[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 13, No. 1, pp.11-19
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Mar 2019
Received 28 Sep 2018 Revised 03 Dec 2018 Accepted 05 Jan 2019

# Assessment of Spatial Ambient Concentration of NH3 and its Health Impact for Mumbai City

Awkash Kumar1), 2), * ; Rashmi S. Patil1) ; Anil Kumar Dikshit1) ; Rakesh Kumar3)
1)Centre for Environmental Science and Engineering, Indian Institute of Technology, Bombay, Mumbai - 400 076
2)Sustianable Approach for Green Environment Powai, Mumbai - 400 076
3)National Environmental Engineering Research Institute, Nagpur, 440020

Correspondence to: * Tel: +91-7208246617, E-mail: awkash.narayan@gmail.com

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

## Abstract

Generally, ambient Ammonia (NH3) concentration level is always under prescribed limit of government regulatory authorities but the concentration level tends to be higher in surrounding regions of a chemical fertilizer industry. There are many chemical fertilizer industries across the world and 9 public and 18 private fertilizers industries in India. Mostly, air quality monitoring is carried out for many gaseous pollutants and dust such as SO2, NO2, SPM, PM10 and PM2.5 but NH3 is monitored at only few selected locations. Maravali region of Mumbai city has a public sector fertilizer company and this region has maximum concentration of NH3 in Mumbai city. In this study, the spatial average concentration of NH3 was estimated for Mumbai city including and excluding the air quality monitoring site of Maravali, where fertilizer industry is present. The spatial average concentration of Mumbai city is 85 μg/m3 and 56 μg/m3 including and excluding Maravali respectively. The maximum concentration of NH3 is at Maravali and annual average concentration here is 342 μg/m3. This is 6.1 times more of spatial average concentration of Mumbai excluding Maravali. The same was visualized and represented in spatial concentration mapping using Inverse Distance Weighting (IDW) interpolation technique of ArcGIS tool. Also, health impact assessment was carried out for Mumbai city due to the concentration level of NH3. Local Concentration–Response (C-R) coefficient for Mumbai was used to assess health impact for ammonia. 3.4 and 6.8 Million people were exposed by phlegm and other chest illness respectively in Mumbai city. The economic cost of the health was also estimated for the phlegm due to ammonia which was 57 Million USD (3.9 Billion INR) for the year 2012 for Mumbai city.

## Keywords:

NH3 concentration, Spatial distribution, IDW, Exposed population, Health cost

## 1. INTRODUCTION

Metropolitan cities have experienced an immense amount of growth in last few decades which is responsible for the high rate of migration from rural to urban localities throughout the world (Aburas et al., 2016; Akintunde et al., 2016). Rapid urbanization has not only aided growth of industries but also has significantly affected the environmental quality such as air, water and soil quality parameters (Patankar and Trivedi, 2011). The poor air quality levels have several health and economic impact in cities (Mokhtar et al., 2014; Guttikunda and Goel, 2013; Patankar and Trivedi, 2011; Gurjar et al., 2010; Namdeo and Stringer, 2008; Pandey et al., 2005; Srivastava and Kumar, 2002). It has been observed that the air pollution is a major concern for the metropolitan city like Mumbai (Kumar et al., 2016a, b). The severe health impact and their economic cost has been carried out for the three air pollutants SO2, NOx and PM for Mumbai city (Kumar et al., 2016a). It is difficult to calculate or measure monitoring concentrations at several locations since it takes a huge toll on the resources.

Every country has its own norms for monitoring various air pollutants. In India, the guidelines provided by ‘Central Pollution Control Board’ to communicate air quality status are used. According to Air (Prevention and control of pollution) Act, 1981, CPCB defined National Ambient Air Quality Standards for 12 air pollutants Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Particulate Matter (PM10 and PM2.5), Ozone (O3), Lead (Pb), Carbon Monoxide (CO), Ammonia (NH3), Benzene (C6H6), Benzo(a) Pyrene (BaP), Arsenic (As), Nickel (Ni). NH3 is one of the criteria pollutants for which the permitted maximum level of ambient concentration has been given. Generally, the concentration of ammonia in the air is not above limits but it has been observed that in regions comprising of Fertilizer industry, have a substantially high NH3 content. In India, fertilizer industries have similar problem and the air quality is getting affected by NH3 content. For example, Maravali region in the suburbs of Mumbai has RCF (Rashtriya Chemicals and Fertilizer) Limited in it due to which the observed NH3 content in the air is quite high as compared to the surrounding regions. NH3 emissions was 54 Tg(N)/year at global level where 40% was from animal waste management system in 1990 (Bouwman et al., 1997). India, China, Pakistan, Indonesia and Bangladesh are major sources for NH3 in Asian countries where India comes on second top (Yamaji et al., 2004). NH3 concentration has been measured at various locations in Asian countries like China, Australia, Japan, Malaysia and Korea using sensitive diffusive (passive) sampler and results show that maximum concentration at China and Thailand (Ferm and Rodhe, 1997).

Atmospheric ammonia is gaseous species with a lifetime less than one day to five days, therefore it is available in different forms. If atmospheric NH3 reacts with sulfuric acid (H2SO4), hydrochloric acid (HCl) or nitric acid (HNO3) they form ammonium sulfate, ammonium chloride or ammonium nitrate respectively. Oxidation of ammonia molecule forms amide radical or ammonium hydroxide (Veremchuk et al., 2016; Aneja, 2001).

There are several health impacts of ammonia on the human body. Ammonia exposure at moderate concentrations (50 to 150 μg/m3), may lead to the irritation of throat, eye and skin also it causes cough and mucus buildup. At high concentration (>150 μg/m3), it causes pulmonary edema and lower lung inflammation. Ammonia exposure may also lead to death from prevention of oxygen uptake by hemoglobin in a relatively short time period at very high concentration (500 to 5,000 μg/m3) (Agency for Toxic Substances and Disease Registry, 2004; Merchant et al., 2002).

Monitoring is a main step to know status of air quality for the region. Many authorities like municipal corporation and pollution control board monitor air quality levels at various locations in respective regions and these data are easily accessible in public domain. Monitored air quality data represent concentration of air pollutants at a point and not for a region. However, spatial views of air quality levels are required for air quality management. Generally, dispersion models and to create surface grids and contours spatial interpolation technique is used. Dispersion models require two types of detail input data on emission inventory and weather parameters. These data are not available readily for every region. However, interpolation techniques can help in rapid assessment of spatial view of air pollution level using readily available ambient concentration data of pollutants.

Arc Geographical Information System (ArcGIS) is an efficient tool for capturing, storing, manipulating, managing and analyzing various types of geographical data and represents graphical view for easy understanding. ArcGIS is software with several inbuilt tools which include operations such as interpolation techniques, network analysis and spatial analysis. It is widely used for modeling and simulation with the help of data processing and various mathematical functions. It provides a strong framework for our increasing ability to monitor diseases and identify their causes (Musa et al., 2013). This tool is helpful in estimating the various levels of air pollution at multiple locations of the study area and also in formulating control strategies for air quality management. In the recent years GIS and associated spatial analytical techniques have been used extensively to study public health issues (Chattopadhyay et al., 2010). It has also been used for the examination of the spatial realities of environmental injustice in the recent past (Maantay, 2007).

GIS include various inbuilt kinds of interpolation techniques which include Inverse Distance Weighting (IDW), Kriging, point interpolation natural neighbor, trend and Spline (Varghese et al., 2011). IDW, Spline, nearest neighbor, and kriging are the spatial interpolation methods use to estimate air pollutant concentration (Wong et al., 2004). IDW technique is used to capture the extend of local surface variation for dense set of points needed for analysis. Among all interpolation techniques, IDW and kriging methods perform better in most of the cases (Kumar et al., 2016a). IDW has performed better among other interpolations for air quality mapping for Indian city of Port Blair ( Jha et al., 2011).

In this study, average monthly and annual concentrations of NH3 were studied and maximum concentration was found at Maravali in Mumbai where fertilizer industry exists. Spatial average concentration was mapped for Mumbai city with and without concentration at Maravali to compare excess level of NH3. IDW technique was used to interpolate the annual concentration over the study region. Further, health impact assessment was carried out due to NH3 using Concentration-Response (C-R) Co-efficient and in which total population of the respective ward. Thereafter, the economic cost of morbidity was estimated within a ward for assessed health impact of NH3.

## 2. STUDY AREA

Mumbai is a highly urbanized settlement in the state of Maharashtra and also economical capital of India. According to the 2011 census, population of Mumbai has reached up to 12.4 Million (Census, 2011). Mumbai city covers 437.71 km2 area in the western coast of India (http://mumbaisuburban.gov.in). Mumbai is divided into 24 wards for the purpose of administration as shown in Fig. 1. Wards are labelled as A, B, C, D, E, F/S, F/N, G/S, G/N, H/E, H/W, K/E, K/W, L, M/E, M/W, N, P/N, P/S, R/S, R/N, R/C, S, and T from South Mumbai to North. RCF (Rashtriya Chemicals and Fertilizer) industry is located in the area of Maravali which lies in suburbs of Mumbai. Deonar landfill is India’s largest and oldest landfill which is also located nearby RCF.

Study area, Mumbai city.

## 3. METHODOLOGY

The main objective of this paper is to estimate the temporal and spatial variation for NH3 over the Mumbai region. Ammonia concentration data were collected from ambient air quality monitoring network system operated by Brihanmumbai Municipal Corporation (BMC) and National Environment Engineering Research Institute (NEERI) at 9 different locations. BMC air quality monitoring stations monitor air quality data at seven different locations (viz. Andheri, Bhandup, Borivali, Deonar, Khar, Maravali and Worli,) and NEERI monitors at three locations (viz. Kalbadevi, Parel and Worli) in the city. NEERI and BMC collect the samples twice a week as per (National Ambient Air Quality Standard) NAAQS. Annual average of air pollution data were calculated for all the locations for the year 2012.

There are many interpolation techniques available in ArcGIS such as Kriging, Spline and IDW. These interpolation techniques are good tools for study of spatial and temporal patterns of air quality without requiring data on meteorology and emissions (Candiani et al., 2013; Janssen et al., 2008; van Loon, 1993). Out of these, the IDW technique gives a fairly good result for the interpolation of the concentration of air pollutants as compared to other interpolation techniques when the regional and local aspects both are incorporated. The evaluation of IDW interpolation technique for the concentration of ambient air quality in Port Blair (India) has been reported by Jha et al. (2011). Apart from this, many studies have also demonstrated that IDW gives fairly good interpolation of air quality (Kumar et al., 2016a; Wong et al., 2004). IDW interpolates all values of the points within the sample range as averaging tool and gives better interpolation estimates when the minimum and maximum values of the surface are represented by sample data points. It estimates the value of a point as a basis of a function of two variables: the distance between sampling points and the point at which the value has to be estimated. The concentration of point will have heavier weight if it is proximal to the required point and vice versa. Here, weight is an inverse function of the distance, as demonstrated in the following equation:

 (1)

where Zj=value of concentration at the jth point, Wi=weight of observed ith point, dji=distance from the ith point to the jth point, p=the power and n=total number of points.

Shapefiles were created with the help of the geo-referenced map for air quality monitoring stations and for the Mumbai region in ArcGIS. Twenty four wards of BMC for Mumbai city were created in shapefiles in ArcGIS where the geo-referenced shapefile for Mumbai city was prepared to associate with physical earth space. The air quality monitoring locations were denoted by point shapefile and ward wise area of Mumbai was denoted by polygon shapfile for Mumbai region. Monthly and annual averages of each air quality monitoring station of the city were fed in the attribute of point shapefile and monthly and annual average concentration data was analyzed and was used for interpolation. The IDW was used to interpolate the data point shapefiles for the concentrations of NH3 in the study area. The spatial annual average concentration was calculated with and without Maravali (a part of Mumbai) and compared. Air quality concentration data for NH3 was estimated for monthly and annual time scale to respective point shapefile.

Further, air quality data within a ward was used to estimate health impact due to NH3 and its economic cost of morbidity based on total population of the respective ward. According to the 2011 census for Mumbai city, ward wise population data was used to estimate population exposure in Mumbai city due to NH3. Relationship between concentration levels of pollutants and adverse health effects has been analyzed by an Epidemiological study (Patankar and Trivedi, 2011) and used here. By multiplying the incidence rate with the corresponding population of the ward, number of cases of the health effect per year was calculated. The minimum concentration in the Mumbai region was 32 μg/m3 and it was assumed as baseline concentration for NH3 for health impact assessment to bring NH3 level at 32 μg/m3 across the city. Annual average concentration was used with C-R coefficients for the pollutant and respective population of the particular ward was used to assess health impact due to NH3. The health impact assessment was carried out using the following equation:

 $∆{H}_{jp}={b}_{jp}*{POP}_{i}*∆{AQ}_{p}$ (2)

Where,

ΔAQp= change in the concentration of pollutant p as compare to baseline concentration,

ΔHjp =the change in health impact j due to pollutant p,

bjp = C-R coefficient for health effect j related to pollutant p and

POPi= population of the particular region/ward of the city.

C-R coefficients for pollutants and diseases and economic evaluation have been taken from Patankar and Trivedi (2011). Then the cost was predicted due to health damage caused by NH3 in the city. Cost was estimated for the total exposed population for damages in health.

## 4. RESULTS AND DISCUSSION

Annual average concentration database was used in ArcGIS to interpolate the concentration of air quality using IDW. The collected air quality dataset was fed in attribute of point shapefiles at monthly scales. Location wise monthly variation in concentration levels over the study region are shown in Fig. 2. According to the national ambient air quality standards (NAAQS) the standard value of Ammonia (NH3) is 100 μg/m3 (CPCB, 1994). The minimum annual concentration of NH3 was 32 μg/m3, 33 μg/m3, 34 μg/m3 at Kalbadevi, Parel & Worli respectively whereas the maximum annual concentration of NH3 was 384 μg/m3 at Maravali. The annual concentrations of NH3 was 53 μg/m3, 62 μg/m3, 56 μg/m3 and 39 μg/m3 at Khar, Andheri, Bhandup and Borivali respectively which is well below the NAAQS standards for NH3. Fig. 3 is showing monthly variation of NH3 concentration for all location of Mumbai. In the study area, there is an industry called “Rashtriye Chemical Fertilizers” which emits NH3. The spatial and temporal variations of the concentration depend upon the meteorology of the region and proximity and operation of the source of industry. Maravali is very close to an industry “Rashtriye Chemical Fertilizers” which emits NH3. This is the reason to have more concentration at Maravali location. Generally, the winter season has maximum and the monsoon has minimum concentration over the period but here the concentration is more in June, July, August and September. This is due to more emission with respect to production of fertilizers during this period. More production causes more emission of the pollution which reflects in ambient air concentration.

Monthly average concentration of NH3 for Mumbai city.

Monthly variation of concentration levels of NH3 for each location.

The study also estimated seasonal variations in concentration of NH3. Pre-monsoon season consist of months of March, April and May; monsoon season is June, July, August and September; post monsoon season is October and November while December, January and February months are considered as the winter season. The maximum concentration of NH3 was observed in Monsoon season i.e. 105.75 μg/m3 whereas the minimum concentration was in winter season i.e. 69 μg/m3. The standard deviations of seasonal average is 14 μg/m3 for NH3. The NH3 concentration for premonsoon season was 74 μg/m3 and that for post monsoon season was 84 μg/m3. Hence, the premonsoon season showed a lower concentration of NH3 than the post monsoon season.

Further, interpolations from point data to surface data were carried out using ArcGIS software version 9.3. ArcGIS has a spatial analyst extension tool which provides spatial interpolation techniques (i.e. modeling suitability, distance). IDW was used to perform the prediction of concentrations from known to unknown points to get point to surface profile. The power value (p) was taken two and searching radius was six in IDW interpolation. Nine point data on an annual average concentration were interpolated over Mumbai region where Maravali was included as shown in Fig. 4a. Again, interpolation was carried out over Mumbai region excluding Maravali location as shown in Fig. 4b. The spatial annual average concentration of NH3 for Mumbai city was calculated as 85 μg/m3 including Maravali and 56 μg/m3 excluding Maravali. The spatial annual average concentration for Mumbai city is exceeding 1.5 times by inclusion of annual average of Maravali concentration. The maximum concentration of NH3 was 342 μg/m3 at Maravali which is 6.1 times of spatial average concentration of Mumbai excluding Maravali.

Air quality mapping for NH3 using IDW (a) including Maravali (b) excluding Maravali location.

Health impact assessment was carried out using NH3 concentration data, ward population and C-R coefficients. The value of C-R coefficients were used from Patankar & Trivedi (2011) for NH3 and are given in Table 2. Phlegm and other chest illnesses are caused due to pollutant NH3 and phlegm is upper respiratory while other chest illnesses are lower respiratory. A C-R function of value 0.012 was used to assess the upper respiratory health impact and a value of 0.024 was used to assess the lower respiratory health impact for NH3. Results showed that the population of Maravali and Deonar is 0.4 times of Mumbai city except Maravali and Deonar.

Seasonal average concentration (μg/m3) for the year 2012.

Concentration-Response coefficients of pollutants for health impact for NH3.

The population exposed to NH3 air pollutant at various locations is shown in Fig. 5. The locations Maravali, Deonar and Andheri are highly affected by NH3 and this is due to the proximity of Deonar to the fertilizer industry. Andehri is 10 km away from Maravali but has shown more population exposure due to its high population density. Maximum population is exposed at Maravali and Deonar with 4.5 Million and 2.8 Million respectively. Hence, 74% population exposure of the total exposure of Mumbai city is in Maravali and Deonar. The lowest population exposed for all the air pollutants is at Borivali, Worli (NEERI), Parel and Kalbadevi. It shows that the minimum and maximum concentration is susceptible to upper respiratory diseases like Phlegm and lower respiratory diseases like other chest illnesses. The population affected by other chest illnesses is higher than those affected by Phlegm. The minimum and maximum population is exposed at Parel and Maravali respectively for NH3.

Exposed population and health cost by NH3 at various locations in Mumbai city.

Further, the cost of health impact was estimated using available costs for diseases. The average cost was taken for each disease and it was assumed that 25% of city residents use public health facilities and have daily wage of approximately USD 3.14 (INR 200). The details are given in Patankar and Trivedi (2011). As the ambient pollution level and the population density are higher at Maravali and Deonar, the total health cost at these sites are high. The total health cost for Phlegm is the highest when compared to other chest illnesses. From Fig. 5, It can also be inferred that the health cost for Phlegm is highest for Maravali (1762 Million INR or 26 Million USD: 1 USD=68.17 INR) followed by Deonar (1082 Million INR or 16 Million USD), Andheri (359 Million INR or 5.3), Bhandup (217 Million INR or 3.2 Million USD) and Worli (BMC) (232 Million INR or 3.4 Million USD) and the least cost of health impact is at Worli (NEERI) (139 Million INR or 2 Million USD).

## 5. CONCLUSIONS

The role of air quality monitoring is to provide information on the concentration of pollutants in the ambient environment. Monitoring data shows concentration level at a point. However, spatial view of concentration level over the region is required for rational air quality management by regulatory authorities. Ammonia (NH3) is one the air quality pollutant standard parameters in India where fertilizer industries are existing. This study shows the location wise monthly average variation in concentration of NH3 in Mumbai city. Also, air quality mapping for NH3 was visualized and represented using IDW technique including and excluding surrounding regions of the chemical fertilizer industry i.e, Maravali. The annual average concentration for Mumbai city was 56 μg/m3 excluding Maravali while it exceeded 1.5 times more (i. e. 85 μg/m3) including Maravali. The maximum concentration of NH3 was 342 μg/m3 at Maravali which is 6.1 times of spatial average concentration of Mumbai excluding Maravali. It might be caused by Fertilizers industry or solid waste dumping site which is near to Maravali region.

Further, the concentration of NH3 was used to assess adverse health effects and population exposure. The results show that the minimum and maximum population is exposed at Parel and Maravali respectively for NH3 which causes Phlegm and other chest illnesses. The total exposed population exposed by both diseases is 10 Million in Mumbai city with 45% of population in Maravali. The cost of health damage by Phlegm for exposed population is 3.9 Billion INR. This work will be useful to assess air quality level and health impact for the region where fertilizers exists for the regulatory purpose.

## Acknowledgments

We express our sincere thanks to Air Quality Monitoring and Research Laboratory of Brihanmumbai Municipal Corporation for providing relevant data for this study. Also, we would like to thank Mr Saurabh Desai for helping in the preparation of the manuscript.

## References

• Aburas, M.M., Ho, Y.M., Ramli, M.F., Ash’aari, Z.H., (2016), The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review, International Journal of Applied Earth Observation and Geoinformation, 52, p380-389. [https://doi.org/10.1016/j.jag.2016.07.007]
• Agency for Toxic Substances and Disease Registry, (2004), Toxicological Profile for Ammonia, Federal Register, 269.
• Akintunde, J.A., Adzandeh, E.A., Fabiyi, O.O., (2016), Spatiotemporal pattern of urban growth in Jos Metropolis, Nigeria, Remote Sensing Applications: Society and Environment, 4, p44-54. [https://doi.org/10.1016/j.rsase.2016.04.003]
• Aneja, V., (2001), Measurement and analysis of atmospheric ammonia emissions from anaerobic lagoons, Atmospheric Environment, 35, p1949-1958. [https://doi.org/10.1016/S1352-2310(00)00547-1]
• Bouwman, A.F., Lee, D.S., Asman, W.A.H., Dentener, F.J., Hoek, K.W., Van Der, Olivier, J.G.J., (1997), A global highresolution emission inventory for ammonia, GLOBAL BIOGEOCHEMICAL CYCLES, 11, p561-587. [https://doi.org/10.1029/97GB02266]
• Candiani, G., Carnevale, C., Finzi, G., Pisoni, E., Volta, M., (2013), A comparison of reanalysis techniques: applying optimal interpolation and Ensemble Kalman Filtering to improve air quality monitoring at mesoscale, The Science of the Total Environment, 458-460, p7-14.
• Chattopadhyay, S., Gupta, S., Saha, R.N., (2010), Spatial and Temporal Variation of Urban Air Quality: A GIS Approach, Journal of Environmental Protection, 1, p264-277. [https://doi.org/10.4236/jep.2010.13032]
• CPCB, (1994), National Ambient Air Quality Standard.
• Ferm, M., Rodhe, H., (1997), Measurements of air concentrations of SO2, NO2 and NH3 at rural and remote sites in Asia, Journal of Atmospheric Chemistry, 27, p17-29. [https://doi.org/10.1023/A:1005816621522]
• Gurjar, B.R., Jain, A., Sharma, A., Agarwal, A., Gupta, P., Nagpure, A.S., Lelieveld, J., (2010), Human health risks in megacities due to air pollution, Atmospheric Environment, 44, p4606-4613. [https://doi.org/10.1016/j.atmosenv.2010.08.011]
• Guttikunda, S.K., Goel, R., (2013), Health impacts of particulate pollution in a megacity-Delhi, India, Environmental Development, 6, p8-20. [https://doi.org/10.1016/j.envdev.2012.12.002]
• Janssen, S., Dumont, G., Fierens, F., Mensink, C., (2008), Spatial interpolation of air pollution measurements using COR INE land cover data, Atmospheric Environment, 42, p4884-4903. [https://doi.org/10.1016/j.atmosenv.2008.02.043]
• Jha, D.K., Sabesan, M., Das, A., Vinithkumar, N.V., Kirubagarn, R., (2011), Evaluation of Interpolation Technique for Air Quality Parameters in Port Blair, India, Universal Journal of Environmental Research and Technology, 1, p301-310.
• Kumar, A., Gupta, I., Brandt, J., Kumar, R., Kumar, A., Patil, R.S., (2016a), Air quality mapping using GIS and economic evaluation of health impact for Mumbai city, India, Journal of Air and Waste Management Association, 66(5), p470-481. [https://doi.org/10.1080/10962247.2016.1143887]
• Kumar, A., Patil, R.S., Kumar, A., Kumar, R., Brandt, J., Hertel, O., (2016b), Assessment of impact of unaccounted emission on ambient concentration using DEHM and AERMOD in combination with WRF, Atmospheric Environment, 142, p406-413. [https://doi.org/10.1016/j.atmosenv.2016.08.024]
• Maantay, J., (2007), Asthma and air pollution in the Bronx: methodological and data considerations in using GIS for environmental justice and health research, Health & Place, 13, p32-56. [https://doi.org/10.1016/j.healthplace.2005.09.009]
• Merchant, J.A., Kline, J., Donham, K.J., Bundy, D.S., Hodne, C.J., (2002), Human Health Effects. Iowa Concentrated Animal Feeding Operations Air Quality Study, Iowa State University and The University of Iowa Study Group, p121-145.
• Mokhtar, M.M., Hassim, M.H., Taib, R.M., (2014), Health risk assessment of emissions from a coal-fired power plant using AERMOD modelling, Process Safety and Environmental Protection, 92, p476-485. [https://doi.org/10.1016/j.psep.2014.05.008]
• Musa, G.J., Chiang, P.-H., Sylk, T., Bavley, R., Keating, W., Lakew, B., Tsou, H.-C., Hoven, C.W., (2013), Use of GIS Mapping as a Public Health Tool - From Cholera to Cancer, Health Service Insights, 6, p111-116. [https://doi.org/10.4137/hsi.s10471]
• Namdeo, A., Stringer, C., (2008), Investigating the relationship between air pollution, health and social deprivation in Leeds, UK, Environment International, 34, p585-591. [https://doi.org/10.1016/j.envint.2007.12.015]
• Pandey, J.S., Kumar, R., Devotta, S., (2005), Health risks of NO2, SPM and SO2 in Delhi (India), Atmospheric Environment, 39, p6868-6874. [https://doi.org/10.1016/j.atmosenv.2005.08.004]
• Patankar, A.M., Trivedi, P.L., (2011), Monetary burden of health impacts of air pollution in Mumbai, India: implications for public health policy, Public Health, 125, p157-164. [https://doi.org/10.1016/j.puhe.2010.11.009]
• Srivastava, A., Kumar, R., (2002), Economic valuation of health impacts of air pollution in mumbai, Environmental Monitoring and Assessment, 75, p135-143. [https://doi.org/10.1023/A:1014431729649]
• van Loon, M., (1993), Testing interpolation and filtering techniques in connection with a semi-Lagrangian method, Atmospheric Environment Part A General Topics, 27, p2351-2364. [https://doi.org/10.1016/0960-1686(93)90403-L]
• Varghese, S., Langmann, B., Ceburnis, D., O’Dowd, C.D., (2011), Effect of horizontal resolution on meteorology and air-quality prediction with a regional scale model, Atmospheric Research, 101, p574-594. [https://doi.org/10.1016/j.atmosres.2011.02.007]
• Veremchuk, L.V., Yankova, V.I., Vitkina, T.I., Nazarenko, A.V., Golokhvast, K.S., (2016), Urban air pollution, climate and its impact on asthma morbidity, Asian Pacific Journal of Tropical Biomedicine, 6, p76-79. [https://doi.org/10.1016/j.apjtb.2015.10.001]
• Wong, D.W., Yuan, L., Perlin, S.A., (2004), Comparison of spatial interpolation methods for the estimation of air quality data, Journal of Exposure Analysis and Environmental Epidemiology, 14, p404-415. [https://doi.org/10.1038/sj.jea.7500338]
• Yamaji, K., Ohara, T., Akimoto, H., (2004), Regional-specific emission inventory for NH3, N2O, and CH4 via animal farming in South, Southeast, and East Asia, Atmospheric Environment, 38(40), p7111-7121. [https://doi.org/10.1016/j.atmosenv.2004.06.045]

### Fig. 1.

Study area, Mumbai city.

### Fig. 2.

Monthly average concentration of NH3 for Mumbai city.

### Fig. 3.

Monthly variation of concentration levels of NH3 for each location.

### Fig. 4.

Air quality mapping for NH3 using IDW (a) including Maravali (b) excluding Maravali location.

### Fig. 5.

Exposed population and health cost by NH3 at various locations in Mumbai city.

### Table 1.

Seasonal average concentration (μg/m3) for the year 2012.

Pollutant Pre-monsoon Monsoon Post-monsoon Winter Annual Std. Dev.
NH3 74 106 84 69 85 14

### Table 2.

Concentration-Response coefficients of pollutants for health impact for NH3.

Disease C-R coefficients Health exposure Cost of health impact (USD)
Patankar and Trivedi, 2011
Phlegm 0.012 3412238 57 Million
Other Chest Illness 0.024 6824476 -