Abstract
Air pollution exposure has been identified as the second-largest health risk in India after maternal and child malnutrition. In India, health impact of air pollution has been examined using time-series studies for association between acute exposure and health endpoints, case-control and cross-section studies for association between long-term exposure and health endpoints, and cohorts for unravelling the causality. The long-term impacts of air pollution on mortality and morbidity has also been estimated in the Global Burden of Disease study, which outlines the exposure and burden disparity. India should invest resource to systematically collate health data, design cohorts, and improve exposure assessment to generate exposure-response functions for PM2.5 mass and species that are more representative of the Indian demography. This would allow health burden to be attributed to sectoral emissions and help the policymakers prioritize interventions based on the health benefit assessment linked to specific clean air targets. This chapter summarizes the existing health studies and presents a roadmap for way forward.
2.1 Introduction
Over the years, an extensive body of scientific evidence established the negative health effects associated with exposure to polluted air, using various epidemiological study designs. The Global Burden of Disease (GBD) study evolved a uniform framework to assess and track the long-term effects of exposure to air pollution across the globe. Based on the GBD assessment (GBD 2019 Risk Factors Collaborators 2020), air pollution exposure is ranked fourth on the list of risks globally causing disability-adjusted life years (DALY) and mortality (GBD 2019 Risk Factors Collaborators 2020).
The GBD study revealed that low- and middle-income countries (LMICs) experience a disproportionate burden of air pollution-related health burden compared to high-income countries. LMICs face multiple challenges in managing air quality and associated health burdens. The ground-based monitoring in LMICs is highly inadequate for mapping regional-scale air quality and exposure (Martin et al. 2019). A higher proportion of solid fuel use for domestic purposes results in high household exposure in LMICs. This, in turn, increases the risk of exposure misclassification in health burden estimates. Patchy health data in LMICs make the health burden estimates more uncertain.
The GBD study addressed these critical gaps using geospatial modelling and other relevant techniques and developed exposure-response functions for ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), acute lower and upper respiratory tract infections (ARI), stroke, diabetes, and neonatal deficiencies such as preterm birth and low birth weight by compiling cohort studies from around the world (Burnett et al. 2014, 2018). The evolution of exposure-response functions and their applications in estimating excess mortality risks has been summarized in Burnett and Cohen (2020). In terms of air pollutants, exposure metrics consider the ambient fine particulate matter (PM2.5), household PM2.5, and ambient ozone.
India, home to 1.4 billion population, has one of the highest PM2.5 exposure globally (Cohen et al. 2017). Air pollution is ranked second after maternal and child malnutrition and was estimated to cause 1.67 million (95% uncertainty interval, UI: 1.42–1.92) deaths in 2019 in India, causing a loss of up to 1.36% of the gross domestic product (Pandey et al. 2021). In addition to the GBD study, where mortality and morbidity burden was estimated at state levels, numerous studies were carried out in the last few decades in India to understand the health impacts emanating from air pollution exposures, using indigenous health data and standard epidemiological study designs. This chapter summarizes the current state-of-the-art knowledge of air pollution health impacts in India, outlines the critical challenges, and shows the ways forward to accelerate India’s progress to meet the United Nations’ Sustainable Development Goal (SDG) 3 of promoting healthy lives and well-being.
2.2 Air Pollution Health Effects and Epidemiological Study Designs
The assessment of any health endpoints in relation to air pollution exposure relies on data from health institutions or civic bodies responsible for the collation of vital statistics. The most common methods to estimate the occurrence of health outcomes in environmental research are based on the events in which (1) the subjects have sought medical attention (e.g., emergency hospital visits or outpatient visits, or hospital admissions), (2) death events recorded by civic bodies and certified by medical professionals, with or without cause of death, (3) self-reported or bio-monitored health irregularities and illnesses recorded in small/large scale health surveys. The use of medical record data, however, has inherent limitations, such as recording errors, variability in medical coding, lack of information on the onset/duration of events, and biases due to varied health-seeking behaviour. These limitations impact the accuracy of the health outcome assessments and introduce bias in the estimation air pollution-related health consequences or any other risk factor. The alternatives to surpass the above limitations are by way of bio-monitoring/human tissue sampling, which is expensive and only viable for a very small sample population and is not realistic at a population scale.
The existing body of epidemiological research records health effects from both long- and short-term exposure to ambient air pollution (Liu et al. 2013; Sehgal et al. 2015; Kesavachandran et al. 2015; Cohen et al. 2017; Haque and Singh 2017). Short-term exposure and related health effects are measured at weekly/hourly/daily temporal scale, whereas long-term exposure and health effects are captured over monthly or annual scales. (Beverland et al. 2012). The short-term exposures manifest in acute illnesses or episodes such as exacerbation of asthma, breathing troubles, and cardiovascular disease (CVD) conditions such as ischemia, arrhythmias, and cardiac failure. The short-term effects are recorded in terms of emergency hospital visits, hospitalizations, and even fatal outcomes generally in finer temporal windows (Kumar et al. 2004; Gupta 2008; Patankar and Trivedi 2011; Maji et al. 2015, 2018). The longer duration and more intense exposures are exhibited in terms of chronic and disabling conditions such as compromised lung/heart function, tuberculosis, cough/wheezing, allergies, cardiovascular complaints, and even premature deaths (Kumar and Foster 2007; Guttikunda and Jawahar 2012; Guttikunda and Goel 2013; Ghosh and Mukherji 2014; Tobollik et al. 2015; Gawande et al. 2016; Rajak and Chattopadhyay 2020).
The type of epidemiological study designs is extremely crucial for accurate assessment of air pollution-related health risks. Their selection primarily depends on exposure and health data and determining factors for the results, their synthesis, interpretation, and policy use. There are multiple analysis methods used in air pollution health studies, ranging from case-crossover, case-control, time-series, Bayesian epidemiological studies, and cohort designs to examine associations between pollutant exposures and health outcomes of interest. Generally, the case-crossover and time-series studies utilize local and fine-scale temporal exposure data. They are generally good at assessing the effects of regional pollutants that exhibit greater spatial homogeneity.
It is important to highlight that time-series studies using exposure and health data resolved at fine spatial scales like zip code level show modestly stronger associations (Joshi et al. 2021). Such studies mainly focus on the short-term and acute effects of exposure to ambient air pollution. Cross-sectional studies utilizing improved spatially resolved health and exposure data rely on ecological assessments and can provide critical evidence of association in the absence of cohort studies. On the other hand, there are cohort-based studies, where a group of subjects is followed, and subsequent health evaluations are conducted to determine participants’ exposure characteristics driven by temporal and spatial variations in exposure, retrospectively or prospectively. The advantage of cohort studies is that they are capable of establishing true causal effects but expensive in terms of logistics and financial resources.
2.3 GBD India Study
Health burden attributable to air pollution depends on three factors—relative risk (RR) of a disease corresponding to an exposure level compared to the theoretically minimum exposure level (TMEL), population, and its age distribution (RRs vary with age for some diseases), and background disease rates from all possible risk factors. The GBD India study compiled the data for the period of 1990 onwards and tracked the progress in air pollution health burden (Balakrishnan et al. 2019). Figure 2.1 shows the state-level DALY rate attributable to air pollution (normalized to 100,000 population) from the GBD India study 2019. The data revealed a large disparity in DALY rates attributable to air pollution in 2019, ranging from 1664 (95% UI: 1347–2042) in Arunachal Pradesh to 5002 (95% UI: 4227–5848) in Uttar Pradesh. For ambient PM2.5 exposure, the lowest DALY rate was found for Arunachal Pradesh (593, 95% UI: 389–823), while the highest rate was found for Uttar Pradesh (3106, 95% UI: 2387–3824). For household PM2.5, the DALY rate varied from 19 (95% UI: 9–37) in Delhi to 2513 (95% UI: 1769–3228) in Chhattisgarh.
DALYs attributable to air pollution, covering both sexes and all age groups in India, 2019. (Data source: https://vizhub.healthdata.org/gbd-compare/)
The temporal changes in the mortality rates attributable to ambient and household air pollution (Fig. 2.2) further revealed that the age-standardized death rates resulting from household level PM2.5 decreased by 72.3% from 215.5 in 1990 to 59.6 in 2019. During the same time, the death rates attributable to ambient PM2.5 rose by 57.5% from 60.7 in 1990 to 95.6 in 2019. A large share of DALYs, around 39.5% of the total DALYs, were due to lung diseases, including COPD (22.7%) and ARI (15.5%). Out of the rest, about 38.6% of the total was CVDs, including IHD (24.9%) and stroke (13.7%), neonatal deficiencies (14.5%), diabetes mellitus (5.5%), and cataracts (1.5%). Across the total DALY burden attributable to all risk factors, air pollution was responsible for 11.5%, out of which 6.7%, 4.5%, and 0.7% could be attributed to ambient-household PM2.5 and ozone exposure, respectively. In 2017, air pollution-related losses in life expectancy were estimated at 0.9 (0.8–1.1) and 0.7 (0.6–0.8) for ambient and household PM2.5, respectively (Balakrishnan et al. 2019).
Age-standardized death rates attributable to air pollution, India 2000–2019. (Data Source: GBD (https://vizhub.healthdata.org/gbd-compare/))
The GBD India study demonstrated that the health burden and exposure are not linearly proportional. Therefore, an improvement in air quality by the same margin in socio-demographically diverse regions will not result in the same health benefit. For example, the change in ambient PM2.5-related mortality burden in India, for the period 2000–2015 is depicted in Fig. 2.3. However, if the other factors remained unaltered and the exposure only changed, the air pollution burden would have increased only by 17.4%. The population rise contributed to 33.6% of the increase in burden alone, and the shift of the age structure towards older age contributed to another 14.5% increase in the burden. This was partially compensated by the reduction of the burden by 14.2% due to an overall reduction in baseline mortality. The net result is an increase in air pollution-linked mortality burden by 47.9% between 2000 and 2015 (Chowdhury et al. 2020). Such analysis implies that the air pollution health burden can be reduced further by accelerating the reduction of baseline mortality by improving the healthcare infrastructure.
The relative changes in mortality burden attributable to ambient PM2.5 exposure in India between 2000 and 2015 and the changes in burden due to the changes in specific factors. (The statistics are taken from Chowdhury et al. 2020, Creative Commons Attribution BY 4.0)
In addition to the GBD study, there are a host of epidemiological studies in India which utilized pre-existing exposure-risk functions for various pollutants and health endpoints. The majority of these studies utilize curated software like the Air Q/Air Q+ developed by the WHO European Centre for Environment Health, Bilthoven Division. Such software packages are based on the Risk of Mortality/Morbidity due to the Air Pollution (RiMAP) model, modelled to estimate air pollutant impact on a defined population group in a given region and time point. The underlying approach to any risk assessment for human impact is population attributable factor (PAF), the fraction of excess morbidity or mortality in a given population due to atmospheric pollutants, calculated using a pre-existing risk function for exposure and excess health risk, factoring in for the possible confounding factors in that association (Maji et al. 2016). The advantage of using these packages is that they serve as tools for quick analysis to support policymaking for air quality management. The other advantages are that they are versatile and capable of estimating air pollution exposures at short (i.e., daily) and long (i.e., yearly) temporal scales for various health endpoints and list of pollutants and for even sources like household air pollution. The risk functions based on non-indigenous studies and data, however, pose a question of true applicability for the Indian demography (Sacks et al. 2020).
Given these limitations, such simple tools have repeatedly been used on Indian city-level data to demonstrate the health and economic impact of poor air quality. Studies based in Mumbai and Delhi found excess all-cause mortality, respiratory and cardiovascular mortality, also hospital admissions due to COPD and CVD to be more strongly associated with PM10, and PM2.5 than gaseous pollutants (Maji 2017a; Afgan and Patidar 2020). Another study in Nagpur used the same model to establish a long-term association (two decades) between COPD risk and PM exposure. Similar studies incorporating multicity analyses have found this excess mortality and morbidity to be on an increasing trend over the years (Maji et al. 2016). The model has also been put to use to establish a strong association between the excess cases of COPD hospital admissions correlated with PM2.5 exposures (Manojkumar and Srimuruganandam 2021). Trend analysis in most studies showed peak particulate matter impacts during post-monsoon and winter seasons (Sasmitaa et al. 2022; Kumar and Middey 2022).
2.4 Cohort Studies
The number of epidemiological studies causally linking air quality and health is very limited in India. The Tamil Nadu Air Pollution and Health Effects (TAPHE) cohort study conducted by Balakrishnan et al. (2015) investigated the impact of fine particulate matter (PM2.5) exposure in a rural-urban regional setting. The study finding revealed that an increment of 10 μg/m3 in PM2.5 exposure during the gestation period was associated with a 4 g (CI: 1.08–6.76) decrease in birthweight and subsequently led to a 2% increase in the low birth-weight prevalence (OR: 1.02; CI: 1.00–1.04) (Balakrishnan et al. 2018). Another cohort—Ambient and Indoor Air Pollution in Pregnancy on the Risk of Low Birth Weight and Ensuing Effects in Infant (APPLE)—showed the impact of prenatal exposure to indoor and outdoor air pollutants during pregnancy on low birth weight (Shriyan et al. 2020). In 2015, Choi et al. showed that households cooking with clean fuel like LPG than households using kerosene experienced a lower incidence of respiratory complaints and illnesses among women and children (Choi et al. 2015).
It is important to note that the pieces of evidence demonstrating adverse health effects due to household air pollution primarily arise from the use of solid fuels such as crop residue, wood, and animal dung, particularly in rural areas. Women are very frequently exposed to high levels of particulate matter (PM) in indoor environments, especially when the kitchens are poorly ventilated and not separated from the rest of the living space. The majority of (non-working) women in India, as well as in most LMICs, spend most of their time indoors. As a result, a difference in exposure levels is expected, and the duration of time spent in the kitchen is likely to vary across India. In order to understand the health benefits of using clean fuel for household activities in four LMICs, Johnson et al. (2020) established a ‘Household Air Pollution Intervention Network (HAPIN)’ trial that includes South India. The data collected during HAPIN showed the impact of personal exposure to indoor air pollution on gestational blood pressure among women dependent on solid fuels for cooking purposes in rural Tamil Nadu (Ye et al. 2022). The Delhi Air Pollution and Health Effects (DAPHNE) was the only cohort focusing on the impact of in-utero exposure to ambient PM2.5 on birthweight in India (https://www.urbanair-india.org/daphne) so far. DAPHNE also examined the ambient PM2.5 exposure on respiratory outcomes of adolescents in Delhi (Mueller et al. 2022).
While there are substantial findings from developed countries indicating the health impacts of fine particulate matter, there remains a dearth of evidence from low- and middle-income countries’ populations. Tonne et al. (2017) designed an observational cohort study titled “Cardiovascular Health Effects of Air Pollution in Telangana, India” (CHAI). The objective of the CHAI study was to assess the relationship between particulate matter and markers of atherosclerosis/cardiovascular diseases. A retrospective cohort study by Liao et al. (2022) provided evidence of the association between exposure—both during pregnancy and after delivery—and infant survival at the national level in India. A prospective cohort study conducted in Delhi, India, from 2010 to 2016 found a significant correlation between high ambient PM2.5 exposure levels and an increased risk of systolic blood pressure and the occurrence of hypertension (Prabhakaran et al. 2020).
2.5 Cross-Sectional and Case-Control Studies
More recently, several cross-sectional studies strengthened the indigenous evidence of health impacts in India using the health data from the National Family Health Survey (NFHS). Goyal and Canning (2021) showed that the adjusted OR of low birthweight increases non-linearly from 1.09 (CI: 0.95, 1.26) to 1.40 (CI: 1.12, 1.75) for ambient PM2.5 exposure 39.3–77.3 μg/m3 respectively. A population-based cross-sectional study by Siddique et al. (2010) showed the influence of vehicular exposure on the behavioural activities of school children in Delhi. The study involved 969 school children across various regions within Delhi-NCR for attention deficit hyperactivity disorder (ADHD) screening and found that the children residing in polluted settings had a higher ADHD prevalence. Another study from the Delhi NCR region showed the association between ambient particulate matter exposure with lung function among the population of the age group 18–50 years (Kesavachandran et al. 2013). Agricultural biomass burning has majorly contributed to air pollution in India. Singh et al. (2021a, b) focused on four North Indian states and showed that the adult population living in close proximity to high-intensity biomass burning (primarily crop burning) is more likely to be hypertensive, while Singh and Dey (2021) demonstrated that exposure to such high-intensity burning events during pre- and post-natal period impact the height later in life. Child stunting (Spears et al. 2019), child anaemia (Mehta et al. 2021), child ARI (Odo et al. 2022), and child mortality (deSouza et al. 2022) are also found to be significantly associated with ambient PM2.5 exposure.
Similarly, certain cross-sectional studies showed the association of household air pollution (HAP) exposure with reduced birth weight (Wylie et al. 2014; Islam and Mohanty 2021), low IQ (Brabhukumr et al. 2020), and childhood stunting (Islam et al. 2021). Agrawal (2012) found that there is a significantly higher risk of asthma in adult women living in households using unclean fuels than those living in households using cleaner fuels. James et al. (2020) reported that more than two-thirds of women using biomass fuel for cooking were positively associated with self-reported health symptoms such as respiratory, cardiovascular, and adverse obstetric outcomes. All these studies focusing on the health impacts of HAP concluded that reducing household unclean fuel use and enhancing the use of improved stove technology may decrease the chances of health effects due to HAP.
A cross-sectional study by Chaudhary et al. (2022) estimated the impact of ambient air pollutants on anaemia burden among Indian women of reproductive age (WRA; 15–49 years). They found out that with every 10 μg/m3 increase in PM2.5, there was a 7.23% (UI: 6.8%, 7.6%) increase in anaemia burden and a decrease in haemoglobin level by 0.4 g/L (UI: 0.5, 0.4) among WRA. Figure 2.4 represents the exposure-response relationships of ambient PM2.5 with anaemia prevalence and haemoglobin level (from NFHS-4) among WRA in India developed in this study, which can be utilized to estimate the health benefits of exposure reduction on a population scale.
The exposure-response curve between chronic exposure to PM2.5 (2007–2016) and log odds of anaemia (left panel). The right panel represents a significant decrease in haemoglobin level with increased PM2.5 exposure. (The statistics are taken from Chaudhary et al. 2022, Creative Commons Attribution BY 4.0)
A case-control study conducted in Delhi, West Bengal, and Uttarakhand showed an urban disadvantage over rural in terms of risk of reduced lung function both in boys and girls (Siddique et al. 2010). The case (exposed) group included 5671 children from Delhi, whereas the control group included 2245 children from rural settings of West Bengal and Uttarakhand. Another case-control study conducted in India showed the differential risks of HAP from wood and coal burning associated with hypopharyngeal and lung cancer (Sapkota et al. 2008). This study showed that the participants who always relied on coal for cooking purposes had a higher risk of lung cancer than the individuals who never used coal.
2.6 Time-Series Studies
Many city-level time-series studies have featured an association between various air pollutants with cardiorespiratory or all-cause mortality and hospital admissions in India. The city-level studies hold importance as the evidence can be crucial for air quality management and policy development at a fine spatial scale. An impetus to time series studies for evidencing the impact of air pollution on mortality was by way of the Health Effects Institute call for multicity—Public Health and Air Pollution in Asia (PAPA) study in India. This research call initiated pioneering studies estimating the excess mortality due to acute, i.e., daily, air pollution in two cities: Delhi and Chennai. These two studies became a reference point for all subsequent short-term impact studies until now. Till recently, all time-series studies in India were restricted to PM10 as the primary pollutant. Lately, a few studies estimated trends in short-term exposure effects of PM2.5 on all-cause non-accidental mortality (Singh et al. 2021; Joshi et al. 2021). Due to gaps in ground-based pollutant data, there were studies that also utilized proxies of respirable suspended particulate matter in terms of haze and visibility to assess the impact on short-term natural mortality (Kumar et al. 2010). The time series design is increasingly being put to use for a variety of health endpoints, the latest being daily COVID-19 infections in association with ambient air pollutants PM2.5, PM10, O3, NO2, SO2, and CO levels in NCT Delhi (Singh 2021). As compared to PM2.5, gaseous pollutant NO2 showed a stronger effect on daily all-cause mortality in highly polluted urban centres such as Delhi and Mumbai (Maji et al. 2017a, b).
In such time series studies assessing short-term health impacts, the most popularly used method is the generalized additive model (GAM). The GAM-based regressions have the advantage that they allow the accounting of non-parametric smooth functions, which is important to model non-linear dependence of deaths/hospital admissions etc., on metrological factors such as temperature and relative humidity. There is also a scope to include dependent variables from a wide variety of distributions to model the effects of pollutant predictor variables on them. The other advantages of GAM design include: (a) each additive term is estimated using a univariate smoother, avoiding multicollinearity, (b) the results are easily interpretable to understand the unit change in the dependent variable with corresponding independent variables (Singh 2021). Figure 2.5 shows the dose-response curves of Delhi for ambient PM2.5 typically calculated using the GAM model (Joshi et al. 2021).
Dose-response relationship 3-day cumulative exposure of PM2.5 for Delhi. (The statistics are taken from Joshi et al. 2021, Creative Commons Attribution BY 4.0)
2.7 Challenges and Way Forward
Although a plethora of epidemiologic studies have been conducted in recent times in India, several challenges still remain.
2.7.1 Accurate Exposure Attribution
The robustness of any health burden estimation depends to a major extent on the representativeness of the attributed exposure on a personal level. In prospective cohorts, personal exposure is measured using portable sensors. However, scaling up personal exposure measurement at a population level is not realistic. For ambient exposure, measurements from continuous ground monitors are usually used. In India, the ground-based monitoring network is disproportionately distributed, and the mean distance to the nearest monitor is more than 80 km (Martin et al. 2019). Therefore, ambient air pollution exposure relying on such measurements has large uncertainty.
Advancements in modelling techniques allow estimating PM2.5 (and other pollutants) exposure at a large spatial scale using satellite remote sensing data products. For particulate matters, aerosol optical depth (AOD) is converted to either PM2.5 or PM10 using statistical (Ma et al. 2022) or a scaling-factor-based approach (Donkelaar et al. 2010). Over the years, satellite-derived particulate matter retrieval became standardized (Donkelaar et al. 2015; Shaddick et al. 2018), and local calibration further improved the accuracy of such products (Dey et al. 2012). Recently, a national database of particulate matter under 2.5 μg, was created for India at 1 km spatial and 24-h temporal resolution (Dey et al. 2020), and the data has been used ever since in many epidemiological studies. The other alternative is machine learning (Liao et al. 2022) or land use regression modelling (Morley and Gulliver 2018) to estimate exposure.
It must be noted that the GBD estimates are increasingly based on exposures generated through a hybrid approach. The uncertainty in exposure could vary by a large margin. Figure 2.6 depicts that accuracy is much less in LMIC regions where ground-level monitoring is sketchy or even absent. Significant efforts are required to further improve ambient exposure in India.
The root means square error of predicted PM2.5, results from three models across super-regions. The dots depict the median of the distribution and the vertical lines denote the range of values; from 25 simulations. Model A shows concentrations derived from satellite and chemical transport data; Model B is a variation of Model A, with local population numbers, and Model C is upgraded Model B, along with particle species and monitors elevation. The super regions are: (1) High-Income countries, (2) Central Europe, Eastern Europe, and Central Asia, (3) Latin America and Caribbean, (4) South East Asia, East Asia, and Oceania, (5) North Africa and the Middle East, (6) Sub-Saharan Africa, and (7) South Asia. (The figure is adapted from Ostro et al. 2018)
Nonetheless, all these approaches opened up the possibility of carrying out retrospective analysis with past cohort data.
However, these methods cannot be applied to household exposure assessment. In India, household exposure is modelled based on the concentration derived from the solid fuel use prevalence and limited indoor measurements to adjust for the exposure window (Balakrishnan et al. 2019). More actual measurements in varying indoor microenvironments are required to further improve household exposure assessment in the future. The ongoing cohorts can be utilized for this purpose.
2.7.2 Reconstructing Personal Exposure and Exposure Apportionment
Health impacts are manifested due to air pollution exposure in both ambient and household modes. Burden estimates independently for either ambient or household PM2.5 are affected by exposure misclassification or exposure overlap. The ideal way forward would be reconstructing personal exposure for a target population. Data need to be collected systematically for indoor concentrations representative of various types of microenvironments (e.g., urban neighbourhoods, offices, educational institutions, commercial locations, etc.), simultaneously with ambient concentrations across the major air sheds of India. The outdoor-indoor ratio will allow the reconstruction of personal exposure based on the time-activity profiles of the population that can be collected through surveys. The limited personal exposure data collected through various cohorts can be used as benchmarks to evaluate personal exposure reconstruction.
For air quality management, source apportionment is the foremost requirement. However, exposure may drastically vary due to socio-demographic determinants. India should focus on exposure apportionment to prioritize sectoral interventions for maximum health benefits. For ambient exposure, chemical transport models are a great tool to provide sectoral contributions at a regional scale. The data can be integrated with population distribution for better exposure apportionment. For household exposure, this can be achieved in a hybrid mode—first traditional sampling of PM, chemical speciation in the lab, and application of tools like positive matrix factorization (Jain et al. 2020) or chemical mass balance (Chow et al. 2015) to determine source characterization, and then integrate with models predicting the influx of ambient air indoor. Such an approach will allow health burden estimates tagged to sources and an understanding of the health burden disparity in view of contributions to emissions and exposure.
2.7.3 Moving Beyond PM2.5 Mass and Elucidating the Biological Pathway
Recent global evidence suggests health effects beyond cardiovascular and lung diseases, including metabolic and neurodegenerative diseases (Thurston et al. 2017). These health endpoints have not been accounted in the GBD estimates so far, and in doing so, further evidence from local Indian cohort studies needs accounting. Multicentric cohorts are the need of the hour to guide the integrated exposure-risk function used in the GBD at the high exposure level.
The GBD framework also has certain underlying uncertainties for the LMICs such as India, which interestingly contribute to one-half of the total global mortality. Firstly, owing to a large and growing ratio of youthful population in the total demographic scene, there may be masking of the actual cumulative cardiopulmonary effects from air pollution exposures in the current GBD estimates. The actual effects are likely to be observable only years later (Apte et al. 2015). Secondly, the GBD exposure-risk functions assume uniform toxicity across the individual species. Studies (Ostro et al. 2007; Janssen et al. 2011; Lippmann et al. 2013; Chowdhury et al. 2022) suggest that the health impacts could be different for different PM2.5 species. In India, the source characterization drastically varies with region and season. India should adopt a dedicated program to carry out epidemiological studies to unravel the differential impacts of individual PM2.5 species tagged to key sources. India-specific exposure-risk functions will provide better health benefit assessments linked to clean air targets and should be the most critical indicator in redefining its national air quality standards.
From establishing association (using a cross-sectional design) to causality (using cohorts), standard epidemiological study designs are capable of providing robust evidence of air pollution health impacts. However, it is also essential to understand the biological pathways so that early indication can be detected. Lab-based studies and biomarker analysis can help in that direction (Clark et al. 2013; Barr et al. 2020). Most importantly, a funding commitment is critical to plan and execute long-term cohorts and achieve this goal.
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Acknowledgements
The authors acknowledge financial support from the Clean Air Fund under ‘Health and Climate Co-Benefits of Pollution Management’ project and grant under SUPRA Scheme, SERB, Department of Science and Technology, GoI (SPR/). SD acknowledges financial support for the Institute Chair position and DST-FIST program (SR/FST/ESII-016/2014).
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Joshi, P., Chaudhary, E., Dey, S. (2024). Impacts of Air Pollution on Human Health: Present Status in India and Future Directions. In: Padhy, P.K., Niyogi, S., Patra, P.K., Hecker, M. (eds) Air Quality and Human Health. Springer, Singapore. https://doi.org/10.1007/978-981-97-1363-9_2
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