Journal Archive

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, NO_x 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 PM_2.5), 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.

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.

Fig. 1. 
Study area, Mumbai city.

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:

where Z_j=value of concentration at the j^th point, W_i=weight of observed i^th point, d_ji=distance from the i^th point to the j^th 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:

Where,

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

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

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

POP_i= 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.

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.

Fig. 2. 
Monthly average concentration of NH₃ for Mumbai city.

Fig. 3. 
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.

Fig. 4. 
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.

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.

Fig. 5. 
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).

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.