The present study provides a national scenario of aerosol pollution with the long-term (2005–2019) trend, source apportionment, and future scenario (2023) for each of the Indian states. We used MODIS ...AOD and FRP, differential AODs from MERRA-2, and trace gases (NO2, SO2) data from OMI. Almost all the states of IGP fall under the red zone (“highly vulnerable”; AOD >0.5) whereas central, western, and a few south-Indian states fall under the orange zone (“vulnerable”; 0.4 < AOD >0.5). The most alarming feature is that most of the southern Indian states exhibit a shift from blue/green (less vulnerable/safe; AOD <0.4) to vulnerable zones in 2023 as observed using the auto-regressive integrated moving average (ARIMA) model. Principal component analysis (PCA) revealed that the coal-fired thermal power plant (TPP), vehicular, solid fuel/waste, and biomass burning are the major sources of aerosols for the vulnerable states at present and in the future. We estimated and proposed the TPP capacity (GW) that needs to be reduced to bring down the AOD to move the vulnerable zones to less vulnerable and safe zones. The present study would complement and strengthen the ongoing national missions to combat air pollution in India.
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•Long-term trend reveals IGP, western and central India are vulnerable (high AOD).•State-wise source apportionment of aerosols conducted in different phases.•ARIMA model reveals that south India would become vulnerable zone in 2023.•Thermal power plant emissions reduction alone could make vulnerable zones safe.
The present study was conducted with the purpose to understand the spatial heterogeneity and the inter-state variability of the relative contributions of the long-range transport, fossil fuel and ...biomass burning on the atmospheric aerosol pollution across India. Satellite and reanalysis datasets (MODIS and MERRA-2) were used to study the total aerosol and its differential components over each of the Indian states under the limited anthropogenic emission condition (April 2020) and compared with the normal condition (April 2015–2019). We observed that the changes in aerosol pollution with the changes in sources from normal to limited anthropogenic activities were not homogeneous across the country. Based on such heterogeneity in “aerosol source-aerosol pollution” relationship, we divided the entire country in four different groups. The states under Group 1 (most of Indo-Gangetic Plain, north-eastern and parts of western and southern India) are found to be mostly influenced by the local/regional anthropogenic sources. The sources other than the biomass burning are the most influential for the aerosol pollution over Group 2 states (Punjab, West Bengal, Rajasthan, Madhya Pradesh, Karnataka and Tripura). Both the biomass burning and long range transport are the major factors for Group 3 state, Telangana. Rest of the states (Group 4) exhibit the relative dominance of the regional and trans-boundary transport over local anthropogenic emissions. Relative influences of fossil fuel and biomass burning over each other and how it changed from the normal to limited activities have also been quantified for each of the states of different groups. The results from the study would be an input of immense importance for the policy makers building state-wise strategies in air pollution control in India.
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•Aerosols anomalies were determined between COVID-19 lockdown and normal period.•The “aerosol-loading” and “aerosol-source” relationship varied between the states.•A novel method was adopted determining relative influences of anthropogenic sources.•States were identified with relative influences of fossil fuel and biomass burning.
Carbonaceous Aerosols (CAs) have played an active role in the Earth's atmospheric system, climate, human health, and radiative forcing. Due to their significant role in the atmosphere, it was ...imperative to characterize the carbonaceous components organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), and total carbonaceous aerosols (TCA) of PM
2.5
samples collected at three different urban locations (Mohal-Kullu, Almora, and Darjeeling) of the Himalayan region of India during August 2018–December 2019. The annual average mass concentrations of PM
2.5
were recorded as 40 ± 27, 27 ± 21, and 38 ± 13 µg m
−3
for Mohal-Kullu, Almora, and Darjeeling, respectively, which is near the Indian National Ambient Air Quality Standards (NAAQS) (24 h: 60 µg m
-3
; annual: 40 µg m
−3
). The OC/EC ratio and significant correlation of OC with EC and WSOC with OC indicated a substantial effect of biomass burning and secondary organic aerosol formation over the study sites. OC was found to be secondary in nature over the study sites during the study period. The contribution of TCA in PM
2.5
showed CAs as a significant contributor to the mass concentration of particulate matter (PM) over the Himalayan regions. HYSPLIT model demonstrates that regionally transported air masses, mostly from Indo-Gangetic plain (IGP), the Thar desert, the semi-arid region, Nepal, and local areas, considerably influence PM concentrations and chemical composition over the pristine Himalayan altitudes. The study implies that to improve the air quality in booming urban areas, it is necessary to pay attention to both local emission sources and meteorological conditions.
This study presents the source apportionment of coarse-mode particulate matter (PM10) extracted by 3 receptor models (PCA/APCS, UNMIX, and PMF) at semi-urban sites of the Indian Himalayan region ...(IHR) during August 2018–December 2019. In this study, water-soluble inorganic ionic species (WSIIS), water-soluble organic carbon (WSOC), carbon fractions (organic carbon (OC) and elemental carbon (EC)), and trace elements of PM10 were analyzed over the IHR. Nainital (62 ± 39 µg m−3) had the highest annual average mass concentration of PM10 (average ± standard deviation at 1 σ), followed by Mohal Kullu (58 ± 32 µg m−3) and Darjeeling (54 ± 18 µg m−3). The annual total ∑WSIIS concentration order was as follows: Darjeeling (14.02 ± 10.01 µg m−3) > Mohal-Kullu (13.75 ± 10.21 µg m−3) > Nainital (10.20 ± 6.30 µg m−3), contributing to 15–30% of the PM10 mass. The dominant secondary ions (NH4+, SO42−, and NO3−) suggest that the study sites were strongly influenced by anthropogenic sources from regional and long-range transport. Principal component analysis (PCA) with an absolute principal component score (APCS), UNMIX, and Positive Matrix Factorization (PMF) were used for source identification of PM10 at the study sites of the IHR. All three models showed relatively similar results of source profiles for all study sites except their source number and percentage contribution. Overall, soil dust (SD), secondary aerosols (SAs), combustion (biomass burning (BB) + fossil fuel combustion (FFC): BB+FFC), and vehicular emissions (VEs) are the major sources of PM10 identified by these models at all study sites. Air mass backward trajectories illustrated that PM10, mainly attributed to dust-related aerosols, was transported from the Thar Desert, Indo-Gangetic Plain (IGP), and northwestern region of India (i.e., Punjab and Haryana) and Afghanistan to the IHR. Transported agricultural or residual burning plumes from the IGP and nearby areas significantly contribute to the concentration of carbonaceous aerosols (CAs) at study sites.
A thirteen years-long (2009–2021) study was conducted on PM10 pollution over a high-altitude station, Darjeeling (27.1° N and 88.15° E, 2000 m amsl) in the eastern Himalayas in India. To better ...understand the sources and build a proper mitigation strategy for PM10, we have segregated PM10 into PM1 and PM1-10 using the Anderson cascade impactor. A total of 620 sets of samples were collected during the entire study period. PM10 was found to remain within its Indian standard (60 μg m−3) in every year of the study period (long-term average PM10: 46 ± 8 μg m−3). PM10 pollution during winter (74.0 ± 15.1 μg m−3) and premonsoon (73.1 ± 12.1 μg m−3) was highest compared to other seasons. Bi-modal mass size distribution of composite PM10 was observed for all the seasons with maximum peak intensity at the finer mode (PM1). Most of the anthropogenic water-soluble ionic components as well as carbonaceous aerosols exhibited maximum intensity at PM1 too. PM10 exhibited a steady decrease from 2009 to 2013 but since 2014, a sharp rise was observed. Further, we have observed that premonsoon and winter-time PM1 pollution was the key factor for a such high rise in PM10. PM1 increased sharply at a rate of 4.1 μg m−3 yr−1 in premonsoon and 3.3 μg m−3 yr−1 in winter since 2014. Auto regressive integrated moving average (ARIMA) model for future prediction revealed that PM10 would cross its Indian standard and PM1 alone would cross the PM2.5 standard in 2024 if the current scenario remains the same and the city would be enlisted among the “non-attainment” cities of the country under “National Clean Air Program (NCAP)” of Government of India. The Positive matrix factorization (PMF) model was run to apportion the sources of PM1 and PM10. It was observed that the vehicular emissions in premonsoon (33% in PM1; 28% in PM10; contribution of vehicular emission in PM1 to PM10 > 90%) and biomass burning in winter (27% in PM1; 23% in PM10; contribution of PM1 to PM10 > 80%) are the most influencing sources that need to be curbed to mitigate PM1 and hence PM10 pollution over Darjeeling.
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•PM10 decreased during 2009–2013 followed by sharp increase since 2014.•High PM10 over eastern Himalaya is due to high premonsoon and winter-time PM1.•80–85% PM1 pollution over eastern Himalaya is contributed by combustion activities.•PM10 pollution level over eastern Himalaya would cross Indian standard by 2024.
The present study has been conducted to investigate the relative changes of carbonaceous aerosols (CA) over a high altitude Himalayan atmosphere with and without (very low) anthropogenic emissions. ...Measurements of atmospheric organic (OC) and elemental carbon (EC) were conducted during the lockdown period (April 2020) due to global COVID 19 outbreak and compared with the normal period (April 2019). The interesting, unexpected and surprising observation is that OC, EC and the total CA (TCA) during the lockdown (OC: 12.1 ± 5.5 μg m−3; EC: 2.2 ± 1.1 μg m−3; TCA: 21.5 ± 10 μg m−3) were higher than the normal period (OC: 7.04 ± 2.2 μg m−3; EC: 1.9 ± 0.7 μg m−3; TCA: 13.2 ± 4.1 μg m−3). The higher values for OC/EC ratio too was observed during the lockdown (5.7 ± 0.9) compared to the normal period (4.2 ± 1.1). Much higher surface O3 during the lockdown (due to very low NO) could better promote the formation of secondary OC (SOC) through the photochemical oxidation of biogenic volatile organic compounds (BVOCs) emitted from Himalayan coniferous forest cover. SOC during the lockdown (7.6 ± 3.5 μg m−3) was double of that in normal period (3.8 ± 1.4 μg m−3). Regression analysis between SOC and O3 showed that with the same amount of increase in O3, the SOC formation increased to a larger extent when anthropogenic emissions were very low and biogenic emissions dominate (lockdown) compared to when anthropogenic emissions were high (normal). Concentration weighted trajectory (CWT) analysis showed that the anthropogenic activities over Nepal and forest fire over north-east India were the major long-distant sources of the CA over Darjeeling during the normal period. On the other hand, during lockdown, the major source regions of CA over Darjeeling were regional/local. The findings of the study indicate the immense importance of Himalayan biosphere as a major source of organic carbon.
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•Impact of COVID-19 lockdown on the carbonaceous aerosols were studied.•Organic carbon was increased by two times in absence of anthropogenic emissions.•Higher ozone produced higher secondary organic aerosols during lockdown.•Biogenic VOC was main precursor of secondary organic aerosols during lockdown.
A year-long (March 2019–February 2020) study on the characterization of fine mode carbonaceous aerosols has been conducted over a high altitude urban atmosphere, Darjeeling (27.01°N, 88.15°E, 2200 m ...asl) in eastern Himalaya. The fine mode aerosol (PM2.5; 41.7 ± 23.7 μgm−3), total carbonaceous aerosols (TCA; 19.8 ± 7.7 μgm−3), organic carbon (OC; 8.0 ± 3.9 μgm−3) and elemental carbon (EC; 2.0 ± 0.9 μgm−3) exhibited similar seasonal variability with the highest abundance during winter followed by premonsoon, postmonsoon and minimum in monsoon. The OC:EC varied over a range of 2.8–19.4 whereas the secondary organic carbon ranged between 1.9 and 17.1 μgm−3 respectively. Higher PM2.5 associated with higher winds and elevated mixing layer depth suggest a strong influence of regional and long-range transport. In addition to the usual morning and evening rush-hour peaks, the impact of low land plain regions driven by up-slope valley winds was observed for the carbonaceous components. A novel approach has been taken to find out the individual contributions from the local and transported fossil fuel, biomass burning, and biogenic sources to OC and EC during premonsoon. We observed that the local fossil fuel (43%) contributions dominated over the biomass burning (39%) for EC whereas the contributions of local biomass burning and the local fossil fuel were same (46%) for OC. EC exhibited a higher contribution (18%) from the regional/long-range transport compared to OC (8%). IGP and Nepal were found to be the maximum contributing long distant source regions for the carbonaceous aerosol loading over eastern Himalaya. Such individual source apportionment of carbonaceous aerosols over eastern Himalaya makes the study unique and first-ever of its kind and immensely helpful for building robust mitigation action plans.
•Seasonal and diurnal variability of carbonaceous components over eastern Himalaya.•PM2.5 exceeded Indian standard (NAAQS) during premonsoon and winter.•Higher contribution of long-range and regional transport for EC than OC.•Local fossil fuel dominated for EC wheras OC was contributed equally by the local biomass burning and local fossil fuel emissions.
A district-wise emission inventory was made for the states and union territories (UTs) of the Indian Indo-Gangetic Plain for the base year of 2018 to estimate the emissions of PM2.5 from various ...sectors. In addition to conventional sectors, emissions from road dust, fossil-fuelled irrigation pumps, and construction dust were also taken into account. Total primary anthropogenic PM2.5 emission was estimated to be 3157.3 Gg (or kilo-tones) for the year 2018 of which 32 % originated from the industrial sector, 27 % from domestic fuel consumption, 23 % from open burning, 14 % from road dust, 2 % from vehicular and 2 % from various unorganized sectors. The highest emissions were observed during the premonsoon (1013 Gg/year) followed by postmonsoon (802Gg/year), winter (788 Gg/year), and lowest during the monsoon (554Gg/year). Among the states and UTs, Uttar Pradesh contributes the most in total emissions (39 %), followed by Punjab (19 %), Bihar (17 %), West Bengal (13 %), Haryana (11 %), Delhi (0.9 %) and Chandigarh (0.1 %). Emission for per capita and for billion-rupee of state gross domestic product (GDP) were the highest for Punjab and Haryana. Results have identified the districts of Punjab (Firozpur, Ludhiana, Jalandhar), scattered pockets of Uttar Pradesh (Sonbhadra, Agra, Varanasi, Kanpur, Lucknow, Prayagraj) and lower Gangetic delta (Gaya, Muzaffarpur, Burdwan, both 24-parganas and Murshidabad) as potent hotspots of cumulative PM2.5 emissions. On the other hand, the districts of Punjab (Faridkot, Mansa, Muktsar, Fatehgarh) were found to be the hotspots for per capita emissions. High emissions were observed from the domestic sector, brick kilns, and micro and small-scale industries, and regulating norms should be more stringent for these sectors. Such a study will be a value add for the policymakers and health experts to assess emission hot spots, pollution simulation, and associated mortality analysis of the region.
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•A comprehensive districtwide emission inventory of PM2.5 was made for the IGP.•Industrial and domestic emission contributes over 50 %.•Emissions from the brick kilns and road dust need to be top most priority.•Uttar Pradesh emits highest cumulatively and Punjab on per capita.•Vehicular emission still a problem for million plus cities.
The present study is an attempt to investigate the relative role of black carbon (BC) and sea-salt aerosols on the CCN activation over a high altitude station, Darjeeling (27.1° N and 88.15° E, ...2200 m asl) at eastern Himalaya. Aerosols (CN, CCN, BC and PM2.5) were measured during premonsoon and monsoon in 2017 and 2018. A unique sampling strategy and a novel methodology were adopted that enabled us to quantitatively and separately determine the contributions of local emissions (LE), valley wind transport (VWT) and long-range transport (LRT) to BC aerosols and their role in CCN activation. On the other hand, the contributions of transported sea-salt (NaCl) aerosols to CCN activation were also determined when they interact with the local anthropogenic soluble species and when they do not. CCN (0.5% super-saturation) concentrations were found to be increased when BC aerosols were more aged (~ 80 cm−3 and 218 cm−3 increase in CCN for 1 μg m−3 increase in BCLE and BCLRT with activation ratios of 0.17 and 0.55 respectively). Local anthropogenic acidic species (SO42−/H2SO4 (g) and NO3−/HNO3 (g)) interact with NaCl resulting to Cl− depletion. Cl− depletion was increased with the increase in NO3− + SO42−(45% for1 μg m−3increase in NO3− + SO42−) that in turn sharply decreased the AR of NaCl (0.04 for 1% increase in Cl- depletion). On the other hand, higher the NO3− + SO42−, higher were the CCN activation of transported BC which could be due to the soluble coating on BC. The important and interesting fact is that when transported and interacted with anthropogenic soluble species, BC aerosols (though hydrophobic) act as much better CCN than NaCl (though hydrophilic).
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•Fresh, valley-wind and long-range transported black carbon were quantified.•Chloride depletion decreases CCN activation of sea-salt aerosols.•H2SO4 (g) and HNO3 (g) increase CCN activation of black carbon.•Transported black carbon is better CCN than transported sea-salt aerosols.
A year-long study (January–December 2019) on the chemical characterization and meteorological impact on PM2.5 was conducted over a semi-urban station, Shyamnagar, in the easternmost part of the ...Indo-Gangetic Plains (IGP). PM2.5 concentrations (Mean = 81.69 ± 66.27 μgm−3; 7.10–272.74 μgm−3), the total carbonaceous aerosols (TCA) (Mean = 22.85 ± 24.95 μgm−3; 0.77–102.97 μgm−3) along with differential carbonaceous components like organic carbon (OC) (Mean = 11.28 ± 12.48 μgm−3; 0.48–53.01 μgm−3) and elemental carbon (EC) (Mean = 4.83 ± 5.28 μgm−3; 0.1–22.13 μgm−3) exhibited prominent seasonal variability with the highest concentrations during winter, followed by post-monsoon, pre-monsoon and lowest during monsoon. A similar seasonal variation was observed for the total water-soluble ionic species (Mean = 31.91 ± 20.12 μgm−3; 0.1–126.73 μgm−3). We observed that under the least favorable conditions (low ventilation coefficient), high PM2.5 pollution (exceeding Indian standard) was associated with a high increase in secondary components of PM2.5. Eastern, central and western parts of IGP, as well as Nepal, were the major long-distant source regions whereas the northern part of West Bengal and parts of Bangladesh were the major regional source region for high PM2.5 pollution over Shyamnagar. The ratios like char-EC/soot-EC, non-sea-K+/EC and non-sea-SO42−/EC strongly indicated the dominance of fossil fuel burning over biomass burning. Compared with other studies, we observed that the PM2.5 pollution over this semi-urban region was comparable (and even higher in some cases) with other parts of IGP. The high exceedance of PM2.5 over the Indian standard in Shyamnagar strongly demands an immediate initiation of systematic and regular based air pollution monitoring over semi-urban/non-urban regions in India, especially IGP, in addition to the polluted cities.
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•PM2.5 pollution was assessed under different meteorological dispersion conditions.•Secondary aerosol components showed higher increase under high pollution events.•PM2.5 was double the Indian standard on annual scale.•PM2.5 over semi-urban IGP region was comparable with many urban regions in India.
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