ANALYSIS OF AIR QUALITY IN INDIAN CITIES USING REMOTE SENSING AND ECONOMIC GROWTH PARAMETERS

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1 ANALYSIS OF AIR QUALITY IN INDIAN CITIES USING REMOTE SENSING AND ECONOMIC GROWTH PARAMETERS Prakhar Misra and Wataru Takeuchi Institute of Industrial Science, The University of Tokyo, Meguro 4-6-1, Tokyo , Japan; Corresponding author: KEY WORDS: seasonal trend, urban growth, population growth, MODIS, OMI ABSTRACT: 13 of the worst 20 cities in terms of air quality lie in India, as highlighted by a recent WHO report. The Indian capital, New Delhi leads the list with the worst air quality. In recent times, the share of eco-health problems prevalent in various Indian cities has risen significantly resulting in a spike of respiratory and cardiac diseases. Ground measurements of SO 2, NO 2, PM 2.5 and PM 10 levels are significantly above National Ambient Air Quality Standard (NAAQS) for most cities and for New Delhi, it has been found that at current rate and policies PM 2.5 will not reach the recommended NAAQS values even by Previous researches have focused on monitoring and characterizing air quality at regional levels using remote sensing data in conjunction with ground monitoring stations for a few cities in Indian subcontinent. The poor state of air quality is well established and lasting corrective measures are required. Therefore, an important piece of strategy to tackle this looming health disaster would be identifying the contributions of major causes responsible. Our research aims to tackle the first piece of the problem by identifying the relationship of air quality, assessed by MODIS (from year 2001 to 2013) and OMI (from year 2005 to 2014) datasets, with economic growth parameters in the sixteen largest Indian cities by pollution. Economic growth parameters considered are urban area expansion and population growth. The results corroborate with findings of previous researches and establish that the contribution of anthropogenic emissions has been increasing although overall pollution levels seem to be decreasing. In addition, population density has been found to be highly correlated with aerosol loading and NO 2 levels. 1. INTRODUCTION 1.1 Background As a developing nation, most of India s energy needs are still met by conventional fossil fuel. It stands only after China in contributing to greenhouse gas emissions in Asia (Olivier et al., 2013). A steady economic growth has also ensured a good pace of development, infrastructure construction and expansion activities, migration to cities and consumption habits in India. But, this has come with its own cost as recently World Health Organization (WHO) findings on air pollution reported (WHO, 2014) that out of 20 cities worst affected by particulate matter (PM), 13 lie in India, most of them being highly populous. As per National Ambient Air Quality Monitoring Program (NAAQMP) in India, PM 10 has the highest rates of exceeding National Ambient Air Quality Standard (NAAQS) followed by NO 2 and SO 2 (CPCB, 2014). Central Pollution Control Board (CPCB) of India in its findings for the four big metropolises (New Delhi, Mumbai, Kolkata and Chennai) using data from ground air quality monitoring stations pointed (CPCB, 2014): Decreasing trend in SO 2 and NO 2 levels in residential areas Worsening of air quality in winter months, and improvement with onset of monsoon season Average annual SOx and NOx emission levels and periodic violations in industrial areas of India significantly lower than that of residential areas of India Of the four major Indian cities, consistently worst air pollution in Delhi, every year over 5 year period ( ) followed by Kolkata, Mumbai and Chennai Since sources for NOx and SOx are located near the Earth s surface except aircraft emissions, CPCB findings regard the decrease in SOx and NOx levels to stricter vehicular emission norms, reduction of sulphur in diesel and usage of LPG instead of coal as domestic fuel. It has been indicated that high urban PM levels are due to anthropogenic reasons like vehicle engine smoke and wear, engine generator-sets, small scale industries, biomass incineration, boilers and emission from power plants, resuspension of traffic dust, commercial and domestic use of fuels, etc (CPCB, 2014) apart from mineral dusts. On the other hand, SO 2 and NO 2 are supposed to be caused by near-surface anthropogenic sources like automobiles, residential cooking, brick kilns, coal-burning power plants, agricultural land-clearing, and biomass burning (Gaur et al., 2014). Long and short-term effects of adverse air quality on health are well documented (Brunekreef & Holgate, 2002) and

2 have been performed for several cities in developed countries (Katsouyanni et al., 2001). In recent times, researchers have started taking note of significant rise in share of air borne eco-health problems prevalent in New Delhi resulting in a spike of respiratory and cardio diseases(nidhi & Jayaraman, 2007). Based on the data of 2006, a similar study was also performed for Kanpur city where Liu et al. (2013) established a strong correlation between respiratory disease causing hospital visits and emission intensity in local area. Moreover at the current rate and policies, it has been found that PM 2.5 for New Delhi will not reach the recommended NAAQS values even by 2030 (Dholakia et al., 2013). Thus, the consequences of poor air quality will permeate well into the future. Such studies are based on data accumulated from ground monitoring stations operated by Central Pollution Control Board (CPCB) which tends to be spatially localized or too recent to study the true picture of long-term representative measurements of stations apart from New Delhi and Kanpur (Singh et al., 2004). Since this issue is also common across other developing countries, remote sensing fills in this gap by providing continuous spatial and temporal domain data. Changes in atmospheric optical depth (AOD), angstrom exponent (ANG), SO 2 and NO 2 columns derived through satellite measurements have been shown to be reflective of local trace gases and PM emissions (Martin, 2008). To characterize PM 2.5 and PM 10 concentrations, AOD and ANG derived from satellite measurements have been used in conjunction with AERONET (Holben et al., 2001) or ground monitoring stations to establish local aerosol climatologies at different locations globally (Dubovik et al., 2002). Using MODIS satellite data for the Indian subcontinent, seasonal variability trends for AOD have been studied from regional scale (Prasad & Singh, 2007) to dense urban agglomerates likes New Delhi and Kanpur (Dey et al., 2012). Lu et al., (2013) and Ghude et al. (2013) have also used OMI data successfully to monitor annual trends for SO 2 emission and identify dominant sources of NO 2 respectively in Indian regions. Previously Fujikawa & Takeuchi (2012) have used MOPITT data derived AOD and ANG values and gross domestic product (GDP) growth rate to assess contribution of vehicular emission in air pollution for 61 cities globally. For Indian cities, Holian (2013) has found overall relationship between socio-economic development indicators like income, literacy, population, etc. and air quality parameters using economic and CPCB data. Any such study that employs satellite data has not been undertaken for India. Such studies will help establish socio-economic reasons for poor air quality at a regional scale and not just on a localized scale limited to monitoring stations. This further can be used to link impact of such air quality on health and overall the relationship with socio-economic growth parameters. The authors believe this can play an instrumental role in policy making decisions by government bodies and tackle eco-health issues related to air pollution. 1.2 Objective of research As a first step, the present study aims to establish link between remotely sensed air quality parameters and economic growth parameters, urban area expansion and population change, for Indian cites. 2. METHODOLOGY Based on data available for the cities chosen first data processing is carried out followed by analysis. In analysis, two main procedures were carried out: 1) Urban area expansion and 2) Trends in air quality parameters. The methodology is presented in flowchart in Figure 1 and discussed in detail subsequently. DATA PROCESSING ANALYSIS RESULT Selection of Cities based on WHO reported values MODIS Data: Obtain AOD and ANG values OMI Data: Obtain SO2 and NO2 concentrations LANDSAT 7, 8 Data: Subset using district level shape file Plot time-series for each air quality parameter at seasonal and composite level Perform unsupervised and check urban area growth % Air quality monitoring by AOD, ANG, SO2 and SO2 Urban growth and air quality Census 2001, 2011 Urban population growth % Figure 1 Methodology undertaken for analysis

3 2.1 Site selection NAAQS for India states annual level range in industrial/residential areas for PM 10 as 60 μg/m 3, PM 2.5 as 40 μg/m 3, NO 2 as 40 μg/m 3 and SO 2 as 50 μg/m 3 (CPCB, 2014). An account of PM 2.5 and PM 10 level across Indian cities for the 13 worst affected cities as reported by WHO (WHO, 2014), paints a grim picture as shown in Table 1. Additionally, concentrations for other big metropolises (apart from New Delhi), Mumbai, Kolkata and Chennai is also presented so as to present a contrast between them and New Delhi. Table 1 Values of PM 10 and PM 2.5 concentrations as reported by WHO City Annual Mean Annual Mean PM 10 (μg/m 3 ) PM 2.5 (μg/m 3 ) New Delhi Patna Gwalior Raipur Ahmedabad Lucknow Firozabad Kanpur Amritsar Ludhiana Allahabad Agra Jodhpur Mumbai Kolkata Chennai The location of these cities is presented in Figure 2 and they are mostly located in the Northern portion of India. It can also be noticed that apart from New Delhi, other metropolises have low PM concentrations and are located not too far from water bodies like the Arabian Sea or the Bay of Bengal which can contribute to their overall low aerosol loading. Arabian Sea Bay of Bengal Figure 2 Location of cities under study 2.2 Data For this study, data from 4 different satellites sensors have been used: Landsat7, Landsat8, MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument). To study urban area expansion, images for the month of April were chosen due to relatively clear skies for imaging. They were retrieved for the year

4 2015 from Landsat8 and in the case of Landsat7 ( SLC-on configuration) for the years 2002 or 2003, as per their availability for the desired stations as mentioned in Table 1. This allows for monitoring of approximately last 12 years urban area growth, which also coincides with the start of India s high GDP growth. To study ANG and AOD levels, Level 2 data (Levy, 2015) obtained from MODIS sensor onboard NASA s EOS Terra satellite data from March, 2001 to September, 2009 was used. Further, AOT at 0.55 micron for both ocean (best) and land (corrected) with all quality data dataset was used for AOD and Angstrom exponent for 0.47 micron dataset was used for AOD ( MODIS HDF File Specification MOD04_L2: MODIS Level 2 Aerosol Product at 10 km spatial resolution, 2004). This data, available as a scaled integer value, needs to be converted to real physical optical depth using a scale factor and an offset (0.001 and 0.0 respectively, for both AOD and ANG). To measure SO 2 and NO 2 concentration levels, Level-3 OMI total column SO2 product (OMSO2e) and Level-3 OMI NO2 Cloud-Screened Total and Tropospheric Column NO2 -Ver003 (OMNO2d) data from March, 2005 to October, 2014 obtained from OMI spectrometer onboard NASA s EOS Aura satellite was used respectively. A more detailed summary of image datasets used is presented in Table 2. Table 2 Specification of datasets used Imaging sensor Purpose in current Spatial Time period research resolution Dataset information Landsat 7 Initial urbanized April, 2002 or April, m 30m Bands 5,4 and 3 used area (as per availability) Landsat 8 Current urbanized April m 30m Bands 6,5, and 4 used area MODIS ANG, AOD levels March, 2001 September, km 10km Level 2 data AOT at 0.55 micron for both ocean (best) and land (corrected) with all quality data and Angstrom exponent for 0.47 micron ; Angstrom exponent for 0.47 micron OMI SO 2,NO 2 levels March, 2005 September, km 24km OMSO2e; OMNO2d In addition, population census data of Indian cities of years 2001 (Census of India, n.d.-a) and 2011 (Census of India, n.d.-b) was used. 2.3 Urban area expansion Landsat 7 and 8 images were subset using the open source shape files of Indian districts following Hijmans (2009) to extract the district boundaries of the 16 cities under study. Further they were subjected to ISODATA unsupervised clustering algorithm in ENVI version 4.8 to mark out the portion of urban/constructed area in the district. Using calculated urban area in Landsat 7 and Landast 8, urban area expansion was found out for the districts to establish a link with air quality parameters. 2.4 Trends in air quality parameters MODIS and OMI values are calculated for pixels lying above central regions of each city to collect dominant concentration levels in the cities. It is performed for mean monthly AOD, ANG, SO 2 and NO 2 images to extract a time-series at month level for pollutant concentration of each city. This time-series data was subsequently analyzed in: a) composite manner (using the most recent 5 years data available), and b) time-series of seasonal trends (using the complete dataset available). The composite manner helps in finding out the cities with high pollutant levels while the time series points out the differing trends that exist for each season in each city. A seasonal trend analysis is further useful because MODIS sensor underestimates AOD in winter and overestimates it in summer season over the Indo Gangetic region (Prasad & Singh, 2007). Also changes in human activities with seasons, e.g. harvesting, biomass burning, etc., capture their influence on anthropogenic emissions (Diem & Comrie, 2001). Therefore on the basis of major seasons in North India, analysis is performed for months as mentioned in Table 3. For simplicity, months of autumn and spring seasons have been split and combined with their corresponding preceding and succeeding months.

5 3. RESULTS AND DISCUSSION 3.1 Seasonal trends in AOD and ANG levels Summer Table 3 Months of prominent seasons in India Season Months Summer March June Rain July October Winter November February Summer seasonal pattern starts with low AOD values in March that keeps on increasing till June. For most of the cities, AOD for corresponding has been found decreasing over the years, except Ahmedabad, Allahabad, Chennai, Lucknow, Patna and Raipur. AOD values of Firozabad and Jodhpur were found unfit for analysis due to data gaps that gave rise to outliers. For all other cities, ANG has also been increasing although at a lower rate compared to other seasons. Due to the inverse relationship between ANG value and size of aerosol particle, this implies a high contribution of dust-storm particles and also an increasing contribution of aerosol loading due to anthropogenic activities in summer Rain First month of rainy season, July, inherits high AOD values from summer, which then decrease sharply due to usually continuous rains for next two months. Thereafter with retreating rainfall, AOD levels start rising again by October. Over the years, a sharp decreasing trend of AOD values is noticed for almost all cities except Ahmedabad, Lucknow and Patna. Sharpest fall in rain time AOD is observed in twin cities of Agra and Firozabad primarily due to high AOD reported in the initial years and the subsequent decrease. Interestingly, Mumbai, Kolkata and Chennai, cities traditionally with low PM levels show a trend of increasing AOD over the years. Also, considering the recent 5 year trends, New Delhi, Ludhiana and Amritsar show an increasing trend for AOD in the month of July. ANG values during rain are not only higher than that shown in summer but also show an increasing trend for all the cities. This shows that despite rains washing down the dust particles the emission pollutants remain in the environment. Such particles likely emanate from human borne emissions Winter The AOD values may or may not increase from October to November but after November a clearly decreasing trend emerges till February. The variability in AOD levels between October and November can be explained by fluctuation in celebration-dates of the Indian festival of Diwali which involves burning firecrackers over a period of two weeks and causes a manifold increase in AOD (Devara et al., 2015). The ANG levels are highest in this season implying maximum contribution of anthropogenic emissions e.g. burning of biomass. The ANG trend also has been found to be increasing at rates higher than other seasons. For all the seasons lowest ANG values are found for Jodhpur because it lies almost in the Thar Desert. Thus, even though it may sometimes have high AOD levels, they are explained by desert sand particles. In Table 4 using the most recent 5 year data available with authors, highest AOD levels are found in New Delhi (0.76 μm) followed by Patna(0.70 μm), Kanpur(0.69 μm) and Lucknow(.69 μm). These cities also have the worst ANG levels after Amritsar(0.88 μm) and Ludhiana(0.89 μm). This leads to an important fact that although for latter two cities, the largest contribution in degrading AOD is from various forms of anthropogenic emissions, for former four cities mineral dusts play an important role. 3.2 Air quality monitoring by SO 2 and NO 2 Since SO 2 and NO 2 occur largely due to anthropogenic activities mentioned before, its levels are influenced highly by presence of thermal power stations, factories and other heavy industries near cities. For SO 2, the highest values are observed in winter followed by summer and then rain. For NO 2, the highest values were found in summer followed by winter in all cities except New Delhi. Venkataraman et al.(2006) attribute this to crop waste burning activities taking place in nearby regions of New Delhi during October-November. The lowest values for SO 2 and NO 2 were seen in rain due to wet scavenging and lower near surface emissions. Additionally, for both the trace gases, decreasing trend was most prominent for winter, much stronger than that of summer.

6 Referring to Table 4 for the most recent 5 year data available with authors, highest overall values of SO 2 used were found for the cities Amritsar ( mg/m 2 ) and Ludhiana( mg/m 2 ) followed by New Delhi( mg/m 2 ). All the other cities hold similar SO 2 levels (at mg/m 2 ). For NO 2 the reverse holds true, that is New Delhi ( mg/m 2 ) is the most polluted city followed by Amritsar ( mg/m 2 ) and Ludhiana ( mg/m 2 ). Further, cities like Ahmedabad, Agra, Mumbai and Raipur that have petrochemical industries, tanneries, coal-mining industries or other high substance-burning factories show much higher NO 2 levels ( mg/m 2 ). 3.3 Urban growth and air quality Table 4 presents an overview regarding how air quality parameters compare with each other and with urban growth for the most recent 5 years data. Amritsar and Ludhiana, lying within 150 kilometers of each other and possessing similar industries show a remarkable similarity in their air quality measurements. Whether emissions from one city influences the other, remains to be researched. Gwalior, which figures up prominently in the WHO report for high particulate matter does not show high levels of AOD. Firozabad, which is undergoing rapid urbanization in terms of constructed area and population has started showing signs (although not statistically significant) of deteriorating air quality when compared to its older and more industrial neighboring city of Agra. Kanpur, which has undergone much more constructed urban area growth compared to neighboring city of Lucknow (80 kilometers north-west), shows similar ANG and AOD value to it. However Kanpur s NO 2 levels are higher than Lucknow pointing to greater anthropogenic emissions. New Delhi emerges the worst place in terms of overall air quality correlating with high population, moderate population growth and urban area expansion. Mumbai, with its high population manages to stay relatively cleaner AOD wise due to influence of the sea. However, its ANG and NO 2 levels are higher than quite a few other cities, pointing towards anthropogenic emissions. Jodhpur, which lies very close to a desert, reports a seemingly incorrect urban area due to a possible misclassification of sand and built-up structures. It is also an air quality outlier, as apart from SO 2, other measures are relatively low. Raipur shows high population and urban area growth along with high trace gases levels yet low AOD levels. Its high trace gas levels can be explained due to presence of numerous coal, metal and chemical industries in its vicinity. Patna s AOD levels corroborate well with WHO PM 2.5 values. City Table 4 Mean performance of air quality parameters in the recent 5 year s data used Urban Urban area WHO area - growth growth PM (2002- (2001- ANG AOD (μg/m 3 ) (km 2 ) 2015) (2011) 2011) (μm) (μm) WHO PM 10 (μg/m 3 ) SO 2 (mg/m 2 ) NO 2 (mg/m 2 ) Agra % 1,574, % Ahmedabad % 5,570, % Allahabad % 1,117, % Amritsar % 1,132, % Chennai % 4,681, % Firozabad % 603, % Gwalior % 1,053, % Jodhpur % 1,033, % Kanpur % 2,767, % Kolkata % 4,486, % Lucknow % 2,815, % Ludhiana % 1,613, % Mumbai % 12,478, % New Delhi % 11,007, % Patna % 1,683, % Raipur % 1,010, % To explore the link that exists between urban growth parameters and air quality parameters, a correlation matrix was prepared. For this, Mumbai, Chennai, Kolkata and Jodhpur were not considered as they possess properties of outliers. The results are shown in Table 5. It emerges that cities with high population density have high AOD and NO 2, which implies that higher the number of people living in a city, greater are their chances of being subjected to poor air quality. Also, cities that have large area, can be supposed to be better spaced out thus leading to lower ANG accumulations due to anthropogenic activities. Surprisingly, population growth correlates negatively with ANG and

7 AOD. Table 5 Correlation matrix for urban growth and air quality parameters Growth Urban area in 2015 (km 2 ) in urban area ( ) (2011) growth ( ) density ANG (μm) AOD (μm) Urban area in 2015 (km 2 ) 1.00 Growth in urban area ( ) (2011) growth ( ) density ANG (μm) SO 2 (mg/m 2 ) AOD (μm) SO 2 (mg/m 2 ) NO 2 (mg/m 2 ) NO 2 (mg/m 2 ) In this study, influence of meteorological factors (temperature and humidity) has not been considered which can shed more light on trace gases and AOD levels. Combining AERONET ground network observations with MISR and MODIS data for stations under study will further establish the reliability of satellite data to relate observed air quality through remote sensed images to activities on ground. 4. CONCLUSION The results show that summer and rain time AOD is decreasing while the condition is getting far worse for winters. SO 2 and NO 2 have been found to be decreasing at a stronger pace for winter than summer. These findings are in fair agreement with CPCB report as mentioned before. Therefore, although aerosol loading might be decreasing or increasing for different cities, contribution of anthropogenic aerosol particles has been increasing more on account of increasing population than urban area expansion. Further, regions with high population density see higher NO 2 levels. 5. REFERENCES Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. Lancet. Census of India. (n.d.-a). Census-2001 Data Summary. Retrieved August 30, 2015, from Census of India. (n.d.-b). Major Agglomerations - Census Retrieved August 30, 2015, from bove.pdf CPCB. (2014). NATIONAL AMBIENT AIR QUALITY STATUS & TRENDS Retrieved from Devara, P. C. S., Vijayakumar, K., Safai, P. D., Raju, M. P., & Rao, P. S. P. (2015). Celebration-induced air quality over a tropical urban station, Pune, India. Atmospheric Pollution Research, 6(3), Dey, S., Di Girolamo, L., van Donkelaar, A., Tripathi, S. N., Gupta, T., & Mohan, M. (2012). Variability of outdoor fine particulate (PM2.5) concentration in the Indian Subcontinent: A remote sensing approach. Remote Sensing of Environment, 127, Dholakia, H. H., Purohit, P., Rao, S., & Garg, A. (2013). Impact of current policies on future air quality and health outcomes in Delhi, India. Atmospheric Environment, 75, Diem, J. E., & Comrie, a C. (2001). Allocating anthropogenic pollutant emissions over space: application to ozone pollution management. Journal of Environmental Management, 63(4),

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