AOT3 1.All cases 25 12.49 .63 .40 .38 15.48 9.86 28.512(0.000) 24.649(0.001) 2. DA_NO2, O2 24 12.33 .62 .38 .35 13.61 9.91 24.41(0.004) 28.69(0.001) 3. Ln(DA_NO2), O2 24 .24 .60 .36 .33 12.44 .20 3.40(0.000) 0.547(0.002) Note: O2 – by omitting case number 2 (location 131), Significance levels are given in brackets.

Figure 5.8: Relationship between NO2 and AOT3 5

80

4

70

3

60

433 404 637

411 638

418 639

236 301 620

50

2

DA_NO2

1 Std. Dev = 12.49 Mean = 50.8 N = 25.00

0 30.0

35.0

40.0

45.0

50.0

55.0

60.0

65.0

70.0

40

318

633

437 230

227

131

133 107 30 0.0

.2

.4

.6

.8

1.0

1.2

1.4

AOT3

DA_NO2

Fig. 5.8a: Distribution of DA_ NO2

Fig. 5.8b: AOT3 versus DA_ NO2 plot

Dependent Variable: DA_NO2

2

433 418 637

2 230 1

638 411

437 640 641 636 441

318 232 235

0 107 133

620

301

433

0

238

-2 131 -3 50

60

131

227

640 633 318

236

227

40

411 639 638 238236 301 641 441 636620

418

-1

30

404

1

637

639 633

404



641 441 636 640

235 232

-.2

75.0

238

70

80

235 232

-1

133 107 437

-2

230

-3 -.1

DA_NO2

0.0

.1


Fig. 5.8c: Plot of observer versus predictor

Fig. 5.8d: Leverage plot

43

.2

.3

.4


Absorption effect of NO2 NO2 is significantly related with AT2. Locations 433, 411 and 107 are not properly predicted by this relationship (Fig. 5.9a). Residuals of these locations are greater than -/+2. Further, high residuals indicated the potential outliers at dependent variable. Leverage is less than 0.2 (Fig. 5.9b). The natural logarithm of dependent variable slightly improves the statistics of the relationship. Results are tabulated in Table 5.14. Figure 5.9: Relationship between NO2 and AT2 80

3

433

433 411

404 70

2

637

411

638

404

418

641

50

639 441 640 238 236

230

40

235 232 227

131 133

107

30 23

24

25

26

27

28

29

636

0

301 318 620 633 437

DA_NO2

636


60

30

638

301 318 633

620 639

640 238 236 230

235 232

437 441 133

131

-1

227 107

-2

-3 -.02

31

637

641 418

1

0.00

.02

.04

.06

.08

.10

.12

.14


AT2

Fig. 5.9a: AT2 versus DA_NO2 distribution

Fig. 5.9b: Leverage plot

Table 5.14: Relationship between DA_ NO2 and AT2 Comments

No of cases

STD of Y

R

R2

1.All cases 25 12.49 .49 .24 2. ln(DA_NO2) 25 .25 .50 .25 Note: Significance levels are given in brackets.

Adj. R2

F

.21 .22

7.28 7.56

Std. error of estimate 11.12 .22

Coefficients and t-significance Constant AT2 -31.874(0.311) 3.108(0.013) 2.243(0.001) 6.224E-2(0.011)

Linear relationship between Ozone as dependant and AOT3/AT2 as independent Hourly average of ozone is significantly negatively correlated with band 1 to band 3 of AOT and positively correlated with AT2. Ozone has highest significance with AOT3. By considering inverse relationship of AOT3 (1/AOT3) with ozone, correlation between these two increases. In this situation case 2 (location 131) is omitted by the system, because corresponding value of AOT3 to location 131 is zero. Location 441 is not predicted properly by the relation (Fig. 5.10b). Leverage shows that case 227 is an outlier. By considering natural logarithm of dependent or independent or both variables (Fig. 5.10c to e) it was tried to improve the correlation, but the statistics of the relation did not improve. Correlation improves when case 20 (location 441) is omitted. Most of the cases are predicted correctly by this relationship. Results are tabulated in Table 5.15. Figures 5.10a to 5.10f illustrate the ozone versus AOT3 relation and the leverage graphs for residuals and outliers in dependent and independent variables.

44


Table 5.15: Relationship between HA_ O3 and AOT3 Comments

No of cases

STD of Y

R2

R

Adj. R2

F


1.All cases 22 1.98 .58 .34 .30 10.19 2. 1/AOT3 21 1.89 .62 .38 .35 11.71 3. ln(Ha_O3) 21 .32 .54 .30 .26 7.93 4. ln(1/AOT3) 21 1.89 .55 .31 .27 8.42 5. Both ln 21 .32 .51 .26 .22 6.66 6. O20, 1/AOT3 20 1.76 .72 .51 .49 18.91 Note: ln - Natural logarithm; O20 - Omitting case 20 (location 441) Significance levels are given in brackets.

Coefficients and t-significance Constant AOT3 8.852(0.000) -3.419(0.005) 2.702(0.007) 2.473(0.003) 1.248(0.000) 0.363(0.011) 5.287(0.000) 3.158(0.009) 1.625(0.000) .486(0.018) 2.347(0.006) 2.613(0.000)

1.65 1.52 .27 1.61 .28 1.26

Figure 5.10: Relationship between O3 and AOT3 12

12 227

227 10

10 131

441

441 107

133

6

301

235 232

230 433 638

236

4

.2

.4

640

404

.6

.8

1.0

1.2

HA_O3

404

4

133 301 236

640

411 437 2 .5

1.4

1.0

1.5

2.0

2.5

3.0

OAOT3

AOT3

Fig. 5.10a: Ha_O3 versus AOT3

Fig. 5.10b: Ha_O3 versus 1/AOT3 3

3

441

441

227 2

2 107 638 433 230

1

107 638 433 230

1

227

232 235

232 235

641 636 318 133 639 633 238404

0

640



232 235 641 636 318 639633 238

2 0.0

638 433 230

6

437411

-.2

107

238 633 639 636 318 641

HA_O3

8

8

301

-1 236 411 437 -2 -.1

0.0

.1

.2

.3

.4

.5

.6

641 636 318 404 639 133 633 640 238 411 437 301

0

-1

236 -2 -.1

.7

0.0

.1

.2

.3

.4

.5



Fig. 5.10c: Ln(Ha_O3) and (1/AOT3)

Fig. 5.10d: Ha_O3 and Ln(1/AOT3)

3

12 227 441

2

10 227

638 433 230 107 1

107

8

641 636 318 133 639 404 633 238 640 301

0

-1

638 433 230

6

404

4 411 437 236

-2 -.1

0.0

.1

232 235

133

641 636 318 639633 238

HA_O3


232 235

.2

.3

.4

.5

Fig. 5.10e: Ln(Ha_O3) and Ln(1/AOT3)

301 236

411 437 2 .5


640

1.0

1.5

2.0

2.5

OAOT3

Fig. 5.10f: After removing location 441 45

3.0


Daily average of O3 is correlated with AT2 but relations are not significant. This can be shown graphically as Figures 5.11a and b, and the results are tabulated in Table 5.16. Removing location 441 does not improve the relationship. Table 5.16: Relationship between DA_O3 and AT2 Comments

No of cases

STD of Y

R

R2

Adj. R2

1.All cases 22 1.48 .40 .16 2. ln(DA_O3) 22 .31 .41 .17 Note: Significance levels are given in brackets.

F

.11 .13


3.70 4.00

Coefficients and t-significance Constant AT2 -3.501(0.402) 0.295(0.069) -.290(0.739) 6.439E-2(0.059)

1.40 .29

Linear relationship between NH3 as dependant and AOT3/AT2 as independent Ammonia is measured only on 3 locations and these data are not significantly related with AOT or AT. Figure 5.11: Relationship between O3 and AT2 2.4

10

441 2.2

8

2.0

638

227

131 641

133

1.6

6

638 131 641

636 236 107

639 238 640

437

232 411

2

404 232

1.0 437 .8 23

23

24

25

26

27

28

29

30

24

411 25

26

27

28

29

30

31

31

AT2

AT2

Fig. 5.11a: DA_O3 versus AT2

639 238 640

1.2

404

3

636 236 107

318 235 230 633

1.4

301 318 235 230 633

4

433

301

433

LNDA_O3

133

5

5.2.

227

1.8

7

DA_O3

441

9

Fig. 5.11b: ln(DA_O3) versus AT2

Regression analysis under ideal grid situation

When the ground location is situated in the centre of the relevant grid cell then the situation is called ‘an ideal grid’ in this study. AOT2, AOT3 and AT2 are recalculated with a new definition of the grid, which places the survey locations at the centre of the grid cells. AOT and AT values for the arbitrary grid cell, ideal grid cell and distance from ground locations to the arbitrary grid cell boundary are tabulated in Appendix 4 Table A4.1. Figures 5.12a to c show the AOT2, AOT3 and AT2 ideal grid cell values (denoted as AOT2C, AOT3C and AT2C) versus arbitrary grid cell values (displayed at y axis). Figures 5.12d to f show how the difference of ideal situation to arbitrary situation varies with the minimum distance (in m) from ground location to the arbitrary grid cell boundary. From these graphs it is clear that once the distance is less, AOT or AT values are highly variable.

46


Figure 5.12: Comparison of arbitrary and ideal grid situation 2.0

633 1.0

.5

0.0 404 301 441

227

AOT2_DIFF

AOT2

-.5 -.5

0.0

.5

1.0

1.5

2.0

236

636

415 238 230

620 107

639

232

633

-.8 0

100

200

300

DISTANCE_M

1.5 230

404 433 437

107 620 .5

Fig. 5.12d: Difference of AOT2 change with distance (m) .8

640 637

638411

416 641 318636 441 232 235 237 639 415 633418 238 236133 301

1.0

.6

637 640

.4

301

227

638

236 416 636

131 418 437 237 318 235 633 404 441 641

-.0

131

411

227 133

.2

-.5 0.0

.5

1.0

1.5

2.0

238

415

639

232 433

-.2

AOT3_DIFF

AOT3

416

235

-.6

2.5

Fig. 5.12a: Values of AOT2 ideal versus arbitrary grid

-.5

433 227

-.4

AOT2C

0.0

411

641

-.2

131

637 640

418

437 237 318

.2

620

0.0

131

.4 133

637 640

639 416 235 433 232 415 237 441 318437 418 236 301 133 238

107 641

.6

411 638

230 636

1.5

620 107

-.4

230

-.6 0

AOT3C

100

200

300

DISTANCE_M

Fig. 5.12b: Values of AOT3 ideal versus arbitrary grid

Fig. 5.12e: Difference of AOT3 change with distance (m)

31

3 638

318

30 441 238

29 418

28

2

416639 404 637 640 236

237

107 636

1

227 433

27

641 25

301 411 437 133

24

0

235 232 415131 230633 620 237

24

25

26

27

28

29

620

415

636 230 416107 433

236 232

639

411

301 641 418

-1

238

441 -2 0

30

100

200

300

DISTANCE_M

AT2C

Fig. 5.12c: Values of AT2 ideal versus arbitrary grid

640 637

638 404437 235 227

318

23 23

131 633

133

AT2_DIFF

26

AT2

638

.8 404

Fig. 5.12f: Difference of AT2 change with distance (m)

In case of ideal grid situation, AOT3 value at location 131 is only changed very slightly with respect to arbitrary grid (Fig. 5.12b). To understand how the ideal grid situation affects the calibration of a relation, correlations between polluting components and outputs of ideal grid situation are tested again. All the graphs related to the following regressions are placed in Appendix 4. Relations of hourly average of CO with AOT2, AOT3 and AT2 with ideal grid Daily and hourly averages of CO are positively related with AOT, except for the relation of daily average of CO with AOT3. All of these relationships are not significant (Appendix 3, Table A3.6). Hourly average of CO is significantly correlated with AT2. Location 640 is away from regression line (Fig. A4.2a). Once this location is omitted, almost all the locations are predicted by the relation (Fig A4.2b). 47


Leverage values are less than 0.2 and residuals between -/+ 1.5 (Fig A4.2c). Residuals are spread evenly (Fig A4.2d). Results can be tabulated as Table 5.17. Table 5.17: Relations of hourly average of CO with AT2 Comments

No of case

STD of Y

R

R2

Adj. R2

F


1.All cases 14 53.87 .60 .36 .30 6.60 45.03 2. O27 13 53.67 .76 .57 .53 14.64 36.72 Note O27: Omitting case 27 (location 640), Significance levels are given in brackets.

Coefficients and t-significance Constant AT2 -253.157(0.150) 15.625(0.025) -363.132(0.025) 19.98(0.003)

Relations of daily average of BP with AOT2, AOT3 and AT2 with ideal grid The correlation between daily average of black particles and AOT3 is significant. Even though absorption properties can be expected from black particles, correlations are not significant with AT2 (Appendix 3, Table A3.7). Relation between black particles and AOT3 can be improved step by step as shown in Table 5.18. Graphical representations of these relations are shown in Appendix 4 Fig.A4.3a to A4.3e. Residuals of the last step are between -/+ 1.5 and the leverage value of location 131 is still high (Fig. A4.3f). Table 5.18: Relations of daily average of BP with AOT3 Comments

No of case

STD of Y

R2

R

Adj. R2

F


Coefficients and t-significance Constant AOT3 1.All cases 9 18.41 .54 .29 .19 2.91 16.54 33.226(.045) 19.202(.132) 2. ln(DA_BP) 9 .34 .60 .36 .27 3.96 .29 3.513(.000) .392(.087) 3. ln dep. & ln ind. 9 .34 .62 .38 .29 4.31 .29 3.98(.000) .184(0.077) 4. 1/da_bp, 1/aot3 9 6.822E-3 .66 .44 .36 5.51 5.154E-3 1.83E-2(.000) 6.063E-4(.051) 5. 1/da_bp, ln(1/aot3) 9 6.822E-3 .70 .49 .42 6.74 5.206E-3 1.953E-2(.000) 4.203E-3(.036) Note: ln dep. & ln ind.: Natural logarithm of dependent and independent variables, Significance levels are given in brackets.

Relations of hourly average PM10 with AOT2, AOT3 and AT2 with ideal grid Hourly and daily averages of PM10 are correlated with AOT2 but not with AOT3 or AT2. AOT2 has the highest significance when correlated with hourly average of PM10 (Appendix 3, Table A3.8). Location 133 is away from the relation (Fig. A4.4a). Natural logarithm of dependent variable does not improve the relation (Fig. A4.4a), but omitting location 133 improves the relation significantly. Results are given in Table 5.19. Table 5.19: Relations of hourly average of PM10 with AOT2 Comments

No of case

STD of Y

R

R2

Adj. R2

1.All cases 2. ln(ha_pm10) 3. O3

11 11 10

14.55 .21 13.20

.65 .62 .77

.43 .38 .60

.36 .31 .55

6.70 5.46 11.84

22.61 .18 8.89

4. Ln(ha_pm10), O3

10

.18

.76

.57

.52

10.73

.13

F

Note O3: Omitting case 3 location 133 Significance levels are given in brackets.

48


Coefficients and t-significance Constant AOT2 45.895(0.002) 24.819(0.029) 3.899(0.000) 0.340(0.044) 47.73(0.000) 25.274(0.009) 3.929(0.000)

.347(0.011)


Relations of hourly average of NO with AOT2, AOT3 and AT2 with ideal grid Correlations of NO with AOT2 and AOT3 are highly significant and correlation with AT2 is not significant (Appendix 3, Table A3.9). Relations of NO with AOT3 are tabulated in Table 5.20. As clear in Fig 5.4a in the above section (arbitrary grid situation), spread of NO in the study area is positively skewed. Logarithm of the dependant variable improved the relationship between AOT3 and NO (Table 5.20, step 2). Residuals placed between -/+2 (Fig. A4.5a). Even though the residuals are normally distributed (Fig. A4.5b), the standardized predicted plot shows (Fig. A4.5c), that some locations like 640, 641, 235 are not properly predicted by this relation. Leverage versus residuals plot indicated (Fig. A4.5d) that location 131 is an outlier. After considering natural logarithm of dependant variable, the variance of the residuals is still an increasing function (Fig. A4.5a). Therefore in next step natural logarithm of both, independent and dependant are considered. Fig. A4.5e also shows that the location 131 is away from other locations. But when location 131 is omitted, the relation does not improved (Table 5.20, step 4 and 5). Table 5.20: Relations of hourly average of NO with AOT3 Comments

STD of Y

R

R2

Adj. R2

F

1.All cases – C26 2. Ln(NO) – C26 3.Ln(NO) & Ln(AOT3) – C26 4. Ln(NO) O2 – C25

138.42 .69 .69 .67

.53 .57 .53 .51

.28 .33 .29 .26

.25 .30 .25 .23

9.24 11.71 9.30 8.01

Std. error of estimate 76.22 .58 .60 .59

Coefficients and t-significance Constant AOT3 27.364(0.495) 112.321(0.006) 3.773(0.000) 0.957(0.002) 4.800(0.000) 0.503(0.006) 3.824(0.000) 0.912(0.010)

5.ln.dep & ln.indep O2 – C25 .67 .52 .27 .24 8.37 .58 4.798(0.000) 0.861(0.008) Note: O2 - Omitting case 2 location 131; ln.dep & ln.indep - natural logarithm of dependent and independent, Significance levels are given in brackets.

Relations of daily average of SO2 with AOT2, AOT3 and AT2 with ideal grid When considering ideal situation of grid, there are significant correlations between AOT2/AOT3 and daily average of SO2 (Appendix 3, Table A3.10). Daily average of SO2 shows the highest correlation with AOT2 and is negatively skewed (Fig: A4.6a). Natural logarithm of dependent variable improves the relationship (Table 5.21, step 2). Residuals fall between -/+2 (Fig: A4.6b). Most of the cases are predicted by the relation (Fig: A4.6c). Leverage plot shows 638 as an outlier (Fig: A4.6d). Omitting location 638 improves the statistics of the relation between daily average of SO2 and AOT2. Results of the regressions are summarized in Table 5.21. Table 5.21: Relations of daily average of SO2 with AOT2 Comments

No of case

STD of Y

R

R2

Adj. R2

F


1.All cases 21 5.19 .38 .15 .10 3.3 4.91 2. Ln(SO2) 21 .63 .45 .20 .16 4.8 .58 3. Ln(SO2), O25 20 .64 .50 .25 .20 5.9 .57 Note O25: Omitting case 25 location 638, Significance levels are given in brackets.

Coefficients and t-significance Constant AOT2 5.323(0.024) 3.532(0.086) 1.509(0.000) 0.498(0.042) 1.415(0.000) 0.629(0.026)

Relations of daily average of NO2 with AOT2, AOT3 and AT2 with ideal grid As arbitrary grid situation, daily average of NO2 shows highly significant correlation with AOT2, AOT3 and AT2. Daily average of NO2 has the highest correlation with AOT3 (Appendix 3, Table A3.11). Figure A4.7a shows that the locations such as 433, 133 are not predicted properly by the rela49


tion. Residual at 433 is greater than -/+2 (Fig.A4.7b) and says that it is a potential outliers at dependent variable. Natural logarithm of the dependent variable slightly improves the relation. Results are tabulated in Table 5.22. Table 5.22: Relations of daily average of NO2 with AOT3 Comments

No of case

STD of Y

R

1.All cases 25 12.49 .63 2. Ln(DA_NO2) 25 .25 .64 Significance levels are given in brackets.

R2

Adj. R2

F

.39 .41

.37 .38

14.98 15.66


Coefficients and t-significance Constant AOT3 32.360(0.000) 18.639(0.001) 3.530(0.000) .373(0.001)

The relation between daily average of NO2 with AT2 for ideal grid selection can be tabulated as Table 5.23. When compared to the arbitrary grid situation, statistics of the relationships between NO2 and image outputs are not much improved. Table 5.23: Relations of daily average of NO2 with AT2 Comments

No of case

STD of Y

R

1.All cases 25 12.49 .43 2. Ln(DA_NO2) 25 .25 .43 Significance levels are given in brackets.

R2

Adj. R2

F

.18 .19

.15 .15

5.07 5.26


Coefficients and t-significance Constant AT2 -21.025(0.518) 2.716(0.034) 2.460(0.001) 5.439E-2(0.031)

Relations of daily average of O3 with AOT2, AOT3 and AT2 with ideal grid Hourly average of O3 is negatively correlated with AOT2 and AOT3 but not correlated with AT2 (Appendix 3, Table A3.12). Hourly average of O3 is highly significant with AOT3. Locations 227, 441 are away from the regression line and not predicted properly (Fig. A4.8a). Natural logarithm of the dependant variable did improve the relation. Residuals indicated that location 227 is a possible outlier (Fig.A4.8b). Omitting 227 improves the relationship (Fig. A4.8c). Inverse relationship of AOT3 with O3 is significant, as in the case of arbitrary grid, but this is not the best relationship (Fig. A4.8d). Regression results are tabulated in Table 5.24. Table 5.24: Relationship of hourly average of O3 with AOT3 Comments

R2

22 22

1.98 .33

.62 .64

.38 .41

.35 .38

12.17 14.10

1.60 .26

21 21

1.63 .29

.67 .66

.45 .44

.42 .41

15.32 14.92

1.25 .23

8.209(0.000) 2.148(0.000)

-2.733(0.001) -0.489(0.001)

5. 1/AOT3 22 1.98 .42 .17 .13 4.19 1.85 6. ln(1/AOT3) 22 1.98 .56 .32 .28 9.18 1.68 Note O4: Omitting case 4 location 227, Significance levels are given in brackets.

5.361(0.000) 5.483(0.000)

0.173(0.054) 1.469(0.007)

2. Ln(HA_O3) 3. O4 4. Ln(HA_O3), O4

STD of Y

R

Coefficients and t-significance Constant AOT3 8.790(0.000) -3.099(0.002) 2.222(0.000) -0.536(0.001)

1.All cases

No of case

Adj. R2

F


A summary of the regression, i.e. a comparison of arbitrary and ideal grid situation is tabulated in Table 5.25. Significant improvement of correlation between ground data and image outputs can be seen

50


under ideal grid situation except for NO2. Some locations such as 131, 640 do not fit well with derived relations under ideal grid situation either. Table 5.25: Regression Summary Component

R

CO

0.59 0.35 0.29 Ha_CO = 8.99 * Exp(0.105 AT2)

237

0.76 0.57 0.53 Ha_CO = -363.132 + 19.98*AT2C

640

R2

Ad. R2

Removed Outliers

Relationship

0.60 0.36 0.30 Ha_CO = -253.157+15.625*AT2C BP

Comment No correlation with AOT No correlation with AOT

-

0.82 0.68 0.62 Da_BP = 29.67*Exp(0.658*AOT3)

230

131 & 133 are situated in border

131, 640 correlation with AT not significant 0.70 0.49 0.42 Da_BP=1/(1.953*E(-2) + 4.203*E(-3) ln(1/AOT3C)) 131 has high leverage No absorption, BP at 640 is low

PM10

0.72 0.51 0.45 Ha_PM10 = 40.314 + 34.125*AOT2

131, 230 230 includes water body

0.77 0.60 0.55 Ha_PM10 = 47.73 + 25.274*AOT2C

133

0.65 0.43 0.36 Ha_PM10 = 45.895 + 24.819*AOT2C NO

404 not predicted properly At 133 ground values are low, w.r.t. AOT2 -

0.61 0.37 0.34 Ha_NO = 35.34 * Exp(1.33 * AOT3)

230

0.43 0.19 0.15 Ha_NO = -336.917 + 17.648*AT2 0.65 0.42 0.36 Ha_NO = -347.126+139.160*AOT3 + 13.584*AT2

641 230, 641, 638

0.57 0.33 0.30 Ha_NO =43.51 * Exp(0.957*AOT3C)

-

235, 236 also identified as an outliers 404, 411 and 637 identified as an outliers

640, 641, 235 not properly predicted 131 has high leverage, hold the equation

SO2

NO2

0.41 0.17 0.12 Ha_SO2 = 4.963*Exp(0.923 * AOT2)

131

Omitting 131 does not improve, very weak relationship

0.50 0.25 0.20 Ha_SO2 = 4.116*Exp(0.629 * AOT2C)

638

638 has relatively low ground values

0.45 0.20 0.16 Ha_SO2 = 4.522*Exp(0.498 * AOT2C)

-

433, 416, 301 shows very high values

0.63

-

230, 437, 433 not predicted properly

0.50 0.25 0.22 Da_NO2 = 9.422*Exp(6.224E(-2) * AT2)

-

residuals at 433, 411, 107

0.64 0.41 0.38 Da_NO2 = 34.124 * Exp(0.373 * AOT3C) 0.43 0.19 0.15 Da_NO2 = 11.705 * Exp(5.439E(-2) * AT2C)

-

433, 133 have residuals Ideal grid results do not better than arbitrary grid

0.4 0.38 Da_NO2 = 28.512 + 24.649 * AOT3

O3

0.72 0.51 0.49 Ha_O3 = 2.347 + 2.613* (1/AOT3)

131, 441 227 hold at upper corner (high leverage)

0.41 0.17 0.13 Ha_O3= 0.748 * Exp(6.439E(-2) * AT2) 0.67 0.45 0.42 Ha_O3 = 8.209 - 2.733 * AOT3C

227

0.64 0.41 0.38 Ha_O3 = 9.226 * Exp(-0.536 * AOT3C) Note: w.r.t. – with respect to AOT2C, AOT3C and AT2C – AOT and AT for ideal grid condition

51

-

441 is an outlier


6. Results and Discussion In this chapter, Section 6.1 includes mapping of dispersion of air pollution using image analysis and regression analysis outputs as a results of this study. Section 6.2 presents the discussion of the overall study.

6.1.

Mapping of dispersion of air pollution

Regression equations that have been derived under the ideal grid situation are considered for mapping purpose. CO, BP and PM10 are selected to be mapped over the study area, while AOT and AT are known. Maps are not limited only on to the urban area, because pollution created in urban areas spreads over other areas as well. In digital format these maps can be zoomed to the area where the user is interested. The maximum level of this zoom is up to size of 600 x 600 m2 area on the ground. Equations that have been used for mapping can be written as follows (from Table 5.25). Ha_CO = -363.132 + 19.98*AT2C Da_BP=1/(1.953*E(-2) + 4.203*E(-3) ln(1/AOT3C)) Ha_PM10 = 47.73 + 25.274*AOT2C

(6.1) (6.2) (6.3)

Minimum and maximum concentrations of CO, BP and PM10 over the study area can be tabulated as Table 6.1. Histograms of estimated polluting components (Appendix 2, Figure A2.2) are used to define the classes in final maps (Figures 6.1and 6.2). In Figure 6.1, BP and PM10 show high concentrations in the west part of the study area maybe due to road traffic, industries in the area and high sea traffic at North Sea. RIVM annual report of air pollution (Hammingh, Beck et al. 2001) point outs that the considerable amount of pollutants come to the Netherlands from the surrounding countries. This may be the reason for the high concentration of PM10 and BP at southeast and east of the country. Most of the areas have mixed classes. In case of CO, Figure 6.2 shows that the highest concentration is in the northeast, southwest belt and east border, and also areas like Rotterdam, Utrecht and Eindhoven. In coarse resolution, CO displays more relation to the urban areas and major road network. Table 6.1: Concentration range of BP, PM10 and CO over the Study Area (µ µg/m3) Black Particles PM10 CO

Minimum 5.85 47.73 0

Maximum 66.80 142.44 856.56

Usability These equations can be used to estimate the concentration of hourly average of CO, PM10 and daily average of BP for any part of the study area, once the AOT or AT2 is known by images, in 600 by 600 m2 area.

52


Figure 6.1: Concentration of Black Particles and Particulate matter over the study area 53


Figure 6.2: Concentration of CO over the Study Area 54


6.2.

Discussion

As explained by Sabins (1987), existence of aerosols in the atmosphere can be checked with Landsat bands. If there is no pollution (less amount of aerosols), the histograms of band 4 and band 7 start at approximately the same DN value. Aerosols cause a shift in the DN values of band 4, to the direction of higher DN numbers compared to band 7 as shown in Figure 6.3. Figure 6.4 shows scatter plots of band 4 versus band 7 for a clear day and a polluted day. Straight lines represent the least squares fit of band 4 versus band 7 DN values. It shows that the ‘offset’ of the polluted day is less than that of the clear day. Presence of aerosols can be further explained by histograms of the DN values of a clear day and a polluted day as shown in Figure A4.1 in Appendix 4. Histograms of a polluted day show shrinkage (less standard deviation with a compressed range of DN values) compared to the clear day (Sifakis and Deschamps 1992).

12000000

12000000

11000000

11000000

10000000

10000000

9000000

9000000

8000000

8000000

Number of pixels

Number of pixels

This implies that, if an image archive does exist, there’s a possibility to find the polluted and less polluted days using histograms of DN values and scatter plots of MIR (2.09-2.35) versus NIR (0.78-0.90).

7000000 6000000 5000000

7000000 6000000 5000000

4000000

4000000

3000000

3000000

2000000

2000000

1000000

1000000

0

0

50

100

150

200

0

250

0

50

23-Dec-00 band 7 (MIR)

100

150

200

250

23-Dec-00 band 4 (NIR)

Figure 6.3: Shift of DN values of band 4 to the direction of higher values compared to the band 7.

band 4 vs band 7

band 4 vs band 7 250

250

200

200

150

band 7

band 7

150

100

100

50

50

0

0

0

50

100

150 band 4

200 250 Y = 64.61 + 0.529 * X band 4 vs band 7

0

50

100

150 band 4

3-July-01 (clear day)

200

250 Y=34.2 + 0.554 * X band 4 vs band 7

23-Decmber-00 (polluted day)

Figure 6.4: Scatter plot of DN values in band 4 versus band 7.

Regression analysis PM10 is significantly correlated with AOT2 while BP is related with AOT3 and relation of CO is with AT2. NO2 relates with AOT3 and AT2, NO and O3 relates with AOT3 and SO2 relates with AOT2 (Table 5.25). A reason for the fact that different components have relationships with different bands of 55


AOT or AT2 may be particle size of aerosols generated by pollutants and another reason may be their chemical and physical properties. Removing water bodies and clouds is important, because the method used to detect AOT and AT2 gives correct results only on the land area (Sifakis and Deschamps 1992; Sifakis and Paronis 1998). Ideal grid situation gives the best result for regression analysis. In arbitrary grid situation it was observed that some locations are closer to the border of the grid cells and those cause errors in the analysis. Therefore accuracy of geo-reference as well as accuracy of ground station co-ordinates is very important. Scattering and absorption effects of pollutant components are together Black particles and particulate matter both show a scattering effect. Correlation between AOT as dependent and BP and PM10 both together as an independent variables is not significant. The reason is that these two components have different relations, PM10 is mainly related with AOT2 and BP with AOT3. Another reason may be that number of cases common to both components is limited to 5. Most of the considered polluting components in this study are generated because of the fuel burning. Most of them are correlated (Appendix 3, Table A3.2). When considering the polluting components together as independent variables, they do not show a significant relationship with image outputs. Less fit ground locations in the regression under ideal grid situation Location 131 is situated in Southeast of the study area, in a place called “Vredepeel”. This place is close to the military airport ‘De Peel’ and surrounded by green areas in coarse resolution. The values of the ground data are high at this station with respect to the image calculations, especially in band 3. In field verification it was clear that the measuring equipment in location 131 is surrounded by open area covered by bare soil and agricultural lands rather than urban environment. Air pollution annual report for 2001 by RIVM (Hammingh, Beck et al. 2001), point out that ‘De Peel’ has high deposition rate. This may be the reason for incomparable results between collected ground data and image outputs. Data at location 640 do not match with image outputs. This station is situated in Utrecht, in a place called “Universiteitsbibliotheek” (University Library). This location is spatially close to location 637 (only about 46m away) and recorded very low concentration of polluting components at ground level with respect to 637. By the field verification it became clear that the measuring equipments at location 637 are situated at the road side, while location 640 is situated in a very different situation, where it is a small open area away from the roads, fully surrounded by buildings. Location 638 is also situated in the Utrecht area, only about 1.5km away from location 637 and 640 and shows low polluted values especially for SO2. This is a very special ‘local situation’ that is some places create a large amount of pollutants and spread over the atmosphere. So, in ground level this may not identified, but in images it is identified as a polluted area. This local situation may be more explainable in case of SO2. SO2 concentration is high in Rotterdam area. Ground measurements at places closer to Rotterdam, like Den Haag and Schipluiden are low, relative to the image outputs (Figure 6.5, location 404 and 411 respectively). Ground stations at Southwest to the study area (Zierikzee, Philippine) show high daily averages relative to the image outputs (Figure 6.5, location 301 and 318 respectively). In other part of the study area daily average of SO2 concentration is more or less 10µm/m3 and hourly average is about 20µm/m3.

56


Figure 6.5: Spread of daily and hourly average of SO2 concentration with AOT2 ideal grid situation 30

70 433 60 433

50

20 416 301

227 131

620

633

232

30

411 638

0.0

.5

318

20

437 237230

1.0

1.5

2.0

2.5

10 620

131227 232

437 235 237230 133

638

0 -.5

AOT2C

633

404

301

107 641

133

0 -.5

411

418

404

HA_SO2

107 641

416 415

235

10

DA_SO2

40 415 418 318

0.0

.5

1.0

1.5

2.0

2.5

AOT2C

Viewing angle In the calculation of aerosol optical thickness (Section 4.2), it is assumed that the viewing angle is 00 with 0.83% error. The maximum value that has been obtained for AOT is 3.7471 for band 2. Therefore the maximum error that can occur is +/- 0.0156 at the edge of the scene. In the study of Sifakis and Deschamps (1992), they have assumed the viewing angle as zero, even when the viewing angle is 150 and 160 for SPOT XS1, with 3.87% of error.

AT2F

Changing of the grid cell size 450 by 450 m2 area (15 by 15 pixels in visible bands) grid cell size is considered to check how the grid cell size affects mapping of urban air pollution. These grid cells are also selected as the ground locations situated in centre of the corresponding grid cells (ideal situation). Landsat-7/ETM+ band 2, 3 and thermal infrared bands are selected for this purpose. When grid cell size is changed, temperature differences do not change significantly as shown in Figure 6.6. This is because the difference of atsatellite temperature (AT2) is calculated as the average of the pixels in the considered grid cell. In the case of AOT, once the grid cell size is changed, 31 AOT is changed as shown in Figure 6.7 (Figures 638 30 6.7a and 6.7b for band 2 and 3 respectively). The 640 639637 29 236416 possible reason for this is, that AOT is calculated 404 318 28 based on the standard deviation of the selected 107 636 441 418 238 grid cell. If the area’s texture changes rapidly, 27 433 227 131 then changing grid cell size affects AOT. In Fig26 633 235415232 230 237 ures 6.6 and 6.7, x-axis and y-axis are AT or AOT 620 25 2 411 641 vales of ideal grid cells in 600 by 600 m size and 301 24 133 450 by 450 m2 size respectively. Because of time 437 23 23 24 25 26 27 28 29 30 limitation, regression analysis was not repeated 2 AT2C for the new grid cell size (450 by 450 m area). Sifakis and Paronis (1998) have shown in their Figure 6.6: Effect of changing grid cell size on atstudy that for Landsat data, the best AOT values satellite temperature are given by 600 x 600 m2 area for urban areas.

57


2.5

2.0 638

2.0

301 411

411 301 636

1.5 441 1.0 633 .5

437 639

404

441 637 640

433

416 235 237 418 318 227 236 415133 230 131 232 238

620

.5

AOT3F

107

-.5 -.5

0.0

.5

1.0

1.5

2.0

2.5

Figure 6.7a: On band 2

620

638

640 637

107 0.0

131

0.0

AOT2C

437404 416 433 636

641 639 236 318 230 232 418 238 235 237 227 415 133 633

1.0

641

0.0

AOT2F

1.5

.5

1.0

1.5

2.0

AOT3C

Figure 6.7b: on band 3

Figure 6.7: Effect of changing grid cell size on AOT

Temperature cut-off value To have a minimum hourly average concentration of CO (Eq. 6.1), at satellite temperature difference should be 18.1750. In other words, once the at-satellite temperature difference is higher than the 18.20 only then CO is present. Another observation is that the air temperature difference between reference and polluted day is almost 180 over the study area (Appendix 4, Table A4.2). When calculating atsatellite temperature (screening effect), using Equation 3.10 (Section 4.3) it is assumed that the ‘target radiative temperature’ is constant. Sifakis and Paronis (1998) explain that this is subjected to variation in solar angle and air temperature (Section 3.2.1, Equation 3.10). To generalize the seasonal effect or to remove the effect by target radiative temperature, 18.20 is used as a cutoff value in this study. In other words, if the at-satellite temperature difference is higher than 18.20, the area is considered polluted. In the study area, the area where the at-satellite temperature is less than 18.20 is very small, i.e. 0.13% (about 14.6 km2) of the whole area. This area is mainly situated in the southern part of the Limburg Province. Limitations of calibrated equations If there are new sources, which emit other components of molecules or particles into the atmosphere, which result in a scattering and absorption effect, these equations may not give correct results without calibrating them again. If the grid cell size differs from the 600 x 600 m2 area (20 x 20 pixels in 30m resolution) results may be incorrect, except if they are calibrated for the situation.

58


7. Conclusion and Recommendation In this study, the main consideration is detecting urban air pollution by remotely sensed images and mapping urban air pollution by establishing the relationships between the data collected at ground level and outputs of image processing.

7.1.

Conclusion

PM10 and BP are significantly correlated with AOT2 and AOT3 respectively. The expected absorption property of BP does not show in this data. CO shows significant relation with AT2. Other components O3, NO2, NO and SO2 are significant but weakly correlated with AOT. NH3 does not show correlation with AOT or AT2. NH3 is measured only on three ground locations in the study area. Pollutant components that have been considered in this study are mostly related with AOT of green and red bands of Landsat-7/ETM+ (band 2 and 3 respectively). Scattering is caused by solid and liquid aerosols in the atmosphere. Aerosols contain different components, however when considering pollutant components together, this did not show a high correlation with AOT as expected. One reason may be, that once the components are considered separately, they are significantly correlated with AOT, but different components with different bands. So, once the components are considered together, high correlation cannot be expected with a particular band of AOT. Other reasons are the existing correlation among the pollutant components and the fact that the number of cases is reduced once the components are considered together. Pollution maps have been made for PM10, BP and CO over the study area. In digital format these can be zoomed where the user is interested. The maximum level of this zoom is up to 600m x 600m area on the ground. To generalize the seasonal effect in this study, the temperature difference 18.20 is considered as lowest in the study area. In other words, if at-satellite temperature difference is higher than 18.20 it is considered as polluted area. This did first come across with the minimum temperature that can predict CO. This is almost same as the difference of air temperature between reference and polluted days in the study area. There can exist special local situations, as an example, CO concentration at the Utrecht area. Ground data at some locations show a good relationship with image outputs (AT2), while other locations in the same area show very low ground data values with respect to the image outputs (AT2). This situation can be explained such that, if a place has a high concentration of pollution, once it is released to the atmosphere, it spreads over the area, but not at ground level. Therefore, this kind of situation can be detected by remotely sensed images, but not by the ground level data. The case of SO2 over Rotterdam area is another example for a special local situation. 59


7.2.

Recommendation

Selecting a reference and a polluted day is very important in this kind of study. One possibility of doing this is to check the pollution level from the ground data, for the recording date of the available images. Another possibility is checking the histograms of DN values of visible bands. For polluted days shrinkage of histograms with a low standard derivation can be expected. To do a histogram check, there should be an image archive. Geo-referencing and removing water bodies and cloud cover are important. To do a regression analysis between ground data and aerosol optical thickness from images, it is important to pick the AOT values corresponding to the ground station locations. It gives the best result, when the ground station is situated in the centre of the corresponding grid cells of the image(s). In this study, this situation is called an “ideal situation”. For most of the urban areas 600 by 600 m2 (20 by 20 pixels in 30m resolution) grid cell size is suitable. Checking the suitable grid cell size for the study area is important because it has an effect on the regression results.

60


Appendix 1: Tables and Figures related to selection of polluted and reference day Figure A1. 1: Image availability Landsat-7/ETM+

23-Feb-00, UT, good

23-Dec-00, both, good

11-Apr-00, UT, few clouds

13-May-00, UT, RT bit more cloud

1-Aug-00, both, few clouds

5-Nov-00, UT, few clouds

3-Jul-01, both, good

14-Feb-00, RT, Good

24-Aug-00, RT, Good

15-Jan-01, RT, Good

7-May-01, RT, Good

23-May-01, RT, Good

30-Oct-01, RT, Good

7-Mar-01, RT, Good

26-Jul-01, RT, Good

I


15-Nov-01, RT, few clouds

23-Jul-00, RT, bit more clouds

Figure A1. 2: Hourly average of polluting components for Rotterdam area Hourly Average - PM10

120.00

23Jul00

1Aug00

50.00

24Aug00 60.00

23Dec00 15Jan01

40.00

7May01 3Jul01

0.00 1

2

3

4

5

6

7

8

24Aug00 23Dec00 15Jan01

30.00

7May01 23May01 3Jul01

10.00

26Jul01

26Jul01

0.00

30Oct01

-20.00

1Aug00

40.00

20.00

23May01

20.00

1

2

3

4

5

6

7

8

7Mar02

7Mar02

Hourly Average - CO

180.00

14Feb00

1Aug00

120.00

24Aug00

100.00

23Dec00

80.00

15Jan01

60.00

7May01

40.00

23May01

1Aug00

1

2

3

4

5

6

7

8

80.00

24Aug00 23Dec00

60.00

15Jan01 7May01

40.00

23May01 3Jul01

20.00

26Jul01

26Jul01

0.00

23Jul00

100.00

3Jul01

20.00

0.00

30Oct01

1

2

3

15Nov01

Locations

14Feb00 13May00

23Jul00

140.00

Hourly Average - O3

120.00

13May00

Amount (mic.g/m3)

Amount (mic.g/m3)

160.00

4

5

6

7

8

Hourly Average - NO

7Mar02

Amount (mic.g/m3)

14Feb00

Hourly Average - NO2

120.00

13May00 23Jul00

300.00

23Dec00

24Aug00 23Dec00

60.00

15Jan01

150.00

15Jan01

7May01 100.00

26Jul01 1

2

3

4

5

Locations

6

7

8

23May01

20.00

3Jul01

0.00

7May01

40.00

23May01

50.00

13May00 1Aug00

80.00

24Aug00

200.00

14Feb00 23Jul00

100.00

1Aug00

250.00

30Oct01 15Nov01

Locations

7Mar02

350.00

30Oct01 15Nov01

Locations

15Nov01

Locations

14Feb00 13May00

60.00

23Jul00

80.00

Hourly Average - SO2

70.00

13May00

Amount (mic.g/m3)

amount (mic.g/m3)

100.00

14Feb00

3Jul01 26Jul01

0.00 1

30Oct01

2

3

4

5

Loc at i ons

15Nov01 7Mar02

6

7

8

30Oct 01 15Nov01 7Mar 02

Note: Location numbers are referrer to ‘ID no’ in Table 3.1

II


Table A1. 1: Daily average of air polluted components around Rotterdam area Rotterdam data summary Date Component 14-Feb-00 PM10 13-May-00 PM10 23-Jul-00 PM10 01-Aug-00 PM10 24-Aug-00 PM10 23-Dec-00 PM10 15-Jan-01 PM10 07-May-01 PM10 23-May-01 PM10 03-Jul-01 PM10 26-Jul-01 PM10 30-Oct-01 PM10 15-Nov-01 PM10 07-Mar-02 PM10 SO2 14-Feb-00 13-May-00 SO2 23-Jul-00 SO2 01-Aug-00 SO2 24-Aug-00 SO2 23-Dec-00 SO2 15-Jan-01 SO2 07-May-01 SO2 23-May-01 SO2 03-Jul-01 SO2 26-Jul-01 SO2 SO2 30-Oct-01 15-Nov-01 SO2 07-Mar-02 SO2 14-Feb-00 NO 13-May-00 NO 23-Jul-00 NO 01-Aug-00 NO 24-Aug-00 NO 23-Dec-00 NO NO 15-Jan-01 07-May-01 NO 23-May-01 NO 03-Jul-01 NO 26-Jul-01 NO 30-Oct-01 NO 15-Nov-01 NO 07-Mar-02 NO 14-Feb-00 NO2 13-May-00 NO2 23-Jul-00 NO2 01-Aug-00 NO2 24-Aug-00 NO2 23-Dec-00 NO2 15-Jan-01 NO2 07-May-01 NO2 23-May-01 NO2

404 41.11 40.33 28.64 39.53 20.75 92.45 42.24 25.81 36.86 31.44 48.35 69.56 40.15 45.24 8.25 5.21 -0.25 13.75 3.67 8.79 2.00 1.50 1.92 2.21 6.71 6.46 12.21 2.63 58.50 2.42 1.58 12.50 6.79 180.75 28.00 7.17 4.83 7.33 7.29 8.17 75.54 2.63 62.25 39.54 13.71 48.71 35.29 71.25 42.00 22.50 30.08

411

415

12.42 5.88 0.75 16.50 2.96 11.71 4.29 0.92 2.21 2.17 7.75 7.92 17.83 6.91 50.92 13.70 6.54 10.17 13.67 164.26 25.91 4.92 6.29 3.21 3.04 8.42 93.46 1.75 62.46 43.91 16.75 40.29 39.63 66.74 40.70 22.50 29.54

7.92 17.67 0.04 15.08 4.00 13.25 8.33 2.46 1.92 7.75 17.08 7.88 7.17 25.75

Daily Average 416

18.25 7.96 0.58 19.38 4.00 17.50 6.17 0.42 2.38 4.00 8.38 20.04 22.88 11.88

III

418 41.93 49.79 24.15 33.34 27.08 91.05 46.53 32.42 34.72 23.79 46.79 42.73 53.81 42.96 15.25 5.88 1.25 0.00 5.25 13.71 6.00 1.38 3.50 2.96 5.08 8.13 19.96 14.88 48.54 5.54 0.33 9.63 8.42 137.00 31.07 2.04 4.39 6.26 5.67 16.50 118.46 4.79 60.88 50.38 12.08 46.54 38.92 63.21 46.71 25.13 31.52

433 36.89 50.27 15.57 34.57 32.15 84.26 37.72 19.61 26.53 28.69 45.83 40.76 51.05 35.84 14.25 10.46 0.08 14.25 5.58 21.04 7.67 0.50 0.67 4.79 13.71 21.25 16.13 17.96 63.88 4.83 2.88 29.83 35.58 228.33 30.96 10.25 14.71 0.00 0.00 34.92 241.79 14.38 54.50 52.58 10.71 54.96 54.88 77.17 47.29 32.21 40.04

437 25.77 75.49 24.56 56.30 28.69 85.71 40.20 24.36 31.86 31.97 0.00 37.37 35.31 36.01 5.13 9.63 0.00 4.50 7.75 5.58 2.83 1.17 2.30 1.33 5.08 4.58 2.83 3.50 9.58 3.25 0.50 3.54 11.46 99.42 13.67 3.17 3.87 3.13 0.00 4.13 75.87 0.48 32.33 37.21 8.79 21.00 39.46 42.38 36.33 19.17 27.00

441 37.02 37.93 17.16 38.37 27.86 65.88 32.40 16.73 25.26 22.88 27.57 38.57 53.61 37.77

26.38 4.54 0.50 7.75 14.04 121.63 20.29 3.21 3.38 1.08 2.17 7.88 131.67 7.89 51.00 36.58 7.21 31.50 48.75 54.29 41.29 23.50 28.50


03-Jul-01 26-Jul-01 30-Oct-01 15-Nov-01 07-Mar-02 14-Feb-00 13-May-00 23-Jul-00 01-Aug-00 24-Aug-00 23-Dec-00 15-Jan-01 07-May-01 23-May-01 03-Jul-01 26-Jul-01 30-Oct-01 15-Nov-01 07-Mar-02 14-Feb-00 13-May-00 23-Jul-00 01-Aug-00 24-Aug-00 23-Dec-00 15-Jan-01 07-May-01 23-May-01 03-Jul-01 26-Jul-01 30-Oct-01 15-Nov-01 07-Mar-02

NO2 NO2 NO2 NO2 NO2 CO CO CO CO CO CO CO CO CO CO CO CO CO CO O3 O3 O3 O3 O3 O3 O3 O3 O3 O3 O3 O3 O3 O3

27.71 35.79 31.96 82.67 25.71

12.92 0.00 54.21 53.54 52.46 3.04 7.63 55.83 62.96 43.29 70.42 25.75 6.88 56.08

21.75 34.13 32.46 63.96 26.96 40.67 32.88 24.42 26.48 20.33 124.00 42.83 20.46 25.50 21.04 32.00 32.21 52.63 23.21 11.25 59.61 53.33 55.33 43.25 2.30 6.30 58.17 55.71 44.04 66.42 18.38 0.00 49.25

35.05 47.46 47.00 67.92 32.33 60.29 50.04 21.04 41.79 35.71 136.21 64.21 31.00 46.08 43.13 53.67 47.63 88.29 35.25

High Low Data not available Source: http://www.lml.rivm.nl/

IV

0.00 0.00 46.38 69.88 28.50

24.27 0.00 23.35 45.17 13.39

10.04 57.13 51.71 39.63 30.71 4.96 7.50 52.96 62.79 41.08 56.29 12.83 0.96 42.92

22.67 53.54 48.38 61.08 31.04 2.33 8.29 55.52 62.09 0.00 66.75 23.09 2.70 60.61

27.79 28.33 34.29 54.83 36.67 56.88 46.00 0.00 36.92 28.67 155.75 51.04 28.08 29.17 24.42 24.46 34.17 131.92 42.68 12.58 65.71 52.38 57.13 31.67 9.33 6.92 53.79 67.04 54.67 80.83 21.13 2.46 37.22


Table A1. 2: Daily average of air polluted components around Utrecht area Utrecht summary Date Component 23-Feb-00 PM10 11-Apr-00 PM10 13-May-00 PM10 1-Aug-00 PM10 5-Nov-00 PM10 23-Dec-00 PM10 3-Jul-01 PM10 23-Feb-00 SO2 11-Apr-00 SO2 13-May-00 SO2 1-Aug-00 SO2 5-Nov-00 SO2 23-Dec-00 SO2 3-Jul-01 SO2 23-Feb-00 NO 11-Apr-00 NO 13-May-00 NO 01-Aug-00 NO 05-Nov-00 NO 23-Dec-00 NO 03-Jul-01 NO 23-Feb-00 NO2 11-Apr-00 NO2 13-May-00 NO2 01-Aug-00 NO2 05-Nov-00 NO2 23-Dec-00 NO2 03-Jul-01 NO2 23-Feb-00 CO 11-Apr-00 CO 13-May-00 CO 01-Aug-00 CO 05-Nov-00 CO 23-Dec-00 CO 03-Jul-01 CO 23-Feb-00 O3 11-Apr-00 O3 13-May-00 O3 01-Aug-00 O3 05-Nov-00 O3 23-Dec-00 O3 03-Jul-01 O3

620

627

633

8.71 5.33 3.50 6.75 4.50 2.58 2.00 60.29 9.71 1.13 4.48 1.29 79.54 2.08 56.13 32.21 23.04 21.78 20.67 44.88 17.67

10.54 4.96 3.38 0.00 3.58 5.54 1.96

8.92 6.21 3.46 8.17 4.04 3.08 1.58 65.54 7.08 0.46 3.38 1.46 74.38 3.13 53.71 29.38 19.17 22.96 20.25 45.67 17.63 69.46 29.04 31.21 37.04 29.33 85.21 25.67 7.71 40.54 78.79 62.13 27.25 3.63 46.58

5.25 35.21 74.13 63.52 19.50 0.00 52.83

Daily Average 636

133.21 49.33 13.29 24.50 21.42 108.92 13.75 75.75 54.71 48.83 56.71 33.04 53.46 32.13 130.13 72.83 54.96 64.75 46.25 130.75 31.96 4.04 27.33 61.88 38.48 17.88 4.42 41.38

High Low Data not available

Source: http://www.lml.rivm.nl/

V

637

638

231.38 165.13 91.42 75.58 62.96 171.58 11.79 93.04 86.46 85.79 84.54 48.50 67.04 34.29 172.88 98.71 78.50 70.96 71.17 175.04 41.04

16.38 6.08 5.54 9.36 5.71 7.92 1.43 106.25 30.46 15.38 18.20 13.71 153.96 23.50 72.92 53.75 52.83 49.15 31.67 66.65 37.96 111.00 70.96 55.79 56.38 48.58 204.67 60.33 5.46 28.50 55.38 49.75 17.25 5.39 38.75

639 81.85 46.02 40.31 44.43 26.68 84.42 27.37

640

184.04 62.75 45.00 55.44 26.21 173.17 31.96 77.50 67.54 64.96 59.33 37.71 56.96 38.21 151.92 90.96 85.42 64.96 65.04 186.75 46.13 3.75 25.00 52.25 0.00 16.83 4.13 37.88

76.46 16.25 2.88 4.38 2.54 76.96 3.17 73.71 50.71 34.33 35.21 25.67 52.42 21.13 76.04 43.88 30.75 34.29 35.04 101.96 24.88 4.42 30.33 71.75 57.33 19.25 3.75 45.29

641 86.03 46.44 41.05 51.64 23.41 65.76 13.06 13.50 7.67 3.21 10.96 6.13 6.63 0.00 165.29 67.42 8.04 70.58 25.00 121.50 2.33 59.54 51.67 25.88 77.08 31.25 55.50 18.21 87.46 43.00 33.79 57.50 40.00 94.42 22.96 2.04 22.79 73.21 24.88 24.17 4.63 49.75


Figure A1. 3: Comparison of hourly average (12hrs) values of pollutant in reference (clear) and polluted days Comparision of clear polluted days - PM10

Comparision of clear polluted days - BP 90

120

80

100

60

80

BP_p

50

Amount

BP_c

40 30

60 PM10_p

40

20

PM10_c

20

10

Location

639

641

-20

640

437

637

441

636

418

437

433

433

318

318

404

230

230

133

236

131

131

0

0

133

Amount

70

Location

Com parision of clear polluted days - SO2

Comparision of clear polluted days - CO 300

70 60

200

Amount

Amount

250

CO_p

150

CO_c

100

50 40

SO2_p

30

SO2_c

20 10

50

0 640

641

638

639

636

637

441

633

411

418

237

238

236

230

0

Location

Location

Comparision of clear polluted days - NO

Comparision of clear polluted days - NO2 350

120

300 250

Amount

80 NO2_p

60

NO2_c

NO_p

150

40

100

20

50

NO_c

100 90 80 70 60 50

O3_p

40

O3_c

30 20 10 641

639

636

620

411

437

318

238

235

0 133

640

638

636

620

437

418

404

301

237

Note: Amount in µg/m3 Location numbers are same as GS_ID (ground station ID) in Table 5.1. P, C in legend is polluted and clear (reference) days.

Comparision of clear polluted days - O3

230

235

107

638

640

620

636

418

437

301

404

235

237

133

230

107

Location

Location

107

230

0

0

Amount

200

133

Amount

100

Location

VI


Appendix 2: Tables and Figures related to images analysis Table A2. 1: Summary of methods considered to distinguish water and land Method Supervised classification 1. Box 2. Minimum Mahalanobis distance 3. Maximum likelihood (MLH) 4. Minimum distances to mean Unsupervised classification 5. With 20 classes 6. With 30 classes 7. NDVI 8. Density Slicing

Remarks More unknowns, when threshold increases unknown areas filled with water. Major water bodies clearly separated from land. Less amount of inland water bodies show compared to MLH. Major water bodies clearly separated from land. Less amount of inland water bodies show compared to NDVI. But urban areas show good classification compared to NDVI, unsupervised classification with 20 classes, minimum distance to mean and density slicing. Major water bodies clearly separated from land. Continuity of inland water bodies. Urban areas misclassified as water bodies. Major water bodies clearly separated from land. Water and land can be separated at class 4 out of 20 classes. Continuity of inland water bodies. But urban areas are misclassified as water. Major water bodies clearly separated from land. Water and land can be separated at class 3 out of 30 classes. Class 4 are mixed with water and land. Major water bodies clearly separated from land. Inland water bodies do not show the continuity and urban areas are misclassified as water. Major water bodies clearly separated from land. Urban areas are misclassified as water.

I


Figure A2. 1: AOT/AT over study area and relevant histograms

Band 3: 0 to 2.9585 800000

900000

1000000

800000

700000

700000

600000

600000

Number of pixels

1500000

Number of pixels

Number of pixels

2000000

Band 4: 0 to 2.8579

500000 400000

1400000 50000 1200000

400000 300000

0 0.0

0.5

1.0

1.5 2.0 value

Band 1

Legend for AOT

2.5

3.0

0 0.0

0.5

1.0

1.5

Band 2

2.0 value

2.5

3.0

3.5

0 0.0

600000

30000

20000

10000

200000

100000

100000

800000

400000

200000

200000

40000

1000000

500000

300000

500000

Band 6: 0 to 61.045

Number of pixels

Band 2: 0 to 3.7471

Number of pixels

Band 1: 0 to 3.1958

0.5

1.0

Band 3

1.5 value

2.0

2.5

3.0

0 0.0

Band 4

Legend for AT2

II

0.5

1.0

1.5 value

2.0

2.5

0

0

Band 6

10

20

30 value

40

50

60


Figure A2. 2: Histograms of estimated pollutants 900000

70000

700000

800000

60000

600000

700000 50000

500000 400000 300000

600000

Number of pixels

Number of pixels

Number of pixels

800000

500000 400000 300000

200000

30000 20000

200000

100000 0

40000

10000

100000

0

10

20

30 40 value

50

60

Estimation of BP ( range 5.85 to 66.80 )

0

50

60

70

80

90 100 110 120 130 140 value

Estimation of PM10 ( range 47.73 to 142.44 )

III

0

0

100 200

300 400 500 600 value

700 800

Estimation of CO ( range 0 to 856.56 )


Appendix 3: Data and Correlation Tables Table A3. 1: Daily and hourly average concentration of air polluted components at ground level (µ µg/m3) and AOT (unit less)/ AT (K) GS_ID Case

AT2

DA_CO

DA_NM3

DA_NO

DA_NO2

DA_O3

DA_PM10

HA_CO

HA_NM3

HA_NO

HA_NO2

HA_O3

HA_PM10

AOT2

AOT3

AOT4

1

0.2231

0.5491

0.7003

0.7376

27.697 -999.00 -999.00

59.58

31.96

4.29 -999.00

7.88 -999.00 -999.00 -999.00

41.00

32.00

8.00 -999.00

131

2

0.0225

0.0000

0.0000

0.0890

25.116 -999.00

3.63

38.00

35.46

4.96

66.05

4.54

31.00 -999.00

4.17

39.00

33.00

9.00

64.53

7.00

133

3

0.7073

0.6827

0.6478

0.3151

23.499 -999.00 -999.00

88.50

32.46

4.92

60.56

3.00

42.00 -999.00 -999.00

111.00

53.00

6.00

48.81

5.00

227

4

0.1942

0.4329

0.3545

0.1384

27.081 -999.00 -999.00

79.38

37.04

6.50 -999.00

6.54 -999.00 -999.00 -999.00

39.00

31.00

11.00 -999.00

8.00

230

5

1.1421

1.4214

1.2755

0.7959

24.844

84.54 -999.00

46.42

38.79

3.79

4.92

87.00 -999.00

51.00

38.00

6.00

66.46

7.00

232

6

0.6381

0.9589

0.9114

0.3903

25.624 -999.00 -999.00

51.33

40.46

2.67 -999.00

2.92 -999.00 -999.00 -999.00

48.00

37.00

6.00 -999.00

4.00

235

7

0.9491

1.0736

0.9022

0.7829

25.497 -999.00

1.44

70.67

40.75

3.96 -999.00

1.31

35.00

34.00

6.00 -999.00

9.00

236

8

0.4636

0.7684

0.6633

0.3221

28.384

271.58 -999.00

220.38

49.92

4.50 -999.00 -999.00 -999.00

257.00 -999.00

242.00

54.00

4.00 -999.00 -999.00

113.00 -999.00 -999.00 -999.00

63.74

DA_SO2

DA_BP

AOT1

107

36.00

10.08 -999.00 -999.00

HA_SO2 13.00

237

9

0.4829

0.9232

0.8971

0.0000

23.824

227.17 -999.00

180.38 -999.00 -999.00 -999.00

4.96 -999.00

174.00 -999.00

238

10

0.5015

0.6799

0.7665

1.0366

28.795

221.08 -999.00

195.87

51.83

3.96 -999.00 -999.00 -999.00

133.00 -999.00

99.00

43.00

5.00 -999.00 -999.00

301

11

0.6931

0.7161

0.6028

0.5098

24.711 -999.00 -999.00

78.54

47.29

4.17 -999.00

14.67 -999.00 -999.00 -999.00

93.00

39.00

5.00 -999.00

14.00

318

12

0.4824

0.8419

1.0116

0.1520

25.101 -999.00 -999.00

82.13

47.58

3.96

77.84

12.63

61.00 -999.00 -999.00

73.00

45.00

5.00

82.34

18.00

404

13

1.4773

1.6767

1.3691

0.5269

28.641 -999.00 -999.00

180.75

71.25

3.04

92.45

8.79 -999.00 -999.00 -999.00

312.00

99.00

4.00

97.12

18.00

411

14

1.2792

1.4996

1.2758

0.6563

24.542

164.26

66.74

2.30 -999.00

237.00

80.00

3.00 -999.00

25.00

415

15

0.6429

0.9328

0.8910

0.6647

25.100 -999.00 -999.00 -999.00 -999.00 -999.00 -999.00

13.25 -999.00 -999.00 -999.00 -999.00 -999.00 -999.00 -999.00

32.00

416

16

1.2321

1.1527

1.0776

0.5152

29.016 -999.00 -999.00 -999.00 -999.00 -999.00 -999.00

17.50 -999.00 -999.00 -999.00 -999.00 -999.00 -999.00 -999.00

37.00

418

17

0.7198

0.8353

0.8253

0.9871

27.937

136.21 -999.00

137.00

63.21 -999.00

91.05

13.71 -999.00

433

18

0.6190

1.0776

1.2993

0.6931

26.653 -999.00 -999.00

228.33

77.17

4.96

84.26

437

19

0.8056

0.8718

1.2040

0.1667

23.784 -999.00 -999.00

99.42

42.38

2.33

85.71

441

20

1.0361

0.8630

0.9943

0.7553

29.122

155.75 -999.00

121.63

54.29

9.33

65.88 -999.00 -999.00

620

21

0.3857

0.2513

0.6506

0.6645

24.413 -999.00 -999.00

79.54

44.88 -999.00 -999.00

2.58 -999.00 -999.00 -999.00

633

22

1.2993

1.0415

0.8109

0.9502

24.802

12.85

74.38

45.67

3.63 -999.00

3.08 -999.00

636

23

0.8056

1.3863

1.0144

1.1998

27.807

130.75 -999.00

108.92

53.46

4.42 -999.00 -999.00

637

24

0.8422

1.2669

1.0696

1.6042

28.552

175.04 -999.00

171.58

67.04 -999.00 -999.00 -999.00

638

25

1.4604

1.4034

1.3103

1.0818

30.072

204.67 -999.00

153.96

66.65

5.39 -999.00

639

26

0.6493

1.1221

0.8965

0.6466

28.928

186.75 -999.00

173.17

56.96

4.13

640

27

0.8422

1.2669

1.0696

1.6042

28.552

101.96 -999.00

76.96

52.42

3.75 -999.00 -999.00

641

28

0.3646

0.4855

1.0266

1.2005

25.262

94.42 -999.00

121.50

55.50

4.63

Note:

-999 data not available;

124.00 -999.00

85.21

DA Daily average;

HA Hourly average I

11.71 -999.00

154.00 -999.00

6.00

156.00 -999.00

191.00

68.00 -999.00

82.18

22.00

21.04

84.00 -999.00 -999.00

222.00

83.00

6.00

78.74

63.00

5.58

54.00 -999.00 -999.00

100.00

39.00

3.00

76.87

10.00

104.00

49.00

9.00

51.28 -999.00

150.00 -999.00

42.00 -999.00 -999.00

4.00

22.71

110.00

48.00

5.00 -999.00

4.00

57.00

191.00 -999.00

171.00

53.00

5.00 -999.00 -999.00

80.00

256.00 -999.00

305.00

75.00 -999.00 -999.00 -999.00

7.92 -999.00

223.00 -999.00

180.00

65.00

6.00 -999.00

84.42 -999.00 -999.00

223.00 -999.00

188.00

50.00

5.00

45.00

115.00 -999.00

104.00

48.00

4.00 -999.00 -999.00

6.63 -999.00

141.00 -999.00

304.00

64.00

5.00

65.76

109.00

Source: http://www.lml.rivm.nl/

87.00

9.00

74.47 -999.00

59.60

12.00


Table A3. 2: Linear Correlations of ground station data, AOT and AT AOT1

AOT2

AOT3

AOT4

AT2

DA_CO

DA_NM3

DA_NO

DA_NO2

DA_O3

DA_PM10

DA_SO2

DA_BP

HA_CO

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed)

DA_CO -.384 .176 14 -.263 .364 14 -.430 .125 14 -.479 .083 14 .410 .146 14 1.000 . 14 . . 1 .885 .000 14 .257 .397 13 .194 .567 11 .580 .306 5 .105 .823 7 .999 .001 4 .704 .005

DA_NM3 .573 .611 3 .310 .799 3 .247 .841 3 .502 .665 3 -.922 .253 3 . . 1 1.000 . 3 .421 .723 3 .748 .462 3 -.550 .629 3 . . 1 -.786 .424 3 . . 1 . .

DA_NO .174 .396 26 .339 .090 26 .387 .051 26 .118 .565 26 .438 .025 26 .885 .000 14 .421 .723 3 1.000 . 26 .788 .000 25 .033 .885 22 .650 .030 11 .545 .016 19 .937 .000 9 .723 .003

DA_NO2 .476 .016 25 .577 .003 25 .634 .001 25 .443 .027 25 .490 .013 25 .257 .397 13 .748 .462 3 .788 .000 25 1.000 . 25 -.022 .922 22 .692 .018 11 .641 .004 18 .907 .001 9 .549 .052

DA_O3 DA_PM10 -.172 .294 .443 .380 22 11 -.333 .474 .130 .141 22 11 -.277 .410 .211 .211 22 11 .017 .004 .939 .992 22 11 .395 .468 .069 .146 22 11 .194 .580 .567 .306 11 5 -.550 . .629 . 3 1 .033 .650 .885 .030 22 11 -.022 .692 .922 .018 22 11 1.000 -.499 . .142 22 10 -.499 1.000 .142 . 10 11 .079 .604 .772 .085 16 9 .054 .793 .899 .060 8 6 .142 .646 .676 .239

DA_SO2 .154 .505 21 .293 .198 21 .322 .155 21 .156 .501 21 .389 .081 21 .105 .823 7 -.786 .424 3 .545 .016 19 .641 .004 18 .079 .772 16 .604 .085 9 1.000 . 21 .927 .008 6 .367 .418 II

DA_BP .139 .722 9 .397 .291 9 .547 .128 9 .329 .387 9 .461 .211 9 .999 .001 4 . . 1 .937 .000 9 .907 .001 9 .054 .899 8 .793 .060 6 .927 .008 6 1.000 . 9 .985 .015

HA_CO -.197 .500 14 .049 .868 14 -.156 .595 14 -.104 .725 14 .492 .074 14 .704 .005 14 . . 1 .723 .003 14 .549 .052 13 .142 .676 11 .646 .239 5 .367 .418 7 .985 .015 4 1.000 .

HA_NM3 .620 .574 3 .365 .762 3 .303 .804 3 .552 .628 3 -.898 .290 3 . . 1 .998 .037 3 .473 .686 3 .785 .425 3 -.598 .592 3 . . 1 -.749 .461 3 . . 1 . .

HA_NO .327 .103 26 .430 .028 26 .485 .012 26 .385 .052 26 .348 .082 26 .144 .624 14 .991 .086 3 .730 .000 26 .805 .000 25 -.122 .590 22 .468 .147 11 .356 .134 19 .860 .003 9 .632 .015

HA_NO2 .508 .009 25 .570 .003 25 .605 .001 25 .313 .128 25 .300 .144 25 .049 .873 13 .971 .154 3 .719 .000 25 .884 .000 25 -.130 .565 22 .559 .074 11 .456 .057 18 .883 .002 9 .447 .126

HA_ O3 HA_PM10 -.445 .302 .038 .367 22 11 -.553 .533 .008 .091 22 11 -.581 .425 .005 .192 22 11 -.261 -.104 .241 .762 22 11 .138 .316 .540 .344 22 11 -.012 .195 .973 .753 11 5 -.552 . .628 . 3 1 -.376 .447 .085 .168 22 11 -.415 .591 .055 .056 22 11 .724 -.637 .000 .047 22 10 -.626 .890 .053 .000 10 11 -.184 .548 .495 .127 16 9 -.345 .651 .402 .161 8 6 -.113 .314 .741 .607

HA_SO2 .116 .618 21 .284 .212 21 .391 .080 21 .141 .543 21 .314 .166 21 -.086 .855 7 -.974 .144 3 .678 .001 19 .701 .001 18 .045 .869 16 .481 .190 9 .889 .000 21 .900 .015 6 .181 .698

HA_NOX .359 .078 25 .461 .020 25 .513 .009 25 .381 .060 25 .343 .093 25 .215 .480 13 .988 .097 3 .768 .000 25 .831 .000 25 -.125 .579 22 .494 .123 11 .378 .122 18 .875 .002 9 .640 .019


HA_NM3

HA_NO

HA_NO2

HA_ O3

HA_PM10

HA_SO2

HA_NOX

N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

DA_CO 14 . . 1 .144 .624 14 .049 .873 13 -.012 .973 11 .195 .753 5 -.086 .855 7 .215 .480 13

DA_NM3 1 .998 .037 3 .991 .086 3 .971 .154 3 -.552 .628 3 . . 1 -.974 .144 3 .988 .097 3

DA_NO 14 .473 .686 3 .730 .000 26 .719 .000 25 -.376 .085 22 .447 .168 11 .678 .001 19 .768 .000 25

DA_NO2 13 .785 .425 3 .805 .000 25 .884 .000 25 -.415 .055 22 .591 .056 11 .701 .001 18 .831 .000 25

DA_O3 DA_PM10 11 5 -.598 . .592 . 3 1 -.122 .468 .590 .147 22 11 -.130 .559 .565 .074 22 11 .724 -.626 .000 .053 22 10 -.637 .890 .047 .000 10 11 .045 .481 .869 .190 16 9 -.125 .494 .579 .123 22 11

DA_SO2 7 -.749 .461 3 .356 .134 19 .456 .057 18 -.184 .495 16 .548 .127 9 .889 .000 21 .378 .122 18

DA_BP 4 . . 1 .860 .003 9 .883 .002 9 -.345 .402 8 .651 .161 6 .900 .015 6 .875 .002 9

HA_CO 14 . . 1 .632 .015 14 .447 .126 13 -.113 .741 11 .314 .607 5 .181 .698 7 .640 .019 13

HA_NM3 1 1.000 . 3 .997 .048 3 .983 .117 3 -.599 .591 3 . . 1 -.960 .182 3 .996 .060 3

HA_NO 14 .997 .048 3 1.000 . 26 .887 .000 25 -.517 .014 22 .386 .241 11 .471 .042 19 .997 .000 25

HA_NO2 13 .983 .117 3 .887 .000 25 1.000 . 25 -.446 .037 22 .528 .095 11 .598 .009 18 .920 .000 25

HA_ O3 HA_PM10 11 5 -.599 . .591 . 3 1 -.517 .386 .014 .241 22 11 -.446 .528 .037 .095 22 11 1.000 -.596 . .069 22 10 -.596 1.000 .069 . 10 11 -.168 .398 .535 .289 16 9 -.514 .419 .014 .199 22 11

HA_SO2 7 -.960 .182 3 .471 .042 19 .598 .009 18 -.168 .535 16 .398 .289 9 1.000 . 21 .500 .035 18

HA_NOX 13 .996 .060 3 .997 .000 25 .920 .000 25 -.514 .014 22 .419 .199 11 .500 .035 18 1.000 . 25

Table A3. 3: Correlations of ground station data, AOT and AT – Converted hyperbolic relationship to linear relationship OAOT1

OAOT2

OAOT3

OAOT4

OAT2

DA_CO

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation

DA_CO

DA_NM3

DA_NO

DA_NO2

DA_ O3

DA_PM10

DA_SO2

DA_BP

DA_NOX

HA_CO

HA_NM3

HA_NO

HA_NO2

HA_O3

HA_PM10

HA_SO2

HA_NOX

.364 .200 14 .112 .702 14 .507 .064 14 .569 .042 13 -.390 .168 14 1.000

-.340 .779 3 1.000 . 2 1.000 . 2 -.353 .770 3 .925 .248 3 .

-.312 .121 26 -.253 .223 25 -.251 .226 25 -.345 .091 25 -.425 .030 26 .885

-.309 .133 25 -.388 .061 24 -.530 .008 24 -.449 .024 25 -.488 .013 25 .257

.122 .590 22 .339 .132 21 .329 .145 21 .067 .768 22 -.392 .071 22 .194

-.289 .389 11 -.464 .177 10 -.345 .329 10 -.157 .644 11 -.473 .142 11 .580

-.206 .369 21 -.343 .139 20 -.226 .337 20 -.220 .352 20 -.405 .068 21 .105

-.469 .203 9 -.139 .743 8 -.335 .417 8 -.425 .254 9 -.465 .208 9 .999

-.323 .115 25 -.284 .179 24 -.314 .134 24 -.376 .064 25 -.536 .006 25 .843

.155 .596 14 -.104 .724 14 .198 .497 14 .375 .207 13 -.484 .079 14 .704

-.395 .742 3 1.000 . 2 1.000 . 2 -.407 .733 3 .901 .285 3 .

-.265 .191 26 -.253 .222 25 -.400 .047 25 -.395 .051 25 -.343 .087 26 .144

-.274 .185 25 -.366 .079 24 -.502 .012 24 -.383 .058 25 -.295 .153 25 .049

.437 .042 22 .543 .011 21 .618 .003 21 .400 .065 22 -.153 .497 22 -.012

-.158 .642 11 -.557 .095 10 -.532 .113 10 -.004 .991 11 -.333 .317 11 .195

-.156 .499 21 -.293 .210 20 -.320 .169 20 -.214 .364 20 -.330 .144 21 -.086

-.273 .187 25 -.280 .184 24 -.427 .038 24 -.399 .048 25 -.338 .098 25 .215

III


DA_NM3

DA_NO

DA_NO2

DA_O3

DA_PM10

DA_SO2

DA_BP

DA_NOX

HA_CO

HA_NM3

HA_NO

HA_NO2

HA_O3

HA_PM10

Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed)

DA_CO

DA_NM3

DA_NO

DA_NO2

DA_ O3

DA_PM10

DA_SO2

DA_BP

DA_NOX

HA_CO

HA_NM3

HA_NO

HA_NO2

HA_O3

HA_PM10

HA_SO2

HA_NOX

. 14 . . 1 .885 .000 14 .257 .397 13 .194 .567 11 .580 .306 5 .105 .823 7 .999 .001 4 .843 .000 13 .704 .005 14 . . 1 .144 .624 14 .049 .873 13 -.012 .973 11 .195 .753

. 1 1.000 . 3 .421 .723 3 .748 .462 3 -.550 .629 3 . . 1 -.786 .424 3 . . 1 .494 .671 3 . . 1 .998 .037 3 .991 .086 3 .971 .154 3 -.552 .628 3 . .

.000 14 .421 .723 3 1.000 . 26 .788 .000 25 .033 .885 22 .650 .030 11 .545 .016 19 .937 .000 9 .993 .000 25 .723 .003 14 .473 .686 3 .730 .000 26 .719 .000 25 -.376 .085 22 .447 .168

.397 13 .748 .462 3 .788 .000 25 1.000 . 25 -.022 .922 22 .692 .018 11 .641 .004 18 .907 .001 9 .854 .000 25 .549 .052 13 .785 .425 3 .805 .000 25 .884 .000 25 -.415 .055 22 .591 .056

.567 11 -.550 .629 3 .033 .885 22 -.022 .922 22 1.000 . 22 -.499 .142 10 .079 .772 16 .054 .899 8 .024 .917 22 .142 .676 11 -.598 .592 3 -.122 .590 22 -.130 .565 22 .724 .000 22 -.637 .047

.306 5 . . 1 .650 .030 11 .692 .018 11 -.499 .142 10 1.000 . 11 .604 .085 9 .793 .060 6 .668 .025 11 .646 .239 5 . . 1 .468 .147 11 .559 .074 11 -.626 .053 10 .890 .000

.823 7 -.786 .424 3 .545 .016 19 .641 .004 18 .079 .772 16 .604 .085 9 1.000 . 21 .927 .008 6 .648 .004 18 .367 .418 7 -.749 .461 3 .356 .134 19 .456 .057 18 -.184 .495 16 .548 .127

.001 4 . . 1 .937 .000 9 .907 .001 9 .054 .899 8 .793 .060 6 .927 .008 6 1.000 . 9 .946 .000 9 .985 .015 4 . . 1 .860 .003 9 .883 .002 9 -.345 .402 8 .651 .161

.000 13 .494 .671 3 .993 .000 25 .854 .000 25 .024 .917 22 .668 .025 11 .648 .004 18 .946 .000 9 1.000 . 25 .759 .003 13 .544 .634 3 .797 .000 25 .774 .000 25 -.396 .068 22 .483 .133

.005 14 . . 1 .723 .003 14 .549 .052 13 .142 .676 11 .646 .239 5 .367 .418 7 .985 .015 4 .759 .003 13 1.000 . 14 . . 1 .632 .015 14 .447 .126 13 -.113 .741 11 .314 .607

. 1 .998 .037 3 .473 .686 3 .785 .425 3 -.598 .592 3 . . 1 -.749 .461 3 . . 1 .544 .634 3 . . 1 1.000 . 3 .997 .048 3 .983 .117 3 -.599 .591 3 . .

.624 14 .991 .086 3 .730 .000 26 .805 .000 25 -.122 .590 22 .468 .147 11 .356 .134 19 .860 .003 9 .797 .000 25 .632 .015 14 .997 .048 3 1.000 . 26 .887 .000 25 -.517 .014 22 .386 .241

.873 13 .971 .154 3 .719 .000 25 .884 .000 25 -.130 .565 22 .559 .074 11 .456 .057 18 .883 .002 9 .774 .000 25 .447 .126 13 .983 .117 3 .887 .000 25 1.000 . 25 -.446 .037 22 .528 .095

.973 11 -.552 .628 3 -.376 .085 22 -.415 .055 22 .724 .000 22 -.626 .053 10 -.184 .495 16 -.345 .402 8 -.396 .068 22 -.113 .741 11 -.599 .591 3 -.517 .014 22 -.446 .037 22 1.000 . 22 -.596 .069

.753 5 . . 1 .447 .168 11 .591 .056 11 -.637 .047 10 .890 .000 11 .548 .127 9 .651 .161 6 .483 .133 11 .314 .607 5 . . 1 .386 .241 11 .528 .095 11 -.596 .069 10 1.000 .

.855 7 -.974 .144 3 .678 .001 19 .701 .001 18 .045 .869 16 .481 .190 9 .889 .000 21 .900 .015 6 .769 .000 18 .181 .698 7 -.960 .182 3 .471 .042 19 .598 .009 18 -.168 .535 16 .398 .289

.480 13 .988 .097 3 .768 .000 25 .831 .000 25 -.125 .579 22 .494 .123 11 .378 .122 18 .875 .002 9 .806 .000 25 .640 .019 13 .996 .060 3 .997 .000 25 .920 .000 25 -.514 .014 22 .419 .199

IV


HA_SO2

HA_NOX

N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

DA_CO

DA_NM3

DA_NO

DA_NO2

DA_ O3

DA_PM10

DA_SO2

DA_BP

DA_NOX

HA_CO

HA_NM3

HA_NO

HA_NO2

HA_O3

HA_PM10

HA_SO2

HA_NOX

5 -.086 .855 7 .215 .480 13

1 -.974 .144 3 .988 .097 3

11 .678 .001 19 .768 .000 25

11 .701 .001 18 .831 .000 25

10 .045 .869 16 -.125 .579 22

11 .481 .190 9 .494 .123 11

9 .889 .000 21 .378 .122 18

6 .900 .015 6 .875 .002 9

11 .769 .000 18 .806 .000 25

5 .181 .698 7 .640 .019 13

1 -.960 .182 3 .996 .060 3

11 .471 .042 19 .997 .000 25

11 .598 .009 18 .920 .000 25

10 -.168 .535 16 -.514 .014 22

11 .398 .289 9 .419 .199 11

9 1.000 . 21 .500 .035 18

11 .500 .035 18 1.000 . 25

HA_SO2 .181 .432 21 .290 .215 20 .367 .111 20 .178 .452 20 .322 .154 21 -.086 .855 7 -.974 .144 3 .678 .001 19 .701 .001 18 .045 .869 16

HA_NOX .366 .072 25 .352 .091 24 .461 .023 24 .404 .045 25 .341 .095 25 .215 .480 13 .988 .097 3 .768 .000 25 .831 .000 25 -.125 .579 22

Table A3. 4: Correlations of ground station data, AOT and AT – Converted Exponential relationship to linear relationship LNAOT1

LNAOT2

LNAOT3

LNAOT4

LNAT2

DA_CO

DA_NM3

DA_NO

DA_NO2

DA_O3

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

DA_CO -.398 .159 14 -.200 .493 14 -.470 .090 14 -.478 .099 13 .400 .156 14 1.000 . 14 . . 1 .885 .000 14 .257 .397 13 .194 .567 11

DA_NM3 .400 .738 3 -1.000 . 2 -1.000 . 2 .404 .736 3 -.924 .250 3 . . 1 1.000 . 3 .421 .723 3 .748 .462 3 -.550 .629 3

DA_NO .296 .143 26 .254 .220 25 .268 .195 25 .288 .163 25 .432 .028 26 .885 .000 14 .421 .723 3 1.000 . 26 .788 .000 25 .033 .885 22

DA_NO2 .463 .020 25 .487 .016 24 .590 .002 24 .478 .016 25 .489 .013 25 .257 .397 13 .748 .462 3 .788 .000 25 1.000 . 25 -.022 .922 22

DA_O3 DA_PM10 -.190 .320 .398 .338 22 11 -.339 .440 .133 .203 21 10 -.306 .335 .178 .344 21 10 -.011 .085 .962 .803 22 11 .394 .471 .070 .144 22 11 .194 .580 .567 .306 11 5 -.550 . .629 . 3 1 .033 .650 .885 .030 22 11 -.022 .692 .922 .018 22 11 1.000 -.499 . .142 22 10

DA_SO2 .234 .308 21 .304 .192 20 .255 .277 20 .182 .442 20 .398 .074 21 .105 .823 7 -.786 .424 3 .545 .016 19 .641 .004 18 .079 .772 16

V

DA_BP .389 .300 9 .076 .857 8 .321 .438 8 .378 .316 9 .463 .209 9 .999 .001 4 . . 1 .937 .000 9 .907 .001 9 .054 .899 8

HA_CO -.189 .518 14 .077 .795 14 -.176 .548 14 -.251 .408 13 .489 .076 14 .704 .005 14 . . 1 .723 .003 14 .549 .052 13 .142 .676 11

HA_NM3 .453 .701 3 -1.000 . 2 -1.000 . 2 .456 .698 3 -.900 .288 3 . . 1 .998 .037 3 .473 .686 3 .785 .425 3 -.598 .592 3

HA_NO .345 .084 26 .320 .119 25 .431 .032 25 .403 .046 25 .345 .084 26 .144 .624 14 .991 .086 3 .730 .000 26 .805 .000 25 -.122 .590 22

HA_NO2 .445 .026 25 .469 .021 24 .563 .004 24 .368 .070 25 .298 .148 25 .049 .873 13 .971 .154 3 .719 .000 25 .884 .000 25 -.130 .565 22

HA_O3 HA_PM10 -.564 .214 .006 .527 22 11 -.499 .584 .021 .076 21 10 -.554 .535 .009 .111 21 10 -.321 -.049 .145 .885 22 11 .145 .325 .519 .330 22 11 -.012 .195 .973 .753 11 5 -.552 . .628 . 3 1 -.376 .447 .085 .168 22 11 -.415 .591 .055 .056 22 11 .724 -.637 .000 .047 22 10


DA_PM10

DA_SO2

DA_BP

HA_CO

HA_NM3

HA_NO

HA_NO2

HA_O3

HA_PM10

HA_SO2

HA_NOX

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

DA_CO .580 .306 5 .105 .823 7 .999 .001 4 .704 .005 14 . . 1 .144 .624 14 .049 .873 13 -.012 .973 11 .195 .753 5 -.086 .855 7 .215 .480 13

DA_NM3 . . 1 -.786 .424 3 . . 1 . . 1 .998 .037 3 .991 .086 3 .971 .154 3 -.552 .628 3 . . 1 -.974 .144 3 .988 .097 3

DA_NO .650 .030 11 .545 .016 19 .937 .000 9 .723 .003 14 .473 .686 3 .730 .000 26 .719 .000 25 -.376 .085 22 .447 .168 11 .678 .001 19 .768 .000 25

DA_NO2 .692 .018 11 .641 .004 18 .907 .001 9 .549 .052 13 .785 .425 3 .805 .000 25 .884 .000 25 -.415 .055 22 .591 .056 11 .701 .001 18 .831 .000 25

DA_O3 DA_PM10 -.499 1.000 .142 . 10 11 .079 .604 .772 .085 16 9 .054 .793 .899 .060 8 6 .142 .646 .676 .239 11 5 -.598 . .592 . 3 1 -.122 .468 .590 .147 22 11 -.130 .559 .565 .074 22 11 .724 -.626 .000 .053 22 10 -.637 .890 .047 .000 10 11 .045 .481 .869 .190 16 9 -.125 .494 .579 .123 22 11

DA_SO2 .604 .085 9 1.000 . 21 .927 .008 6 .367 .418 7 -.749 .461 3 .356 .134 19 .456 .057 18 -.184 .495 16 .548 .127 9 .889 .000 21 .378 .122 18

VI

DA_BP .793 .060 6 .927 .008 6 1.000 . 9 .985 .015 4 . . 1 .860 .003 9 .883 .002 9 -.345 .402 8 .651 .161 6 .900 .015 6 .875 .002 9

HA_CO .646 .239 5 .367 .418 7 .985 .015 4 1.000 . 14 . . 1 .632 .015 14 .447 .126 13 -.113 .741 11 .314 .607 5 .181 .698 7 .640 .019 13

HA_NM3 . . 1 -.749 .461 3 . . 1 . . 1 1.000 . 3 .997 .048 3 .983 .117 3 -.599 .591 3 . . 1 -.960 .182 3 .996 .060 3

HA_NO .468 .147 11 .356 .134 19 .860 .003 9 .632 .015 14 .997 .048 3 1.000 . 26 .887 .000 25 -.517 .014 22 .386 .241 11 .471 .042 19 .997 .000 25

HA_NO2 .559 .074 11 .456 .057 18 .883 .002 9 .447 .126 13 .983 .117 3 .887 .000 25 1.000 . 25 -.446 .037 22 .528 .095 11 .598 .009 18 .920 .000 25

HA_O3 HA_PM10 -.626 .890 .053 .000 10 11 -.184 .548 .495 .127 16 9 -.345 .651 .402 .161 8 6 -.113 .314 .741 .607 11 5 -.599 . .591 . 3 1 -.517 .386 .014 .241 22 11 -.446 .528 .037 .095 22 11 1.000 -.596 . .069 22 10 -.596 1.000 .069 . 10 11 -.168 .398 .535 .289 16 9 -.514 .419 .014 .199 22 11

HA_SO2 .481 .190 9 .889 .000 21 .900 .015 6 .181 .698 7 -.960 .182 3 .471 .042 19 .598 .009 18 -.168 .535 16 .398 .289 9 1.000 . 21 .500 .035 18

HA_NOX .494 .123 11 .378 .122 18 .875 .002 9 .640 .019 13 .996 .060 3 .997 .000 25 .920 .000 25 -.514 .014 22 .419 .199 11 .500 .035 18 1.000 . 25


Table A3. 5: Correlations of ground station data, AOT and AT – Converted Geometric relationship to linear relationship LNAOT1

LNAOT2

LNAOT3

LNAOT4

LNAT2

LNHA_CO

LNHA_NO

LNHA_NO2

LNHA_O3

LNHAPM10

LNHA_SO2

LNHA_NOX

LNDA_BP

LNDA_CO

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed)

LNHA_CO

LNHA_NO

LNHA_NO2

LNHA_O3

LNHAPM10

LNHA_SO2

LNHA_NOX

LNDA_BP

LNDA_CO

LNDA_NO

LNDA_NO2

LNDA_O3

LNDAPM10

LNDA_SO2

LNDA_NOX

-.222 .446 14 .028 .924 14 -.187 .521 14 -.224 .461 13 .487 .078 14 1.000 . 14 .729 .003 14 .543 .055 13 -.108 .753 11 .236 .702 5 .331 .469 7 .735 .004 13 .985 .015 4 .755 .002

.445 .023 26 .324 .114 25 .442 .027 25 .446 .025 25 .323 .108 26 .729 .003 14 1.000 . 26 .912 .000 25 -.592 .004 22 .306 .360 11 .490 .033 19 .997 .000 25 .867 .002 9 .273 .345

.489 .013 25 .465 .022 24 .573 .003 24 .425 .034 25 .306 .137 25 .543 .055 13 .912 .000 25 1.000 . 25 -.490 .021 22 .402 .221 11 .583 .011 18 .937 .000 25 .850 .004 9 .155 .612

-.525 .012 22 -.464 .034 21 -.510 .018 21 -.225 .314 22 .164 .465 22 -.108 .753 11 -.592 .004 22 -.490 .021 22 1.000 . 22 -.595 .070 10 -.282 .290 16 -.578 .005 22 -.396 .331 8 .022 .949

.170 .617 11 .565 .089 10 .550 .100 10 -.059 .863 11 .299 .371 11 .236 .702 5 .306 .360 11 .402 .221 11 -.595 .070 10 1.000 . 11 .660 .053 9 .326 .328 11 .608 .200 6 .152 .807

.192 .404 21 .338 .145 20 .380 .098 20 .157 .509 20 .405 .069 21 .331 .469 7 .490 .033 19 .583 .011 18 -.282 .290 16 .660 .053 9 1.000 . 21 .528 .024 18 .888 .018 6 .087 .853

.448 .025 25 .353 .091 24 .469 .021 24 .446 .025 25 .339 .097 25 .735 .004 13 .997 .000 25 .937 .000 25 -.578 .005 22 .326 .328 11 .528 .024 18 1.000 . 25 .873 .002 9 .326 .277

.473 .199 9 .038 .929 8 .290 .485 8 .389 .300 9 .451 .223 9 .985 .015 4 .867 .002 9 .850 .004 9 -.396 .331 8 .608 .200 6 .888 .018 6 .873 .002 9 1.000 . 9 .999 .001

-.359 .208 14 -.156 .595 14 -.413 .142 14 -.402 .174 13 .461 .097 14 .755 .002 14 .273 .345 14 .155 .612 13 .022 .949 11 .152 .807 5 .087 .853 7 .326 .277 13 .999 .001 4 1.000 .

.398 .044 26 .227 .276 25 .254 .221 25 .348 .088 25 .428 .029 26 .792 .001 14 .815 .000 26 .763 .000 25 -.427 .048 22 .356 .283 11 .589 .008 19 .831 .000 25 .946 .000 9 .860 .000

.487 .014 25 .478 .018 24 .584 .003 24 .499 .011 25 .496 .012 25 .641 .018 13 .828 .000 25 .866 .000 25 -.448 .037 22 .574 .065 11 .674 .002 18 .842 .000 25 .873 .002 9 .400 .176

-.266 .231 22 -.381 .089 21 -.372 .097 21 .016 .944 22 .409 .059 22 .224 .508 11 -.103 .647 22 -.127 .573 22 .758 .000 22 -.676 .032 10 -.026 .924 16 -.108 .633 22 -.057 .893 8 .282 .401

.306 .360 11 .431 .213 10 .346 .327 10 .073 .831 11 .470 .145 11 .661 .224 5 .513 .107 11 .515 .105 11 -.649 .042 10 .886 .000 11 .711 .032 9 .512 .108 11 .805 .053 6 .618 .267

.216 .347 21 .352 .128 20 .271 .247 20 .165 .488 20 .448 .042 21 .500 .253 7 .323 .177 19 .397 .103 18 -.182 .499 16 .717 .030 9 .931 .000 21 .351 .153 18 .851 .031 6 .299 .514

.431 .031 25 .290 .169 24 .334 .110 24 .379 .062 25 .538 .006 25 .808 .001 13 .857 .000 25 .802 .000 25 -.445 .038 22 .414 .205 11 .706 .001 18 .857 .000 25 .957 .000 9 .841 .000

VII


LNDA_NO

LNDA_NO2

LNDA_O3

LNDAPM10

LNDA_SO2

LNDA_NOX

N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

LNHA_CO

LNHA_NO

LNHA_NO2

LNHA_O3

LNHAPM10

LNHA_SO2

LNHA_NOX

LNDA_BP

LNDA_CO

LNDA_NO

LNDA_NO2

LNDA_O3

LNDAPM10

LNDA_SO2

LNDA_NOX

14 .792 .001 14 .641 .018 13 .224 .508 11 .661 .224 5 .500 .253 7 .808 .001 13

14 .815 .000 26 .828 .000 25 -.103 .647 22 .513 .107 11 .323 .177 19 .857 .000 25

13 .763 .000 25 .866 .000 25 -.127 .573 22 .515 .105 11 .397 .103 18 .802 .000 25

11 -.427 .048 22 -.448 .037 22 .758 .000 22 -.649 .042 10 -.182 .499 16 -.445 .038 22

5 .356 .283 11 .574 .065 11 -.676 .032 10 .886 .000 11 .717 .030 9 .414 .205 11

7 .589 .008 19 .674 .002 18 -.026 .924 16 .711 .032 9 .931 .000 21 .706 .001 18

13 .831 .000 25 .842 .000 25 -.108 .633 22 .512 .108 11 .351 .153 18 .857 .000 25

4 .946 .000 9 .873 .002 9 -.057 .893 8 .805 .053 6 .851 .031 6 .957 .000 9

14 .860 .000 14 .400 .176 13 .282 .401 11 .618 .267 5 .299 .514 7 .841 .000 13

14 1.000 . 26 .800 .000 25 .042 .853 22 .639 .034 11 .490 .033 19 .993 .000 25

13 .800 .000 25 1.000 . 25 -.064 .779 22 .701 .016 11 .588 .010 18 .861 .000 25

11 .042 .853 22 -.064 .779 22 1.000 . 22 -.563 .090 10 .085 .753 16 .021 .926 22

5 .639 .034 11 .701 .016 11 -.563 .090 10 1.000 . 11 .721 .028 9 .669 .024 11

7 .490 .033 19 .588 .010 18 .085 .753 16 .721 .028 9 1.000 . 21 .580 .012 18

13 .993 .000 25 .861 .000 25 .021 .926 22 .669 .024 11 .580 .012 18 1.000 . 25

Table A3. 6: Correlations of CO with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. AOT2C AOT3C AT2C LNAOT2C LNAOT3C LNAT2C

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

AOT2C 1.000 . 28 .847 .000 28 .408 .031 28 .944 .000 27 .596 .001 28 .394 .038 28

AOT3C .847 .000 28 1.000 . 28 .308 .110 28 .738 .000 27 .849 .000 28 .289 .135 28

AT2C .408 .031 28 .308 .110 28 1.000 . 28 .319 .104 27 .196 .318 28 1.000 .000 28

LNAOT2C .944 .000 27 .738 .000 27 .319 .104 27 1.000 . 27 .539 .004 27 .307 .119 27

LNAOT3C .596 .001 28 .849 .000 28 .196 .318 28 .539 .004 27 1.000 . 28 .182 .353 28

VIII

LNAT2C .394 .038 28 .289 .135 28 1.000 .000 28 .307 .119 27 .182 .353 28 1.000 . 28

HA_CO .317 .270 14 .232 .424 14 .596 .025 14 .362 .203 14 .249 .391 14 .591 .026 14

DA_CO LNHA_CO LNDA_CO .039 .302 .111 .894 .294 .706 14 14 14 -.103 .219 -.042 .727 .453 .887 14 14 14 .447 .569 .494 .109 .034 .072 14 14 14 .179 .347 .253 .541 .224 .383 14 14 14 -.096 .238 -.031 .745 .413 .915 14 14 14 .451 .565 .497 .106 .035 .070 14 14 14


HA_CO DA_CO LNHA_CO LNDA_CO

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

AOT2C .317 .270 14 .039 .894 14 .302 .294 14 .111 .706 14

AOT3C .232 .424 14 -.103 .727 14 .219 .453 14 -.042 .887 14

AT2C .596 .025 14 .447 .109 14 .569 .034 14 .494 .072 14

LNAOT2C .362 .203 14 .179 .541 14 .347 .224 14 .253 .383 14

LNAOT3C .249 .391 14 -.096 .745 14 .238 .413 14 -.031 .915 14

LNAT2C .591 .026 14 .451 .106 14 .565 .035 14 .497 .070 14

HA_CO 1.000 . 14 .704 .005 14 .987 .000 14 .735 .003 14

DA_CO LNHA_CO LNDA_CO .704 .987 .735 .005 .000 .003 14 14 14 1.000 .709 .987 . .005 .000 14 14 14 .709 1.000 .755 .005 . .002 14 14 14 .987 .755 1.000 .000 .002 . 14 14 14

Table A3. 7: Correlations of BP with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. AOT2C AOT3C AT2C LNAOT2C LNAOT3C LNAT2C DA_BP LNDA_BP

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

AOT2C 1.000 . 28 .847 .000 28 .408 .031 28 .944 .000 27 .596 .001 28 .394 .038 28 .506 .165 9 .537 .136 9

AOT3C .847 .000 28 1.000 . 28 .308 .110 28 .738 .000 27 .849 .000 28 .289 .135 28 .542 .132 9 .601 .087 9

AT2C .408 .031 28 .308 .110 28 1.000 . 28 .319 .104 27 .196 .318 28 1.000 .000 28 .426 .253 9 .428 .250 9

LNAOT2C .944 .000 27 .738 .000 27 .319 .104 27 1.000 . 27 .539 .004 27 .307 .119 27 .540 .134 9 .587 .096 9 IX

LNAOT3C .596 .001 28 .849 .000 28 .196 .318 28 .539 .004 27 1.000 . 28 .182 .353 28 .533 .139 9 .617 .077 9

LNAT2C .394 .038 28 .289 .135 28 1.000 .000 28 .307 .119 27 .182 .353 28 1.000 . 28 .427 .252 9 .427 .252 9

DA_BP .506 .165 9 .542 .132 9 .426 .253 9 .540 .134 9 .533 .139 9 .427 .252 9 1.000 . 9 .989 .000 9

LNDA_BP .537 .136 9 .601 .087 9 .428 .250 9 .587 .096 9 .617 .077 9 .427 .252 9 .989 .000 9 1.000 . 9


Table A3. 8: Correlations of PM10 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. AOT2C AOT3C AT2C LNAOT2C LNAOT3C LNAT2C HA_PM10 DA_PM10 LNHAPM10 LNDAPM10


AOT2C 1.000 . 28 .847 .000 28 .408 .031 28 .944 .000 27 .596 .001 28 .394 .038 28 .653 .029 11 .631 .038 11 .615 .044 11 .614 .045 11

AOT3C .847 .000 28 1.000 . 28 .308 .110 28 .738 .000 27 .849 .000 28 .289 .135 28 .389 .236 11 .471 .143 11 .346 .297 11 .464 .151 11

AT2C .408 .031 28 .308 .110 28 1.000 . 28 .319 .104 27 .196 .318 28 1.000 .000 28 .468 .147 11 .493 .123 11 .445 .170 11 .500 .117 11

LNAOT2C .944 .000 27 .738 .000 27 .319 .104 27 1.000 . 27 .539 .004 27 .307 .119 27 .549 .080 11 .562 .072 11 .518 .103 11 .549 .080 11

LNAOT3C .596 .001 28 .849 .000 28 .196 .318 28 .539 .004 27 1.000 . 28 .182 .353 28 .228 .500 11 .345 .298 11 .194 .568 11 .339 .308 11

X

LNAT2C .394 .038 28 .289 .135 28 1.000 .000 28 .307 .119 27 .182 .353 28 1.000 . 28 .468 .147 11 .488 .128 11 .445 .170 11 .495 .122 11

HA_PM10 .653 .029 11 .389 .236 11 .468 .147 11 .549 .080 11 .228 .500 11 .468 .147 11 1.000 . 11 .890 .000 11 .994 .000 11 .891 .000 11

DA_PM10 LNHAPM10 LNDAPM10 .631 .615 .614 .038 .044 .045 11 11 11 .471 .346 .464 .143 .297 .151 11 11 11 .493 .445 .500 .123 .170 .117 11 11 11 .562 .518 .549 .072 .103 .080 11 11 11 .345 .194 .339 .298 .568 .308 11 11 11 .488 .445 .495 .128 .170 .122 11 11 11 .890 .994 .891 .000 .000 .000 11 11 11 1.000 .883 .999 . .000 .000 11 11 11 .883 1.000 .886 .000 . .000 11 11 11 .999 .886 1.000 .000 .000 . 11 11 11


Table A3. 9: Correlations of NO with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. AOT2C AOT3C AT2C LNAOT2C LNAOT3C LNAT2C HA_NO DA_NO LNHANO

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

AOT2C 1.000 . 28 .847 .000 28 .408 .031 28 .944 .000 27 .596 .001 28 .394 .038 28 .436 .026 26 .382 .054 26 .446 .022 26

AOT3C .847 .000 28 1.000 . 28 .308 .110 28 .738 .000 27 .849 .000 28 .289 .135 28 .527 .006 26 .395 .046 26 .573 .002 26

AT2C .408 .031 28 .308 .110 28 1.000 . 28 .319 .104 27 .196 .318 28 1.000 .000 28 .304 .130 26 .383 .054 26 .293 .147 26

LNAOT2C .944 .000 27 .738 .000 27 .319 .104 27 1.000 . 27 .539 .004 27 .307 .119 27 .314 .127 25 .358 .079 25 .358 .079 25

XI

LNAOT3C .596 .001 28 .849 .000 28 .196 .318 28 .539 .004 27 1.000 . 28 .182 .353 28 .447 .022 26 .425 .031 26 .528 .006 26

LNAT2C .394 .038 28 .289 .135 28 1.000 .000 28 .307 .119 27 .182 .353 28 1.000 . 28 .295 .144 26 .379 .056 26 .281 .165 26

HA_NO .436 .026 26 .527 .006 26 .304 .130 26 .314 .127 25 .447 .022 26 .295 .144 26 1.000 . 26 .730 .000 26 .955 .000 26

DA_NO .382 .054 26 .395 .046 26 .383 .054 26 .358 .079 25 .425 .031 26 .379 .056 26 .730 .000 26 1.000 . 26 .769 .000 26

LNHANO .446 .022 26 .573 .002 26 .293 .147 26 .358 .079 25 .528 .006 26 .281 .165 26 .955 .000 26 .769 .000 26 1.000 . 26


Table A3. 10: Correlations of SO2 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. AOT2C AOT3C AT2C LNAOT2C LNAOT3C LNAT2C HA_SO2 DA_SO2 LNHA_SO2 LNDA_SO2


AOT2C 1.000 . 28 .847 .000 28 .408 .031 28 .944 .000 27 .596 .001 28 .394 .038 28 .354 .115 21 .384 .086 21 .435 .049 21 .447 .042 21

AOT3C .847 .000 28 1.000 . 28 .308 .110 28 .738 .000 27 .849 .000 28 .289 .135 28 .339 .132 21 .402 .070 21 .417 .060 21 .437 .048 21

AT2C LNAOT2C LNAOT3C .408 .944 .596 .031 .000 .001 28 27 28 .308 .738 .849 .110 .000 .000 28 27 28 1.000 .319 .196 . .104 .318 28 27 28 .319 1.000 .539 .104 . .004 27 27 27 .196 .539 1.000 .318 .004 . 28 27 28 1.000 .307 .182 .000 .119 .353 28 27 28 .267 .326 .271 .241 .160 .235 21 20 21 .324 .349 .339 .152 .132 .133 21 20 21 .329 .355 .309 .145 .125 .172 21 20 21 .365 .361 .345 .104 .117 .126 21 20 21

XII

LNAT2C .394 .038 28 .289 .135 28 1.000 .000 28 .307 .119 27 .182 .353 28 1.000 . 28 .272 .233 21 .325 .150 21 .330 .144 21 .364 .105 21

HA_SO2 .354 .115 21 .339 .132 21 .267 .241 21 .326 .160 20 .271 .235 21 .272 .233 21 1.000 . 21 .889 .000 21 .915 .000 21 .787 .000 21

DA_SO2 LNHA_SO2 LNDA_SO2 .384 .435 .447 .086 .049 .042 21 21 21 .402 .417 .437 .070 .060 .048 21 21 21 .324 .329 .365 .152 .145 .104 21 21 21 .349 .355 .361 .132 .125 .117 20 20 20 .339 .309 .345 .133 .172 .126 21 21 21 .325 .330 .364 .150 .144 .105 21 21 21 .889 .915 .787 .000 .000 .000 21 21 21 1.000 .933 .959 . .000 .000 21 21 21 .933 1.000 .931 .000 . .000 21 21 21 .959 .931 1.000 .000 .000 . 21 21 21


Table A3. 11: Correlations of NO2 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. AOT2C AOT3C AT2C LNAOT2C LNAOT3C LNAT2C HA_NO2 DA_NO2 LNHA_NO2 LNDA_NO2


AOT2C 1.000 . 28 .847 .000 28 .408 .031 28 .944 .000 27 .596 .001 28 .394 .038 28 .587 .002 25 .606 .001 25 .587 .002 25 .583 .002 25

AOT3C .847 .000 28 1.000 . 28 .308 .110 28 .738 .000 27 .849 .000 28 .289 .135 28 .580 .002 25 .628 .001 25 .609 .001 25 .636 .001 25

AT2C LNAOT2C LNAOT3C .408 .944 .596 .031 .000 .001 28 27 28 .308 .738 .849 .110 .000 .000 28 27 28 1.000 .319 .196 . .104 .318 28 27 28 .319 1.000 .539 .104 . .004 27 27 27 .196 .539 1.000 .318 .004 . 28 27 28 1.000 .307 .182 .000 .119 .353 28 27 28 .266 .486 .471 .198 .016 .017 25 24 25 .425 .531 .524 .034 .008 .007 25 24 25 .282 .490 .517 .171 .015 .008 25 24 25 .431 .519 .548 .031 .009 .005 25 24 25

XIII

LNAT2C .394 .038 28 .289 .135 28 1.000 .000 28 .307 .119 27 .182 .353 28 1.000 . 28 .260 .210 25 .419 .037 25 .274 .185 25 .425 .034 25

HA_NO2 .587 .002 25 .580 .002 25 .266 .198 25 .486 .016 24 .471 .017 25 .260 .210 25 1.000 . 25 .884 .000 25 .986 .000 25 .846 .000 25

DA_NO2 LNHA_NO2 LNDA_NO2 .606 .587 .583 .001 .002 .002 25 25 25 .628 .609 .636 .001 .001 .001 25 25 25 .425 .282 .431 .034 .171 .031 25 25 25 .531 .490 .519 .008 .015 .009 24 24 24 .524 .517 .548 .007 .008 .005 25 25 25 .419 .274 .425 .037 .185 .034 25 25 25 .884 .986 .846 .000 .000 .000 25 25 25 1.000 .893 .992 . .000 .000 25 25 25 .893 1.000 .866 .000 . .000 25 25 25 .992 .866 1.000 .000 .000 . 25 25 25


Table A3. 12: Correlations of O3 with AOT2, AOT3 and AT2 when select the grid as survey location situated in center of the relevant grid cell. AOT2C AOT3C AT2C LNAOT2C LNAOT3C LNAT2C HA_O3 DA_O3 LNHA_O3 LNDA_O3


AOT2C 1.000 . 28 .847 .000 28 .408 .031 28 .944 .000 27 .596 .001 28 .394 .038 28 -.415 .055 22 -.209 .351 22 -.440 .041 22 -.248 .265 22

AOT3C .847 .000 28 1.000 . 28 .308 .110 28 .738 .000 27 .849 .000 28 .289 .135 28 -.615 .002 22 -.244 .273 22 -.643 .001 22 -.311 .159 22

AT2C LNAOT2C LNAOT3C .408 .944 .596 .031 .000 .001 28 27 28 .308 .738 .849 .110 .000 .000 28 27 28 1.000 .319 .196 . .104 .318 28 27 28 .319 1.000 .539 .104 . .004 27 27 27 .196 .539 1.000 .318 .004 . 28 27 28 1.000 .307 .182 .000 .119 .353 28 27 28 .083 -.360 -.561 .715 .100 .007 22 22 22 .285 -.151 -.167 .199 .502 .457 22 22 22 .114 -.385 -.556 .613 .077 .007 22 22 22 .333 -.198 -.226 .130 .377 .313 22 22 22

XIV

LNAT2C .394 .038 28 .289 .135 28 1.000 .000 28 .307 .119 27 .182 .353 28 1.000 . 28 .099 .662 22 .292 .188 22 .131 .561 22 .340 .121 22

HA_O3 -.415 .055 22 -.615 .002 22 .083 .715 22 -.360 .100 22 -.561 .007 22 .099 .662 22 1.000 . 22 .724 .000 22 .978 .000 22 .728 .000 22

DA_O3 -.209 .351 22 -.244 .273 22 .285 .199 22 -.151 .502 22 -.167 .457 22 .292 .188 22 .724 .000 22 1.000 . 22 .721 .000 22 .966 .000 22

LNHA_O3 -.440 .041 22 -.643 .001 22 .114 .613 22 -.385 .077 22 -.556 .007 22 .131 .561 22 .978 .000 22 .721 .000 22 1.000 . 22 .758 .000 22

LNDA_O3 -.248 .265 22 -.311 .159 22 .333 .130 22 -.198 .377 22 -.226 .313 22 .340 .121 22 .728 .000 22 .966 .000 22 .758 .000 22 1.000 . 22


Appendix 4: Tables and Figures related to Regression Analysis and Results and Discussion

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Table A4. 1: AOT/AT values for arbitrary and ideal grid cells and distance to arbitrary grid cell boundary GS_ID

Case_no

107 131 133 227 230 232 235 236 237 238 301 318 404 411 415 416 418

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

AOT2

0.549 0.000 0.683 0.433 1.421 0.959 1.074 0.768 0.923 0.680 0.716 0.842 1.677 1.500 0.933 1.153 0.835

AOT3

0.700 0.000 0.648 0.355 1.276 0.911 0.902 0.663 0.897 0.767 0.603 1.012 1.369 1.276 0.891 1.078 0.825

AT2

AOT2C

27.697 25.116 23.499 27.081 24.844 25.624 25.497 28.384 23.824 28.795 24.711 25.101 28.641 24.542 25.100 29.016 27.937 I

0.240 0.534 1.043 0.638 1.239 0.730 1.108 0.835 1.099 0.539 0.634 0.981 1.642 2.021 0.925 1.271 1.174

AOT3C

0.300 0.043 0.967 0.742 0.916 0.835 0.893 0.865 0.912 0.814 1.114 0.986 1.309 1.675 0.891 1.204 0.855

AT2C

27.385 25.624 23.833 26.557 24.788 25.624 25.043 28.419 24.935 27.414 23.775 27.412 28.336 23.928 25.368 28.740 26.870

Distance m 111.60 34.60 2.75 56.75 134.23 157.33 36.66 152.20 24.00 131.22 12.39 13.80 10.05 125.54 210.95 102.09 33.78


GS_ID

Case_no

433 437 441 620 633 636 637 638 639 640 641

18 19 20 21 22 23 24 25 26 27 28

AOT2

AOT3

1.078 0.872 0.863 0.251 1.042 1.386 1.267 1.403 1.122 1.267 0.486

AT2

1.299 1.204 0.994 0.651 0.811 1.014 1.070 1.310 0.897 1.070 1.027

AOT2C

26.653 23.784 29.122 24.413 24.802 27.807 28.552 30.072 28.928 28.552 25.262

AOT3C

1.365 1.119 0.756 0.000 0.244 1.402 1.865 2.159 1.064 1.865 0.266

Distance m

AT2C

1.145 1.228 0.920 0.405 0.770 1.076 1.792 1.569 0.893 1.792 0.892

26.335 23.530 27.669 24.678 25.058 27.748 29.271 29.784 28.987 29.271 24.302

99.65 23.60 17.99 106.21 38.89 96.31 63.46 53.04 267.41 63.46 26.72

Figure A4. 2: Regression results of CO with AT2 considering ideal grid situation 300

300 236

236

637 639

638

636

200

237 418

641

411

441 238

HA_CO

HA_CO

100

0 24

25

26

27

28

29

30

23

24

25

26

27

28

29

30

NAT2C

Fig. A4.2a: Spread of HA_CO with AT2

Fig. A4.2b: HA_CO versus AT2 ; by omitting loc. 640 Dependent Variable: HA_CO

2.0 2.0

236 1.5

411

237

1.5

637


1.0 641 .5


230

0

AT2C

639 636

0.0

638 418 -.5 633 -1.0 -1.5

441 238

633

230

23

418 641

640

633 100

638

636

200

237 411

637 639

441 238

230

0.0

.1

236 411

637

1.0 641 .5

639 636

0.0

638 418

-.5 633

441

-1.0 230

238

-1.5 -1.5

.2

237

-1.0

-.5

0.0

.5

1.0

1.5



Fig. A4.2c: Leverage values by omitting loc. 640

Fig. A4.2d: Spread of residuals by omitting loc. 640

II


Figure A4. 3: Regression results of BP with AT2 considering ideal grid situation 90

4.6 433

433

637

80

70

4.2

318 636

318 60

637

4.4

437

4.0

636 437

50

640

3.8

640

133

30

LNDA_ZR

DA_ZR

133 40

230 131

0.0

.5

1.0

1.5

2.0

230

3.6

3.4

131

0.0

AOT3C

.5

1.0

1.5

2.0

AOT3C

Fig. A4.3a: AOT3 versus DA_BP

Fig. A4.3b: AOT3 versus ln(DA_BP)

4.6

.04 433

4.4

637 131

4.2

318 636 437

4.0

.02 437 636 318

133

ODA_ZR

230

3.6 131

3.4 -4

-3

-2

-1

230 133 640

640

3.8

LNDA_ZR

.03

0

1

637 433 .01 0

LNAOT3C

10

20

30

OAOT3C

Fig. A4.3c: ln(AOT3) versus ln(DA_BP)

Fig. A4.3d: 1/AOT3 versus 1/DA_BP

.04

2.0 230 1.5 131

640 1.0

.03

133

230

.5 133

ODA_ZR


640 .02

437 636 318 637

433

.01 -1

0

1

2

3

4

437

0.0

636 -.5

637

-1.0

433

-1.5 -.2

LNOAOT3C

131

318

0.0

.2

.4

.6


Fig. A4.3e: ln(1/AOT3) versus 1/DA_BP

Fig. A4.3f: Loc. 131 has large leverage value

III

.8

1.0


Figure A4. 4: Regression results of PM10 with AOT3 considering ideal grid situation 100

4.5

90

318 318

80

4.4

418

4.2

LNHAPM10

441

133

50

40 .2

.4

.6

.8

1.0

1.2

1.4

1.6

230

131 641

4.1

641

60

433

4.3

230

131

418 437 639

433

437 639

70

HA_PM10

404

4.6

404

4.0

441 133

3.9 3.8

1.8

.2

AOT2C

.4

.6

.8

1.0

1.2

1.4

1.6

1.8

AOT2C

Fig. A4.4a: Loc 133 not properly predict

Fig. A4.4b: ln(Ha_pm10) not improve the situation

Figure A4. 5: Regression results of NO with AOT3 considering ideal grid situation Dependent Variable: LNHANO


2.5

Dependent Variable: LNHANO

641

2.0

1.00

8 13 2617 18

236 404

418 639

433

1.0

637

633

411

238 237 133 441

0.0

638

131 107

-.5

318

-1.0

301 437

232230 227

-1.5

640

235

-2.0 -3

-2

-1

0

1

2

.50 2

10 20 3 25

914

11 1 19 12

.25

7

0.00

274

56

0.00


.25

.50

.75

1.00

Observed Cum Prob

Fig. A4.5a: Residuals placed between -/+2

Fig. A4.5b: Residuals are normally distributed

Dependent Variable: LNHANO

2 236

2.5 640

2.0

637 411 638

1.5

1

418 639 433

404

636

1.0

404

437 301

.5 0.0 235 227

-.5

230 232

-1.0

318

636

133 441237 238 633

639 418

433 236

641

620 107

-1.5 -2.0

131

-2.5 3.5

4.0

4.5

5.0

5.5

620

633 237 238 133 441 0



2123 24 22

.75

636

620 .5

Expected Cum Prob


1.5

28

638

131

301437 318

107

-1 232 230 227

-2

640

235

0.00

6.0

637 411

.05

.10

.15


LNHANO

Fig. A4.5c: some locations are not properly predict

Fig. A4.5d: Leverage versus residuals plot

IV

.20

.25


6.0 404 637

641 236

5.5

433

411

418 639 638 636 5.0

620

4.5

237 133 633 441 640 238 437 301

LNHANO

318

230 232

4.0 107

131

227 235

3.5 -4

-3

-2

-1

0

1

LNAOT3C

Fig. A4.5e: Location 131 is away from other points

Figure A4. 6: Regression results of SO2 with AOT2 considering ideal grid situation Dependent Variable: LNDA_SO2

6 2.0

301

5

1.5


4

3

2

1

Std. Dev = 5.19

0

N = 21.00

Mean = 8.8 2.0

4.0

6.0

8.0

10.0 12.0 14.0 16.0 18.0 20.0 22.0

1.0

107 641

.5

235 227

0.0 437 237 230

633

620

-1.0

411

404

131

-.5

638

232 -1.5

133

-2.0 -2

DA_SO2

-1

0

1

2

3


Fig. A4.6a: DA_SO2 is negatively skewed

Fig. A4.6b: Residuals fall between -/+2

Dependent Variable: LNDA_SO2

2.0 301 433

3 1.5

416 415 318 418 1.0

638 411

2

.5

404 1 133 0

230 237437

232 131

-1

235

418

416

0.0

318 415

227

301

641 107

633 620

-2 .5

1.0

1.5

2.0

2.5

3.0

107 641

235 227

433



433 416 415 318 418

-.5 437 237 230 -1.0

633

-1.5133 -2.0 .1


LNDA_SO2

Fig. A4.6c: Relationship predicts most of the cases

Fig. A4.6d: Location 638 as an outlier

V

638

620

232

0.0

3.5

411

404 131

.2

.3


Figure A4. 7: Regression results of NO2 with AOT3 considering ideal grid situation 80

3

433

433 404 70

2

638 411 637

418 404

418 60

639 641 441 636 238 236 318 301 633

DA_NO2

620

437

235 232 230 227

40 131

640


50

1 639 641 441 238

133

107 30 0.0

.5

1.0

1.5

411

301 232 235 227 -1230 437

131

637

107

640

133 -2

2.0

0.0

AOT3C

.1

.2

.3


Fig. A4.7a: DA_SO2 is negatively skewed 4.4

Fig. A4.7b: Residuals fall between -/+2 3

433 404

433

638 411 637

4.2

2

418 639 641 441 636 238 236 318 301 633

620

3.8

3.6 131

3.4 0.0

.5

-1

-2

133

0

133

107

1.0

1.5

2.0

404

639 641 441 238 236 636 633 318 301 232 235 230227 437

640

437

235 232 230 227

418

1


4.0

LNDA_NO2

638 620

236 636 0 633 318

620 638

411

637

131

107 640

-3 -.05

AOT3C

-.00

.05

.10

.15

.20

.25

.30


Fig. A4.7c: Relationship predicts most of the cases

Fig. A4.7d: Location 638 as an outlier

Figure A4. 8: Regression results of O3 with AOT3 considering ideal grid situation 12

3 227

227

10 131

441 107

1

232 235 230 133

6

433

638


8

633 238639 641318636 301 236

4

HA_O3

441

2

404

640

437

411

2 0.0

.5

1.0

1.5

2.0

433

640

133 230 235 232 301 636 318 641 639 404 238 633

0

107

131

411

-1 236 437

-2 -.1

AOT3C

638

0.0

.1

.2

.3


Fig. A4.8a: Location 227 and 441 are away from relationship

Fig. A4.8b: In Ln(Ha_O3) relationship, Loc.227 has highest residuals

VI


10 9

12 131

227

441 10

107

8

441

7 6

433

638

633 238641 639318636 301

5

6

638 433 133 230 235 232 301 636 318 641 639 238 633

404

640

437

3

4

411

HA_O3

236

4

HA_O3

107

8 232 235 230 133

131

2 0.0

.5

1.0

1.5

2.0

640 404 236 411 437

2 0

NAOT3C

10

20

30

OAOT3C

Fig. A4.8c: Omitting location 227

Fig. A4.8d: Ha_O3 versus 1/AOT3 relationship

Table A4. 2: Comparison of Air Temperature in Study Area

Place De Bilt Rotterdam Eindhoven Maastricht

mean -2.4 -1.7 0.2 1.7

23-Dec00 max 1.8 2.0 6.0 8.0

min -6.6 -5.4 -5.5 -4.6

mean 19.4 19.6 21.1 20.2

VII

3rd-Jul01 max 26.0 26.3 27.4 26.7

min 12.8 13.0 12.8 13.6

mean 21.8 21.3 20.9 21.9

Diff max 24.2 24.3 21.4 24.7

min 19.4 18.4 18.3 19.0


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II