Hotspot driven air pollution during crop residue burning season in the Indo-Gangetic Plain, India.

Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM2.5 concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R2) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM2.5 concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM2.5 in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals.

Chemical characteristics, morphology and source apportionment of PM10 over National Capital Region (NCR) of India.

The present study frames the physico-chemical characteristics and the source apportionment of PM10 over National Capital Region (NCR) of India using the receptor model's Positive Matrix Factorization (PMF) and Principal Momponent Mnalysis/Absolute Principal Component Score-Multilinear Regression (PCA/APCS-MLR). The annual average mass concentration of PM10 over the urban site of Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81 µg m-3, respectively. Carbonaceous species (organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)), elemental constituents (Al, Ti, Na, Mg, Cr, Mn, Fe, Cu, Zn, Br, Ba, Mo Pb) and water-soluble ionic components (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) of PM10 were entrenched to the receptor models to comprehend the possible sources of PM10. The PMF assorted sources over Faridabad were soil dust (SD 15%), industrial emission (IE 14%), vehicular emission (VE 19%), secondary aerosol (SA 23%) and sodium magnesium salt (SMS 17%). For IGDTUW-Delhi, the sources were SD (16%), VE (19%), SMS (18%), IE (11%), SA (27%) and VE + IE (9%). Emission sources like SD (24%), IE (8%), SMS (20%), VE + IE (12%), VE (15%) and SA + BB (21%) were extracted over CSIR-NPL, New Delhi, which are quite obvious towards the sites. PCA/APCS-MLR quantified the similar sources with varied percentage contribution. Additionally, catalogue the Conditional Bivariate Probability Function (CBPF) for directionality of the local source regions and morphology as spherical, flocculent and irregular were imaged using a Field Emission-Scanning Electron Microscope (FE-SEM).

Emission inventory of inorganic trace gases from solid residential fuels over the National Capital Territory of India.

In developing nations, solid residential fuels are the major sources of primary energy for various domestic activities. To date, the emission inventory of inorganic trace gases over National Capital Territory (NCT) was prepared using either default or country-specific emission factors. In this paper, we report (for the first time) the spatial variation of emission factors (EFs) of inorganic trace gases (SO2, NO, NO2, CO, CO2, and CH4) from the residential fuels used in slums and rural areas of NCT determined using dilution chamber in the laboratory. 147 residential fuel samples, including fuelwood, dung cake, crop residues, coal, etc., were collected at 149 NCT locations out of 675 slum clusters and 146 rural villages. The range of EF(s) of SO2 (0.02 ± 0.01 to 0.04 ± 0.01 g kg-1), CH4 (0.10 to 0.34 g kg-1), NO2 (0.01 to 0.02 g kg-1) is lower than the CO (3.55 ± 1.72 to 6.07 ± 1.53 g kg-1) and CO2 (0 to 129.45 ± 46.94 g kg-1). The north and north west districts of NCT are emission hotspots for CH4, NO, and NO2 emissions, whereas, the southern and northern areas of NCT are for CO2. These citywide emission inventories (0.05° × 0.05°) of inorganic trace gases are prepared using laboratory-determined EFs and available consumption data determined by recent survey information. Among solid residential fuels, fuel wood, and dung cake are two major contributors to inorganic trace gases in NCT.

Role of South Asian outflow on the oxidative potential of marine aerosols over the Indian Ocean.

Oxidative potential (OP) of fine marine aerosols (PM2.5) over the northern Indian Ocean (N_IO) and equatorial Indian Ocean (E_IO) were studied using shipborne measurements conducted as part of the Integrated Campaign for Aerosols, gases and Radiation Budget (ICARB-2018). During the study, an enhanced concentration of PM2.5 was found over N_IO (27.22 ± 14.29 µg.m-3) compared with E_IO (15.91 ± 2.58 µg.m-3), as N_IO experiences continental outflow from anthropogenically dominated South Asian region. However, E_IO received pristine air masses from the middle of the Arabian Sea, implying a reduced concentration. The OP of PM2.5 was evaluated using a dithiothreitol (DTT) assay. The mass (DTTm or intrinsic OP) and volume (DTTv or extrinsic OP) normalized DTT exhibited a significant spatial variation over the Indian Ocean (IO). Intrinsic OP showed ∼2 times higher values over N_IO than E_IO, indicating aging of aerosols during long-range transport impacts OP of marine aerosol. Similarly, increased concentrations of anthropogenic species such as non-sea sulfate (nssSO42-), nitrate (NO3-), ammonium (NH4+), non-sea potassium ion (nssK+), water-soluble transition metals (Fe, Ti, Zn, Cu, Mn, Cr), elemental carbon (EC), organic carbon (OC), water-soluble organic carbon (WSOC), were also observed over N_IO compared with E_IO. Pearson correlation and multiple linear regression (MLR) analysis revealed that combustion sources, chemical processing and co-transportation of anthropogenic species during long-range transport are the main drivers of intrinsic OP in the outflow region.

Chemical properties of emissions from solid residential fuels used for energy in the rural sector of the southern region of India.

In the present study, we have estimated the emission factors (EFs) of particulate matter (PM), organic and elemental carbon (OC and EC), oxide of sulfur and nitrogen, and water-soluble ionic species emitted from residential fuels (fuelwood, crop residue, dung cake) used in the rural sector of five states (Kerala, Karnataka, Andhra Pradesh, Telangana, Tamil Nadu) of the southern region of India. Average EFs of PM, OC, and EC from fuelwood (FW), crop residues (CR), and dung cakes (DC) from southern region of India are estimated as follows: PM: 6.35 ± 5.64 g/kg (FW), 6.99 ± 5.46 g/kg (CR), 9.69 ± 3.73 g/kg (DC); OC: 1.60 ± 1.72 g/kg (FW), 1.50 ± 1.52 g/kg (CR), 3.54 ± 0.75 g/kg (DC); and EC: 0.46 ± 0.53 g/kg (FW), 0.29 ± 0.17 g/kg (CR), 0.21 ± 0.11 g/kg (DC), respectively. Similarly, the average EFs of SO2, NOx from FW, CR, and DC are determined to be as follows: SO2: 0.40 ± 0.37 g/kg (FW), 1.17 ± 0.25 g/kg (CR), and 0.18 ± 0.10 g/kg (DC); NOx: 1.11 ± 1.22 g/kg (FW), 0.69 ± 0.37 g/kg (CR), and 0.91 ± 0.54 g/kg (DC), respectively. PO43- shows the highest EF from FW (646.02 ± 576.35 mg/kg), CR (531.06 ± 678.29 mg/kg) among all anions followed by Cl- (FW: 512.91 ± 700.35 mg/kg, CR: 661.61 ± 865.46 mg/kg and DC: 104.16 ± 54.01 mg/kg); whereas, Na+ shows highest EF from FW (254.05 ± 298.50 mg/kg) and CR (249.36 ± 294.85 mg/kg) among all cations. The total emissions of trace gases, PM, and their chemical composition from FW, CR, and DC have been calculated using laboratory-generated EFs over the southern region of India. CR (1595.58 ± 14.24 Gg) contributes to higher emission of PM as compared to FW (218.78 ± 53.93 Gg), whereas the contribution from DC is negligible.

Gridded distribution of total suspended particulate matter (TSP) and their chemical characterization over Delhi during winter.

In the present study, total suspended particulate matter (TSP) samples were collected at 47 different sites (47 grids of 5 × 5 km2 area) of Delhi during winter (January-February 2019) in campaign mode. To understand the spatial variation of sources, TSP samples were analyzed for chemical compositions including carbonaceous species [organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC)], water-soluble total nitrogen (WSTN), water-soluble inorganic nitrogen (WSIN), polycyclic aromatic hydrocarbons (16 PAHs), water-soluble inorganic species (WSIS) (F-, Cl-, SO42-, NO2-, NO3-, PO43-, NH4+, Ca2+, Mg2+, Na+, and K+), and major and minor trace elements (B, Na, Mg, Al, P, S, Cl, K, Ca, Ti, Fe, Zn, Cr, Mn, Cu, As, Pd, F, and Ag). During the campaign, the maximum concentration of several components of TSP (996 µg/m3) was recorded at the Rana Pratap Bagh area, representing a pollution hotspot of Delhi. The maximum concentrations of PAHs were recorded at Udhyog Nagar, a region close to heavily loaded diesel vehicles, small rubber factories, and waste burning areas. Higher content of Cl- and Cl-/Na+ ratio (>1.7) suggests the presence of nonmarine anthropogenic sources of Cl- over Delhi. Minimum concentrations of OC, EC, WSOC, PAHs, and WSIS in TSP were observed at Kalkaji, representing the least polluted area in Delhi. Enrichment factor <5.0 at several locations and a significant correlation of Al with Mg, Fe, Ti, and Ca and C/N ratio indicated the abundance of mineral/crustal dust in TSP over Delhi. Principal component analysis (PCA) was also performed for the source apportionment of TSP, and extracted soil dust was found to be the major contributor to TSP, followed by biomass burning, open waste burning, secondary aerosol, and vehicular emissions.

Frequency distribution of pollutant concentrations over Indian megacities impacted by the COVID-19 lockdown.

The megacities experience poor air quality frequently due to stronger anthropogenic emissions. India had one of the longest lockdowns in 2020 to curb the spread of COVID-19, leading to reductions in the emissions from anthropogenic activities. In this article, the frequency distributions of different pollutants have been analysed over two densely populated megacities: Delhi (28.70° N; 77.10° E) and Kolkata (22.57° N; 88.36° E). In Delhi, the percentage of days with PM2.5 levels exceeding the National Ambient Air Quality Standards (NAAQS) between 25 March and 17 June dropped from 98% in 2019 to 61% in 2020. The lockdown phase 1 brought down the PM10 (particulate matter having an aerodynamic diameter ≤ 10 µm) levels below the daily NAAQS limit over Delhi and Kolkata. However, PM10 exceeded the limit of 100 µgm-3 during phases 2-5 of lockdown over Delhi due to lower temperature, weaker winds, increased relative humidity and commencement of limited traffic movement. The PM2.5 levels exhibit a regressive trend in the highest range from the year 2019 to 2020 in Delhi. The daily mean value for PM2.5 concentrations dropped from 85-90 µgm-3 to 40-45 µgm-3 bin, whereas the PM10 levels witnessed a reduction from 160-180 µgm-3 to 100-120 µgm-3 bin due to the lockdown. Kolkata also experienced a shift in the peak of PM10 distribution from 80-100 µgm-3 in 2019 to 20-40 µgm-3 during the lockdown. The PM2.5 levels in peak frequency distribution were recorded in the 35-40 µgm-3 bin in 2019 which dropped to 15-20 µgm-3 in 2020. In line with particulate matter, other primary gaseous pollutants (NOx, CO, SO2, NH3) also showed decline. However, changes in O3 showed mixed trends with enhancements in some of the phases and reductions in other phases. In contrast to daily mean O3, 8-h maximum O3 showed a reduction over Delhi during lockdown phases except for phase 3. Interestingly, the time of daily maximum was observed to be delayed by ~ 2 h over Delhi (from 1300 to 1500 h) and ~ 1 h over Kolkata (from 1300 to 1400 h) almost coinciding with the time of maximum temperature, highlighting the role of meteorology versus precursors. Emission reductions weakened the chemical sink of O3 leading to enhancement (120%; 11 ppbv) in night-time O3 over Delhi during phases 1-3.

Seasonal variations in carbonaceous species of PM2.5 aerosols at an urban location situated in Indo-Gangetic Plain and its relationship with transport pathways, including the potential sources.

The study examines the variation in organic carbon (OC) and elemental carbon (EC) in PM2.5 concentration at an urban location of Indo-Gangetic Plains (IGP) to understand the impact of seasonality and regional crop residue burning activities. Seasonal cluster analysis of backward air masses and concentration-weighted trajectory (CWT) analysis was performed to identify seasonal transport pathways and potential source regions of carbonaceous aerosols. The mean PM2.5 level during the study period was 57 ± 41.6 µgm-3 (5.0-187.3 µgm-3), whereas OC and EC concentration ranges from 2.8 µgm-3 to 28.2 µgm-3 and 1.3 µgm-3 to 15.5 µgm-3 with a mean value of 8.4 ± 5.5 µgm-3 and 5.1 ± 3.3 µgm-3 respectively. The highest mean PM2.5 concentration was found during the winter season (111.3 ± 25.5 µgm-3), which rises 3.6 times compared to the monsoon season. OC and EC also follow a similar trend having the highest levels in winter. Total carbonaceous aerosols contribute ∼38% of PM2.5 composition. The positive linear trend between OC and EC identified the key sources. HYSPLIT cluster analysis of backward air mass trajectories revealed that during the post-monsoon, winters, pre-monsoon, and monsoon, 71%, 81%, 60%, and 43% of air masses originate within the 500 km radius of IGP. CWT analysis and abundance of OC in post-monsoon and winters season establish a linkage between regional solid-biomass fuel use and crop residue burning activities, including meteorology. Moreover, the low annual average OC/EC ratio (1.75) indicates the overall influence of vehicular emissions. The current dataset of carbonaceous aerosols collated with other Indian studies could be used to validate the global aerosol models on a regional scale and aid in evidence-based air pollution reduction strategies.

Drivers of air pollution variability during second wave of COVID-19 in Delhi, India.

To curb the 2nd wave of COVID-19 disease in April-May 2021, a night curfew followed by full lockdown was imposed over the National Capital Territory, Delhi. We have analyzed the observed variation in pollutants and meteorology, and role of local and transboundary emission sources during night-curfew and lockdown, as compared to pre-lockdown period and identical periods of 2020 lockdown as well as of 2018 and 2019. In 2021, concentration of pollutants (except O3, SO2, and toluene) declined by 4-16% during night-curfew as compared to the pre-lockdown period but these changes are not statistically significant. During lockdown in 2021, various pollutants decreased by 1-28% as compared to the night-curfew (except O3 and PM2.5), but increased by 31-129% compared to the identical period of 2020 lockdown except O3. Advection of pollutants from the region of moderate lockdown restrictions and an abrupt increase in crop-residue burning activity (120-587%) over Haryana and Punjab increased the air pollution levels over NCT during the lockdown period of 2021 as compared to 2020 in addition to a significant contribution of long-range transport. The increase in PM2.5 during the lockdown period of 2021 compared to 2020 might led to 5-29 additional premature mortalities.

Seasonal variation and sources of carbonaceous species and elements in PM2.5 and PM10 over the eastern Himalaya.

The study represents the seasonal characteristics (carbonaceous aerosols and elements) and the contribution of prominent sources of PM2.5 and PM10 in the high altitude of the eastern Himalaya (Darjeeling) during August 2018-July 2019. Carbonaceous aerosols [organic carbon (OC), elemental carbon (EC), and water soluble organic carbon (WSOC)] and elements (Al, Fe, Ti, Cu, Zn, Mn, Cr, Ni, Mo, Cl, P, S, K, Zr, Pb, Na, Mg, Ca, and B) in PM2.5 and PM10 were analyzed to estimate their possible sources. The annual concentrations of PM2.5 and PM10 were computed as 37±12 µg m-3 and 58±18 µg m-3, respectively. In the present case, total carbonaceous species in PM2.5 and PM10 were accounted for 20.6% of PM2.5 and 18.6% of PM10, respectively, whereas trace elements in PM2.5 and PM10 were estimated to be 15% of PM2.5 and 12% of PM10, respectively. Monthly and seasonal variations in mass concentrations of carbonaceous aerosols and elements in PM2.5 and PM10 were also observed during the observational period. In PM2.5, the annual concentrations of POC and SOC were 2.35 ± 1.06 µg m-3 (66% of OC) and 1.19±0.57 µg m-3 (34% of OC), respectively, whereas annual average POC and SOC concentrations in PM10 were 3.18 ± 1.13 µg m-3 (63% of OC) and 2.05±0.98 µg m-3 (37% of OC), respectively. The seasonal contribution of POC and SOC were ranging from 55 to 77% and 33 to 45% of OC in PM2.5, respectively, whereas in PM10, the seasonal contributions of POC and SOC were ranging from 51 to 73% and 37 to 49% of OC, respectively. The positive relationship between OC & EC and OC & WSOC of PM2.5 and PM10 during all the seasons (except monsoon in case of PM10) indicates their common sources. The enrichment factors (EFs) and significant positive correlation of Al with othe crustal elements (Fe, Ca, Mg, and Ti) of fine and coarse mode aerosols indicate the influence of mineral dust at Darjeeling. Principal component analysis (PCA) resolved the four common sources (biomass burning + fossil fuel combustion (BB + FFC), crustal/soil dust, vehicular emissions (VE), and industrial emissions (IE)) of PM2.5 and PM10 in Darjeeling.

Investigating the seasonal variability in source contribution to PM2.5 and PM10 using different receptor models during 2013-2016 in Delhi, India.

The present work deals with the seasonal variations in the contribution of sources to PM2.5 and PM10 in Delhi, India. Samples of PM2.5 and PM10 were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM2.5 and PM10 were 131 ± 79 µg m-3 and 238 ± 106 µg m-3, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM2.5 and PM10 were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM2.5 and PM10 as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM2.5 and PM10. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.

Seasonal variation, source apportionment and source attributed health risk of fine carbonaceous aerosols over National Capital Region, India.

Deteriorating air quality with high levels of fine particulate matter (PM2.5) over National Capital Region (NCR) of India is one of the serious environmental and scientific issues. In this paper, PM2.5 samples were collected for 24 h twice or thrice a week during December 2016-December 2017 at three sites [Delhi (IG), Modinagar (MN) and Mahendragarh (HR)] over NCR to analyse the carbonaceous aerosols. Source apportionment of PM2.5 was attempted using Principal Component analysis (PCA) and Positive Matrix Factorization (PMF) based on the analysed carbonaceous fractions [Organic carbon, Elemental carbon, Secondary organic carbon (SOC)]. Organic compounds: alkanes, hopanes, steranes, polycyclic aromatic hydrocarbons (PAHs), phthalates, levoglucosan and n-alkanoic acids were analysed to distinguish the emission sources. Total Carbonaceous Aerosols (TCA) contributed significantly (∼26%) to PM2.5 which revealed their importance in source apportionment. Estimated SOC contributed 43.2%, 42.2% and 58.2% to OC and 5.4%, 5.3% and 7.8% to PM2.5 at IG, MN and HR sites respectively. PCA and PMF apportion five emission sources i.e., vehicular emissions (34.6%), biomass burning (26.8%), cooking emissions (15.7%), plastic and waste burning (13.5%) and secondary organic carbon (9.5%) for PM2.5. Source attributed health risk has also been calculated in terms of Lung cancer risk (LCR) associated with PAHs exposure and concluded that vehicular emissions (40.3%), biomass burning (38.1%), secondary organic carbon (12.8%) contributed higher to LCR (503.2 × 10-5; ∼503 cases in 1,00,000). Health risk assessment combined with source apportionment inferences signifies the immediate implementation of emissions reduction strategies with special target on transport sector and biomass burning over the NCR of India.

Real-time monitoring of air pollutants in seven cities of North India during crop residue burning and their relationship with meteorology and transboundary movement of air.

Air pollutants emissions due to the burning of crop residues could adversely affect human health, environment, and climate. Hence, a multicity campaign was conducted during crop residue burning period in Indo Gangetic Plains (IGP) to study the impact on ambient air quality. Seventeen air pollutants along with five meteorological parameters, were measured using state of the art continuous air quality monitors. The average concentration of PM10, PM2.5, and PM1 during the whole campaign were 196.7±30.6, 148.2±20, and 51.2±8.9 µgm-3 and daily average concentration were found several times higher than national ambient air quality standards for 24h. Amritsar had the highest average concentration of PM2.5 (178.4±83.8 µgm-3) followed by Rohtak and Sonipat (158.4±79.8, 156.5±105.3µgm-3), whereas Chandigarh recorded the lowest concentration (112.3±6.9µgm-3). The concentration of gaseous pollutants NO, NO2, NOx, and SO2 were also observed highest at Amritsar location, i.e., 6.6±2.6ppb, 6.2±0.7ppb, 12.7±3.0ppb, and 7.5±3.3ppb respectively. The highest average O3 and CO were 22.5±19.3ppb and 1.5±1.2ppm during the campaign. The level of gaseous pollutants and Volatile organic compounds (VOCs) found to be elevated during the campaign, which can play an important role in the formation of secondary air pollutants. The correlation of meteorology and air pollutants was also studied, and O3 shows a significant relation with temperature and UV (R=0.87 and 0.74) whereas VOCs shows a significant correlation with temperature (R=-0.21 to -0.47). Air quality data was also analyzed to identify sources of emissions using principal component analysis, and it identifies biomass burning and vehicular activities as major sources of air pollution.

Light-absorbing impurities in snow of the Indian Western Himalayas: impact on snow albedo, radiative forcing, and enhanced melting.

Seasonal snow cover in the Himalayas acts as source of fresh water for several Asian rivers such as Indus, Ganges, Brahmaputra, and Yangtze. Early loss of seasonal snow exposes the ice layer of the glaciers directly to sunlight, consequently leading to their ablation and alterations in discharge of glacier-fed rivers. Therefore, any alteration in the melting rate of the Himalayan snow pack can significantly affect the ecological balance in the region. Besides global warming, enhanced melting of snow, caused by light-absorbing impurities (LAIs) such as dust and elemental carbon (EC), has also been recognized as prominent cause of enhanced melting of snow in the Himalayas of China and Nepal. However, in light of vast area of the Himalayas and persistent emissions from India, studies, emphasizing the potential of LAIs to substantially affect the snow radiation budget of snow cover in IWHs, are still scanty. Therefore, in this study, field campaigns were made on three glaciers, i.e., Hamta, Beas Kund, and Deo Tibba, in IWHs to collect snow samples for estimation of LAIs. Snow of the studied glaciers was observed to be contaminated with 13.02 to 74.57 ng/g of EC and 32.14 to 216.54 µg/g of dust. Albedo simulations done using SNow and ICe Aerosol Radiation (SNICAR) model indicated that besides the changes caused by increased grain size, EC and dust, cumulatively induced 0.60 to 32.65% reduction in albedo of snow. Further assessment, constrained by measurements, illustrated that radiative forcing (RF), of 1.8 to 80 W/m2, was instigated due to enhanced thermal absorption of snow. Ten hours of daily mean RFs in this range could correspond to 3 to 9.65 mm/d of snow melt and contribute significantly in reducing the seasonal snow cover in IWHs. Considering the consequences of LAIs-induced snow melt and lack of in situ observations in the IWHs, the outcomes of this study could assist researchers and policy makers in developing efficient climate models and framing mitigation measures, respectively.

Source apportionment and health risk assessment of organic constituents in fine ambient aerosols (PM2.5): A complete year study over National Capital Region of India.

Fine ambient aerosols (PM2.5) levels in the atmosphere are continuously worsening over Delhi and National Capital Region (NCR) of India. Complete source profiles are required to be assessed for implementation of proper mitigation measures over the NCR. In this study, emission sources of PM2.5 are reported for the NCR of India for samples collected during December 2016 to December 2017 at three sampling sites in Delhi, Uttar Pradesh and Haryana. Organic constituents (n-alkanes, isoprenoid hydrocarbons, polycyclic aromatic hydrocarbons, phthalates, levoglucosan and n-alkanoic acids) in PM2.5 were measured to apportion the sources over the study area. Source apportionment of PM2.5 was performed using organic constituents by Positive Matrix Factorization (PMF) and Principal Component Analysis (PCA). Health risk associated with organic pollutants [PAHs and carcinogen BEHP bis(2-ethylhexyl) phthalate] demonstrated the threat of PM2.5 exposure via inhalation. Transport pathways of air masses were evaluated using 3-day backward trajectories and observed that some air masses originated from local sources along with long-range transport which influenced the PAHs concentration during most of the study period over the NCR. PMF and PCA resulted in the five major emission sources [vehicular emissions (32.2%), biomass burning (30%), cooking emissions (16.8%), plastic burning (13.4%), mixed sources (7.6%) including biogenic and industrial emissions] for PM2.5 over the sampling sites. The present study reveals that transport sector is a major source to be targeted to reduce the vehicular emissions and consequent health risks associated with organic pollutants especially PAHs.

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models.

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM10 sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM10. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM10 concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 µg m-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM10 mass concentration over the IGP region of India.

Levels and sources of organic compounds in fine ambient aerosols over National Capital Region of India.

The study presents the spatial and temporal variation of fine ambient aerosols (PM2.5) over National Capital Region (NCR), India, during January to June 2016. The investigation includes three sampling sites, one in Delhi and two in the adjoining states of Delhi (Uttar Pradesh and Haryana), across NCR, India. The average PM2.5 concentration was highest for Delhi (128.5 ± 51.5 µg m-3) and lowest for Mahendragarh, Haryana (74.5 ± 28.7 µg m-3), during the study period. Seasonal variation was similar for all the sites with highest concentration during winter and lowest in summer. PM2.5 samples were analysed for organic compounds using gas chromatograph (GC). The concentration of three organic compound classes, n-alkanes (C11-C35), polycyclic aromatic hydrocarbons (PAHs), and phthalates, present in PM2.5 samples has been reported. Diagnostic ratios for n-alkanes demonstrated that biogenic emissions were dominant over Mahendragarh while major contributions were observed from petrogenic emissions over Delhi and Modinagar, Uttar Pradesh. Molecular diagnostic ratios were calculated to distinguish between different sources of PAHs, which revealed that the fossil fuel combustion (diesel and gasoline emissions), traffic emissions, and biomass burning are the major source contributors. Health risk associated with human exposure of phthalates and PAHs was also assessed as daily intake (DI, ng kg-1 day-1) and lung cancer risk, respectively. Backward trajectory analysis explained the local, regional, and long-range transport routes of PM2.5 for all sites. Principal component analysis (PCA) results summarized that the vehicular emissions, biomass burning, and plastic burning were the major sources of the PAHs and phthalates over the sampling sites.

Stable carbon and nitrogen isotopic composition of PM10 over Indo-Gangetic Plains (IGP), adjoining regions and Indo-Himalayan Range (IHR) during a winter 2014 campaign.

For source identification, a field campaign involving simultaneous sampling of particulate matter (PM10) was conducted at eight sampling sites in the Indian mainland during winter 2014. The sampling sites include Delhi (upper IGP), Lucknow (middle IGP), and Kolkata (lower IGP) in the Indo-Gangetic Plains (IGP); Mohal-Kullu and Darjeeling in the Indo-Himalayan Range (IHR). In addition, Ajmer, located upwind of the IGP in NW-India and Giridih and Bhubaneswar, in the downwind to the IGP has also been chosen. To characterize the sources of the ambient PM10, stable isotope ratios of carbon (δ13CTC) and nitrogen (δ15NTN) for the total carbon (TC) and total nitrogen (TN) fractions have been considered. Ancillary chemical parameters, such as organic carbon (OC), elemental carbon (EC), and water-soluble ionic components (WSIC) mass concentrations are also presented in this paper. There was very small variation in the daily average δ13CTC ratios (- 24.8 to - 25.9‰) among the sites. Comparison with end-member stable C isotopic signatures of major typical sources suggests that the PM10 at the sites was mainly from fossil fuel and biofuel and biomass combustion. Daily average δ15NTN ratios were not observed to vary much between sites either (8.3 to 11.0‰), and the low δ15NTN levels also indicate substantial contributions from biofuel and biomass burning of primarily C3 andC4 plant matter. Graphical abstract Scatter plot of the average (± 1 standard deviation (SD)) δ13CTC (‰) compared to δ15NTN (‰) at the sampling sites.

Loss of crop yields in India due to surface ozone: an estimation based on a network of observations.

Surface ozone is mainly produced by photochemical reactions involving various anthropogenic pollutants, whose emissions are increasing rapidly in India due to fast-growing anthropogenic activities. This study estimates the losses of wheat and rice crop yields using surface ozone observations from a group of 17 sites, for the first time, covering different parts of India. We used the mean ozone for 7 h during the day (M7) and accumulated ozone over a threshold of 40 ppbv (AOT40) metrics for the calculation of crop losses for the northern, eastern, western and southern regions of India. Our estimates show the highest annual loss of wheat (about 9 million ton) in the northern India, one of the most polluted regions in India, and that of rice (about 2.6 million ton) in the eastern region. The total all India annual loss of 4.0-14.2 million ton (4.2-15.0%) for wheat and 0.3-6.7 million ton (0.3-6.3%) for rice are estimated. The results show lower crop loss for rice than that of wheat mainly due to lower surface ozone levels during the cropping season after the Indian summer monsoon. These estimates based on a network of observation sites show lower losses than earlier estimates based on limited observations and much lower losses compared to global model estimates. However, these losses are slightly higher compared to a regional model estimate. Further, the results show large differences in the loss rates of both the two crops using the M7 and AOT40 metrics. This study also confirms that AOT40 cannot be fit with a linear relation over the Indian region and suggests for the need of new metrics that are based on factors suitable for this region.

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India.

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM2.5 was recorded as 121.9 ± 93.2 µg m-3 (range 25.1-429.8 µg m-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM2.5. Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM2.5 from the north-west India and Pakistan were mostly pertinent.