Clean air is the most essential requirement for healthy living. However, due to uncontrolled urbanization, rapid increase in number of vehicles in cities and high energy demand for modern civilization, the air is continuously polluted. Episodic events of severe air pollution in Europe (Meuse Valley and London) and in the United States (Donora, Pennsylvania) in the 20^th century caused acute disease and deaths of several thousands of people. Hence, there is no denying the fact that air pollution causes adverse effects on human health (Ciocco and Thompson, 1961; Nemery et al., 2001). Urban people die of severe asthma due to nitrogen dioxide and ozone (Korek et al., 2015; Pope et al., 2011; Sunyer et al., 2002; Romieu et al., 1997). Peroxyacytyl nitrate (PAN) causes severe irritation in eyes and it is unequivocally bad for lungs and damages plants too (Taylor, 1969). Polycyclic aromatic hydrocarbons (PAHs) have been found in placental tissues of women and in umbilical cord blood samples of newborn babies with irreversible damage to DNA (Ravindra and Mittal, 2001). Particulate matter (PM) is the complex mixture of organic and inorganic matter, nitrogen compounds, sulphur compounds, PAHs, several heavy metals and radionuclides. Health effects associated with exposure to PM are lung inflammatory reactions, adverse effect on the cardiovascular systems and increase in mortalities, and also reduced lung function in children and increased chances of lung cancers (WHO, 2006). Regular exposure to sulfur dioxide (SO₂) causes asthma, chronic bronchitis and cardiovascular disease for the particularly sensitive people (WHO, 2003). Even the concentration of air pollutants lower than WHO guideline is also known to effect human health adversely (Downs et al., 2007).

An assessment study on the global burden of disease (GBD) showed 0.695 million premature deaths and loss of 18.2 million disability-adjusted life years (DALYs) due to outdoor PM_2.5 and ozone pollution in 2010 and became the fifth leading cause of deaths in India (Lelieveld et al., 2015). Other studies observed that about 0.62 million and 0.69 million premature death cases occurred due to outdoor air pollution and corresponding economic cost was 232.74 and 416.7 billion US$ in 2005 and 2010 respectively (OECD, 2014). According to the recent GBD study, in 2013 alone, total premature deaths due to PM in India was 0.59 million and years of life lost (YLLs) and DALYs were 16.12 million and 16.66 million respectively (Brauer et al., 2015; Salomon et al., 2015).

Different city level studies linked air pollution and health impact have been done in India (Maji et al., 2016a, b; Lelieveld et al., 2015; Nagpure et al., 2014; Gurjar et al., 2010). The city level study helps pollution control authorities to make air quality management and policy development for air quality control at regional level. Various causes of air pollution have triggered megacity cities like Mumbai in India to be high risk areas and their inhabitants are facing various health problems. Such health risks need to be estimated to improve the sustainability of city life and to consolidate the baseline of environmental policy and management.

In this paper human health risk like total mortality, cardiovascular mortality, respiratory mortality, hospital admissions COPD, hospital admissions respiratory disease (HARD) and hospital admissions cardiovascular disease (HACD) due to particulate matter having aerodynamic diameter 10 micrometer or less (PM₁₀), sulphur dioxide (SO₂) and nitrogen dioxide (NO₂) are calculated for the megacity Mumbai in India from 1992 to 2013. And the trend of pollution level from past 22 years are analyzed by excess cases of mortality and morbidity per one million population. For these calculations, Air Quality (AirQ) software for Health Impact Assessment developed by WHO was used.

The relative risk (RR) is derived from dividing the probability that an exposed group will develop disease by the probability that unexposed group will develop the disease (Rothman et al., 2008). The approach to human health risk (HHR) assessment for the present study used AirQ 2.2.3 software (WHO, 2004) developed by the WHO European Centre for Environment Health, Bilthoven Division. This software adopted Risk of Mortality/Morbidity due to Air Pollution (Ri-MAP) model in the present study to estimate the potential impact of exposure to particular air pollutant 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 (Maji et al., 2016a; Jeong, 2013; Northridge, 1995). The PAR can be easily calculated by the following general equation:

where, RR(c) is the changed relative risk for the health outcome in category “c” of exposure and RR(c)=1+(C_a-C_w)×(RR-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) can be 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 RR 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 change time to time and it depends on the area (e.g. industrial or residential), and non-statistical uncertainty of the concentration-response function (WHO, 2003).

The AirQ software uses WHO specified input of RR values (per 10 μg/m³ increase of concentration for hazardous substances) and corresponding baseline incidences (per 10⁵ population) for different air pollutants (particulate matter having aerodynamic diameter ≤10 (PM₁₀), sulphur dioxide (SO₂) and nitrogen dioxide (NO₂) etc.) as well as types of diseases (e.g., cardiovascular, respiratory, COPD, hospital admissions respiratory disease etc.) associated with those values (Table 1), based on various previous studies (Burnett et al., 1997; Sunyer, 1997; Touloumi and Katsouyanni, 1997). Annual averages as per WHO guideline for outdoor air quality for NO₂, SO₂ and PM₁₀ are 40, 20 and 20 μg/m³ respectively (WHO, 2006) were used in this study.

2. 2 Case Study: Mumbai, India

Mumbai metro is the capital of Maharashtra state in the western region of India. With an approximate population of 18.4 million (CI, 2011), it is the second most populous region in India. Such a huge population makes Mumbai the largest energy consumer but environmental conservation mandates and mild weather condition make its per capita energy use the lowest among all Indian states (EGI, 2014). Mumbai is the financial, commercial and entertainment hub of India. It is also one of the world’s top ten centers of commerce in terms of global financial flow, generating 6.16% of India’s GDP and accounting for 25% of industrial output, 70% of maritime trade in India and 70 % of capital transactions to India’s economy (TCPD, 2014). Major industries in Mumbai include chemical products, electrical and non-electrical machinery, pharmaceuticals, textiles, petroleum and allied products.

The methodology as recommended by WHO depends mainly on the concentration of ambient air pollutants and population data. The simple method by 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 is the indicator of exposure of entire city population. Monthly average of ambient air pollution concentrations (μg/m³) for the critical pollutants namely PM₁₀, SO₂ and NO₂ from all monitoring stations in Mumbai during 1992-2013 are taken from Maharashtra Pollution Control Board (MPCB) and the published data from the zonal laboratory of National Environmental Engineering Research Institute (NEERI) in Mumbai. Monthly average of all monitoring stations data are calculated [with standard deviation (SD)] and considered as overall pollution level in Mumbai (Fig. 1).

Fig. 1. 
Trends in annual average concentration (±SD) of SO₂, NO₂ and PM₁₀ in Mumbai.

Population data in year 1991, 2001 and 2011 are taken from Press Information Bureau, Government of India (PIB, 2011) and Census of India (CI, 2011). Population in consequent years from 1992 to 2013 are calculated by population growth equation, P=P₀ exp(kt) where P is final population, P₀ is initial population, t is time (in years) and k is exponential growth factor. For all the pollutants, the required parameters for the software (annual, winter and summer maximum; annual, winter and summer mean; and annual 98th percentiles) are estimated and concentrations were recorded to 10 μg/m³ categories corresponding to equivalent exposure. AirQ model computes mean relative risks with addition of lower and upper bounds (i.e. range) for the 95% confidence interval (CI) based on input parameters for all pollutants.

This paper offers a case study using the WHO approach to assess the impact of atmospheric pollution on human health in Mumbai city. The health risk was estimated based on exposure to PM₁₀, SO₂ and NO₂. It was observed that, in Mumbai, from 1992-2002, the per million average excess number of cases for total mortality, cardiovascular mortality, respiratory mortality and hospital admission of COPD, hospital admission due to respiratory disease and cardiovascular disease were 850, 492, 88, 14, 1069 and 412 respectively, however, from 2003-2013 these figures became 1131, 685, 115, 32, 1442 and 553.

In the previous study, Nagpure et al. (2014) observed that in NCT Delhi in 2010, 11394, 3912, 1697 and 16253 excess number of cases of total mortality, cardiovascular mortality, respiratory mortality and hospital admission of COPD respectively were observed, however, in year 2010 these figures became 18229, 6374, 2701 and 26525. In Pune city, the annual average excess number of cases of total mortality, cardiovascular mortality, respiratory mortality, hospital admission of COPD, hospital admission due to respiratory disease and cardiovascular disease were 3502, 2143, 372, 97, 4303, and 1660, respectively, during 2005 to 2013 (Maji et al., 2016a). Gurjar et al. (2010) observed that for entire Delhi from 1998 to 2005, the per million average excess number of cases for total mortality, cardiovascular mortality, respiratory mortality and hospital admissions COPD varied between 750 to 930, 250 to 310, 110 to 140 and 105 to 130 respectively. Lelieveld et al. (2015) showed that air pollution is responsible for 13500 deaths in Kolkata in year 2010.

From the current and previous studies, it is observed that the excess number of mortality and morbidity is quite high and it is basically due to particulate matter (PM₁₀) rather than due to gaseous pollutants (SO₂ and NO₂). Different combustion processes were the main contributors for PM in Mumbai like power plant, open burning, commercial food sector and road transport, and they contributed by 37%, 24%, 18% and 10% respectively. A study by NEERI (CPCB, 2010) in 2010 found out that open burning and landfill fires of municipal solid waste (MSW) were a major source of air pollution in Mumbai. The survey result showed that about 2% of total generated MSW was burnt on the streets and in slum areas, 10% of the total generated MSW was burnt in landfills by management authorities or due to accidental fires at landfill sites and huge amount of CO, PM, carcinogenic HC and NO_X were emitted. The fine particulate matter PM_2.5 (particles with aerodynamic diameter <2.5 μm) having much more negative influence on lung function owing to smaller size, was not considered in this study. Although, the concentration of PM_2.5 in Mumbai was higher than the WHO guideline (10 μg/m³). The annual average PM_2.5 concentration in Mumbai from 2000 to 2010 was 78.8±42 μg/m³, and in that period PM_2.5 concentration increased 24.6 μg/m³ (Day et al., 2012). In 2015, the annual average PM_2.5 was 81 μg/m³ (http://mpcb.gov.in/). In the recent year 2016, the average PM₁₀ concentration is 138 μg/m³, which is about seven times higher than WHO recommended guideline.

The implementation of clean fuel technology (CNG) and EURO norms in vehicular emission standards and phasing out industries from the city area are responsible for low concentration of SO₂ in Mumbai.

The pollution control authorities in Mumbai urgently need proper policies to elevate ambient air quality in terms of PM₁₀ levels to decrease the economic costs of air pollution-related health impacts.

The proposed and applied methodology is a straightforward spreadsheet model using AirQ software to assess human health effects attributable to air pollutants in Mumbai. AirQ calculates the mortality and morbidity for different pollutants individually and does not consider the synergistic effects of multiple compounds. Estimated excess number of cases are only with reference to the concentrations of pollutants in excess of the levels adopted in the WHO guidelines. However, concentrations lower than the WHO guideline have also excess morbidity and mortality such as long time exposure to PM₁₀. Hence, the pollution control authorities in Mumbai urgently need proper policy to improve ambient air quality in terms of PM₁₀ levels. Since all relevant air pollutant components were not included, these mortality and morbidity numbers should be interpreted as lower limits.

In India, PM_2.5 and PM₁₀ ratio is very high about 0.65 (Satsangi et al., 2011), which is much greater than USA and Europe (Barmpadimos et al., 2012). Long term exposure to PM_2.5 is strongly related with ischaemic heart disease, cerebrovascular disease, lung cancer and acute lower respiratory infections. Coal based power plant is a major source of energy and particulate matter emissions from coal burning power plants was responsible for 80 to 115 thousand premature deaths in 2011-2012 (Guttikunda and Jawahar, 2014).

For more extensive study, one needs to estimate human health risk due to all the relevant pollutants such as PM₁₀, PM_2.5, O₃, CO, NO₂, SO₂ and polyaromatic hydrocarbons. And then, the governing parameter like relative risk in WHO model should be developed for the country specific studies. In the absence of these parametric values, WHO approach to assess the impact of atmospheric pollution on human health can be used to get an overall risk levels in the mega cities.