Environmental Science and Pollution Research

, Volume 24, Issue 5, pp 4709–4730 | Cite as

Disability-adjusted life years and economic cost assessment of the health effects related to PM2.5 and PM10 pollution in Mumbai and Delhi, in India from 1991 to 2015

  • Kamal Jyoti Maji
  • Anil Kumar Dikshit
  • Ashok Deshpande
Research Article

Abstract

Particulate air pollution is becoming a serious public health concern in urban cities in India due to air pollution-related health effects associated with disability-adjusted life years (DALYs) and economic loss. To obtain the quantitative result of health impact of particulate matter (PM) in most populated Mumbai City and most polluted Delhi City in India, an epidemiology-based exposure–response function has been used to calculate the attributable number of mortality and morbidity cases from 1991 to 2015 in a 5-year interval and the subsequent DALYs, and economic cost is estimated of the health damage based on unit values of the health outcomes. Here, we report the attributable number of mortality due to PM10 in Mumbai and Delhi increased to 32,014 and 48,651 in 2015 compared with 19,291 and 19,716 in year 1995. And annual average mortality due to PM2.5 in Mumbai and Delhi was 10,880 and 10,900. Premature cerebrovascular disease (CEV), ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD) causes are about 35.3, 33.3, and 22.9% of PM2.5-attributable mortalities. Total DALYs due to PM10 increased from 0.34 million to 0.51 million in Mumbai and 0.34 million to 0.75 million in Delhi from average year 1995 to 2015. Among all health outcomes, mortality and chronic bronchitis shared about 95% of the total DALYs. Due to PM10, the estimated total economic cost at constant price year 2005 US$ increased from 2680.87 million to 4269.60 million for Mumbai City and 2714.10 million to 6394.74 million for Delhi City, from 1995 to 2015, and the total amount accounting about 1.01% of India’s gross domestic product (GDP). A crucial presumption is that in 2030, PM10 levels would have to decline by 44% (Mumbai) and 67% (Delhi) absolutely to maintain the same health outcomes in year 2015 levels. The results will help policy makers from pollution control board for further cost–benefit analyses of air pollution management programs in Mumbai and Delhi.

Keywords

Particulate matter (PM) Health endpoints Premature mortality Disability-adjusted life years Economic cost 

Introduction

The acute and chronic health impacts from short- and long-term exposures to particulate matter (PM) are well established in the literature (Zanobetti et al. 2008; Pope et al. 1995, 2004, 2011; Anenberg et al. 2011; Cesaroni et al. 2014; Beelen et al. 2014; Hamra et al. 2014; Korek et al. 2015; Brauer et al. 2012, 2015; Brunekreef and Holgate 2002). Epidemiological cohort studies show that these health impacts rely on long-term ambient (both household and outdoor) measurements of PM and associated risk factor vary country to country (Pope et al. 2004; Pope and Dockery 2006; HEI 2011; CPCB 2012; Dholakia et al. 2014). Air pollution is a high priority in global burden of disease (GBD) assessment, and World Health Organization (WHO) has estimated that air pollution is responsible for 6.7% of all deaths and 7.6% of disability-adjusted life years (DALYs) (4.5 and 3.1% for household and outdoor air pollution) globally (Lim et al. 2012; WHO 2016a; WHO 2014a, b) and fourth highest ranking risk factor for premature mortality in the world (IHME 2016). Global population-weighted PM2.5 increased by 20.4% in South Asia, Southeast Asia, and China from 1990 to 2013 (Lim et al. 2012). Resent study shows about 5.5 million premature deaths globally in 2013 due to household and outdoor air pollution and 55% of deaths occur in China and India. About 0.91 and 0.81 million premature death in China and 0.59 and 0.92 million premature death in India occur due to household and outdoor air pollution in 2013 (IHME 2016). Coal, wood, and dung burning as solid fuels for cooking and heating are the major problems of household air pollution in China and India.

In India, rapid industrial development and urbanization have posed huge changes in economy in the past decade making India one of the fastest growing economy in the world (http://www.imf.org/external/index.htm), but the growth has come at a real cost to its environment and public health. In the last 20 years, urban population has increased from 217 to 377 million in India and will reach 600 million by 2031 (NCE 2013). The rapidly growing urban regions are responsible for 44% of India’s carbon emissions. India is now facing several environmental challenges, including air pollution, availability of clean water, pollution of river water, deforestation, and desertification. Negative health outcomes are associated with such environmental problems, as high levels of air pollution incur health system and the economy (WB 2013).

The GBD assessments study showed 0.695 million premature deaths and loss of 18.2 million DALYs due to outdoor PM2.5 and ozone pollution in 2010 and became the fifth leading cause of death in India (IHME 2013; Lelieveld et al. 2015; GI 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 costs were 232.74 and 416.7 billion in 2005 and 2010 (OECD 2014). According to the recent GBD study, in 2013, total premature death due to PM in India was 0.59 million [95% confidence interval (CI) 0.51–0.67 million], and years of life lost (YLLs) and DALYs were 16.12 million and 16.66 million (Brauer et al. 2015; IHME 2016; Salomon et al. 2015). In total premature death due to PM, ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), stroke, lower respiratory infection (LRI), and lung cancer were responsible for 15.47, 5.39, 6.97, 3.94, and 0.6%, respectively.

Different city-level studies linked PM10 pollution and short-term mortality (Cropper et al. 1997; Ghose et al. 2005; Nema and Goyal 2010; Guttikunda and Jawahar 2014; Guttikunda and Goel 2013; Guttikunda et al. 2014; Maji et al. 2016) and COPD (Gurjar et al. 2010) lung function in children and adults (Foster and Kumar 2011; Ghosh and Mukherji 2014). The city-level study helps pollution control authorities to make air quality management and policy development for air quality control at regional level.

In this study, the DALYs and economic costs caused by long-term mortality and 18 different morbidities attributed to PM10 and long-term mortality attributed to PM2.5 are estimated in most populated and most polluted cities in India from 1991 to 2015.

Methods

The selected cities in the case study are Mumbai and Delhi in India. Mumbai is the financial capital and Delhi is the political capital of India; both are home to many textile, heavy manufacturing, large chemical, and energy industries. Mumbai and Delhi are within top 20 polluted cities of the world and Delhi is the most polluted, with an annual mean concentration of PM2.5 of 153 μg/m3 in 2010, which was 15 times higher than WHO standard (Ellis and Roberts 2016). The total population in Mumbai metropolitan and Delhi were 18.41 and 16.79 million in 2011, with growth rate 1.2 and 2.12% per year in the past decade (http://censusindia.gov.in/).

PM and population data

Ambient air pollution consists of a complex mixture of different pollutants like PM, sulfur dioxide, nitrogen dioxide, carbon monoxide, ozone, polyaromatic hydrocarbon, and black carbon. The pollutants are correlated to each other and associated with synergistic effect on human health. The overcounting problem occurs when health effects of multiple pollutants are aggregated (Johns et al. 2012). Consistent with most previous studies conducted in China and Europe, PM is selected as the indicator of all air pollutant because PM shows the most significant adverse health effects among the pollutants (Brunekreef and Holgate 2002; Pope and Dockery 2006).

PM2.5 is a better exposure predictor than PM10 for health risk assessments (Cifuentes et al. 2000); however, PM10 is mainly used in this study, as (i) outdoor air pollution related epidemiological studies in India mostly utilize PM10 (HEI 2011; Dholakia et al. 2014) that is routinely monitored by the Central Pollution Control Board (CPCB) through 573 monitoring stations (http://cpcbenvis.nic.in/airpollution/monetoring.htm); (ii) CPCB started PM2.5 measurement in six major cities in 2010 for air quality forecasting (Sahu et al. 2011); and (iii) for an accurate and complete assessment of regional human health risks, the numbers of PM2.5 monitoring stations are too few (Dey and Di Girolamo 2010).

The annual average PM concentration (μg/m3) value from 1991 to 2015 is used in this study, which is the average level of all monitoring stations in the city (12 in Mumbai and 22 in Delhi), and the data belongs to Brihanmumbai Municipal Corporation (BMC), National Environmental Engineering Research Institute (NEERI), and Maharashtra Pollution Control Board (MPCB) in Mumbai and CPCB, NEERI, and Delhi Pollution Control Committee (DPCC) in Delhi.

For the present study, long-term PM2.5 values are not available in Mumbai and Delhi. For estimating India’s historic PM2.5 concentration levels, the conversion factor is used between PM10 and PM2.5. Most studies focusing on India’s air pollution use 0.50–0.70 as PM10–PM2.5 conversion factors (Sharma and Maloo 2005; Satsangi et al. 2011; WHO 2008). Among them, the smallest conversion factor (0.5) that has been used to compute our central estimates for PM2.5 caused health damage.

Population and age distribution data in 1991, 2001, and 2011 have been taken from Census of India (http://censusindia.gov.in/), and population of subsequent years from 1991 to 2015 was calculated by population growth equation P = P0 exp (kt)(https://www.wou.edu/las/physci/ch371/lecture/popgrowth/howlong.htm), where P, P0, t, and k denote final population, initial population, time (year), and exponential growth factor, respectively.

In the case of PM and population data, 5-year average for each year has been used (e.g., year 1995 is represented an average PM level for the period of 1991–1995), as DALYs and economic cost projected for each 5-year interval (Table 1).
Table 1

PM10 concentrations (μg/m3) (5-year annual average) and urban populations (in millions) in Mumbai and Delhi

 

Mumbai

Delhi

Year

Annual average PM10 concentration

Urban population (million)

Annual average PM10 concentration

Urban population (million)

1995

142

13.29

204

10.19

2000

133

15.18

187

12.36

2005

115

16.81

186

14.40

2010

139

17.80

232

15.85

2015

137

18.84

263

17.45

Health outcomes

The health endpoints are selected in this study based on availability of following parameters: (i) the health outcome due to PM pollution and corresponding exposure–response (E-R) coefficient (ERC) or relative risk (RR), (ii) the baseline incidence rate (BIR) of each health outcome, (iii) DALY value of each health outcome, and (iv) economic cost per case of health effects.

After considerable literature review, mortality and morbidity attributed to PM10 and mortality attributed to PM2.5 among child, adult, and all ages are considered in this study. ERC and BIR of each health endpoints attributed to PM10 are summarized in Table 2.
Table 2

Exposure–response coefficients of PM10 (per 1 μg/m3) and incidence rates (per person) of health endpoints

 

Health outcome

ER coefficient

Frequency (BIR)

Reference

1

Total mortality >30

0.0043 (0.0026, 0.0061)

0.01013

HEI (2011) and Dholakia et al. (2014)

2

Chronic bronchitis (all ages)

0.0045 (0.00127, 0.00773)

0.01390

Zhang et al. (2008) and Tang et al. (2014)

3

Restricted activity days (RADs) (adults ≥20)

0.0094 (0.0079, 0.0109)

3

Kan and Chen (2004)

4

Asthma attack (children <15)

0.0044 (0.0027, 0.0062 )

0.0693

Kan and Chen (2004) and Zhang et al. (2008)

5

Asthma attack (adults ≥15)

0.0039 (0.0019, 0.0059)

0.0561

Kan and Chen (2004) and Zhang et al. (2008)

6

Emergency room visits

2.91E-05 (2.18E-05, 3.82E-05)

1

ExternE (2005)a

7

Acute bronchitis all ages

0.0055 (0.00189, 0.00911)

0.0372

Kan and Chen (2004) and Zhang et al. (2008)

8

COPD

2.7E-06 (1.16E-06, 2.93E-06)

1

ExternE (2005)a

9

Respiratory HA

7.03E-06 (3.83E-06, 1.03E-05)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

10

Cerebrovascular HA

5.04E-06 (3.88E-07, 9.69E-06)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

11

Cardiovascular HA

4.34E-06 (2.17E-06, 6.51E-06)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

12

Cough children

0.133 (0.023, 0.243)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

13

Cough adult

0.168 (0.0291, 0.307)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

14

Respiratory symptom days (children)

0.186 (0.092, 0.277)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

15

Respiratory symptom days (adults)

0.13 (0.015, 0.243)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

16

Lower respiratory symptoms (wheeze) (children <15)

0.186 (0.092, 0.277)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

17

Lower respiratory symptoms (wheeze) (adults ≥15)

0.13 (0.15, 0.243)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

18

Bronchodilator usage (children)

0.078 (0.006, )0.15

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

19

Bronchodilator usage (adults)

0.163 (0.0125, 0.313)

1

ExternE (2005)a, Nam et al. (2010), and Matus et al. (2012)

aExternE (2005) refer to Bickel and Friedrich (2005)

Assessment of health effects

The excess number of health outcomes due to PM are estimated by using (1) an ERC, which can be calculated from relative risk (RR) value; (2) BIR of health endpoints; (3) change in ambient air concentration; and (4) exposed population. The health effect of PM10 depends upon a functional form of the relationship. If the functional form of ERC is log-linear, excess number of cases can be calculated by using a health impact function given in Eq. (1) (Lelieveld et al. 2013; Pascal et al. 2013; Mahapatra et al. 2014; Voorhees et al. 2014).
$$ \Delta {E}_{\mathrm{mor}}=\left[1- \exp \Big(-\beta \left({C}_a-{C}_0\right)\right]\times {I}_r\times {E}_{\mathrm{pop}} $$
(1)
where ΔEmor is the excess number of mortality or morbidity in a year due to PM10, β is the ERC, Ir is the BIR, Ca is the annual average ambient PM10 concentration, C0 is the threshold level, and Epop is the exposed population. The ERC (β) describes the increased risk of a population associated with a certain health response when exposed to PM10. The ERC can be derived from established epidemiological cohort studies and that defines the relationship between the change in PM10 concentration and RR of health impacts and given by the following expression:
$$ RR= \exp \left(\beta \varDelta C\right) $$
(2)

Based on India’s cohort studies, the estimated total long-term mortality risk RR = 1.044 for 10 μg/m3 increase of PM10, giving rise to an ERC = 0.0043. The 95% CIs are also reported as 0.0026 to 0.0061 (HEI 2011; Dholakia et al. 2014).

The BIR of premature death among adult (i.e., age >30 years) is the measure as number of death before reaching average life expectancy, and the reported number is 1013 per 105 people (WHO 2000). WHO-recommended annual average threshold concentration (C0) of PM10 (20 μg/m3) is selected as a primary standard in this study (WHO 2005). The Epop can be the entire population or subgroups like children or elderly persons according to the targeted population and related health impacts.

Recent studies revealed the nonlinear feature of health effects by air pollution (PM2.5) (Apte et al. 2015; Burnett et al. 2014; WHO 2014a, b). Burnett et al. (2014) developed nonlinear integrated exposure–response (IER) function that constrains the shape of the concentration response relationship unlike the vintage concentration response relation developed by Pope et al. (2002), by considering RR of premature mortality for Global Burden of Disease (GBD) studies.

The relationship between PM2.5 and RR, which are organized in bins from GDB 2010 (http://ghdx.healthdata.org/record/global-burden-disease-study-2010-gbd-2010-ambient-air-pollution-risk-model-1990-2010), has been used to calculate RR over India for causes of premature mortality in adults (>25 years), CEV (stroke), IHD, COPD, and lung cancer (LC). In addition, the RR for acute lower respiratory infections (ALRIs) was also calculated for infants (<5 years). Single-baseline mortality values (116.4, 165.8, 142.1, 28.5, and 39 per 100,000 population for CEV, IHD, COPD, LC, and ALRI, respectively) for India are available from WHO (2011), Malik and Raina (2015), and IHME (2016).

Estimation of DALYs

The DALY values for each health outcome are adopted from the World Bank (WB) (Lvovsky et al. 2000) and Zhang et al. (2006) study. For premature death due to air pollution, 7.5 DALYs are attributed to each death (WB 2013). The DALY values due to premature death in India are less than USA because average life expectancy in India is much lower than USA. DALYs per 10,000 cases of various health endpoints are presented in Table 3.
Table 3

Values of DALYs per 10,000 cases of health endpoints due to air pollution

Health endpoints

DALYs per 10,000 cases

Reference

Mortality

75,000

WB (2013)

Chronic bronchitis

22,000

WB (2013)

Restricted activity days (RADs)

3

Lvovsky et al. (2000) and WB (2013)

Asthma

4

Lvovsky et al. (2000) and Zhang et al. (2006)

Acute bronchitis

4

Zhang et al. (2006)

Emergency room visit

3

Lvovsky et al. (2000)

Respiratory hospital admissions

160

WB (2013)

Other hospital admissions

264

Lvovsky et al. (2000)

Cough day

3

Lvovsky et al. (2000)

Symptom day

3

Lvovsky et al. (2000)

Lower respiratory symptoms

3

Lvovsky et al. (2000)

Respiratory medication use

3

Zhang et al. (2006)

Economic costs of health effects

The value of a statistical life (VSL) represents an individual’s willingness to pay (WTP) for a marginal reduction in the risk of death. Cost of illness (COI) method was also employed for some morbidity endpoints, which measure the total COI including hospital admission cost, medical cost, and day loss. VSL in India in 2004 was 94,721 US$ (Laxminarayan et al. 2007). A benefit transfer approach (BTA) is used in this study because a detailed survey of economic costs of various health endpoints from air pollution was not available for India (Matus et al. 2012). Morbidity endpoint values are adjusted with the European valuation table presented in Bickel and Friedrich (2005), by using the average gross domestic product (GDP) per capita difference between India and European Union (EU) (WB 2016a). The Cmorb is calculated through BTA by the following equation: Cmorb(India) = Cmorb(EU) × (PCIIndia/PCIEU)e, where Cmorb(India) and Cmorb(EU) are the morbidity treatment cost in India and EU country, while PCIIndia and PCIEU represent the per capita income in India and EU. “e” is the elastic coefficient of WTP and is assumed to be 1.0 (Zhang et al. 2008). UN (2005), Navrud (2007), Milligan et al. (2014), Johnston et al. (2015), Bateman et al. (2011), and Hammitt and Robinson (2011) recommended benefit transfer using purchasing power parity (PPP) value. Mortality and morbidity costs are converted to constant price year 2005 US$ using Ecost(2005) = Ecost(yt) × (1 +  % ΔP +  % ΔY)k; Ecost(2005) and Ecost(yt) are the economic cost of mortality and morbidity in year 2005 and any year yt. %ΔP and %ΔYare the percentage change in real GDP per capita growth and the percentage increase in consumer price in real from yearyt to 2005. k is an income elasticity to the power of 0.8 (OECD 2014). In the present study, VSL and COPD cost are taken from the study conducted in India. Table 4 represents (unit value of various health points) the competition of BTA using per capita income (PCI) and purchasing power method.
Table 4

Unit value (per case) for various health endpoints in India

Health endpoints

Cost per cases (2005 US$)

Approach

Data source

PCI

PPP

Mortality

103,598

WTP

Laxminarayan et al. (2007)

Chronic bronchitis

6,857.43

21,025.69

WTP

Bickel and Friedrich (2005)

Restricted activity days (RADs)

4.69

14.39

WTP

Bickel and Friedrich (2005)

Asthma

1.91

5.87

WTP

Bickel and Friedrich (2005)

Acute bronchitis

8.06

24.75

WTP

Bickel and Friedrich (2005)

Emergency room visit

24.18

74.14

WTP

Bickel and Friedrich (2005)

COPD hospital admission

723.29

COI

Patankar and Trivedi (2011)

Hospital admissions

72.18

221.32

WTP

Bickel and Friedrich (2005)

Cough day

1.37

4.21

WTP

Bickel and Friedrich (2005)

Symptom day

1.37

4.21

WTP

Bickel and Friedrich (2005)

Lower respiratory symptoms

2.71

8.30

WTP

Bickel and Friedrich (2005)

Respiratory medication use

0.04

0.11

WTP

Bickel and Friedrich (2005)

The key assessment approaches and steps are shown in Fig. 1.
Fig. 1

DALYs and economic cost assessment approaches and steps for health effect of PM

Results

Health effects due to PM10

Mumbai and Delhi are found to be among the most polluted cities in India as well as in South Asia (populations >10 million), which was a combined result of vast urbanization, old automobiles, outdated industrial plants, and lack of government regulation. The annual average PM10 decreased from 142 μg/m3 in 1995 to 137 μg/m3 in 2015 in Mumbai City and increased from 204 μg/m3 in 1995 to 263 μg/m3 in 2015 in Delhi City (Table 1).

Table 2 summarizes the E-R coefficients (95% CI) of selected health outcomes due to PM10 and corresponding incidence rates (per person/year) in the study. Using the E-R functions (β), frequency of health endpoints, threshold concentration (C0), exposed population (Ep), and exposure concentration (C), the attributable number of cases per year due to PM10 pollution in urban area of Mumbai and Delhi have been calculated.

Mumbai

Table 5 lists the attributable cases of health outcomes due to PM10 pollution in Mumbai City from 1995 to 2015. PM10 pollution have caused 19,291 premature deaths in 1995. Further, 78,293 cases of chronic bronchitis (CB), 11,441 respiratory-related hospital admissions (HAs), 8203 cerebrovascular HAs, 7064 cardiovascular-related HAs, 47,294 emergency room visits for internal medicine, 242,383 cases of acute bronchitis (AB), and 279,788 asthma attacks due to PM10 pollution have been recorded in Mumbai City in 1995. After a decade in 2005, attributable number of premature death became 21,137. And 81,010 cases of CB, 11,175 respiratory-related HAs, 8013 cerebrovascular-related HAs, 6900 cardiovascular-related HAs, 49,967 emergency room visits for internal medicine, 253,686 cases of AB, and 279,788 asthma attacks attribute to PM10 pollution in 2005. In 2015, attributable number of premature death became 32,014. And 107,197 cases of CB, 15,490 respiratory-related HAs, 11,107 cerebrovascular-related HAs, 9564 cardiovascular-related HAs, 64,037 emergency room visits for internal medicine, 332,595 cases of AB, and 381,579 asthma attacks were due to PM10 pollution in 2015. In 2005, both the PM10 concentration (115 μg/m3) and the excess number of health outcomes were relatively lower, with exception that children that suffer from cough, respiratory symptoms, lower respiratory symptoms (wheeze), and bronchodilator users are somewhat high. Between 1995 and 2015, premature mortality, CB, RADs, HA, emergency room visits, and AB increased to 65.95, 36.92, 56.49, 35.39, 35.40, and 37.22%, respectively. From 1995 (142 μg/m3) to 2015 (137 μg/m3), PM10 concentration did not change much, but the mortality and morbidity value changes were very high due to increase of population by 41.73% (5.55 million). Asthma among children increased by 4.90%, and other health effects related to children (age <15) increased parallel with increase of child population by 8.61%, although overall child population decreased from 24.75 to 18.97% between 1995 and 2015.
Table 5

Estimated number of cases attributed to PM10 pollution in urban area of Mumbai

Health endpoints

Attributable number of cases (mean and 95% CI)

1995

2000

2005

2010

2015

Mortality (adult ≥30 years)

19,291 (12,850, 24,797)

22,026 (14,572, 28,499)

21,137 (13,790, 27,730)

30,561 (20,299, 39,392)

32,014 (21,239, 41,312)

Chronic bronchitis (all ages)

78,293 (26,618, 113,085)

84,284 (28,279, 123,129)

81,010 (26,452, 121,208)

102,318 (34,594, 148,490)

107,197 (36,160, 155,877)

RADs (adults ≥20)

15,147,964 (13,735,090, 16,323,723)

17,086,984 (15,421,565, 18,492,179)

17,008,120 (15,198,694, 18,578,239)

22,561,763 (20,417,919, 24,356,215)

23,705,551 (21,435,737, 25,610,010)

Asthma attack (children)

94,989 (64,200, 121,307)

90,609 (60,827, 116,477)

87,021 (57,610, 113,415)

95,116 (64,104, 121,804)

99,644 (67,077, 127,749)

Asthma attack (adults)

184,798 (101,027, 250,370)

205,511 (111,436, 280,515)

196,583 (104,817, 272,535)

269,212 (146,671, 365,877)

281,935 (153,385, 383,664)

Acute bronchitis (all ages)

242,383 (102,188, 332,482)

261,901 (108,857, 363,581)

253,686 (102,382, 361,228)

317,255 (132,958, 437,364)

332,595 (139,043, 459,467)

Emergency room visits

47,294 (35,446, 62,049)

49,967 (37,448, 65,559)

46,211 (34,631, 60,636)

61,322 (45,959, 80,455)

64,037 (47,993, 84,018)

COPD

4,395 (1,888, 4,770)

4,643 (1,995, 5,039)

4,293 (1,845, 4,659)

5,699 (2,449, 6,184)

5,951 (2,557, 6,458)

Respiratory HA

11,441 (6,234, 16,759)

12,086 (6,586, 17,705)

11,175 (6,089, 16,371)

14,834 (8,083, 21,729)

15,490 (8,441, 22,691)

Cerebrovascular HA

8,203 (632, 15,767)

8,666 (667, 16,657)

8,013 (617, 15,402)

10,636 (819, 20,443)

11,107 (855, 21,348)

Cardiovascular HA

7,064 (3,533, 10,595)

7,463 (3,732, 11,193)

6,900 (3,450, 10,349)

9,159 (4,580, 13,737)

9,564 (4,783, 14,345)

Cough (children)

3,290,177 (3,093,456, 3,290,177)

3,331,327 (3,085,166, 3,331,328)

3,688,939 (3,269,925, 3,688,951)

3,375,845 (3,155,190, 3,375,845)

3,573,413 (3,331,089, 3,573,413)

Cough (adult)

8,674,103 (8,428,408, 8,674,103)

10,258,923 (9,879,048, 10,258,923)

11,360,233 (10,635,490, 11,360,234)

12,958,587 (12,547,726, 12,958,587)

13,716,975 (13,261,342, 13,716,975)

Respiratory symptom days (children)

3,290,177 (3,290,135, 3,290,177)

3,331,328 (3,331,229, 3,331,328)

3,688,951 (3,688,337, 3,688,951)

3,375,845 (3,375,784, 3,375,845)

3,573,413 (3,573,338, 3,573,413)

Respiratory symptom days (adults)

8,674,102 (7,292,526, 8,674,103)

10,258,919 (8,382,912, 10,258,923)

11,360,182 (8,610,418, 11,360,234)

12,958,584 (10,771,088, 12,958,587)

13,716,972 (11,345,211, 13,716,975)

Lower respiratory symptoms (wheeze) (children)

3,290,177 (3,290,135, 3,290,177)

3,331,328 (3,331,229, 3,331,328)

3,688,951 (3,688,337, 3,688,951)

3,375,845 (3,375,784, 3,375,845)

3,573,413 (3,573,338, 3,573,413)

Lower respiratory symptoms (wheeze) (adults)

8,674,102 (7,292,526, 8,674,103)

10,258,919 (8,382,912, 10,258,923)

11,360,182 (8,610,418, 11,360,234)

12,958,584 (10,771,088, 12,958,587)

13,716,972 (11,345,211, 13,716,975)

Bronchodilator usage (children)

3,289,943 (1,712,277, 3,290,177)

3,330,843 (1,642,946, 3,331,328)

3,686,643 (1,597,396, 3,688,949)

3,375,521 (1,718,784, 3,375,845)

3,573,025 (1,802,454, 3,573,413)

Bronchodilator usage ( adults)

8,674,103 (6,797,597, 8,674,103)

10,258,923 (7,768,844, 10,258,923)

11,360,232 (7,876,987, 11,360,234)

12,958,587 (10,016,089, 12,958,587)

13,716,975 (10,539,350, 13,716,975)

Delhi

The attributable number of cases of mortality and morbidity due to PM10 pollution in megacity Delhi from 1995 to 2015 is shown in Table 6. Totally, PM10 caused 19,716 attributable deaths in Delhi in 1995. It also accounted for 79,636 cases of CB, 13,141 respiratory-related HAs, 9423 cerebrovascular HAs, 8115 cardiovascular-related HAs, 54,288 emergency room visits for internal medicine, 240,959 cases of AB, and 287,554 asthma attacks due to PM10 pollution in Delhi City in 1995. After a decade in 2005, attributable number of premature death became 276,491. And 105,358 cases of CB, 16,803 respiratory-related HAs, 12,048 cerebrovascular-related HAs, 10,375 cardiovascular-related HAs, 69,425 emergency room visits for internal medicine, 320,785 cases of AB, and 373,702 asthma attacks were due to PM10 pollution in 2005. In 2015, attributable number of premature death became 48,651. And 161,388 cases of CB, 29,813 respiratory-related HAs, 21,379 cerebrovascular-related HAs, 18,411 cardiovascular-related HAs, 123,079 emergency room visits for internal medicine, 478,833 cases of AB, and 586,738 asthma attacks were due to PM10 pollution in 2015. From 1995 (204 μg m−3) to 2015 (263 μg m−3), PM10 concentration change was very high, and simultaneously, the mortality/ morbidity value changes were also very high due to increase of population (7.26 million). Between 1995 and 2015, premature mortality, CB, RADs, HA, emergency room visits, and AB increased to 146.76, 102.66, 111.87, 126.88, 126.72, and 98.72%, respectively. Asthma among children increased by 55.62%, and other health effects related to children (age <15) increased parallel with increase of child population to 31.24% between 1995 and 2015.
Table 6

Estimated number of cases attribute to PM10 pollution in urban area of Delhi

Health endpoints

Attributable number of cases (mean and 95% CI)

1995

2000

2005

2010

2015

Mortality (adult ≥30 years)

19,716 (13,708, 24,332)

23,822 (16,378, 29,708)

27,649 (18,996, 34,502)

40,707 (28,858, 49,424)

48,651 (35,154, 57,996)

Chronic bronchitis (all ages)

79,636 (29,454, 107,373)

90,770 (32,836, 124,543)

105,358 (38,062, 144,713)

135,421 (51,983, 177,509)

161,388 (64,462, 205,574)

RADs (adults ≥20)

13,958,154 (12,999,451, 14,686,115)

16,809,330 (15,552,410, 17,787,633)

19,540,419 (18,071,979, 20,685,032)

25,820,288 (24,293,557, 26,931,364)

29,572,999 (28,099,668, 30,595,985)

Asthma attack (children)

96,849 (68,306, 118,778)

97,790 (68,206, 121,183)

113,502 (79,111, 140,742)

126,335 (90,769, 152,344)

150,719 (110,434, 178,612)

Asthma attack (adults)

190,706 (109,833, 246,729)

224,223 (127,377, 293,561)

260,200 (147,694, 340,908)

364,096 (214,566, 461,978)

436,738 (263,772, 543,084)

Acute bronchitis all ages

240,959 (111,127, 307,886)

276,270 (124,459, 359,327)

320,785 (144,310, 417,677)

405,818 (194,586, 504,127)

478,833 (239,242, 578,415)

Emergency room visits

54,288 (40,697, 71,205)

59,927 (44,921, 78,607)

69,425 (52,041, 91,067)

97,435 (73,049, 127,782)

123,079 (92,286, 161,390)

COPD

5,049 (2,170, 5,479)

5,573 (2,394, 6,047)

6,456 (2,774, 7,005)

9,066 (3,896, 9,838)

11,456 (4,923, 12,432)

Respiratory HA

13,142 (7,162, 19,248)

14,504 (7,904, 21,245)

16,803 (9,157, 24,612)

23,594 (12,858, 34,556)

29,813 (16,249, 43,664)

Cerebrovascular HA

9,423 (726, 18,110)

10,400 (801, 19,987)

12,048 (928, 23,155)

16,918 (1,303, 32,512)

21,379 (1,647, 41,081)

Cardiovascular HA

8,115 (4,058, 12,170)

8,956 (4,479, 13,432)

10,375 (5,189, 15,560)

14,570 (7,287, 21,850)

18,411 (9,208, 27,610)

Cough (children)

2,522,190 (2,485,183, 2,522,190)

2,710,866 (2,652,747, 2,710,866)

3,158,971 (3,089,705, 3,158,971)

3,006,702 (2,983,698, 3,006,702)

3,310,216 (3,297,896, 3,310,216)

Cough (adult)

6,649,409 (6,617,566, 6,649,409)

8,348,191 (8,283,595, 8,348,191)

9,728,144 (9,650,697, 9,728,144)

11,541,586 (11,517,337, 11,541,586)

12,706,659 (12,695,932, 12,706,659)

Respiratory symptom days (children)

2,522,190 (2,522,190, 2,522,190)

2,710,866 (2,710,865, 2,710,866)

3,158,971 (3,158,970, 3,158,971)

3,006,702 (3,006,702, 3,006,702)

3,310,216 (3,310,216, 3,310,216)

Respiratory symptom days (adults)

6,649,409 (6,225,741, 6,649,409)

8,348,191 (7,667,029, 8,348,191)

9,728,144 (8,922,659, 9,728,144)

11,541,586 (11,060,661, 11,541,586)

12,706,659 (12,375,738, 12,706,659)

Lower respiratory symptoms (wheeze) (children)

2,522,190 (2,522,190, 2,522,190)

2,710,866 (2,710,865, 2,710,866)

3,158,971 (3,158,970, 3,158,971)

3,006,702 (3,006,702, 3,006,702)

3,310,216 (3,310,216, 3,310,216)

Lower respiratory symptoms (wheeze) (adults)

6,649,409 (6,225,741, 6,649,409)

8,348,191 (7,667,029, 8,348,191)

9,728,144 (8,922,659, 9,728,144)

11,541,586 (11,060,661, 11,541,586)

12,706,659 (12,375,738, 12,706,659)

Bronchodilator usage (children)

2,522,188 (1,683,744, 2,522,190)

2,710,860 (1,715,985, 2,710,866)

3,158,964 (1,992,815, 3,158,971)

3,006,702 (2,163,338, 3,006,702)

3,310,216 (2,540,848, 3,310,216)

Bronchodilator usage (adults)

6,649,409 (5,979,033, 6,649,409)

8,348,191 (7,313,905, 8,348,191

9,728,144 (8,508,069, 9,728,144)

11,541,586 (10,724,799, 11,541,586)

12,706,659 (12,098,838, 12,706,659)

Health effects due to PM2.5

Long-term exposure to PM2.5 is associated with increased mortality in adult (>25 years) from stroke (CEV), IHD, COPD, and LC, and it is also associated with increased incidence of acute ALRI in infants (<5 years).

Based on the health impact function (Eq.1), premature mortalities by CEV, IHD, COPD, LC, and ALRI are calculated, as well as the corresponding uncertainties (95% CI) of the mortality are estimated in Mumbai and Delhi in 1995–2015. Total estimated annual premature mortality due to PM2.5 is shown in Table 7. It is shown that total premature mortality in Mumbai was 8678 (95% CI 4424–13,016) in 1995 and became 13,196 (95% CI 6772–19,945) in 2015. In Delhi, the premature mortality was 7481 (95% CI 4050–10,810) in 1995 and became 14,844 (95% CI 9030–21,469) in 2015. Annual average mortality in Mumbai and Delhi increased about 52.1 and 67.9% from 1995 to 2015. Premature CEV, IHD, and COPD causes are about 35.3, 33.3, and 22.9% of PM2.5-attributable mortalities for all five causes.
Table 7

Premature mortality attribute to PM2.5 in Mumbai and Delhi

City

Year

Mortality (mean and 95% CI)

CEV

IHD

COPD

LC

ALRI

Total mortality

Mumbai

1995

3,156 (1,097, 3,882)

2,894 (2,019, 4,607)

1,885 (965, 2,681)

480 (167, 656)

263 (177, 1,190)

8,678 (4,424, 13,016)

2000

3,558 (1,253, 4,404)

3,249 (2,235, 5,167)

2,095 (1,026, 2,972)

529 (190, 728)

287 (195, 1,315)

9,719 (4,899, 14,586)

2005

3,975 (1,328, 5,040)

3,652 (2,519, 5,704)

2,231 (1,105, 3,244)

568 (185, 794)

252 (174, 1,389)

10,678 (5,311, 16,170)

2010

4,417 (1,545, 5,469)

4,076 (2,844, 6,451)

2,655 (1,359, 3,722)

677 (235, 916)

306 (208, 1,661)

12,132 (6,191, 18,219)

2015

4,856 (1,698, 5,991)

4,481 (3,126, 7,048)

2,840 (1,494, 4,091)

731(258, 997)

289 (196, 1,817)

13,196 (6,772, 19,945)

Delhi

1995

2,552 (985, 3,035)

2,467 (1,731, 3,824)

1,770 (981, 2,386)

447 (187, 581)

245 (166, 984)

7,481 (4,050, 10,810)

2000

3,061 (1,152, 3,549)

2,909 (2,045, 4,572)

2,064 (1,133, 2,799)

521 (204, 680)

285 (193, 1,281)

8,841 (4,728, 12,882)

2005

3,751 (1,412, 4,497)

3,565 (2,507, 5,575)

2,477 (1,388, 3,391)

639 (250, 834)

290 (198, 1,561)

10,722 (5,754, 15,858)

2010

4,220 (1,723, 4,996)

4,145 (2,975, 6,372)

3,111 (1,753, 4,144)

783 (337, 999)

352 (240, 1,867)

12,611 (7,028, 18,378)

2015

4,876 (2,094, 5,729)

4,861 (3,482, 7,382)

3,789 (2,249, 4,952)

950 (950, 1,194)

368 (255, 2,212)

14,844 (9,030, 21,469)

DALYs due to PM10

Using the value of DALYs per 10,000 excess number of cases (Table 3) and quantitative health effects (Tables 5 and 6), the corresponding annual DALYs over the period of 1995 to 2015 in Mumbai and Delhi Cities have been estimated (Tables 8 and 9). The total DALY associated with PM10 pollution in Mumbai was 336,755.24 (95% CI 171,790.56–455,407.53) in 1995 and became 505,050.97 (95% CI 263,416.42–683,089.71) in 2015, increased by about 49.98%. In Delhi, in 1995, the total DALY was 339,296.03(95% CI 182,215.12–435,662.21) and 750,320.60 (95% CI 433,450.70–919,043.62) in 2015, increased by about 121.14%. Among all health outcomes, mortality and CB predominated and shared about 95% of the total DALYs.
Table 8

DALYs due to PM10 pollution in urban area of Mumbai

Health endpoints

DALYs

1995

2000

2005

2010

2015

Mortality (adult ≥30 years)

144,684.32

165,191.52

158,524.02

229,206.52

240,102.81

Chronic bronchitis (all ages)

172,244.26

185,424.72

178,223.09

225,100.25

235,832.61

RADs (adults ≥20)

4,544.39

5,126.10

5,102.44

6,768.53

7,111.67

Asthma attack (children)

38.00

36.24

34.81

38.05

39.86

Asthma attack (adults)

73.92

82.20

78.63

107.68

112.77

Acute bronchitis (all ages)

96.95

104.76

101.47

126.90

133.04

Emergency room visits

14.19

14.99

13.86

18.40

19.21

COPD

116.03

122.58

113.34

150.44

157.10

Respiratory HA

183.05

193.38

178.81

237.34

247.84

Cerebrovascular HA

216.57

22,878

211.54

280.79

293.21

Cardiovascular HA

186.50

197.01

182.16

241.80

252.50

Cough (children)

987.05

999.40

1,106.68

1,012.75

1,072.02

Cough (adult)

2,602.23

3,077.68

3,408.07

3,887.58

4,115.09

Respiratory symptom days (children)

987.05

999.40

1,106.68

1,012.75

1,072.02

Respiratory symptom days (adults)

2,602.23

3,077.68

3,408.07

3,887.58

4,115.09

Lower respiratory symptoms (wheeze) (children)

987.05

999.40

1,106.68

1,012.75

1,072.02

Lower respiratory symptoms (wheeze) (adults)

2,602.23

3,077.68

3,408.07

3,887.58

4,115.09

Bronchodilator usage (children)

986.98

999.25

1,105.99

1,012.66

1,071.91

Bronchodilator usage (adults)

2,602.23

3,077.68

3,408.07

3,887.58

4,115.09

Total DALYs

336,755.24

373,030.43

360,822.46

481,877.92

505,050.97

Table 9

DALYs due to PM10 pollution in urban area of Delhi

Health endpoints

DALYs

1995

2000

2005

2010

2015

Mortality (adult ≥30 years)

147,868.77

178,667.69

207,366.84

305,524.99

364,882.16

Chronic bronchitis (all ages)

175,199.63

199,693.32

231,787.01

297,926.11

355,052.91

RADs (adults ≥20)

4,187.45

5,042.80

5,862.13

7,746.09

8,871.90

Asthma attack (children)

38.74

39.12

45.40

50.53

60.29

Asthma attack (adults)

76.28

89.69

104.08

145.64

174.70

Acute bronchitis (all ages)

96.38

110.51

128.31

162.33

191.53

Emergency room visits

16.29

17.98

20.83

29.23

36.92

COPD

133.30

147.11

170.43

239.33

302.45

Respiratory HA

210.26

232.06

268.84

377.50

477.02

Cerebrovascular HA

248.77

274.56

318.07

446.65

564.41

Cardiovascular HA

214.24

236.44

273.91

384.64

486.06

Cough (children)

756.66

813.26

947.69

902.01

993.06

Cough (adult)

1,994.82

2,504.46

2,918.44

3,462.48

3,812.00

Respiratory symptom days (children)

756.66

813.26

947.69

902.01

993.06

Respiratory symptom days (adults)

1,994.82

2,504.46

2,918.44

3,462.48

3,812.00

Lower respiratory symptoms (wheeze) (children)

756.66

813.26

947.69

902.01

993.06

Lower respiratory symptoms (wheeze) (adults)

1,994.82

2,504.46

2,918.44

3,462.48

3,812.00

Bronchodilator usage (children)

756.66

813.26

947.69

902.01

993.06

Bronchodilator usage (adults)

1,994.82

2,504.46

2,918.44

3,462.48

3,812.00

Total DALYs

339,296.03

397,822.14

461,810.39

630,490.98

750,320.60

Table 10

Estimated economic cost of health impacts due to PM10 pollution in urban area of Mumbai (in million 2005 US$)

Health endpoints

Economic cost (95% CI)

1995

2000

2005

2010

2015

Mortality (adult ≥30 years)

1998.53 (1331.23, 2568.90)

2281.80 (1509.66, 2952.48)

2189.70 (1428.58, 2872.75)

3166.04 (2102.93, 4080.89)

3316.55 (2200.32, 4279.81)

Chronic bronchitis (all ages)

536.89 (182.53, 775.47)

577.97 (193.92, 844.35)

555.52 (181.39, 831.18)

701.64 (237.22, 1018.26)

735.09 (247.97, 1068.92)

RADs (adults ≥20)

71.07 (64.44, 76.59)

80.17 (72.36, 86.76)

79.80 (71.31, 87.17)

105.86 (95.80, 114.28)

111.22 (100.57, 120.16)

Asthma attack (children)

0.18 (0.12, 0.23)

0.17 (0.12, 0.22)

0.17 (0.11, 0.22)

0.18 (0.12, 0.23)

0.19 (0.13, 0.24)

Asthma attack (adults)

0.35 (0.19, 0.48)

0.39 (0.21, 0.54)

0.38 (0.20, 0.52)

0.51 (0.28, 0.70)

0.54 (0.29, 0.73)

Acute bronchitis (all ages)

1.95 (0.82, 2.68)

2.11 (0.88, 2.93)

2.04 (0.82, 2.91)

2.56 (1.07, 3.52)

2.68 (1.12, 3.70)

Emergency room visits

1.14 (0.86, 1.50)

1.21 (0.91, 1.59)

1.12 (0.84, 1.47)

1.48 (1.11, 1.95)

1.55 (1.16, 2.03)

COPD

3.18 (1.37, 3.45)

3.36 (1.44, 3.64)

3.11 (1.33, 3.37)

4.12 (1.77, 4.47)

4.30 (1.85, 4.67)

Respiratory HA

0.83 (0.45, 1.21)

0.87 (0.48, 1.28)

0.81 (0.44, 1.18)

1.07 (0.58, 1.57)

1.12 (0.61, 1.64)

Cerebrovascular HA

0.59 (0.05, 1.14)

0.63 (0.05, 1.20)

0.58 (0.04, 1.11)

0.77 (0.06, 1.48)

0.80 (0.06, 1.54)

Cardiovascular HA

0.51 (0.25, 0.76)

0.54 (0.27, 0.81)

0.50 (0.25, 0.75)

0.66 (0.33, 0.99)

0.69 (0.35, 1.04)

Cough (children)

4.51 (4.24, 4.51)

4.57 (4.23, 4.57)

5.06 (4.48, 5.06)

4.63 (4.33, 4.63)

4.90 (4.57, 4.90)

Cough (adult)

11.90 (11.56, 11.90)

14.07 (13.55, 14.07)

15.58 (14.59, 15.58)

17.77 (17.21, 17.77)

18.81 (18.19, 18.81)

Respiratory symptom days (children)

4.51 (4.51, 4.51)

4.57 (4.57, 4.57)

5.06 (5.06, 5.06)

4.63 (4.63, 4.63)

4.90 (4.90, 4.90)

Respiratory symptom days (adults)

11.90 (10.00, 11.90)

14.07 (11.50, 14.07)

15.58 (11.81, 15.58)

17.77 (14.77, 17.77)

18.81 (15.56, 18.81)

Lower respiratory symptoms (wheeze) (children)

8.91 (8.91, 8.91)

9.02 (9.02, 9.02)

9.99 (9.98, 9.99)

9.14 (9.14, 9.14)

9.67 (9.67, 9.67)

Lower respiratory symptoms (wheeze) (adults)

23.48 (19.74, 23.48)

27.77 (22.69, 27.77)

30.75 (23.31, 30.75)

35.08 (29.16, 35.08)

37.13 (30.71, 37.13)

Bronchodilator usage (children)

0.12 (0.06, 0.12)

0.12 (0.06, 0.12)

0.13 (0.06, 0.13)

0.12 (0.06, 0.12)

0.13 (0.07, 0.13)

Bronchodilator usage (adults)

0.31 (0.25, 0.31)

0.37 (0.28, 0.37)

0.41 (0.28, 0.41)

0.47 (0.36, 0.47)

0.50 (0.38, 0.50)

Total

2680.87

3023.78

2916.28

4074.51

4269.60

Table 11

Estimated economic cost of health impacts due to PM10 pollution in urban area of Delhi (in million 2005 US$)

Health endpoints

Economic cost (95% CI)

1995

2000

2005

2010

2015

Mortality (adult ≥30 years)

2042.52 (1420.13, 2520.70)

2467.95 (1696.77, 3077.74)

2864.37 (1968.00 (3574.37

4220.24 (2989.60, 5120.18)

5040.14 (3641.87, 6008.32)

Chronic bronchitis (all ages)

546.10 (201.98, 736.30)

622.45 (225.17, 854.05)

722.48 (261.01, 992.36)

928.64 (356.47, 1217.26)

1106.70 (442.04, 1409.71)

RADs (adults ≥20)

65.49 (60.99, 68.91)

78.87 (72.97, 83.46)

91.68 (84.79, 97.05)

121.15 (113.98, 126.36)

138.75 (131.84, 143.55)

Asthma attack (children)

0.19 (0.13, 0.23)

0.19 (0.13, 0.23)

0.22 (0.15, 0.27)

0.24 (0.17, 0.29)

0.29 (0.21, 0.34)

Asthma attack (adults)

0.36 (0.21, 0.47)

0.43 (0.24, 0.56)

0.50 (0.28, 0.65)

0.70 (0.41, 0.88)

0.84 (0.50, 1.04)

Acute bronchitis (all ages)

1.94 (0.90, 2.48)

2.23 (1.00, 2.90)

2.58 (1.16, 3.37)

3.27 (1.57, 4.06)

3.86 (1.93, 4.66)

Emergency room visits

1.31 (0.98, 1.72)

1.45 (1.09, 1.90)

1.68 (1.26, 2.20)

2.36 (1.77, 3.09)

2.98 (2.23, 3.90)

COPD

3.65 (1.57, 3.96)

4.03 (1.73, 4.37)

4.67 (2.01, 5.07)

6.56 (2.82, 7.12)

8.29 (3.56, 8.99)

Respiratory HA

0.95 (0.52, 1.39)

1.05 (0.57, 1.53)

1.21 (0.66, 1.78)

1.70 (0.93, 2.49)

2.15 (1.17, 3.15)

Cerebrovascular HA

0.68 (0.05, 1.31)

0.75 (0.06, 1.44)

0.87 (0.07, 1.67)

1.22 (0.09, 2.35

1.54 (0.12, 2.97)

Cardiovascular HA

0.59 (0.29, 0.88)

0.65 (0.32, 0.97)

0.75 (0.37, 1.12)

1.05 (0.53, 1.58)

1.33 (0.66, 1.99)

Cough (children)

3.46 (3.41, 3.46)

3.72 (3.64, 3.72)

4.33 (4.24, 4.33)

4.12 (4.09, 4.12)

4.54 (4.52, 4.54)

Cough (adult)

9.12 (9.08, 9.12)

11.45 (11.36, 11.45)

13.34 (13.24, 13.34)

15.83 (15.80, 15.83)

17.43 (17.41, 17.43)

Respiratory symptom days (children)

3.46 (3.46, 3.46)

3.72 (3.72, 3.72)

4.33 (4.33, 4.33)

4.12 (4.12, 4.12)

4.54 (4.54, 4.54)

Respiratory symptom days (adults)

9.12 (8.54, 9.12)

11.45 (10.52, 11.45)

13.34 (12.24, 13.34)

15.83 (15.17, 15.83)

17.43 (16.97, 17.43)

Lower respiratory symptoms (wheeze) (children)

6.83 (6.83, 6.83)

7.34 (7.34, 7.34)

8.55 (8.55, 8.55)

8.14 (8.14, 8.14)

8.96 (8.96, 8.96)

Lower respiratory symptoms (wheeze) (adults)

18.00 (16.85, 18.00)

22.60 (20.75, 22.60)

26.33 (24.15, 26.33)

31.24 (29.94, 31.24)

34.40 (33.50, 34.40)

Bronchodilator usage (children)

0.09 (0.06, 0.09)

0.10 (0.06, 0.10)

0.11 (0.07, 0.11)

0.11 (0.08, 0.11)

0.12 (0.09, 0.12)

Bronchodilator usage (adults)

0.24 (0.22, 0.24)

0.30 (0.26, 0.30)

0.35 (0.31, 0.35)

0.42 (0.39, 0.42)

0.46 (0.44, 0.46)

Total

2714.10

3240.70

3761.71

5366.93

6394.74

Table 12

Total economic costs (in million US$) and percentage of GDP loss

 

1995

2000

2005

2010

2015

Total economic cost PCI (PPP)

5,394.96 (8,195.583)

6,264.47 (9,378.84)

6,677.99 (10,017.16)

9,441.44 (13,665.61)

10,664.33 (15,406.22)

India’s GDP (in million 2005 US$) (WB 2016b)

396,844.35

540,190.32

718,467.34

1,064,746.21

1,497,610.93

Percentage of India’s GDP (%) PCI (PPP)

1.36 (2.07)

1.16 (1.74)

0.93 (1.39)

0.89 (1.28)

0.71 (1.03)

Table 13

Excess mortally, total DALYs, and total economic cost (in million 2005 US$) using various PM10 threshold levels

Threshold concentration

Indian National Ambient Air Quality Standard (60 μg/m3)

No threshold (0 μg/m3)

City

Year

Attributable number of mortality (mean and 95% CI)

Total DALYs

Total economic cost (in million 2005 US$)

Attributable number of mortality (mean and 95% CI)

Total DALYs

Total economic cost (in million 2005 US$)

Mumbai

1995

14,069 (9,094, 18,628)

250,131.62

1,978.10

21,584 (14,586, 27,361)

374,619.14

2,988.22

2000

15,438 (9,908, 20,589)

267,047.67

2,147.49

24,918 (16,729, 31,787)

419,354.92

3,406.99

2005

13,231 (8,368, 17,912)

233,249.82

1,861.78

24,608 (16,296, 31,810)

416,565.55

3,377.25

2010

21,939 (14,139, 29,137)

352,367.69

2,957.92

34,346 (23,147, 43,654)

538,497.43

4,562.92

2015

22,825 (14,692, 30,351)

366,982.08

3,079.25

36,049 (24,266, 45,867)

565,411.04

4,790.26

Delhi

1995

16,637 (11,252, 21,074)

288,672.96

2,302.87

21,068 (14,844, 25,686)

361,436.35

2,894.12

2000

19,569 (13,079, 25,072)

329,919.95

2,678.70

25,690 (17,904, 31,635)

427,517.63

3,486.68

2005

22,671 (15,141, 29,067)

382,340.07

3,103.98

29,834 (20,779, 36,761)

496,564.63

4,049.59

2010

35,595 (24,553, 44,253)

554,196.58

4,707.72

42,994 (30,848, 51,572)

663,873.43

5,655.66

2015

43,704 (30,785, 53,294)

677,175.09

3,103.98

50,823 (37,173, 59,951)

782,332.86

6,671.94

Economic cost of health impacts

Table 4 shows the cost (per cases) for various health impacts in India. These values are much lower than the Europe and US values (Kochi et al. 2010; Vlachokostas et al. 2010) because of different economic background level. Using the estimated data in Tables 5 and 6, the economic cost of health impacts of PM10 pollution in Mumbai and Delhi from 1995 to 2015 was calculated (Tables 10 and 11).

During this period, the economic cost of health impact in Mumbai was approximately US$2680.87 million in 1995 through US$2916.28 million in 2005 to US$4269.60 million in 2015 (Table 10). Relatively low economic cost was observed in 2005 due to low pollution concentration (115 μg/m3). In Delhi City, the economic cost of health impact was continuously increased from US$2714.10 million in 1995 to US$3761.71 million in 2005 to US$6394.74 million in 2015 (Table 11). The total sum of economic cost in two cities from 1995 to 2015 was about 5394.96, 6264.47, 6677.99, 9441.44, and 10,664.33 million, respectively. Among the health effect endpoints, premature death accounting 4041.05 (95% CI 24,750.37–5089.60), 4749.75 (95% CI 3206.43–6030.22), 5054.07 (955 CI 3396.58–6447.12), 7386.28 (955 CI 5092.54–9201.07), and 8356.69 (95% CI 5842.18–10,288.13) million US$ from 1995 to 2015 governed the value of the total costs approximately 76.60%. Chronic bronchitis also made an important contributor to economic costs at an estimated 1082.99, 1200.42, 1278.00, 1630.28, and 1841.79 million US$, around 17.27 to 22.07% of the total costs between 1995 and 2015. Moreover, the cost of RADs was similarly higher, even though asthma and bronchodilator usage contributed minimum in the total cost. Health impacts and corresponding economic cost were much higher for Delhi City than Mumbai City due to high PM10 concentration in Delhi.

Average economic cost due to premature death attributed to PM2.5 in Mumbai and Delhi was 1127.2 and 1129.2 million US$.

In the previous study, it is shown that premature mortality dominated the value of the total economic costs, 82.90% in Shanghai in 2001 (Kan and Chen 2004), 87.23% in Beijing in 2004 (Zhang et al. 2007), 88.4% in 111 Chinese City in 2004 (Zhang et al. 2008), 80% in Barcelona metropolitan area (Spain) in 2004 (Pérez et al. 2009), 93.4% in India in 2009 (WB 2013), 90% in India and China in 2010 (OECD 2014), and 67.19% in Pearl River Delta region in China (Huang et al. 2012).

The total GDP loss has been estimated for a parallel comparison. As shown in Table 12, the economic damage (PCI method) to human health from PM10 was accounted for 1.36, 1.16, 0.93, 0.89, and 0.71% of the India’s GDP between 1995 and 2015. Annual average total economic cost is 48.2% high in PPP method than PCI method in BTA. India’s GDP maintained fast growth at 5.36, 4.98, 6.41, 7.29, and 5.34% between 1995 and 2015, respectively, although the PM10 pollution-related economic cost could not be ignored.

The economic cost of health impact due to PM10 mainly depends on exposed population, pollution concentration, number of health outcome parameter considered in studies, and mortality cost (VSL). In this study, some health impacts, for example, minor restricted activity days (MRADs) and work loss days (WLDs) among adult, upper respiratory tract infection, mortality from acute exposure, and congestive heart failure, among elders (age >65), which are related with PM pollution, were not considered. These factors could have led to miscalculation of actual results.

Discussion

Determination of quantitative value of air pollution-related health impacts is becoming a vital element in evaluating the economic cost, which will help pollution control authorities (BMC, NEERI, and MPCB in Mumbai and NEERI, DPCC, and CPCB in Delhi) to take decision of cost–benefit analysis to pollution control measures. In this study, impacts of PM10 pollution on human health are substantial in Mumbai and Delhi, highly populated and highly polluted cities in India, whether in physical and monetary terms. The quantified health impacts of PM10 have been estimated using the E–R coefficient and statistical data for Mumbai and Delhi from 1991 to 2015 in a 5-year interval.

In this study, it is found that the annual average premature mortality attributable to PM10 was around 25,006 (95% CI 16,550–32,346) and 32,115 (95% CI 22,619–39,192) for year 1991–2015 in the urban area of Mumbai and Delhi. The per capita mortality (PCM) (per 10,000 person-years) among adult (>30 years) in Mumbai and Delhi in 1991–2015 attributable to PM10 was 38.98 (95% CI 25.80–50.42) and 57.04 (95% CI 40.01–69.86). The PCM (per 10,000 person-years) for all ages attributable to PM10 was 15.15 (95% CI 10.03–19.59) and 22.28 (95% CI 15.65–27.26) in Mumbai and Delhi as shown in Figs. 2a and 3a. In Mumbai, although the average PM10 from 1991 to 1995 (142 μg/m3) was a little higher than during the period 2011 to 2015 (137 μg/m3), the PCM among all ages due to PM10 for the period of 2011–2015 [16.99 (95% CI 11.27–21.93)] was higher as that for the period of 1991–1995 [14.51 (95% CI 9.67–18.65)].
Fig. 2

Annual per capita (per 10,000 person-years) a mortality, b chronic bronchitis, c RADs, d asthma attack, e acute bronchitis, f emergency room visits, and g total hospital admissions for all ages, attributable to PM10, for the period of 1995–2015 in the urban area of Mumbai, as well as the corresponding uncertainties (95% CI)

Fig. 3

Annual per capita (per 10,000 person-years) a mortality, b chronic bronchitis, c RADs, d asthma attack, e acute bronchitis, f emergency room visits, and g total hospital admissions for all ages, attributable to PM10, for the period of 1995–2015 in the urban area of Delhi, as well as the corresponding uncertainties (95% CI)

In Delhi, the average PM10 from 1991 to 1995 (204 μg/m3) was much higher than during the period of 2001 to 2005 (186 μg/m3), but the PCM for all ages due to PM10 for the period of 2001–2005 [19.20 (95% CI 13.19–23.96)] was the same as that for the period of 1991–1995 [19.35 (95% CI 13.45–23.88)]. This was mainly because the PCM (per 10,000 person-years) was mainly influenced by the ratio of adult population (age >30 years) and the total population, and the ratio was 0.35, 0.37, and 0.42 for the periods of 1991–1995, 2001–2005, and 2011–2015, respectively.

The past study have also looked that the PCM due to PM10 was 25.16 (95% CI 18.11–30.77) in Beijing, China, in 2004 (C, 149 μg/m3; C0, 70 μg/m3) (Zhang et al. 2007); 13.88 in Tianjin, China, in 2004 (C, 111 μg/m3; C0, 40 μg/m3) (Zhang et al. 2008); 14.11 (95% CI 8.70–19.51) in Shanghai, China, in 2001 (Kan and Chen 2004) (C, 100 μg/m3; C0, 50 μg/m3); 9.34 in Taiyuan, China, in 2010 (C, 89 μg/m3; C0, 40 μg/m3) (Tang et al. 2014); 9.18 (95% CI 7.05–11.33) in 13 Italian cities during 2002–2004 (C, 45 μg/m3; C0, 20 μg/m3) (Martuzzi et al. 2006); and 9.05 (95% CI 5.69–12.41) in Barcelona, Spain, in 2004 (C, 50 μg/m3; C0, 20 μg/m3) (Pérez et al. 2009).

The annual average per capita chronic bronchitis (PCCB) cases (per 10,000 person-years) was extending equally with PM10 value in Mumbai and Delhi as shown in Figs. 2b and 3b. In the period from 1991 to 1995 to 2011–2015, annual average PCCB cases decreased from 58.89 (95% CI 20.02–85.07) to 56.90 (95% CI 19.19–82.74) in Mumbai and increased from 78.15 (95% CI 28.90–105.36) to 92.47 (95% CI 36.93–117.79) in Delhi. The annual average per capita RADs cases (per 10,000 person-years) among all ages from 1991 to 1995 to 2011–2015 increased from 11,395 (95% CI 10,332–12,279) to 12,582 (95% CI 11,378–13,593) in Mumbai and 13,697 (95% CI 12,756–14,411) to 16,945 (95% CI 16,100–17,531) in Delhi as shown in Figs. 2c and 3c. So, because of illness attributed to PM10, every person yearly spends approximately one half of the day in bed or home from cutting down on regular activities. Figures 2d and 3d show the trend of annual average per capita asthma (PCA) (per 10,000 person-years) attack among all ages. From the period of 1991–1995 to 2011–2015, PCA was decreased from 210.47 (95% CI 124.29–279.59) to 202.53 (95% CI 117.02–271.44) in Mumbai and increased from 282.17 (95% CI 174.81–358.67) to 336.60 (95% CI 214.41–413.51) in Delhi. Similar trend was observed for annual average per capita acute bronchitis and emergency room visit (per 10,000 person-years) in Mumbai and Delhi as shown in Figs. 2e, f and 3e, f. The annual average per capita hospital admission cases (per 10,000 person-years) among all ages from 1991 to 1995 to 2011–2015 decreased from 23.40 (95% CI 7.96–32.80) to 22.35 (95% CI 8.83–34.42) in Mumbai and increased from 35.06 (95% CI 13.85–53.98) to 46.45 (955 CI 18.35–71.50) in Delhi, as shown in Figs. 2g and 3g. Among the total annual average per capita hospital admission cases, respiratory hospital admission dominated the value of approximately 36.78%.

The annual average mortality attribute to PM2.5 in Mumbai and Delhi was 10,880 (95% CI 5520–16,387) and 10,900 (95% CI 6118–15,879). The past study shows that the premature mortality in Delhi was 7350 to 16,200 in 2010 (Guttikunda and Goel 2013). Chowdhury and Dey (2016) estimated annual premature deaths in Mumbai and Delhi, which were 8320 and 11,020 for the period of 2000–2010.

The per capita mortality (per 10,000 person-years) for all ages on an average in Mumbai was 6.6 (95% CI 3.4–10.0) and 7.7 (95% CI 4.3–11.2) in Delhi City.

In previous studies in India, the per capita mortality due to PM2.5 was 4.7 (Apte et al. 2015). Lelieveld et al. (2013) calculated premature mortality due to PM2.5 in India and found that the per capita mortality for all ages is 4.2 in Mumbai, 7.9 in Delhi, and 5.9 in Kolkata in 2005.

In 2030, the population in Mumbai and Delhi will be 27.80 and 36.06 million (UN 2014); among them, 52.5% will be adult population with an age of >30 years (NCP 2006).

A crucial presumption of this point is that PM10 levels in 2030 would have to 57 and 68 μg/m3 above the threshold level (C0) in Mumbai and Delhi and have to decline by 44% (Mumbai) and 67% (Delhi) absolutely to maintain the premature mortality attributable to PM10 constant at year 2011–2015 levels.

The DALY approach is a robust methodological framework with firm theoretical base (Ostro 2004). For measurement of impact of air pollution on public health, DALY scores advantage by making direct comparison with the overall impact of disease in various cities as well as with diseases.

The total DALYs in Mumbai and Delhi were quiet high 505,050.97 and 750,320.60 in 2015. Per capita DALYs (per 10,000 persons) were increased after 2005 as shown in Figs. 4a and 4b and were increased from 253.32 (95% CI 129.23–342.58) to 268.07 (95% CI 139.81–362.57) in Mumbai and 332.95 (95% CI 178.81–427.51) to 429.91 (95% CI 248.36–526.59) in Delhi, from the period of 1995 to 2015. The previous study has also looked that DALY lost for Shanghai, China, in 2000 was 103,064 (Zhang et al. 2006); Taiyuan, China, in 2010 was 22,806.94 (Tang et al. 2014); and Germany in 2010 was 632,545 (WHO-OECD 2015).
Fig. 4

Per capita DALYs (per 10,000 person-years) for the period of 1995–2015 in the urban area of a Mumbai and b Delhi

The annual average total economic cost in the two cities was about 7688.64 million US$, accounting on average about 1.01% of India’s GDP, from 1991 to 2015, while the GDP maintained a growth rate of about 5.87%, at constant price year 2005 US$.

In the previous study, GDP loss due to PM10 pollution was 4.3% in Shijiazhuang (Peng et al. 2002), 4.31% in Singapore (Quah and Boon 2003), 6.55% in Beijing (Beijing’s GDP) (Zhang et al. 2007), 5.9% in China (34 major cities) in 2005 (Matus et al. 2012), 1.35% in Pearl River Delta region in China (regional GDP) (Huang et al. 2012), and 0.7–2.8% in USA (Muller and Mendelsohn 2007) and 4.5, 3.7, and 7.4% in Germany, UK, and Czech Republic due to mortality in 2010 (WHO 2015).

In the assessment of human health impacts, the selection of threshold concentration C0 greatly impacts the results. In this study, WHO-recommended annual average PM10 standard (20 mg/m3) was used as the C0. Indian National Ambient Air Quality Standard (INAAQS) (60 μg/m3) and no threshold (0 μg/m3) level also used for the assessment health endpoints attributed to PM10 pollution in Mumbai and Delhi, whether in physical and economic terms, and Table 13 lists the results. Using INAAQS as C0, the number of premature mortality in Mumbai was 14,069 (95% CI 9094–18,628), 13,231 (95% CI 8368–17,912), and 22,825 (95% CI 14,692–30,351) in the years 1995, 2005, and 2015. The total DALYs and total economic cost were 250,131.62, 233,249.82, and 366,982.08 and 1978.10, 1861.78, and 3079.25 million US$ of years 1995, 2005, and 2015, respectively.

In Delhi, the premature mortality attributed to PM10 was 16,637 (95% CI 11,252–21,074), 22,671 (95% CI 15,141–29,067), and 43,704 (95% CI 30,785–53,294) in annual year 1995, 2005, and 2015, considering INAAQS as C0. The total DALYs and total economic cost were 288,672.96, 382,340.07, and 677,175.09 and 2302.87, 3103.98, and 3103.98 million US$ of years 1995, 2005, and 2015, respectively. At zero threshold level, the premature mortality attributed to PM10 was quite high 21,584 (14,586–27,361), 24,608 (16,296–31,810), and 36,049 (24,266, 45,867) in Mumbai and 21,068 (14,844–25,686), 29,834 (20,779–36,761), and 50,823 (37,173–59,951) in Delhi in years 1995, 2005, and 2015.

Reason of PM pollution

PM10 pollution is a serious source of health problem in urban areas of Mumbai and Delhi, and the source of PM10 pollution is coal-based power plant, industry, vehicle exhaust, domestic burning, solid waste burning, construction, and re-suspension of road dust. The relative contribution of different sectors to total PM10 emissions is power plant, industry, transport, bakeries industry, landfill open burning, construction, and re-suspension of road dust, and it contributes 21, 18, 6, 6, 11, 8, and 30%, respectively, in Mumbai City (CPCB 2011). Where as in Delhi, power plant, industrial, residential, transport sector, and road dust contribute 4.67, 11.53, 15.29, 12.84, and 55.66% of total PM10 pollution (Sahu et al. 2011).

Methodological limitations and assumptions

There are a number of methodological uncertainties that limit the applicability of this approach and may require improvements for future studies. In particular,
  • The risk estimates (except mortality attributed to PM10) are based on cohort studies primarily conducted in the USA, Europe, and China. Thus, there is inherent uncertainty involved in assessing health risks in other countries with different health and economic conditions.

  • Populations are generally exposed to a mix of pollutants, both in indoor and outdoor air, possibly associated with synergistic effects. Relative risk for PM associated with synergistic effect of temperature and other pollutants have not been considered in this study. This may lead to varied results when (under) estimated values are considered in absolute terms.

  • Since daily resolved data were not available for cities, thus the annual average air quality concentrations were used, whereas relative risk values pertain to increases in daily average concentrations. This may be a source of error in risk estimates.

  • Most studies focusing on India’s air pollution use 0.50–0.70 as PM10–PM2.5 conversion factors (Sharma and Maloo 2005; Satsangi et al. 2011). Among them, the smallest conversion factor (0.50) that has been chosen to compute our central estimates for PM2.5 caused health damage. This will give underestimated premature mortality value than the actual value.

  • The accuracy and representativeness of air quality data, as available through BMC, NEERI, MPCB, DPCC, and CPCB, are uncertain. This could be a substantial source of error, especially for cities with insufficient resources, expertise, and air quality-monitoring infrastructure. The absolute percentage error for instrumental error for PM10 measurement is 5.83% (http://cpcb.nic.in/).

  • The present study considered only PM10 and PM2.5 with threshold limits, whereas past studies show that serious health impacts of ultrafine particles matter (e.g., PM1), ozone, SO2, NO2, and polyaromatic hydrocarbon (PAH) were not considered in this study (Brunekreef and Holgate 2002; Pope and Dockery 2006; Ostro et al. 2009).

  • Megacity residents are exposed to multiple risk factors, and it needs to be studied how different risks compete or add. For example, air pollution may directly cause mortality and morbidity, but in extreme cases can also enhance the probability of traffic accidents because air pollution is causing physical distractions, perhaps an itching nose, or limiting visibility (Sager 2016).

  • A BTA is used in this study for total economic cost assessment, because a detailed survey of economic costs of various health endpoints from air pollution was not available for India. The possible biasness is involved by using per capita GDP to transfer the values from other countries.

Conclusions

Particulate air pollution makes significant health impacts in Indian cities. Using PM10 population data for Mumbai and Delhi from 1991 to 2015 and the E–R function of different health endpoints, the quantitative health impacts of air pollution have been estimated. Annual average mortalities due to PM10 in Mumbai and Delhi were 25,006 and 32,109. And annual average mortalities due to PM2.5 in Mumbai and Delhi were 10,880 and 10,900. The total economic cost attributed to PM10 pollution by most populated and highest polluted city was accounting on average about 1.01% of India’s GDP, from 1991 to 2015, while the GDP growth rate was of about 5.87%, at constant price year 2005 US$. Annual average DALYs due to PM10 were 411,507 in Mumbai and 515,948 in Delhi, during 1991 to 2015.

In the developing country like India, PM2.5 and PM10 ratio is very high about 0.70 (Sharma and Maloo 2005; Satsangi et al. 2011), which is much greater than USA (Pace 2005) and Europe (Barmpadimos et al. 2012). Long-term exposure to PM2.5 is strongly related with ischemic heart disease, cerebrovascular disease, COPD, lung cancer, and acute lower respiratory infections. Thus, long-term epidemiological study-related PM2.5 should be performed in India in the future because of the presence of high outdoor PM2.5 concentration in Indian 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. At present, proper air quality management is required to reduce air pollution urgently and effectively, especially for PM10 and PM2.5 pollutant. The major source of PM2.5 is combustion sources, such as industrial boiler, vehicular exhausts, and solid waste burning (Sahu et al. 2011; Gargava et al. 2014). Coal-based power plant is a major source of energy production in India. In 2011–2012, particulate matter emissions from 92 coal-burning power plants were responsible for 80,000 to 115,000 premature deaths and 21 million asthma cases and which cost 3.2 to 4.6 billion US$ (Guttikunda and Jawahar 2014). The DALY due to health impact of coal electricity generation in India was about 2 million in 2012 (Cropper et al. 2012; WB 2013). India should reduce coal-based energy source and have to look for more renewable energy in future for better environment. Major problem is also the number of old vehicles (age >10 years), which is about 7% of total 2.2 million vehicles in Mumbai (MVD 2013) and 9.6 million vehicles in Delhi (DTP 2016). Number of privet vehicles should be reduced in future. As Delhi is the world’s most polluted city, government of Delhi is making an effort to relinquish that title, and they announced that they would allow owners of private vehicles to drive only on alternate days, based on the sequence of their number plates. The initial results of 15-day trial, which began on 1 January 2016, show that the daily levels of PM2.5 fell by roughly 10% (Subramanian 2016). On the other hand, fuel and vehicular emission control technologies should be improved. The government should develop more public transport systems. Road dust is a major problem in Indian cities, which contributes about 30 to 72% of total PM10 (CPCB 2011) and some toxic polycyclic aromatic hydrocarbons and black carbon abundant in road dust in high amount (Ray et al. 2012).

Current study shows the importance of evaluation and assessment of health impacts of air quality on local scale to protect environment and economic balance. This study was based on the assumption that the entire population of Mumbai and Delhi was exposed to the average concentration levels of all air quality-monitoring stations. The suggestion is of using Benefits Mapping and Analysis Program (BenMAP) to calculate the number and economic value of air pollution-related deaths and illnesses in finer resolution.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Kamal Jyoti Maji
    • 1
  • Anil Kumar Dikshit
    • 1
  • Ashok Deshpande
    • 2
  1. 1.Center for Environmental Science and Engineering (CESE)Indian Institute of Technology BombayMumbaiIndia
  2. 2.Berkeley Initiative in Soft Computing (BISC)–Special Interest Group (SIG)–Environment Management Systems (EMS)BerkeleyUSA

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