Approximately 3 billion people worldwide cook with solid fuels, such as wood,
charcoal, and agricultural residues. These fuels, also used for residential
heating, are often combusted in inefficient devices, producing carbonaceous
emissions. Between 2.6 and 3.8 million premature deaths occur as a result of
exposure to fine particulate matter from the resulting household air
pollution (Health Effects Institute, 2018a; World Health Organization, 2018).
Household air pollution also contributes to ambient air pollution; the
magnitude of this contribution is uncertain. Here, we simulate the
distribution of the two major health-damaging outdoor air pollutants
(PM2.5 and O3) using state-of-the-science emissions databases and
atmospheric chemical transport models to estimate the impact of household
combustion on ambient air quality in India. The present study focuses on New
Delhi and the SOMAARTH Demographic, Development, and Environmental
Surveillance Site (DDESS) in the Palwal District of Haryana, located about
80 km south of New Delhi. The DDESS covers an approximate population of
200 000 within 52 villages. The emissions inventory used in the present study
was prepared based on a national inventory in India (Sharma et al., 2015,
2016), an updated residential sector inventory prepared at the University of
Illinois, updated cookstove emissions factors from Fleming et al. (2018b),
and PM2.5 speciation from cooking fires from Jayarathne et al. (2018).
Simulation of regional air quality was carried out using the US
Environmental Protection Agency Community Multiscale Air Quality modeling
system (CMAQ) in conjunction with the Weather Research and Forecasting
modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the
global chemical transport model GEOS-Chem to generate concentrations on the
boundary of the computational domain. Comparisons between observed and
simulated O3 and PM2.5 levels are carried out to assess overall
airborne levels and to estimate the contribution of household cooking
emissions. Observed and predicted ozone levels over New Delhi during
September 2015, December 2015, and September 2016 routinely exceeded the 8 h
Indian standard of 100 µg m-3, and, on occasion, exceeded
180 µg m-3. PM2.5 levels are predicted over the SOMAARTH
headquarters (September 2015 and September 2016), Bajada Pahari (a village in
the surveillance site; September 2015, December 2015, and September 2016),
and New Delhi (September 2015, December 2015, and September 2016). The
predicted fractional impact of residential emissions on anthropogenic
PM2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to
about 0.10 in New Delhi. The predicted secondary organic portion of
PM2.5 produced by household emissions ranges from 16 % to 80 %.
Predicted levels of secondary organic PM2.5 during the periods studied
at the four locations averaged about 30 µg m-3, representing
approximately 30 % and 20 % of total PM2.5 levels in the rural
and urban stations, respectively.
Introduction
Although outdoor air pollution is widely recognized as a health risk,
quantitative understanding remains uncertain on the degree to which household
combustion contributes to unhealthy air. Recent studies in China, for
example, show that 50 %–70 % of black carbon (BC) emissions and
60 %–90 % of organic carbon (OC) emissions can be attributed to
residential coal and biomass burning (Cao et al., 2006; Klimont et al., 2009;
Lai et al., 2011). Moreover, existing global emissions inventories show a
significant contribution of household sources to primary PM2.5
(particulate matter of diameter less than or equal to 2.5 µm)
emissions. The Indo-Gangetic Plain of northern India (23–31∘ N,
68–90∘ E) has among the world's highest values of PM2.5. In
this region, the major sources of emissions of primary PM2.5 and of
precursors to secondary PM2.5 are coal-fired power plants, industries,
agricultural biomass burning, transportation, and combustion of biomass fuels
for heating and cooking (Reddy and Venkataraman, 2002; Rehman et al., 2011).
The southwest monsoon in summer months in India leads to lower pollution
levels than in winter months, which are characterized by low wind speeds,
shallow boundary layer depths, and high relative humidity (Sen et al., 2017).
With the difficulty in determining representative emissions estimates (Jena
et al., 2015; Zhong et al., 2016), simulating the extremely high PM2.5
observations in the Indo-Gangetic Plain has remained a challenge (Schnell et
al., 2018).
Approximately 3 billion people worldwide cook with solid fuels, such as wood,
charcoal, and agricultural residues (Bonjour et al., 2013; Chafe et al.,
2014; Smith et al., 2014; Edwards et al., 2017). Used also for residential
heating, such solid fuels are often combusted in inefficient devices,
producing BC and OC emissions. Between 2.6 and 3.8
million premature deaths occur as a result of exposure to fine particulate
matter from household air pollution (Health Effects Institute, 2018a; World
Health Organization, 2018). In India, more than 50 % of households report
the use of wood or crop residues, and 8 % report the use of dung as cooking fuel
(Klimont et al., 2009; Census of India, 2011; Pant and Harrison, 2012).
Residential biomass burning is one of the largest individual contributors to
the burden of disease in India, estimated to be responsible for 780 000
premature deaths in 2016 (Indian Council of Medical Research et al., 2017).
The recent GBD MAPS Working Group (Health Effects Institute, 2018b) estimated
that household emissions in India produce about 24 % of ambient air
pollution exposure. Coal combustion, roughly evenly divided between
industrial sources and thermal power plants, was estimated by this study to
be responsible for 15.3 % of exposure in 2015. Open burning of
agricultural crop stubble was estimated annually to be responsible for
6.1 % nationally, although it was higher in some areas.
Traditional biomass cookstoves, with characteristic low combustion
efficiencies, produce significant gas- and particle-phase emissions. An early
study of household air pollution in India found outdoor total suspended
particulate matter (TSP) levels in four Gujarati villages well over
2 mg m-3 during cooking periods (Smith et al., 1983). Secondary
organic aerosol (SOA), produced by gas-phase conversion of volatile organic
compounds to the particulate phase, is also important in ambient PM levels,
yet there is a dearth of model predictions to which data can be compared.
Overall, household cooking in India has been estimated by various groups to
produce 22 %–50 % of ambient PM2.5 exposure (Butt et al., 2016;
Chafe et al., 2014; Conibear et al., 2018; Health Effects Institute, 2018b;
Lelieveld et al., 2015; Silva et al., 2016), and Fleming et al. (2018a, b)
report characterization of a wide range of particle-phase compounds emitted
by cookstoves. In a multi-model evaluation, Pan et al. (2015) concluded that
an underestimation of biomass combustion emissions, especially in winter, was
the dominant source of model underestimation. Here, we address both primary
and secondary organic particulate matter from household burning of biomass
for cooking.
Air quality in urban areas in India is determined largely, but not entirely,
by anthropogenic fuel combustion. In rural areas, residential combustion of
biomass for household uses, such as cooking, also contributes to nonmethane
volatile organic carbon (NMVOC) and particulate emissions (Sharma et al.,
2015, 2018). Average daily PM2.5 levels frequently exceed the 24 h
Indian standard of 60 µg m-3 and can exceed
150 µg m-3, even in rural areas. The local region on which
the present study focuses is the SOMAARTH Demographic, Development, and
Environmental Surveillance Site (DDESS) run by the International Clinical
Epidemiological Network (INCLEN) in the Palwal District of Haryana (Fig. 1).
Located about 80 km south of New Delhi, SOMAARTH covers an approximate
population of 200 000 in 52 villages. Particular focus in the present study
is given to the SOMAARTH Headquarters (HQ) and the village of Bajada Pahari
within DDESS, coinciding with the work of Fleming et al. (2018b), who studied
cookstove nonmethane hydrocarbon (NMHC) emissions and ambient air quality.
Demographically, with a coverage of almost 308 km2, the DDESS has a mix
of populations from different religions and socioeconomic and development
statuses.
Geographic area of simulation. Panel (a) shows the entirety of
India, and (b) shows a close-up of the model domain. The domain spans
a 600 km by 600 km area with a grid resolution of 4 km (150 cells along
each axis) and includes both New Delhi and SOMAARTH DDESS.
The climate of the region of interest in the present study is primarily
influenced by monsoons, with a dry winter and very wet summer. The rainy
season, July through September, is characterized by average temperatures
around 30 ∘C and primarily easterly and southeasterly winds. In a
study related to the present one, Schnell et al. (2018) used emission
datasets developed for the Coupled Model Intercomparison Project Phases 5
(CMIP5) and 6 (CMIP6) to evaluate the impact on predicted PM2.5 over
northern India, October–March 2015–2016, with special attention paid to the
effect of meteorology of the region, including relative humidity, boundary
layer depth, strength of the temperature inversion, and low-level wind speed.
In that work, nitrate and organic matter (OM) were predicted to be the
dominant components of total PM2.5 over most of northern India.
The goal of the present work is to simulate the distribution of primary and
secondary PM2.5 and O3 using recently updated emissions databases
and atmospheric chemical transport models to obtain estimates of the total
impact on ambient air quality attributable to household combustion. With
respect to ozone, the present work follows that of Sharma et al. (2016), who
simulated regional and urban ozone concentrations in India using a chemical
transport model and included a sensitivity analysis to highlight the effect
of changing precursor species on O3 levels. The present work is based
on simulating the levels of both O3 and PM2.5 at the regional
level based on recent emissions inventories using state-of-the-science
atmospheric chemical transport models.
The present study uses an emissions inventory conglomerated from two primary
sources: (1) an India-scale inventory for all nonresidential sectors
prepared by TERI (Sharma et al., 2015, 2016) and (2) a high-resolution
residential sector inventory detailed here. Emissions data from each source
were distributed to a 4 km grid for the present study. The TERI national
inventory was prepared at a resolution of 36 km × 36 km using the
Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS ASIA)
emission model (Amann et al., 2011). GAINS ASIA estimated emissions based on
energy and nonenergy sources using an emission factor approach after taking
into account various fuel-sector combinations. Following the approach of
Kilmont et al. (2002), the emissions were estimated using the basic equation:
Ek=∑l∑m∑nAk,l,mefk,l,m1-ηl,m,n⋅Xk,l,m,n,
where E denotes the pollutant emissions (in kt); k, l, m, and n are
region, sector, fuel or activity type, and control technology, respectively;
A is the activity rate; ef is the unabated emission factor (kt per unit of
activity); η is the removal efficiency (%/100); and X is the application
rate of control technology n (%/100) where ∑X=1. The energy sources
considered include coal, natural gas, petroleum products, biomass fuels, and
others and are categorized into five sectors – transport, industries,
residential, power, and others. The model uses the state-wise energy data and
generates emissions of species such as PM, NOx, SO2,
NMVOCs, NH3, and CO.
For activity data of source sectors, TERI employed published statistics
(mainly population, vehicle registration, energy use, and industrial
production) where possible. Energy-use data for industry and power sectors
were compiled based on a bottom-up approach, collected from the Ministry of
Petroleum and Natural Gas (MoPNG, 2010), the Central Statistics Office (CSO,
2011), and the Central Electricity Authority (CEA, 2011). Transportation
activity data were compiled from information on vehicle registrations
(Ministry of Road Transport and Highways, 2011), emission standards (MoPNG,
2001), travel demand (CPCB, 2000), and mileage (TERI, 2002). Emission
factors for energy-based sources from the GAINS ASIA database were used.
Speciation factors are adopted from sector-specific profiles from Wei et al. (2014),
primarily developed for China as there is a lack of information for
India. In the transportation sector, the Chinese species profiles are
dependent on fuel type but not technology.
The TERI inventory was compiled on a yearly basis, with monthly variations
for brick kilns and agricultural burning, at a native resolution of 36 km × 36 km then equally distributed to grid resolution of 4 km × 4 km for this study. Emissions for nonresidential sectors have no
specified diurnal or daily variations; thus, the inventory for
nonresidential sectors is the same for each simulated day. Transportation
sector emissions were estimated using population and vehicle fleet data at
the district level and distributed to the grid using the administrative
boundaries. Industry, power, and oil and gas sector emissions were assigned
to the grid by their respective locations. Emissions from agriculture were
allocated by crop-types produced by state in India. The inventory was
vertically distributed to three layers with the lowest layer extending to
30–43 m, the middle layer to 75–100 m, and the top layer to 170–225 m. Volatile organic compound (VOC) emissions were assumed to occur only in the bottom layer.
Industry and power emissions were distributed based on stack heights and
allocated to the second and third layers.
We incorporated biogenic emissions by using daily-averaged emission rates of
isoprene (0.8121 moles s-1) and terpenes (0.8067 moles s-1) per
4 km grid cell, predicted by GEOS-Chem for the region of study. The TERI
inventory additionally includes isoprene emissions from the residential
sector, so isoprene from natural sources was calculated as the difference of
the total rate predicted by GEOS-Chem and the rate of emissions solely from
the residential sector. Terpene emissions are assumed to occur only in
nonresidential source sectors. Isoprene and terpene emission rates were
applied to all computational cells as an hourly average (with no diurnal
profile) in the nonresidential inventory.
Residential sector emissions
To examine local and regional impacts of residential sector emissions in
greater detail, an update to the TERI inventory was performed using various
sources to consider more granular input data specific to the residential
sector (Table 1). Bottom-up estimates of delivered energy for cooking, space
heating, water heating, and lighting were informed by those used in Pandey et
al. (2014) and converted to fuel consumption at the village level using
population size and percentage of reported primary cooking and lighting fuels
from the 2011 Census of India (2011). Urban areas of the domain were assumed
to have the average cooking and lighting fuel use profiles of the average
urban areas of their district. Fuel consumption was converted to emission
rates using fuel-specific emission factors informed by a review of field and
laboratory studies, which was used to update the Speciated Pollutant
Emissions Wizard (SPEW) inventory (Bond et al., 2004) and to generate summary
estimates by fuel type. Hourly emissions were generated using source-specific
diurnal emissions profiles (Fig. 2). The same diurnal emissions profile is
applied to all species from a source category and was informed by real-time
emissions measurements taken in homes during cooking reported by Fleming et
al. (2018a, b). Profiles for fuel-based lighting were informed by real-time
measurements of kerosene lamp usage data reported in Lam et al. (2018). The
residential sector inventory represents surface emissions with a native
spatial resolution of 30 arcsec (∼1 km).
Residential emissions inventory sources by species.
CMAQ required species1SourceSolely emitted byresidential sectorNOTami C. Bond (University of Illinois) NOx using Sharma et al. (2015),NO : NO2= 10 : 1NoNO2NoGasSO2Sharma et al. (2015)NoNH3Sharma et al. (2015), assumed to be negligibleCOTami C. Bond (University of Illinois)NoALD2NoALDXYesETHNoETHANoNMHCETOHSpeciation from Tami C. Bond (University of Illinois) NMHC NoFORMusing Fleming et al. (2018a, b) emission factorsNoMEOHNoOLENoPARcalculated3NoTOLNoXYL3NoISOP2All-sector total ISOP emission from GEOS-Chem daily average and subtracted nonresidential ISOP emission from Sharma et al. (2015) NoCMAQTERP2Assumed to be negligibleAERO6XYLMNXYLMN = 0.998 × XYLNospeciesNAPHNAPH = 0.002 × XYLPye and Pouliot (2012)NoPARCMAQPARCMAQ=NoPARcalculated- 0.00001 × NAPHSOAALKSOAALK = 0.108 × PARCMAQNoPECTami C. Bond (University of Illinois)NoPOCNoPNAYesPCLYesPKSpeciation of PM2.5 from Tami C. Bond (University of Illinois) using YesPNH4Jayarathne et al. (2018) mass percentageYesPNO3NoPSO4NoPMPMOTHRPMOTHR= PM2.5- (PEC+ POC+ PNA+ PNH4+ PK+ PCL+ PNO3+ PSO4)NoPMCSharma et al. (2015)NoPNCOMUnknown, assumed to be 0PH2OPALPCAPFEAssumed to be negligiblePMGPMNPSIPTI
1 Bolded species contribute to SOA production via the AERO6 module.
2 Total isoprene and terpene emissions from all sectors are taken from
GEOS-Chem and were included only in the O3 simulations.
3 PARcalculated and XYL are excluded from CMAQ and replaced with
PARCMAQ, XYLMN, NAPH, and SOAALK.
Fraction of daily household emissions by quantifiable fuel-use
activity. Red, green, blue, and purple indicate cooking, space heating,
water heating, and lighting, respectively. This represents the fraction of
activity-specific daily emissions at each hour. Each species obeys the same
profile. While profiles for heating are shown, the inventory assumes
temperatures too high for this activity to take effect.
In deriving summary estimates of emission factors, priority was given to
emission factor measurements from field-based studies. Several studies have
shown that laboratory-based measurements of stove and lighting emissions tend
to be lower than those of devices measured in actual homes (Roden et al.,
2009), perhaps due to higher variation in fuel quality and operator behavior.
Field-based emission factors utilized in this study include those for
nonmethane hydrocarbons, measured from fuels and stoves within the study
domain (Fleming et al., 2018a, b). PM2.5 speciation from cooking fires
was informed by Jayarathne et al. (2018) (Tables 2 and 3). Residential
emission rates for PM2.5, BC, OC, CO,
NOx, CH4, CO2, and NMHCs were generated from SPEW, which estimates emissions from
combustion by fuel type. As such, solvent emissions are not included for lack
of specific input data. Additionally, while SPEW incorporates
temperature-dependent heating combustion activity, the inventory assumes
temperatures too high for this activity to take effect. Thus, our inventory
has no emissions from heating.
Residential PM2.5 and NMHC emissions speciation.
Emitted speciesFuel-specific dataUsePM2.5 (Bond et al., 2004)Wood, dung,agricultural residue, LPGTotal PM2.5 emission rate distributed by wood, dung, and agricultural residue. LPG emissions assumed to be negligible.Speciated PM2.5 (Jayarathne et al., 2018)Wood, wood–dung mixAverage profile of wood and wood–dung mix applied to all fuel type emissions.NMHC (Bond et al., 2004)Wood, dung,agricultural residue, LPGTotal PM2.5 emission rate distributed by wood, dung, and agricultural residue. LPG emissions assumed negligible.Speciated HCs (Fleming et al., 2018a, b)Wood, dungOne profile applied to each cell according to which fuel type dominates emissions in that cell. Where agricultural residue dominates, wood profile is assumed.
PM2.5 speciation by fuel type.
Emitted species1% mass of total emitted PM2.5Wood2Wood–dung2Averageemployed3PEC145.109.55POC526156.50PNA0.050.390.22PCL3.208.585.89PK1.780.521.15PNH41.124.462.79PNO30.420.210.32PSO40.330.460.40PMOTHR27.1019.2923.19
1 Total PM2.5 mass emission rates from residential combustion
were estimated and distributed by fuel type (wood, dung, or agricultural
residue) by University of Illinois. 2 Emitted PM2.5 weight percent
reported by Jayarathne et al. (2018). 3 An average profile applied to
all cells, indiscriminate of fuel type.
We employed various methods to account for pollutant species not explicitly
reported by SPEW (Tables 1 and 2). Gas-phase SO2 and NH3
emissions were informed by existing residential emissions in the TERI
inventory (Sharma et al., 2015); NO and NO2 were estimated from
NOx emissions, assuming a NO : NO2 emission ratio
of 10 : 1. Total NMHC and PM2.5 emission factors from SPEW are
distributed by fuel type (wood, dung, agriculture residue, or LPG) (Table 2).
Given the low PM2.5 emission rate of LPG (Shen et al., 2018), emissions
from LPG are assumed to be negligible. To further speciate NMHCs, we employed
HC species-specific emission factors (Fleming et al., 2018b), differentiated
by fuel and stove type (i.e., traditional stove, or chulha, with wood
or dung, and simmering stove, or angithi, with dung). We assume that
all NMHC emissions in each computational grid cell are produced by either
wood or dung, whichever contributes the greater fraction of total PM2.5
emissions in that cell (Fig. 3). The NMHC emission profile of dung was
assumed to be the average of measurements from chulha and angithi stoves. The
emission profile for agricultural residue is similar to that of wood;
therefore, wood speciation profiles are applied in cells where agricultural
residue dominates.
Fuel type assumed for speciation of household NMHC emissions. Study
domain: 600 km by 600 km at 4 km resolution. Red indicates cells where dung
use dominated emissions and thus was assumed to be the sole fuel type used.
Orange indicates cells where wood and agricultural residue use dominated
emissions and was thus assumed to be the sole fuel type used.
Particle-phase speciation of total PM2.5 was based on PM mass emissions
from wood- and wood–dung-fueled cooking fires as reported by Jayarathne et
al. (2018), and primary cooking fuel type distribution data from the 2011
census (Tables 2 and 3). A single PM2.5 speciation profile, defined as
the average of that of wood and that of the wood–dung mixture, was applied in
all cells for lack of information on pure dung emissions (Table 3).
Noncarbon organic particulate matter (PNCOM) and particulate water
(PH2O) were assumed to be negligible owing to a lack of information on
these species. Emissions of remaining particle-phase species (i.e., Al, Ca,
Fe, Mg, Mn, Si, and Ti) were also assumed to be negligible for lack of
information. Unspeciated fine particulate matter (PMothr) is
defined in CMAQ as the portion of total PM2.5 unassigned to any other
species:
2PMothr=PM2.5-PEC+POC+PNa+PNH4+PK+PCl+PNO3+PSO4
Tables 4 and 5 summarize emission rates for the study domain.
Particulate matter surface emissions over study domain.
* Mealtimes are assumed to be 04:00–10:00 and 16:00–20:00 (local time).
Atmospheric modeling
To study the impact of household emissions on ambient air pollution, we
simulated two emission scenarios each for three time periods which coincide
with available INCLEN observation data (Tables 6 and 7). A “total” emission
scenario represents the overall atmospheric environment by including
emissions from all source sectors in the inventory. A “nonresidential”
emission scenario represents zeroing-out or “turning-off” of all household
emissions. By considering these scenarios independently, we can isolate the
effect of the residential sector on the ambient atmosphere. Each scenario was
simulated over a region in northern India (Fig. 1) for those periods when
measurements were carried out in the region of interest. Figure 1 shows the
600 km by 600 km domain with 4 km grid resolution. The domain is centered
over the Palwal District and the SOMAARTH DDESS and includes New Delhi and
portions of surrounding states.
Ambient observation data availability.
Location (grid cell)PM2.5O3Bajada Pahari1 (74,74)20–31 December 2015 19–30 September 16Not availableSOMAARTH HQ1 (75, 74)22–27 September 2015 23–30 September 2016Not availableWest New Delhi2 (71, 91)7–30 September 2015 7–31 December 2015 7–30 September 20167–30 September 2015 7–31 December 2015South New Delhi2 (71, 89)7–30 September 2015 7–31 December 2015 7–30 September 20167–30 September 2015 7–31 December 2015 7–30 September 2016
1 Data from the International Epidemiological Clinical Network.
Observations at Bajada Pahari are the average of two monitoring locations
that coincide within the same grid cell. 2 Data from the Central
Pollution Control Board of India at New Delhi Punjabi Bagh monitoring
station.
Simulation durations.
CMAQ17–30 September 20157–31 December 20157–30 September 2016WRF2 (Meteorology)2–30 September 20152–31 December 20152–30 September 2016GEOS-Chem3 (boundary conditions)7–30 September 20157–31 December 20157–30 September 2016
1 Five days prior to date shown were run and omitted from analysis as
spinup. 2 One day prior to date shown was run and omitted from analysis
as spinup. 3 GEOS-Chem was run for 1 year before extracting
atmospheric diagnostics.
Simulation of regional air quality was carried out using the US
Environmental Protection Agency Community Multiscale Air Quality modeling
system (CMAQ), version 5.2 (Appel et al., 2017; US EPA, 2017). CMAQ is a
three-dimensional chemical transport model (CTM) that predicts the dynamic
concentrations of airborne species. CMAQ includes modules of radiative
processes, aerosol microphysics, cloud processes, wet and dry deposition, and
atmospheric transport. Required input to the model includes emissions
inventories, initial and boundary conditions, and meteorological fields. The
domain-specific, gridded emissions inventory provides hourly-resolved total
emission rates for each species (not differentiated by source) by cell,
time step, and vertical layer. Initial conditions (ICs) and boundary
conditions (BCs) are necessary to define the atmospheric chemical
concentrations in the domain at the first time step and at the domain edges,
respectively. The present study uses the global
chemical transport model GEOS-Chem v11-02c
(http://acmg.seas.harvard.edu/geos/index.html, last access: 28 May 2019) to generate concentrations on the boundary of the
computational domain. Meteorological
conditions (including temperature, relative humidity, wind speed and
direction and land use and terrain data) drive the atmospheric processes
represented in CMAQ. The Weather Research and Forecasting modeling system
(WRF) Advanced Research WRF (WRF-ARW, version 3.6.1) was used to simulate
the meteorological input for CMAQ (Skamarock et al., 2008).
GEOS-Chem
We used GEOS-Chem v11-02c, a global chemical transport model driven by
assimilated meteorological observations from the NASA Goddard Earth Observing
System Fast Processing (GEOS-FP) of the Global Modeling and Assimilation
Office (GMAO), to simulate the boundary conditions for the CMAQ modeling.
Simulations are performed at 2∘×2.5∘ horizontal
resolution with 72 vertical layers, including both the full tropospheric
chemistry with complex SOA formation (Marais et al., 2016) and UCX
stratospheric chemistry (Eastham et al., 2014). Emissions used the standard
HEMCO configuration (Keller et al., 2014), including EDGAR v4.2 anthropogenic
emissions
(http://edgar.jrc.ec.europa.eu/overview.php?v=42, last access: 28 May 2019), biogenic emissions from the MEGAN v2.1
inventory (Guenther et al., 2012), and GFED biomass burning emissions
(http://www.globalfiredata.org, last access: 28 May 2019). Simulations were run for 1 year, after which hourly time series
diagnostics were compiled for the CMAQ modeling period. Using the
PseudoNetCDF processor, we remapped a subset of the 616 GEOS-Chem-produced
species to CMAQ species
(https://github.com/barronh/pseudonetcdf, last access: 28 May 2019). The resulting ICs and BCs include 119 gas- and
particle-phase species, 80 adapted from GEOS-Chem and the remaining 39
(including OH, HO2, ROOH, oligomerized secondary aerosols, coarse
aerosol, and aerosol number concentration distributions) from the CMAQ
default initial and boundary conditions data (which were developed to
represent typical clean-air pollutant concentrations in the United States).
Weather Research and Forecasting (WRF) model
Three monthly WRF version 3.6.1 simulations were conducted in the absence of
nudging or data assimilation. The large-scale forcing to generate initial and
boundary meteorological fields is adopted from the latest version of the
European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 released in
January 2019 (Copernicus Climate Change Services, 2017). These reanalysis data are on a 31 km grid and resolve the
atmosphere using 137 levels from the surface to a height of 80 km. WRF
simulations were performed with 4 km horizontal resolution and 24 vertical
layers (the lowest layer of about 50 m depth), consistent with the setup of
the CMAQ model. No cumulus parameterization was used in the simulations.
Meteorological outputs from WRF were prepared as inputs to CMAQ by the
Meteorology-Chemistry Interface Processor (MCIP) version 4.4 (Otte et al.,
2010).
Community Multiscale Air Quality (CMAQ) modeling system
Within the chemical transport portion of CMAQ, there are two primary
components: a gas-phase chemistry module and an aerosol chemistry,
gas-to-particle conversion module. The present study employs a CMAQ-adapted
gas-phase chemical mechanism, CB6R3 (derived from the Carbon Bond Mechanism
06) (Yarwood et al., 2010), and the aerosol-phase mechanism, AERO6, which
define the gas-phase and aerosol-phase chemical resolution. The present
study considers 70 NMHC compounds lumped into 12
groups of VOCs. The emissions inventory
provides emission rates for 28 chemical species, including 18 gas-phase
species and 10 particle-phase species. The CB6R3 adaptation describes
atmospheric oxidant chemistry with 127 gas-phase species and 220 gas-phase
reactions, including chlorine and heterogenous reactions. The CMAQ aerosol
module (AERO6) describes aerosol chemistry and gas-to-particle conversion
with 12 traditional SOA precursor classes, and 10 semivolatile primary
organic aerosol (POA) precursor reactions. The majority of the gas-phase
organic species are apportioned to lumped groups by their carbon bond
characteristics, such as single bonds, double bonds, ring structure, and
number of carbons. Some organic compounds are apportioned based on
reactivity, and others, like isoprene, ethene, and formaldehyde, are treated
explicitly.
Treatment of anthropogenic SOA in CMAQv5.2. Predicted aerosol
species are included in the black box. Species in white boxes are
semivolatile and species in gray boxes are nonvolatile. Blue indicates
species and processes predicted by CB6R3. All other coloring indicates the
AERO6 mechanism where green arrows are two-product volatility distribution,
orange arrows are particle- and vapor-phase partitioning, and purple arrows
are oligomerization. In AERO6, anthropogenic and biogenic VOC emissions
(lumped by category) are oxidized by OH, NO, and HO2 and OH,
O3, NO, and NO3 respectively, to semivolatile products that
undergo partitioning to the particle phase (Pye et al., 2015). Semivolatile
primary organic pathways in CMAQv5.2 are described by Murphy et al. (2017).
The secondary organic aerosol module, AERO6, developed specifically for CMAQ,
interfaces with the gas-phase mechanism, predicts microphysical processes of
emission, condensation, evaporation, coagulation, new particle formation, and
chemistry, and produces a particle size distribution comprising the sum of
the Aitken, Accumulation, and Coarse log-normal modes (Fig. 4). AERO6
predicts the formation of SOA from anthropogenic and biogenic VOC precursors (properties of which are shown in Table 8),
as well as semivolatile POA and cloud processes. CB6R3 accounts for the
oxidation of the first-generation products of the anthropogenic lumped VOCs:
high-yield aromatics, low-yield aromatics, benzene, PAHs, and long-chain
alkanes (Pye and Pouliot, 2012).
Properties of anthropogenic traditional semivolatile SOA
precursors in CMAQv5.2. NA denotes not applicable.
The semivolatile reaction products of “long alkanes” (SV_ALK1
and SV_ALK2) are parameterized by Presto et al. (2010). Values for
“low-yield aromatics” products (SV_XYL1 and SV_XYL2) are based on xylene,
with the enthalpy of vaporization (ΔHvap) from studies of
m-xylene and 1,3,5-trimethylbenzene. ΔHvap for products of
“high-yield aromatics” (SV_TOL1 and SV_TOL2) are based on the higher end
of the range for toluene. The products of benzene (SV_BNZ1 and SV_BNZ2)
assume the same value for ΔHvap. All semivolatile aromatic
products are assigned stoichiometric yield (α) and effective
saturation concentration (C*) values from laboratory measurements by Ng et
al. (2007). Remaining parameters for PAH reaction products (SV_PAH1 and
SV_PAH2) are taken from Chan et al. (2009). Properties of semivolatile
primary organic aerosol precursors are given in Murphy et al. (2017).
Evaluation of WRF-simulated meteorological fields versus ground
observations.
Quantification of WRF model biases in meteorological
fields.
Bajada Pahari SOMAARTH HQ West New Delhi South New Delhi Sep 15Dec 15Sep 16Sep 15Dec 15Sep 16Sep 15Dec 15Sep 16Sep 15Dec 15Sep 16PRE–15.2830.1029.27–30.2230.4516.5930.0730.3217.5929.96(4.59)(3.19)(3.48)(3.06)(3.79)(4.91)(3.05)(3.74)(4.82)(30.3)TemperatureOBS–15.6230.8632.15–33.2632.8019.0431.4628.4812.5829.22(∘C)(4.91)(5.67)(4.12)(5.31)(3.60)(3.66)(2.33)(4.30)(5.52)(4.22)MB–-0.34-0.76-2.89–-3.04-2.35-2.45-1.381.845.020.74ME–1.603.082.92–3.073.032.581.542.115.022.37RMSE–2.203.713.39–3.993.582.991.882.505.332.75PRE–2.912.31––2.01––2.572.802.722.74(1.17)(1.07)(0.66)(1.28)(1.27)(1.08)(1.39)WindOBS–1.180.73––0.55––1.031.260.941.18speed(0.75)(0.40)(0.30)(0.51)(0.83)(0.71)(0.79)(m s-1)MB–1.721.58––1.46––1.541.541.771.56ME–1.751.62––1.50––1.581.611.821.62RMSE–1.961.85––1.66––1.881.852.011.84PRE–247116272–111––179206254191(111)(45)(70)(51)(98)(118)(97)(96)WindOBS–259102255–110––181198224228direction(57)(41)(58)(48)(97)(45)(44)(50)(∘)MB–0.141416–-0.14––-6935-34ME–513844–32.71––49947475RMSE–665164–47.50––641068790
PRE is mean predictions. OBS is mean observations. MB is mean bias. ME is
mean error. and RMSE is root mean square error. Standard deviation of
predictions and observations are noted in parentheses.
In addition to SOA formation from traditional precursors, CMAQv5.2 accounts
for the semivolatile partitioning and gas-phase aging of POA using the
volatility basis set (VBS) framework independently from the rest of AERO6
(Murphy et al., 2017). The module distributes directly emitted POA (as the
sum of primary organic carbon, POC, and noncarbon organic matter, NCOM) from
the emissions inventory input into five new emitted species grouped by
volatility: LVPO1, SVPO1, SVPO2, SVPO3, and IVPO1 (where LV is low
volatility, SV is semivolatile, IV is intermediate volatility, and PO is
primary organic). POA is apportioned to these lumped vapor species using an
emission fraction and is oxidized in CB6R3 by OH to LVOO1, LVOO2, SVOO1,
SVOO2, and SVOO3 (where OO denotes oxidized organics) with stoichiometric
coefficients derived from the 2D-VBS model. AERO6 then partitions the
semivolatile primary organics and their oxidation products to the aerosol
phase (Fig. 4). Thus, the treatment of POA as semivolatile products leads to
an additional twenty species, a particle- and vapor-phase component for each
primary organic and oxidation product (Murphy et al., 2017).
Measured and predicted PM2.5(a, c) and average
diurnal cycle (b, d) in Bajada Pahari for
20–31 December 2015 (a, b) and
20–30 September 2016 (c, d). Here the yellow lines correspond to
CMAQ predictions of the “total” (solid) and “nonresidential” (dotted)
simulations. The solid black line represents ambient observations. Standard
deviations of the diurnal profiles for observations and predictions are
indicated, respectively, by colored shading. Diurnal profiles were averaged
over simulation durations (Table 7). Computations were carried out at 4 km
resolution.
Emissions inventory modifications were required to match the most recent
aerosol module, AERO6, in the CMAQ model. Initially, the lumped emissions of
PAR (a lumped VOC group characterized by alkanes) and XYL (a lumped VOC
group characterized by xylene) derived from grouping specific NMHCs,
calculated using the University of Illinois estimation and the Fleming et
al. (2018a) emission factors, accounted for characteristics of naphthalene
(NAPH) and SOA-producing alkanes (SOAALK), which are not individually
described by any of the sources used to construct the inventory. Moreover,
only a subset of VOCs in the plume could be measured. However, CMAQv5.2
simulations incorporate a surrogate species, potential secondary organic
aerosol from combustion emissions (pcSOA), to address sources of missing
SOA, including unspeciated emissions of semivolatile and intermediate
volatility organic compounds. AERO6 predicts the formation of SOA from NAPH
and SOAALK independently as well as from XYL and PAR; these secondary
aerosol precursor emission rates are calculated with the following:
3XYLMN=0.998×XYL,4NAPH=0.002×XYL,5PARCMAQ=PARcalculated-0.00001×NAPH,6SOAALK=0.108×PARCMAQ,
where XYLMN, NAPH, PARCMAQ, and SOAALK are the new inventory species (Pye
and Pouliot, 2012). SOA-producing alkanes are treated separately in AERO6.
Surface observational data
Gas-phase air quality data analyzed in the present study come from the
Central Pollution Control Board (CPCB) of the Ministry of Environment, Forest
& Climate Change of the Government of India at two sites in New Delhi (one in
the west, and one in the south) (CPCB, 2019). The particle-phase data analyzed come from the
SOMAARTH Demographic, Development, and Environmental Surveillance Site
(Mukhopadhyay et al., 2012; Pillarisetti et al., 2014; Balakrishnan et al.,
2015) managed by INCLEN.
Palwal District has a population of ∼ 1 million over an area of
1400 km2. In this district, ∼ 39 % of households utilize wood
burning as their primary cooking fuel, with dung (∼ 25 %) and crop
residues (∼ 7 %) (Census of India, 2011). The specific sites studied
are the SOMAARTH HQ in Aurangabad (15 km south of Palwal) and
the village of Bajada Pahari (8 km northwest of SOMAARTH HQ). Ambient
measurement sites are shown in Fig. 1, and Table 6 details available data for
each location. We used meteorological data (hourly surface temperature and
near-surface wind speed and direction) from INCLEN and CPCB at the two rural
and two urban sites, respectively, to evaluate the WRF simulations
performance.
Measured and predicted PM2.5(a, c) and average
diurnal cycle (b, d) at SOMAARTH HQ for
20–31 December 2015 (a, b) and
20–30 September 2016 (c, d). Here the green lines correspond to
CMAQ predictions of the “total” (solid) and “nonresidential” (dotted)
simulations. The solid black line represents ambient observations. Standard
deviations of the diurnal profiles for observations and predictions are
indicated by gray and green colored shading, respectively. Diurnal profiles were averaged
over simulation durations (Table 7). Computations were carried out at 4 km
resolution.
Simulation resultsWRF evaluation
We evaluated WRF-simulated meteorology against the available surface
observations at different sites during the same periods. Figure 5 shows that
there is generally good agreement in surface temperature between WRF and
observations for all three months. The surface wind direction is found
to be consistent between model and observations for each site and each month
(Table 9). The simulated near-surface wind speeds are overestimated in WRF,
with an averaged mean bias (MB) of about +1.5 m s-1. Such a bias is
partly a result of the difference in the definition of “near-surface”
between the model and observations.
Measured and predicted PM2.5(a, c) and average
diurnal cycle (b, d) in West New Delhi for
20–31 December 2015 (a, b) and
20–30 September 2016 (c, d). Here the pink lines correspond to CMAQ
predictions of the “total” (solid) and “nonresidential” (dotted)
simulations. The solid black line represents ambient observations. Standard
deviations of the diurnal profiles for observations and predictions are
indicated by gray and pink colored shading, respectively. Diurnal profiles were averaged
over simulation durations (Table 7). Computations were carried out at 4 km
resolution.
Measured and predicted PM2.5(a, c, e) and average
diurnal cycle (b, d, f) in South New Delhi for
20–31 December 2015 (a, b) and
20–30 September 2016 (c–f). Here the blue lines correspond to CMAQ
predictions of the “total” (solid) and “nonresidential” (dotted)
simulations. The solid black line represents ambient observations. Standard
deviations of the diurnal profiles for observations and predictions are
indicated by gray and blue colored shading, respectively. Diurnal profiles were averaged
over simulation durations (Table 7). Computations were carried out at 4 km
resolution.
Particulate matter
Figures 6–9 show measured and predicted total PM2.5 and the average
diurnal profile at each site for the periods with available measurements. The
diurnal profile in these figures includes that of both emission scenarios:
the total scenario with all emissions and the nonresidential scenario with
zeroed-out residential sector. The simulations capture the general trend well
and produce significant diurnal profiles (Table 10). Rural sites show typical
PM2.5 levels are predicted between 50 and 125 µg m-3 in
December and 25 and 75 µg m-3 in September months (Figs. 6
and 7). On the other hand, typical values at urban sites range from 100 to
300 µg m-3 in December and 50 to 125 µg m-3
in September months (Figs. 8 and 9). Observations and predictions show higher
PM2.5 levels in December than September, owing to frequent temperature
inversions in winter and shallower planetary boundary layers. Two daily peaks
and lows of PM2.5 compare with ambient observations at Bajada Pahari
in December 2015 and September 2016, SOMAARTH HQ in September 2015 and 2016, West
New Delhi in December 2015, and South New Delhi in December and September 2015.
Average daily PM2.5 levels regularly exceed the 24 h Indian standard of
60 µg m-3 in each month in both rural and urban locations,
surpassing even double the standard in the village of Bajada Pahari during
mealtimes in December. Afternoon minima tend to be underestimated in
September and December 2015. Diurnal trends of PM2.5 were weaker in
September 2016 than the other months, with lower predictions but
overestimated minima. Urban sites show greater overestimation than rural
sites. This is likely due in part to the granularity of the primary emissions
inventory datasets. The nonresidential sector was prepared from data with a
native resolution of 36 km, while the residential sector used data with
∼ 1 km resolution. Underpredictions of peak PM2.5 concentrations
in September could also result because the emission inventory does not
account for day-to-day variations, especially in the agricultural burning
sector in which emissions can change significantly on a daily basis. Observed
and predicted PM2.5 levels in New Delhi can exceed
300 µg m-3, especially in winter. In this highly populated
urban environment, particulate matter levels are more than double those
reported in the nearby rural areas. The employed emissions inventory
specifies particulate matter surface emissions, which surpass those of Bajada
Pahari and SOMAARTH HQ more than 30-fold (Table 5). Biogenic emissions are
predicted to be of little importance, accounting for less than 10 % on
average of total PM2.5 concentrations for most stations and months
(Table 10).
CMAQ model performance and summary statistics.
Bajada Pahari SOMAARTH HQ West New Delhi South New Delhi Dec 15Sep 15Sep 16Dec 15Sep 15Sep 16Dec 15Sep 15Sep 16Dec 15Sep 15Sep 16PRE133.4954.8359.22131.8032.1663.66212.29101.71106.44191.3592.6892.85(40.66)(21.24)(9.89)(42.81)(15.99)(11.24)(75.55)(41.49)(28.58)(61.03)(39.46)(24.37)OBS136.01–35.55–75.8358.03120.4981.53–254.1570.2470.97PM2.5(28.35)–(13.76)–(37.16)(35.19)(29.92)(12.72)–(70.89)(13.04)(18.72)MB-2.52–23.67–-43.675.6491.8020.19–-62.8122.4421.88ME35.20–24.66–43.6725.0491.9341.02–67.6726.4225.71RMSE40.23–26.35–56.2327.71115.7648.60–81.0237.5035.37PRE72.7680.7247.2471.8380.7547.2232.5957.1431.6640.9062.7636.29(39.47)(3.87)(17.56)(39.99)(34.06)(17.60)(41.34)(53.36)(30.16)(44.87)(53.52)(29.89)OBS––––––21.7471.09–43.5759.4729.28O3(8.05)(42.41)(37.07)(36.30)(20.27)MB––––––10.93-13.95–-2.673.297.01ME––––––16.8318.74–12.6224.7219.29RMSE––––––22.9622.10–14.0827.6423.31SOAPRE44.6017.8923.3044.8118.0622.9544.2223.7633.2843.9522.4431.78(7.76)(2.40)(3.96)(7.59)(2.34)(3.77)(3.76)(4.74)(8.80)(3.82)(4.11)(7.84)FbioPRE0.090.180.080.090.180.080.040.060.040.040.030.05(0.03)(0.10)(0.02)(0.03)(0.11)(0.02)(0.01)(0.01)(0.01)(0.01)(0.01)(0.01)FSOA,resPRE0.150.240.260.150.240.260.130.150.160.130.160.16(0.02)(0.05)(0.04)(0.02)(0.05)(0.04)(0.03)(0.02)(0.03)(0.03)(0.02)(0.03)Fan,resPRE0.120.160.200.120.170.200.070.080.090.070.080.10(0.03)(0.04)(0.03)(0.03)(0.04)(0.04)(0.02)(0.03)(0.02)(0.02)(0.03)(0.02)Fres,SOAPRE0.480.510.520.470.500.520.430.510.550.450.530.54(0.16)(0.20)(0.18)(0.16)(0.21)(0.18)(0.18)(0.21)(0.17)(0.18)(0.22)(0.17)
Statistics are calculated for average diurnal profiles of
predicted parameters. PM2.5, O3, and SOA are the mass
concentrations in micrograms per cubic meter (µg m-3) of total fine particulate matter,
ozone, and secondary organic matter, respectively. Fbio is the
fraction of total PM2.5 that is produced by biogenic emissions,
FSOA,res is the fraction of total secondary organic matter
attributable to the residential sector, Fan,res is the fraction of
total anthropogenic PM2.5 attributable to the residential sector, and
Fres,SOA is the fraction of residential PM2.5 attributable to
SOA. PRE is mean predictions, OBS is mean observations, MB is mean bias, ME
is mean error, and RMSE is root mean square error. Standard deviation of
predictions and observations are noted in parentheses.
Figure 10 shows CMAQ predictions of secondary organic PM2.5 (SOA). Like
PM2.5, SOA is typically predicted to be higher in New Delhi than in the
rural sites, due to higher PM2.5 and precursor VOC emissions and ambient
concentrations in urban environments (Tables 5 and 6). Higher levels are
similarly attained in December than in September due to longer residence
times and more aging during winter. SOA has high day-to-day variability.
Values range from below 20 µg m-3 to over
200 µg m-3 in December, with average peaks up to
55 µg m-3 at the rural sites. September months predict lower
SOA, ranging from 10 to 130 µg m-3. Diurnal average SOA
maximum in December for the rural stations is nearly double that of
September 2016, which can be attributed to temperature inversions and a
shallower planetary boundary layer in winter.
Predicted secondary organic PM2.5(a, c, e) and
average diurnal cycle (b, d, f) for
20–31 December 2015 (a, b), 7–30 September 2015 (c, d),
and 20–30 September 2016 (e, f). Bajada Pahari is shown in yellow,
SOMAARTH HQ in green, West New Delhi in pink, and South New Delhi in blue.
Diurnal profiles were averaged over simulation durations (Table 7).
Computations were carried out at 4 km resolution. Statistics are shown in
Table 10.
Average diurnal
Residential anthropogenic PM2.5Total anthropogenic PM2.5(a–c),
Residential SOATotal SOA(d–f), and
Residential SOAResidential PM2.5(g–i).
Bajada Pahari is shown in yellow, SOMAARTH HQ in green, West New Delhi in
pink, and South New Delhi in blue. Shading indicates mealtimes. Residential
PM is calculated as the difference in predictions from the nonresidential
and total emission scenario and averaged over simulation durations (Table 7).
Computations were carried out at 4 km resolution. Statistics are shown in
Table 10.
The significance of household emissions on outdoor PM2.5 concentrations
is demonstrated by the diurnal profiles in Fig. 11. Figure 11a, b, and c the predicted contribution of the residential sector to anthropogenic
PM2.5, while Fig. 11 d, e, and f describe the predicted
contribution of the residential sector to secondary organic PM2.5, as in
Eqs. (7) and (8) respectively:
Residential anthropogenic PM2.5Total anthropogenic PM2.5,Residential SOATotal SOA.
Figure 11g, h, and i show the predicted SOA portion of residential
PM2.5, as
Residential SOAResidential PM2.5,
where residential PM is calculated as the difference in predictions from the
nonresidential and total emission scenario and averaged over simulation
durations (Table 7). The importance of household emissions to ambient PM is
strongly correlated with mealtimes. Predicted maximum contributions to
anthropogenic PM2.5 in Bajada Pahari and SOMAARTH HQ are about double
that of South and West New Delhi for each month. Household energy use is
estimated to account for up to 27 % of anthropogenic PM2.5 (at
SOMAARTH HQ during September 2016), remaining consistently above 10 % for
each rural site during all months. Similar behavior is predicted for SOA
(Fig. 11b, e, and h). An estimated 15 % to 34 % of secondary
organic matter is attributable to residential emissions in September and
2016. Again, the impact is smaller in West and South New Delhi (up to
19 % and 21 %, respectively in September 2016), where there are
greater emissions of SOA precursors from other sectors. The diurnal profile
of the contribution to SOA is subdued for all sites in December, suggesting
that SOA generation is less efficient in winter when radiation and
temperatures are lower. The aging of VOCs is captured by the phase shift of the
impact on SOA daily trend, where peaks consistently occur an hour after the
residential sector shows the greatest importance to anthropogenic PM2.5.
At each measurement site during all months, SOA is predicted to make up more
than 40 % of PM2.5 produced by the residential sector on average
(Fig. 11g–i). SOA is least significant to residential
PM2.5 in the first half of mealtimes (∼ 20 % during breakfast
and ∼ 40 % during dinner) at rural sites, when primary particulate
matter is largest. The aging of precursor VOCs from cooking emissions, paired
with maximum incoming radiation, leads to maximum
Residential SOAResidential PM2.5 values in
early afternoon, when SOA accounts for more than 75 % of residential
PM2.5 at both rural and urban sites during each simulated month.
The fractional contribution of total SOA to total PM2.5 is shown in
Fig. 12. While concentrations of SOA depend significantly on the site and
time period, their contribution to total PM2.5 shows little variation.
At all stations, SOA is predicted to make up to 55 % of PM2.5 in
September months and to be most significant around midday. However, diurnal
variation of the significance of SOA is greater in New Delhi than in Bajada
Pahari or SOMAARTH HQ, owing to greater diversity of energy-use activities
and emissions characteristics in the urban environment.
Ozone
The 8 h India Central Pollution Control Board (CPCB) standard for ozone is
100 µ m-3 for an 8 h average. In the alternative unit of
ozone mixing ratio, a mass concentration of ozone of
100 µg m-3 at a temperature of 298 K at the Earth's surface
equates to a mixing ratio of 51 parts per billion (ppb). A number of
atmospheric modeling studies of ozone over India exist (Kumar et al., 2010;
Chatani et al., 2014; Sharma et al., 2016).
Sharma et al. (2016) carried out baseline CMAQ simulations for 2010 and
compared ozone predictions with measurements at six monitoring locations in
India (Thumba, Gādanki, Pune, Anantpur, Mt. Abu, and Nainital). Also carried
out were sensitivity simulations in which each emissions sector (transport,
domestic, industrial, power, etc.) was systematically set to zero. The
domestic sector was predicted to contribute ∼ 60 % of the
nonmethane volatile organic carbon emissions, followed by 12 % from
transportation and 20 % from solvent use and the oil and gas sector. The
overall NOx-to-VOC mass ratio in the region simulated by
Sharma et al. (2016) was 0.55. This exceptionally low
NOx-to-VOC ratio was attributed, in part, to the widespread
use of biomass fuel for cooking (leading to high VOC emissions), coupled with
relatively low NOx emissions. (Although vehicle emissions
are high in urban areas, overall vehicle ownership is relatively low at the
national level. In addition, Euro-equivalent norms have led to a reduction in
NOx emissions.) Predicted O3 levels at the six
observation sites tended to exceed measured values, with the ratio of
predicted to observed annual average O3 being in the range of
1.04–1.37 at the six locations. Moreover, the overall low
NOx-to-VOC ratios in India lead to
NOx-sensitive O3 formation conditions. Based on
emissions inventories, the overall anthropogenic
NMVOC /NOx mass emissions ratio in India in 2010 as
computed by Sharma et al. (2016) was 1.82. Considering only ground-level
sources, the ratio increases to 3.68.
Predicted
Total SOATotal PM2.5(a, c, e) and
average diurnal cycle (b, d, f) for
20–31 December 2015 (a, b), 7–30 September 2015 (c, d),
and 20–30 September 2016 (e, f). Bajada Pahari is shown in yellow,
SOMAARTH HQ in green, West New Delhi in pink, and South New Delhi in blue.
Diurnal profiles were averaged over simulation durations (Table 7).
Computations were carried out at 4 km resolution.
Predicted O3 (left) and average diurnal cycle (right) for
20–31 December 2015 (a), 7–30 September 2015 (b), and
20–30 September 2016 (c) in West New Delhi (pink), and South New Delhi
(blue). Standard deviations of the diurnal profiles for observations and
predictions are indicated, respectively, by colored shading. Diurnal profiles
were averaged over simulation durations (Table 7). Computations were carried
out at 4 km resolution.
Ozone surface measurements and predicted mass concentrations based on the
CMAQ 4 km resolution simulations at two sites in New Delhi over the periods
7–29 September 2015, 7–30 December 2015, and 7–29 September 2016 in the
present study are shown in Fig. 13a–c. The predicted
O3 concentrations are reproduced well at the West New Delhi and South
New Delhi stations, especially in September (Table 10). However, when NO
concentrations are higher due to meteorological inversion conditions, ozone
concentrations are underestimated, as local NO+O3 titration reactions
near the monitoring site are not resolved. The performance of the model
improves with regard to its prediction of higher values of ozone (as in the case of
September), which are of greater importance for assessing exposures. High
ozone concentrations in September are quite well reproduced by the model.
This shows that, on the larger scale, the model captures photochemistry quite
well; however, micro-scale titration is not well represented due to the
limitations of inventory resolution. This would require further enhancement
of emission inventories at even higher resolution. The results of ozone
simulations in the present study are generally consistent with those of
previous simulations over India. For example, also using WRF-CMAQ, Kota et
al. (2018) showed that the relative bias in ozone simulation ranges from
-30 % to +50 % in the major cities of India. In South New Delhi, the
bias in O3 predictions in the present study lies between -2.67 and
+7.01 µg m-3, as compared to the observations of 29.28 to
62.76 µg m-3.
Conclusions
Air quality in India is determined by a mixture of industrial and motor
vehicle emissions, and anthropogenic fuel combustion, that includes
residential burning of biomass for household uses, such as cooking. Average
daily PM2.5 levels frequently exceed the 24 h standard of
60 µg m-3 and can exceed 200 µg m-3, even in
rural areas. PM2.5 is a mixture of directly emitted particulate matter
and that formed by the atmospheric conversion of volatile organic compounds
to secondary organic aerosol. Here, we assess the extent to which observed
O3 and PM2.5 levels in India can be predicted using
state-of-the-science emissions inventories and atmospheric chemical transport
models. We have focused on the 308 km2 of the SOMAARTH Demographic,
Development, and Environmental Surveillance Site (DDESS) in the Palwal
District of Haryana, India.
Atmospheric simulation of particulate matter levels over a complex region
like India tends to be demanding, owing to the combination of a wide range
of primary particulate emissions and the presence of secondary organic
matter from atmospheric gas-phase reactions generating low-volatility
gas-phase products that condense into the particulate phase, forming
secondary organic aerosol (SOA). Consequently, the main focus of the present
work has been the evaluation of the extent to which ambient particulate
matter levels over the current region of India can be predicted. Simulations
capture the general trend of observed daily peaks and lows of particulate
matter, with PM2.5 reaching values as high as 250 µg m-3.
Secondary organic matter accounts for 10 % to 55 % of total PM2.5
mass on average. In India, over 50 % of households report use of wood,
crop residues, or dung as cooking fuel; such fuels produce significant gas-
and particle-phase emissions. We evaluated the fractional impact of the
residential sector emissions on the formation of secondary organic aerosol,
as a function of time of day, for New Delhi, SOMAARTH HQ, and Bajada Pahari.
The predicted fractional contribution of residential sector emissions to
secondary organic PM2.5 in Bajada Pahari and SOMAARTH HQ reaches values
as high as 34 % and, moreover, displays a distinct diurnal profile, with
maxima corresponding to the morning and evening mealtimes. In both rural and
urban areas, SOA is predicted to account for more than 40 % of residential
PM2.5, reaching up to 80 % in early afternoon in September months.
Simulations of ozone levels in New Delhi reported here are largely in
agreement with ambient monitoring data, although the simulations fail to
capture several 1- to 2-day ozone episodes that exceed predictions by a
factor of 2 or more. The overall agreement between observed and predicted
O3 levels, also demonstrated in the study of Sharma et al. (2016),
suggests that gas-phase atmospheric chemistry over India is reasonably well
understood. While ozone and particulate matter were simulated for September
and December months, we employed a single emissions inventory, regardless of
season. Thus, the inventory does not capture December-specific
characteristics, including heating combustion. Furthermore, information
regarding household solvent use, emissions profiles by fuel type, and
speciation of certain emissions (such as semivolatile organic compounds and
intermediate volatility organic compounds) is lacking. Variation in the
resolution of specific input data additionally contributes to uncertainty.
Air quality studies such as the present one provide a quantification of the
elements of atmospheric composition in India, especially that owing to
household sources. The importance of replacing traditional household
combustion devices with modern technology is evident in studies such as the
present one.
Data availability
The gridded data files of PM2.5 used in this study are available from the authors upon request by email. Surface measurements of various atmospheric chemicals and meteorology are available from the Central Pollution Control Board (CPCB) of the Ministry of Environment and Forests of the Government of India at http://www.cpcb.gov.in/CAAQM/frmUserAvgReportCriteria.aspxTS1 (CPCB, 2019; last access: 28 May 2019). Initial and boundary condition data for WRF meteorological simulations are from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5, generated using Copernicus Climate Change Service Information and available at https://cds.climate.copernicus.eu/cdsapp\#!/home (Copernicus Climate Change Services, 2017; last access: 28 May 2019). Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.
Author contributions
BR carried out the simulations and wrote the paper. RZ, YW and KB assisted
with the simulations. AP carried out field measurements. SS, SK, TB, NL, BO,
LX, and VG helped formulate the emissions inventory. LF, RW, SM, and DB
designed and carried out measurements. SN, RE, AY, and NA performed data
analysis. KS designed the research. JS designed the research and wrote the
paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by EPA STAR grant R835425 Impacts of Household
Sources on Outdoor Pollution at Village and Regional Scales in India. The
contents are solely the responsibility of the authors and do not necessarily
represent the official views of the US EPA. Yuan Wang appreciates the support
from the Jet Propulsion Laboratory, California Institute of Technology, under
a contract with the National Aeronautics and Space Administration, and
support from the US National Science Foundation (award no. 1700727).
Financial support
This research has been supported by EPA STAR (grant
no. R835425).
Review statement
This paper was edited by Qiang Zhang and reviewed by three
anonymous referees.
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