Urban versus rural health impacts attributable to PM_2.5 and O₃ in northern India

Air quality modeling was performed using the regional CMAQ model version 5.1. Previously, CMAQ has been used in India to assess O₃ regimes (Sharma et al 2016, Sharma and Khare 2017) and evaluate urban and rural emissions uncertainties (Karambelas et al 2018). We completed simulations for four seasonally representative months using 2010 meteorology and emissions: January, April, July, and October for winter, pre-monsoon spring, monsoon, and post-monsoon fall respectively. Model processes include surface- and upper-level emissions, photolysis, gas- and particle-phase chemistry, deposition, and dispersion Byun and Schere (2006). Simulations include the Carbon Bond 05 (CB05) chemical mechanism (Yarwood et al 2005) and AERO 6 aerosol mechanism, the inclusion of windblown dust (Dong et al 2015) with enhancements following Karambelas et al (2018), and in-line lightning NO_x production (Allen et al 2012). Boundary and initial conditions are from a larger, 36 km by 36 km domain (Karambelas et al 2018). Meteorology is simulated using the Weather Research and Forecasting (WRF) model v3.2 and Preprocessing System with European Center for Medium-Range Weather Forecasting ERA-Interim globally gridded reanalysis data (Dee et al 2011). WRF is used to interpolate weather data from an 80 km resolution over 60 vertical layers at 6 hour increments to our model domain over 36 vertical sigma layers from the surface to approximately 150 hPa using the Grell cumulus parameterization Grell and Devenyi (2002). For use in CMAQ, WRF output was ultimately preprocessed with the Meteorology-Chemistry Interface Processor (MCIP). Planetary boundary height, temperature, and precipitation from MCIP and evaluated with TRMM show MCIP captures seasonality in meteorology for the region, though underestimates precipitation (supplemental figure 1 available at stacks.iop.org/ERL/13/064010/mmedia).

We use biogenic, biomass burning, and anthropogenic emissions for year 2010 gridded to 12 by 12 km and vertically distributed (supplemental table 1). Monthly biogenic emissions are from the Community Land Model with the Model of Emissions and Gases from Nature (Guenther et al 2006) (downloaded from http://lar.wsu.edu/megan/guides.html). Monthly biomass burning emissions are from the Global Fire Emissions Database including small fires (GFED v4.1s) (van Der Werf et al 2010) and were vertically distributed following methods used in Karambelas et al (2018). Annual total anthropogenic emissions for 2010 are from the GAINS Model gridded globally following the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project version 5a (Stohl et al 2015, Klimont et al 2017). In addition to regridding anthropogenic emissions from the global 0.5 degree by 0.5 degree to 12 km by 12 km over northern India, we distribute anthropogenic emissions vertically according to Simpson et al (2012). Updates according to urban and rural activities and population distributions where cities and towns greater than 100 000 people are designated as urban were included for the domestic combustion and transportation sectors to provide enhanced detail at the higher domain resolution. Emissions from electricity generation were also updated based on power plant location information. More details on population data and energy sector emissions updates can be found in the supplemental information. Our urban designation is different than used for population distribution in India, that states an urban population must have (1) at least a population of 5000; (2) 75% of the male working population is employed in non-agricultural work; and (3) the density is at least 400 people per square kilometer. Our urban-rural designations result in a distribution of 79% rural and 21% urban, and is approximately 2% more rural (less urban) than the 2001 population distribution off of which our data is based.

Solid fuel based cooking is one of the main sources of primary PM emissions in India, particularly among rural communities where biomass is freely available and access to clean fuels like liquid petroleum gas (LPG) or cookstoves that use them may be limited in availability and cost. Recent studies found residential combustion emissions are attributable for 20%–25% of premature deaths associated with ambient PM_2.5 air pollution (Conibear et al 2018, GBD MAPS Working Group 2018). We reallocate emissions from the domestic cooking sector using state totals and gridded population data following greater LPG consumption in urban regions and more traditional biomass burned in rural regions. Information on distinctions between urban and rural domestic cooking fuel use were from Pachauri et al (2013), and updated emissions were gridded according to urban and rural population activity fractions across each state. We did not include other socio-demographic factors such as proximity to forest edge (Winijkul et al 2016a, 2016b), yet we recognize this may be a relevant factor in emissions allocation.

Transportation emissions were adjusted according to vehicle type: mopeds, motor-cycles, light duty cars, light duty trucks, buses, and heavy duty trucks. Based on best available information from Indian transportation research on vehicle population composition in the Mumbai metropolitan region (Shirgaonkar 2012) and personal communications (with Dr. Jens Borken, AIR, IIASA in July 2015), emissions for urban and rural regions per vehicle type are distributed according to ratios in supplemental table 2. Equal distribution is given to urban and rural mopeds due to their accessibility for both low-income and middle-class households. Urban populations operate more motorcycles, light duty cars and trucks, while buses and heavy-duty trucks are operated in greater frequency in rural areas. Assumptions regarding vehicle fleet age are not included in the redistribution. Urban and rural emissions were gridded based on respective population distributions.

The final emissions inventory included the updated sectors merged with non-updated sectors including agriculture, non-road, industry, and other sectors.

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We use parameters from integrated exposure response functions from the GBD 2013 study (Cohen et al 2016, Forouzanfar et al 2015, World Health Organization 2016) to calculate the relative risk associated with diseases attributable to an excess concentration of ambient PM_2.5. The attributable fraction of cause-specific mortality related to excess ambient air pollution is multiplied by our population dataset to estimate the number of premature deaths per grid cell.

We estimate the effects of long-term exposure to PM_2.5 on deaths due to chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer, and stroke. Baseline mortality rate estimates for these outcomes in India are from the World Health Organization (WHO) (World Health Organization 2017) and have been previously used for ambient air pollution health assessments in India (Chowdhury and Dey 2016, Ghude et al 2016). Our work follows methods similar to Ghude et al (2016), Burnett et al (2014) and Apte et al (2015). We estimate annual premature mortality (∆M_j) attributable to estimated annual average PM_2.5 concentrations as a result of disease j, where instances of IHD and stroke are age-dependent, following:

$$\begin{equation} \Delta M_{j} = Y_{j} \times \frac {RR_{j^{-1}}}{RR_{j}} \times P \end{equation} \tag{ 1 }$$

where Y_j is the baseline mortality rate for a particular disease and P is total population. We apply the same baseline mortality rates across India, but we recognize that there are limitations to this method. We use relative risk tables parameters from GBD (2013) in calculating relative risk for disease j using an integrated exposure response (IER) function following Burnett et al (2014):

$$\begin{equation} RR_{j} = 1 + a_{j} \times [1-{\rm exp}(-\gamma_{j}(\Delta PM_{2.5})^{\delta_{j}})] \end{equation} \tag{ 2 }$$

where α, γ, and δ are disease-dependent parameters (Burnett et al 2014, Apte et al 2015). ∆PM_2.5 is the excess PM_2.5 beyond an annual average counterfactual minimum risk concentration commonly placed between 5.8–8.0 µg m⁻³ (Chowdhury and Dey 2016, Ghude et al 2016). Other recent estimates have used nonlinear power law functions derived from cohort studies to estimate relative risk attributable to PM_2.5 in India which result in lower premature mortality estimates compared to using IER methodology (Chowdhury and Dey 2016) and references therein, and we include analysis using this method in the supplemental information. We calculate the 95% confidence interval of disease-specific relative risk at each grid cell to define a range of mortality estimates attributable to PM_2.5 exposure.

We estimate premature deaths from COPD due to long-term exposure of estimated annual average concentrations of maximum daily 8 hour average (MDA8) O₃ following Ghude et al (2016) and Ostro (2004). We use the relative risk calculation:

$$\begin{equation} {{RR}} = {\left[ {\frac{{X + 1}}{{{X_0} + 1}}} \right]^\delta } \end{equation} \tag{ 3 }$$

where the minimum-risk concentration, X₀, is 37.6 ppb (range: 33.3–41.9 ppb) and the factor δ is 0.1521, both derived from the GBD assessment (Lim et al 2012). This minimum-risk concentration range was used to estimate minimum and maximum mortality impacts. To estimate ∆M attributable to annual average MDA8 O₃, we substitute RR from equation (3) into equation (1). Premature mortality for both PM_2.5 and O₃ is estimated for adults 25 years and older, meaning our estimates are likely conservative considering they do not take into account infant, child, and young adult mortality. Finally, we apply grid-cell specific population distributions to assess differences across urban and rural regions, with urban defined as towns and cities with greater than 100 000 people in total respectively.

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Domain-wide 2010 annual emissions of anthropogenic NO_x, SO_x, and primary PM_2.5 (elemental carbon [EC], organic carbon [OC]) indicate spatial variability across the Indo-Gangetic Plain. Emissions vary across the region, with many instances of localized high values indicative of regions of relatively higher population density. Emissions of NO_x (figure 1(a)) and SO₂ (figure 1(b)) exhibit similar distributions, with annual totals in the north India region (model domain) of 2.3 Tg N (2.6 Tg N) and 6.3 Tg (6.9 Tg) respectively, where there are many concentrated regions of emissions and fewer emissions from area sources, however this is much more pronounced among the SO₂ emissions due to power plants being a main source. Emissions of NO_x are slightly greater than reported in a recent total India 2005 inventory of 1.9 TgN derived using satellite tropospheric columns (Ghude et al 2013), but emissions of both NO_x and SO₂ are less than the range (5–6.5 TgN and 7–9.5 Tg SO₂ respectively for 2010) of a recent comparative analysis (Saikawa et al 2017), as expected considering our simulations cover the northernmost half of India.

Particulate emissions are largely dominated by the domestic combustion sector, emissions from which are strongly dependent on population density and availability of combustible materials. Total PM_2.5 emissions for the north India domain (2.7 Tg) are about one-third of those used in recent studies covering all of India (9–9.1 Tg) (Venkataraman et al 2017, GBD MAPS Working Group 2018). Emissions indicate a high number of localized peaks in PM_2.5 emissions, and, unlike NO_x and SO₂ emissions, broad regions of lower levels of emissions (figure 1(c)). Variations in emissions magnitude indicate relatively densely populated villages among low-populated rural areas and the effect these different population distributions have on total emissions. Adjusting emissions according to urban-rural population and activity distribution information resolves detailed urban and rural differences for more precise air quality modeling.

Estimated annual average concentrations of PM_2.5 and MDA8 O₃ using four seasonally representative months are shown in figure 2; monthly average concentrations are shown in supplemental figure 2. Modeled average concentrations for PM_2.5 and MDA8 O₃ are 25.5 µg m⁻³ and 41.7 ppb in northern India respectively (figures 2(a) and (b) respectively). Concentrations of PM_2.5 in the New Delhi National Capital Region are much higher (167.4 µg m⁻³). Concentrations of both pollutants are relatively higher along the Indo-Gangetic Plain (IGP), corresponding with population density. South of the IGP, annual mean concentrations of PM_2.5 are comparatively low, between 30 and 40 µg m⁻³ with some localized instances of concentrations greater than 50 µg m⁻³ including near Ahmedabad to the west and Kolkata to the east. Localized instances of higher concentrations are predominantly from primary PM_2.5 species such as elemental and organic carbon (supplemental figure 3). Ozone concentrations exhibit local minima in highly populated urban areas, for instance in Ahmedabad and most noticeably Delhi, where concentrations average 34.3 ppb as a result of NO_x titration due to modeled high NO₂ concentrations in excess of 20 ppb.

We evaluate model performance for 2010 with available observations from India's CPCB. Modeled O₃ exhibits a minimal high bias (NMB = +3.5%) across nine monitor locations. However at Delhi monitor locations model biases are actually low on average in comparison due to an overestimation of modeled O₃ depletion from NO_x titration. Average high O₃ biases exhibited across northern India is exhibited in other CMAQ (Sharma et al 2016 and WRF-Chem Ghude et al 2016) analyses. Compared to two sites in Delhi, modeled daily PM_2.5 concentrations exhibit a slight high bias of +6.0%. Despite low model biases, normalized mean errors are quite high (O₃: 83.4%; PM_2.5 45.7%) and spatial correlations are poor (O₃: −0.29; PM_2.5: +0.41). Evaluation with CPCB measurements is limited due to challenges such as unavailable data and the influence of very local sources on measured concentrations. Finally, to determine if the model can represent annual average values of PM_2.5 and O₃ from the four month simulations, we compare our modeled values with long-term average concentrations at available monitors from 1 January 2010– 31 December 2014 (detailed in the supplemental information). The four-month average concentrations compare well with observations at nine monitor locations in Delhi for PM_2.5—163 µg m⁻³ modeled and 160 µg m⁻³ observed—and at 23 monitor locations in the IGP for annual average O₃—22 ppb modeled and 21 ppb observed. Evaluation metrics for NO₂, SO₂, and PM₁₀ for 2010 are shown in supplemental table 3.

Using representative annual average concentrations of PM_2.5 and O₃, we calculate premature mortality attributable to these pollutants within northern India.

Estimated population-weighted annual average concentrations of PM_2.5 are 72.1 µg m⁻³ in northern India. We estimate 463 200 (95% CI: 444 600–482 600) annual premature adults deaths resulting from excess PM_2.5 pollution in northern India. Concentrations and adult premature deaths from COPD, IHD, stroke, and LC are greatest along IGP (figure 3(a)), where most grid cells reflect annual premature deaths between 40 and 75 adults and several localized hotspots estimate more than 300 adults deaths attributable to PM_2.5 (i.e. in New Delhi). In far northwestern India where the population and emission density is low, there are some cells where zero deaths are attributable to PM_2.5. Estimated annual average modeled concentrations in Delhi and Kolkata are 152.7 µg m⁻³ and 144.4 µg m⁻², respectively, well above the Indian National Ambient Air Quality Standard (NAAQS) of 40 ug m⁻³ (annual average) and the WHO guideline of 10 ug m⁻³. The populations of Delhi and Kolkata are 16.8 and 4.5 million people according to the 2011 Census, respectively. Using the average concentration and total populations of Delhi (National Capital Territory) and Kolkata, we find that approximately 12 200 (11 700–12 800) and 3300 (3600–3900) premature deaths occur each year in Delhi and Kolkata, respectively, as a result of excess PM_2.5. Our estimate of premature mortality in Delhi is about 50% greater than the 8300 deaths from Amann et al (2017), likely due to our average modeled concentration being greater by nearly 30 µg m⁻³ and differences in model resolution, emissions, and population datasets. Values for other cities atop the WHO most polluted cities list are included in the Supplemental Information.

Larger numbers of premature deaths attributable to MDA8 O₃ are evident across the IGP than in central India (figure 3(b)). The average population weighted MDA8 O₃ concentration in northern India is 56.5 ppb. Exposure to O₃ contributes to approximately 37 600 (28 500–48 100) adult lives lost annually. In Delhi, the MDA8 O₃ concentration of 34.3 ppb is lower then both the O₃ ambient air quality standards of both India and the WHO (50 ppb) due to NO_x titration, while the average MDA8 O₃ concentration in Kolkata (59.5 ppb) is in excess of these standards. Ozone-attributable COPD deaths in these cities are an estimated 100 and 315, respectively. In contrast to PM_2.5, there are many grid cells across India where fewer than five premature adult deaths are attributable to O₃, suggesting that PM_2.5 rather than O₃ plays a larger health risk to the northern India population.

3.3.1. Urban and rural adult premature deaths

This study has several limitations. One limitation is deficiencies in other emissions sectors, for instance, data is particularly lacking for the industrial sector. Further, emissions inventories in India remain highly uncertain. Recently, an emissions inventory comparison noted large uncertainties, on the order of 150%–325%, across several inventories for different pollutants from the domestic combustion sector (Saikawa et al 2017). Updates to support reducing uncertainties in the domestic combustion sector have been included in this study, yet still requires considerable research.

A second limitation is using four model months to represent annual average concentrations. Each month was chosen to represent a unique air quality condition: January for the polluted winter due to inversions, April to represent spring windblown dust, July to simulate air quality conditions during the monsoon, and October to represent the onset of the agricultural biomass burning season in northwestern India. However, we note above that the average concentrations estimated using these four months (PM_2.5: 163 µg m⁻³, O₃: 22 ppb) are comparable to annual average long-term observations from the CPCB (PM_2.5: 160 µg m⁻³, O₃: 21 ppb).

Finally, there are limitations in using the IER methodology to estimate premature mortality in India. The concentration-response functions for ambient air pollution used here were developed in the context of the GBD 2013 study (Forouzanfar et al 2015) based on epidemiological studies in comparatively low-pollution regions including the US and Europe. Applying these functions to estimate health effects in a high pollution region such as India relies upon extrapolation to higher levels based on studies from the passive and active smoking literatures (Burnett et al 2014) and references therein. The parameters used in the IERs underlie considerable uncertainty, and recent updates in the context of GBD studies since 2010 led to distinctly different estimates of premature deaths. However, in the absence of long-term air pollution cohort studies at high concentrations, using published and documented exposure-response relationships remains the best approach for estimating effects in India, and yields findings that can promote awareness of health implications and the need for observational studies in India to obtain more accurate representation of underlying health status, relevant time spent outdoors, access to healthcare, and genetic predisposition.