Human health risk assessment due to air pollution in 10 urban cities in Maharashtra, India

Abstract This study assesses human health risk in 10 cities in Maharashtra, India, in terms of mortality and morbidity due to three critical pollutants (i.e. PM10, SO2, and NO2). Risk of mortality/morbidity due to air pollution (Ri-MAP) model adopted in air quality health impact assessment (AirQ) software is used to evaluate the direct health impacts of various critical air pollutants in various cities in Maharashtra during the period 2004–2013. The result shows that excess number of mortality and morbidity in Nagpur, Thane, Aurangabad, Kolhapur, and Chandrapur is in increasing trend, while cities like Mumbai and Solapur are in decreasing trend, and other cities as Pune, Nashik, and Navi-Mumbai are in a steady-state condition. Cities having highest annual average excess number of total mortality, cardiovascular mortality, and respiratory motility in one million population are Mumbai (1,192, 724, and 121) (high population city), Chandrapur (944, 533, and 98) (low population city), Navi-Mumbai (797, 492, and 84), and Pune (733, 449, and 78) in decreasing order. Cities having highest annual average of hospital admission due to respiratory disease and cardiovascular disease among one million population are in decreasing order: Mumbai (1,519 and 582), Chandrapur (1,173 and 451), Navi-Mumbai (986 and 378), Pune (901 and 348), and Solapur (797 and 320).


Introduction
In epidemiology, "risk" is defined as a measure of the statistical likelihood of having severity of adverse events (e.g. illness or death) due to exposure to some factors (e.g. toxic chemical) (Lowrance, 1976). Health risk is the probability or chance of exposure to a hazardous substance, which makes humans sick and it is equal to multiplication of hazard and exposure. The human health risk (HHR) ABOUT THE AUTHOR The author joined the Indian Institute of Technology Bombay in December 2011 as a research scholar. His doctoral work is focused on "Air quality monitoring networking in an urban city and human health risk assessment due to air pollution". His research interests include the use of satellite remote sensing data for health risk assessment, atmospheric chemistry, low-cost indoor air pollution removal technology, and heavy metal contamination in deep sea fish.

PUBLIC INTEREST STATEMENT
The present research is very useful to the pollution control authorities and public, as air pollution level is increasing year by year and pollution is highly related to human death and illness. Metropolitan cities in Maharashtra like Mumbai, Chandrapur, Navi-Mumbai, and Pune have the highest impact of air pollution. Huge number of vehicles, high energy demand, open burning of solid waste, open coal mining, thermal power plant, road dust, and coal burning are the major sources of air pollution. There is a need to generate public awareness about air pollution in these cities. assessment involves four major steps as follows (NRC, 1983): (1) Hazard identification-elements with known toxicity (like PM 10 , SO 2 , and NO 2 are responsible for different health effects like cardiovascular mortality (CM), respiratory mortality (RM), COPD, etc.). The knowledge about how hazardous a substance (pollutants) is comes from animal experiments or long time human studies. (2) Exposure assessment involves estimation of amount of hazardous pollutants inhaled by a certain population. Exposure information comes in two ways: (a) monitor substance concentration at different places in human community or (b) from dispersion models that account exposure based on released amount of hazardous substance from different sources. (3) Concentration-response assessment: it reflects the probability of health effects based on the dose of inhaled air pollutants. Finally, (4) HHR assessment is calculated by a mathematical model which based on the exposure and dose-response assessments.
Proper knowledge of exposure, baseline incidence of mortality or morbidity for every pollutant as well as concentration-response functions from epidemiological studies help account trends in perilous human health effects associated with alternative scenarios (EOHSP, 2007).
In India, about 0.62 million premature excess number of death cases occur due to outdoor air pollution and became the fifth leading cause of death after high blood pressure, indoor air pollution, tobacco smoking, and poor nutrition in 2012 (NYT, 2014). The economic cost of health impacts due to air pollution is about USD 80 billion in 2010, equivalent to 5.7% of gross domestic product (GDP) in India. Serious health consequences due to PM 10 coming from fossil fuels burning amount to about 3% (1.7% by outdoor air pollution and 1.3% by indoor air pollution) of India's GDP. The huge amount of health cost for outdoor/indoor is pollution due to fine particulate matter are primarily driven by an elevated exposure of skillful young population in urban area that results a substantial cardiopulmonary and RM and mobility load among adults (WB, 2013).
Due to high population, high energy demand, large number of industries, and huge number of vehicles, cities are facing most environmental challenges in air pollution. Thus, cities tend to be high risk areas and their human community vulnerable to air pollution-induced unfavorable health impacts like CM, RM, chronic obstructive pulmonary disease (COPD), etc. Madronich, 2006;Molina & Molina, 2004). Such risk needs to be calculated to help the pollution control authorities to ameliorate the sustainability of city life. Modeling of human exposure by air pollutants is most important for the evaluation of HHR (Vostal, 1994).
The case study in this paper focuses on quantitative assessment of HHR, like total mortality (TM), CM, RM, COPD, hospital admissions respiratory disease (HARD), and hospital admissions cardiovascular disease (HACD), due to three critical pollutants (PM 10 , SO 2 , and NO 2 ) in 10 urban areas (cities) (Mumbai, Pune, Nagpur, Thane, Nashik, Aurangabad, Solapur, Navi-Mumbai, Kolhapur, and Chandrapur) in Maharashtra in India from 2004 to 2013.
Here, the risk of mortality/morbidity due to air pollution (Ri-MAP) model adopted in air quality health impact assessment (AirQ) software has been used to assess direct HHR due to three criteria air pollutants present in urban areas (cities).

Methodology
The relative risk (R r ) is the probability an exposed group will develop disease relative to the probability of an unexposed group developing the same disease due to air pollutants (Rothman, Greenland, & Lash, 2008). The approach adopted in the present research is HHR assessment, using the AirQ 2.2.3 software (WHO, 2004) developed by the WHO European Centre for Environment Health, Bilthoven Division. This software adopted Ri-MAP model which is used in the present study to estimate the potential impact of exposure to particular air pollutants on the health of people living in an urban area during a certain time period.
The HHR assessment is based on the population attributable risk (PAR) (also called "population attributable risk proportion") concept, defined as the fraction of the excess rate of disease in a given population distinguishable to exposure to a particular atmospheric pollutant, assuming a proven causal relation between exposure and excess rate of disease with no major confounding effects in that association (Gefeller, 1992;Northridge, 1995). The PAR can be easily calculated by the following general equation: where R r (c) is the changed relative risk for the health outcome in category "c" of exposure and R r (c) = 1 + (C a − C w ) × (R r − 1)∕10. C a is the ambient air pollutant concentration, C w is the WHO recommended threshold level of that pollutant, and R r is the relative risk of exposure-disease relation (the ratio of the conditional disease probabilities among exposed and non-exposed). p(c) is the proportion of the population in category "c" of exposure.
If the baseline frequency (at WHO recommended threshold concentration value) of selected health outcomes (i.e. I w ) in the population under investigation is known, then the excess number of cases (ENCs) per unit population (rate) attributed to the exposure in population (i.e. I E ) is calculated as (WHO, 1999): Consequently, the frequency of the outcome to the non-exposed population (i.e. I NE ) can be calculated as follows: Finally, at a certain category of exposure (c) with known R r and the estimated incidence in non-exposed population having population size under investigation (P), the ENCs (ΔN(c)) can be calculated: Equation (4) is used to estimate ENCs of mortality or morbidity in the exposed population. In practice, however, the range of the estimated health outcome (i.e. uncertainty of the impact) is greater due to measurement errors in exposure assessment because pollutant concentration changes time to time and it depends on the area (e.g. industrial or residential) and non-statistical uncertainty of the concentration-response function (WHO 1999(WHO , 2003. The AirQ software uses WHO specified input of R r values (per 10 μg/m 3 increase of concentration for hazardous substances) and corresponding baseline incidences (per 10 5 population) for different air pollutants (particulate matter having aerodynamic diameter ≤ 10 (PM 10 ), sulfur dioxide (SO 2 ), and nitrogen dioxide (NO 2 ), etc.) as well as types of diseases (e.g. cardiovascular, respiratory, COPD, and HARD) associated with those values (Table 1), based on various previous studies (Burnett, Dales, Brook, Raizenne, & Krewski, 1997;Poloniecki, Atkinson, de Leon, & Anderson, 1997;Sunyer et al., 1997;Touloumi & Katsouyanni, 1997).
The simple method using all air quality monitoring station data in the city and taking average of the concentrations for each time unit is followed to evaluate human exposure. This average value (daily) is the indicator of exposure of the entire city population. For example, for Chandrapur, daily data of six monitoring stations located throughout the city are taken and the average of three critical pollutants (i.e. PM 10 , SO 2 , and NO 2 ) is used for each year. (1)

Study area
Maharashtra is a state in the western region and is the second-most populous region in India with a population of 112 million (9.28% of India's population) and its capital Mumbai is the highest populated city in India and its population is approximately 12.48 million (CI, 2011). Maharashtra is one of the most developed states in India, contributing 25% of the country's industrial output and 23.2% of its GDP (2010-2011) (DDCH, 2014). The state's economy mainly depends on agriculture and industries. Major industries are included in chemical products, electrical and non-electrical machinery, pharmaceuticals, textiles, petroleum, and allied products. The huge amount of population makes Maharashtra the largest energy user but conservation mandates, mild weather, and strong environmental movements make its per capita energy use lowest of any Indian state (EGI, 2014). The high electricity demand of the state constitutes about 13% of the total electricity generated in India, which mainly comes from fossil fuels such as coal and natural gas-based power plants (IndianPowerSector.com, 2015).
The case study in this paper focuses on quantitative assessment of HHR based on Ri-MAP model in 10 cities in Maharashtra from 2004 to 2013.
The Ri-MAP model depends mainly upon ambient air pollution concentrations and population data. The daily average ambient air pollution concentrations (μg/m 3 ) for criteria pollutants, namely; PM 10 , SO 2 , and NO 2 from all monitoring stations in 10 cities from 2004 to 2013, used in this study were monitored and estimated by Maharashtra Pollution Control Board (MPCB) (MPCB, 2014). Population data of all cities in 2001 and 2011 are taken from Census of India (CI, 2001) to calculate the exponential growth factor for all cities. Population growth is estimated by P = P 0 exp (kt), where P, P 0 , t, and k denote final population, initial population, time (year), and exponential growth factor, c Baseline Incidence per 100,000 is based on threshold limit given in WHO guideline.

Pollutants
Mortality respectively. The highest populated city is Mumbai followed by Pune, Nagpur, Thane, Nashik, Aurangabad, Solapur, Navi-Mumbai, Kolhapur, and Chandrapur in decreasing order.

Results
The model used the WHO default value for relative risk for three critical pollutants (i.e. PM 10 , SO 2 , and NO 2 ) and the results obtained from the case study, using Ri-MAP model incorporating exposure response Equations (1)-(4). City-wise ENCs of mortality/morbidity of TM, CM, RM, COPD, HARD, and HACD have been illustrated from Figures 1-6.

Discussion
In the above results, the annual average ENCs of mortality and morbidity are almost proportionate to the total population in the city; but in this case study, any area-specific ambient air pollution concentration is not considered to calculate HHR. To avoid biases generated due to different population sizes, different population densities and different areas (i.e. residential or industrial) in different cities, and ENCs in one million population (ENCsOMP) have been estimated (Figure 7). All figures show the range of the annual average ENCsOMP at 95% CI.

Total mortality
The excess number of mortality (death) has been estimated taking into account the sum total of effects caused by PM 10 and SO 2 . City-wise different trends are observed for the excess number of mortality cases considered among one million population in all cities (Figure 7

Cardiovascular mortality
Annual average ENCsOMP of CM follow the same trend too (Figure 7

Respiratory mortality
Annual average excess number of RM in one million population is relatively low (Figure 7(c)). Figure shows that annual average ENCsOMP of RM was highest in Mumbai (121/year). Subsequent to Mumbai, top five cities in this list are Chandrapur (98/year), Navi-Mumbai (84/year), Pune (78/year), Nashik (68/year), and Solapur (67/year). The excess number of RM has been calculated taking into account the sum total of PM 10 , SO 2 , and NO 2 .

Chronic obstructive pulmonary disease
Trend of annual average ENCsOMP was different from others and was very low because SO 2 and NO 2 were only responsible for COPD (Figure 7 Other case studies were carried out in different cities by different researchers on human health due to atmospheric pollution. AirQ software has been popular to many researchers to assess the human health impact of PM 2.5 (Boldo et al., 2006) or PM 10 (Tominz, Mazzoleni, & Daris, 2005). Naddafi et al. (2012) calculated the excess number of mortality under all causes, cardiovascular and respiratory diseases due to PM 10 , SO 2 , NO 2 , and O 3 in Tehran, Iran. Study on HHR due to air pollution in two industrial municipalities of Northern Italy was done by Fattore et al. (2011). The study showed that 433, 180, and 72 lives were lost per year for all causes, cardiopulmonary diseases and lung cancer, respectively. An assessment study on other human health impacts was carried out by Gharehchahi et al. (2013) in Shairaz, Iran;Orru, Laukaitienė, and Zurlytė (2012) in Vilnius and Kaunas; Orru et al. (2009) in Tallinn using fine spatial resolution with GIS. Nagpure, Gurjar, and Martel (2014) evaluated HHR in NCT Delhi for the years 1991-2010 and Gurjar et al. (2010) estimated the HHR due to air pollution in different megacities in the world. Results obtained from the studies of health effects due to air pollution in various cities in the world differ substantially, but in all of these as well as our present study, particulate matter has been found to cause the most adverse health effects.

Cause of air pollution
From the above study, it is observed that excess number of mortality and morbidity is due to particulate matter (PM 10 ) than gaseous pollutants. Mumbai and Chandrapur have the highest rate of ENCs of mortality and morbidity. In Mumbai, different combustion processes are the main contributors for PM, like power plant, open burning, commercial food sector, and road transport, and they contribute 37, 24, 18, and 10%, respectively. A study by National Environmental Engineering Research Institute (NEERI) found that open burning and landfill fires of municipal solid waste (MSW) were a major source of air pollution in Mumbai (CPCB, 2010). The survey results show that about 2% of total generated MSW is burnt on the streets and slum areas, 10% of the total generated MSW is burnt in landfills by management authorities or due to accidental landfill fires, thereby emitting large amounts of CO, PM, carcinogenic HC, and NO X . In Chandrapur, primary sources of high critical pollutant concentration (i.e. SPM, PM 10 , SO 2 , and NO 2 ) are open coal mining, lime stone mining, fluoride mining, cement industry, thermal power plant, road dust, natural burning of coal, and domestic coal burning by local people for cooking (MPCB, 2010). In Pune city, highest pollution load of PM 10 comes from different sources like road dust (61%), vehicular source (18%), industry (1.25%), vegetative burning, and solid fuels burning. For NO 2 emissions, major contributions are from vehicles (95%), industries (2%), and domestic and commercial fuel burning (3%) (ARAI, 2010). Vehicles and industries contribute to high SO 2 emission loads due to fuel burning. Main cause of air pollution in Nashik city is due to plastic industry, food processing factories, and domestic waste burning. Till December 2013, there are 1.13 million number of registered vehicles in the city, causing a major source of pollution (TI, 2014).

Uncertainty analysis
There may be several uncertainties because study areas are not categorized into residential or industrial together with instrumental error and so uncertainty is owing to relative risk measurements. Central health risk with the addition of lower and upper ranges for the 95% CI based on the input parameters (Table 1)  Error in health risk assessment is due to uncertainty involved in relative risk. Relative risk is calculated using generalized additive model (GAM) in cohort study. Other parameters involved in the cohort study are temperature, humidity, due point, and rain fall. These parameters are most pronounced in CM; it has remained practically constant in the case of RM (Dholakia, Bhadra, & Garg, 2014;Jerrett et al., 2008;Ren & Tong, 2008).

Limitation and assumption made
There are a number of methodological uncertainties and limitations in the approach which need further improvement to make the method robust. In particular: (a) Relative risk values used in this study are experimentally developed in United States of America, but a lot of uncertainty is involved when these values are used in any other country like India, as the climatic conditions and economic backgrounds differ starkly from United State of America.
(b) Pollutants are generally of mixed kind-outdoor and indoor air pollutants-associated with synergistic effect which is not considered in the study. While calculating the combined exposure to different pollutants, quite often, an assumption is made that the effects of individual pollutants on human health are additive. However, the simple addition of the effects of each single pollutant would not be totally precise because normally there is a positive relation among atmospheric pollutants (Fattore et al., 2011).
(c) Here, the area-specific (i.e. industrial or residential area) mortality and morbidity have not been considered.
(d) The accuracy of the air quality data as available through MPCB is uncertain due to a wide variety of reasons such as frequent power cut, man power availability problem, calibration error, and failures of air quality monitoring instruments that might cause an error.
(e) In this study, only PM 10 , SO 2 , and NO 2 with WHO threshold limits are considered, but fine particulate matter PM 2.5 and ozone have not been considered which might have more health impacts.

Conclusion
HHR has been estimated in terms of ENCs in different urban area (cities) in Maharashtra using Ri-MAP model. Excess number of mortality and morbidity is observed in Nagpur, Thane, Aurangabad, Kolhapur, and Chandrapur in increasing trend and in decreasing trend in Mumbai and Solapur, but in steady-state condition in Pune, Nashik, and Navi-Mumbai. Annual average excess number of TM, CM, and RM of one million of population is highest in Mumbai and Chandrapur, followed by Navi-Mumbai, Pune, and Nashik. The primary responsible pollutant for HHR in all cities is PM 10 , but NO 2 and SO 2 are also similarly responsible in Chandrapur. Hence, the pollution control authorities in Maharashtra urgently need proper policies to elevate ambient air quality in terms of PM 10 level to decrease the economic costs of air pollution-related health impact. COPD in all the cities is shown very low because SPM, the primary responsible pollutant, is not considered in the case study. The estimated ENCs are only with reference to pollutants' concentration in excess of the levels adopted as per WHO guidelines. However, pollutant concentrations lower than the WHO guidelines also have excess morbidity and mortality because of long time exposure. Actual number of mortality/morbidity cases is higher than the calculated one, owing to lack of data for other pollutants. For more extensive study to estimate health impact, one needs all the relevant pollutants such as TSP, PM 2.5 , PM 1 , O 3 , CO, heavy metals, and polyaromatic hydrocarbons. In developing countries like India, the ratio of PM 2.5 and PM 10 is very high, about 0.65 (Satsangi, Kulshrestha, Taneja, & Rao, 2011), which is much higher than USA (Pace, 2005) and Europe (Barmpadimos, Keller, Oderbolz, Hueglin, & Prévôt, 2012). And longterm exposure to PM 2.5 is strongly related with ischemic heart disease, cerebrovascular disease, COPD, lung cancer, and acute lower respiratory infections. Thus, long-term epidemiological study related to PM 2.5 should be performed in India in future because of the presence of high outdoor PM 2.5 concentration in India and its subcontinent countries (Dey et al., 2012). A new wave of pollution control initiatives is needed to stem the current crippling levels of air pollution. It will be appropriate to initiate similar studies in megacities all over the world; however, the governing parameter (i.e. relative risk) in WHO model should be developed for country-specific studies. The current study shows the importance of evaluation and assessment of health impacts of air quality on a local scale to protect the environment. This study was based on the assumption that the entire population of a city was exposed to the average concentration levels of all air quality monitoring stations. Future studies may use Benefits Mapping and Analysis Program (BenMAP) to calculate the number of air pollution-related deaths and illnesses in finer resolution.