Quantification and assessment of hazardous mercury emission from industrial process and other unattended sectors in India: A step towards mitigation.

Hazardous pollutants like Mercury (Hg) have emerged as a pressing challenge in recent times where the expanding industrial sector is regarded as the major source in developing country India. In this study, we are trying to identify all possible industrial sectors at district level to quantify Hg emission load across India for the year 2019 using IPCC methodology where the country-specific technological emission factors are used. We have included 5 major sectors out of which emission from coal combustion in thermal power plants accounts for 186.5 t/yr of Hg emission followed by non-ferrous metal production (88.3 t/yr), captive power plants (65.5 t/yr) and fly ash generation from various manufacturing industries (45.9 t/yr). A total of 459.4 t/yr of Hg is released into the ecosystem in 2019 with an uncertainty of ± 48%. This study also estimated that about 233 million people living in and around 10 km periphery of major industrial zones with as many as 17 million people residing near the 10 major hotspots are susceptible to hazardous Hg emissions directly or indirectly. This information would be quite useful in formulating future Hg emission control strategies in India. ENVIRONMENTAL IMPLICATIONS: Present study is the first-of-its-kind quantification of Hg emission load from the Industrial process and many unattended sectors over India, which will not only give an insight into potential hotspots regions across the country but also assess the population exposed to it. It will provide aid in tracking the mercury burden to match the international conventions. The findings suggest that about 233 million people are likely to be exposed to hazardous Hg emissions. It will also enlighten the government, policymakers, stakeholders and people about their mercury footprint and envision protecting the biomes and formulating future control strategies in India.

Triple dip La-Nina, unorthodox circulation and unusual spin in air quality of India.

The recent La-Nina phase of the El Nino Southern Oscillation (ENSO) phenomenon unusually lasted for third consecutive year, has disturbed global weather and linked to Indian monsoon. However, our understanding on the linkages of such changes to regional air quality is poor. We hereby provide a mechanism that beyond just influencing the meteorology, the interactions between the ocean and the atmosphere during the retreating phase of the La-Niña produced secondary results that significantly influenced the normal distribution of air quality over India through disturbed large-scale wind patterns. The winter of 2022-23 that coincided with retreating phase of the unprecedented triple dip La-Niña, was marred by a mysterious trend in air quality in different climatological regions of India, not observed in recent decades. The unusually worst air quality over South-Western India, whereas relatively cleaner air over the highly polluted North India, where levels of most toxic pollutant (PM2.5) deviating up to about ±30 % from earlier years. The dominance of higher northerly wind in the transport level forces influx and relatively slower winds near the surface, trapping pollutants in peninsular India, thereby notably increasing PM2.5 concentration. In contrast, too feeble western disturbances, and unique wind patterns with the absence of rain and clouds and faster ventilation led to a significant improvement in air quality in the North. The observed findings are validated by the chemical-transport model when forced with the climatology of the previous year. The novelty of present research is that it provides an association of air quality with climate change. We demonstrate that the modulated large-scale wind patterns linked to climatic changes may have far-reaching consequences even at a local scale leading to unusual changes in the distribution of air pollutants, suggesting ever-stringent emission control actions.

Air quality and health co-benefits of climate change mitigation and adaptation actions by 2030: an interdisciplinary modeling study in Ahmedabad, India.

Climate change-driven temperature increases worsen air quality in places where coal combustion powers electricity for air conditioning. Climate solutions that substitute clean and renewable energy in place of polluting coal and promote adaptation to warming through reflective cool roofs can reduce cooling energy demand in buildings, lower power sector carbon emissions, and improve air quality and health. We investigate the air quality and health co-benefits of climate solutions in Ahmedabad, India-a city where air pollution levels exceed national health-based standards-through an interdisciplinary modeling approach. Using a 2018 baseline, we quantify changes in fine particulate matter (PM2.5) air pollution and all-cause mortality in 2030 from increasing renewable energy use (mitigation) and expanding Ahmedabad's cool roofs heat resilience program (adaptation). We apply local demographic and health data and compare a 2030 mitigation and adaptation (M&A) scenario to a 2030 business-as-usual (BAU) scenario (without climate change response actions), each relative to 2018 pollution levels. We estimate that the 2030 BAU scenario results in an increase of PM2.5 air pollution of 4.13 µg m-3 from 2018 compared to a 0.11 µg m-3 decline from 2018 under the 2030 M&A scenario. Reduced PM2.5 air pollution under 2030 M&A results in 1216-1414 fewer premature all-cause deaths annually compared to 2030 BAU. Achievement of National Clean Air Programme, National Ambient Air Quality Standards, or World Health Organization annual PM2.5 Air Quality Guideline targets in 2030 results in up to 6510, 9047, or 17 369 fewer annual deaths, respectively, relative to 2030 BAU. This comprehensive modeling method is adaptable to estimate local air quality and health co-benefits in other settings by integrating climate, energy, cooling, land cover, air pollution, and health data. Our findings demonstrate that city-level climate change response policies can achieve substantial air quality and health co-benefits. Such work can inform public discourse on the near-term health benefits of mitigation and adaptation.

Machine learning based quantification of VOC contribution in surface ozone prediction.

The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014-2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NOx, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results.

Impact assessment of surface ozone exposure on crop yields at three tropical stations over India.

Surface ozone is a damaging pollutant for crops and ecosystems, and the ozone-induced crop losses over India remain uncertain and a topic of debate due to a lack of sufficient observations and uncertainties involved in the modeled results. In this study, we have used the observational data from MAPAN (Modelling Air Pollution And Networking) for the first time to estimate the relative yield losses, crop production losses, and economic losses for the two major crops (wheat and rice). The detailed estimation has been done focusing on three individual suburban sites over India (Patiala, Tezpur, and Delhi) and compared with other related studies over the Indian region. We have used the concentration-based metric (M7, 7-h average from 09:00 to 15:59 h) along with the cumulative ozone exposure indices (AOT40, accumulated exposure over a threshold of 40 ppb) and applied the exposure-response (E-R) functions for the calculation of the crop losses. Our study shows that the yearly crop losses can reach the level of 12.4-40.8% and 2.0-11.1% for the wheat and rice crops, respectively, at certain places like Patiala in India. The annual economic loss can be as high as $4.6 million and $0.7 million for wheat and rice crops, respectively, even at individual locations in India. Our estimated %RYL (relative yield loss) lies in the range of 0.3 + /0.6 times the recent regional model estimates which use only the AOT40 metric. Region-specific E-R functions based on factors suitable for the Indian region needs to be developed.

Understanding the influence of summer biomass burning on air quality in North India: Eight cities field campaign study.

Near real-time monitoring of major air pollutants, i.e., particulate matter (PM10, PM2.5, PM1), trace gases (O3, CO, NO, NO2, NOx, NH3, CO2, SO2) and Volatile Organic Compounds (VOCs: benzene, ethylbenzene, m-, p-xylene, o-xylene and toluene) along with climatological parameters was done in eight-cities field campaigns during the rabi (wheat) crop residue burning period in the northwest of Indo-Gangetic Plain (IGP) region. The phase-wise monitoring was done at eight locations representing rural, semi-urban and urban backgrounds. During the whole campaign, the semi-urban site (Sirsa) observed the highest average concentration of PM10 (226 ± 111 µg m-3) and PM2.5 (91 ± 67 µg m-3). The urban site (Chandigarh) reported the minimum concentrations of all the three size fractions of particulate matter with PM10 as 89 ± 54 µg m-3, PM2.5 as 42 ± 22 µg m-3 and PM1 as 20 ± 13 µg m-3 where the monitoring was done in the early phase of the campaign. The highest VOC concentration was recorded at the semi-urban (Sirsa) site, whereas the lowest was at a rural location (Fatehgarh Sahib). NH3 concentration was observed highest in rural sites (31.7 ± 29.8 ppbv), which can be due to the application of fertilizers in agricultural activities. Visible Infrared Imaging Radiometer Suite (VIIRS) based fire and thermal anomalies, along with HYSPLIT back trajectory analysis, show that major air masses over monitoring sites (22 %-70 %) were from the rabi crop residue burning regions. The characteristic ratios and Principal component analysis (PCA) results show that diverse sources, i.e., emissions from crop residue burning, solid biomass fuels, vehicles and industries, majorly degrade the regional air quality. This multi-city study observed that semi-urban regions have the most compromised air quality during the rabi crop residue burning and need attention to address the air quality issues in the IGP region.

Assessment of hazardous radionuclide emission due to fly ash from fossil fuel combustion in industrial activities in India and its impact on public.

Traditionally coal has been extensively used as a dominating fossil fuel in a wide range of industries due to its abundance. In India, industries like thermal power plants, cement industries, iron, and steel industries along with many captive power plants consume a huge quantity of coal each year to meet energy demand. Coal combustion releases blackish-grey colored fly ash waste is one of the most imperative sources of radionuclides like Radium (226Ra), Thorium (232Th), Potassium (40K) and Uranium (238U). The estimated industrial fly ash is ∼308.416 Million Tonnes (MT) in 2019, considered as an emerging environmental problem. This study represents the first-ever radionuclide emission from Indian fly ash generated across various major industries. The results reveal that the estimated 226Ra, 232Th, 238U, and 40K radionuclides were estimated to be ∼27.473 TBq, ∼44.351 TBq, ∼41.089 TBq, and ∼111.091 TBq respectively. The potential radionuclide hotspot regions across the nation are identified, which could be used as an important tool to assess its impact on the chronic exposure of millions of residents living near these sources. Cleaner or green energy could be the best alternative to combat the unseen health disaster. More effective and safe utilization of fly ash can minimize the hazardous effect of radionuclides emission.

Temporal variability of PM2.5 and its possible sources at the tropical megacity, Bengaluru, India.

The mass concentrations of PM2.5 were measured at a tropical megacity, Bengaluru, India, for the year 2015. The mean mass concentrations showed large fluctuations on day to day basis with values less than the Indian National Ambient Air Quality Standard (INAAQS) of 60 µg m-3. The observed annual mean mass concentration of 28 ± 11 µg m-3 is also within the INAAQS value of 40 µg m-3. The diurnal trend of PM2.5 concentration showed bimodal distribution, with the primary peak in the morning and the secondary one during the late evening hours. The timing of the peaks matched with rush traffic hours. Strong seasonality is observed in the diurnal concentration of PM2.5 with the highest value during winter (50 ± 22 µg m-3) and the lowest of (11 ± 5 µg m-3) in the monsoon. The weekend PM2.5 mass concentrations were less than those on the weekdays up to a maximum of 100%. The decrease in PM2.5 mass concentration was also observed on the day of the strike when many busses were off the road. Vehicular traffic is suggested as one of the primary contributors of PM2.5 in this region. The health risk assessment in this study, points to ischemic heart disease as the primary cause of PM2.5-induced death.

Analysis of Ozone Photochemistry over Southern Tropical Megacity, Bengaluru, India.

Rapid infrastructure development, increased population, shift in land use practices and higher vehicular emissions have all influenced Ozone (O3 ) production through its precursor gases in southern megacity, Bengaluru, India. We have investigated the photochemistry using hourly measurements of O3 and associated precursor gases conducted in Bengaluru during January to December 2019. The rate of formation of O3 is analyzed for Bengaluru for the first time using a photochemical model involving NOx cycle. On the diurnal scale, O3 showed a midday peak (1200-1500 IST), while its precursors showed a bimodal trend with peaks at 0900 IST and 2100 IST. The photolytic rate constants for j NO 2 and j O 3 were derived from the tropospheric ultraviolet and visible (TUV) radiation model. Using the photolytic rate constants for j NO 2 and j O 3 under the triad NO-NO2 -O3 , photostationary state (PSS), Leighton ratio (Ф) and its deviations from unity were estimated over Bengaluru. Positive anomalies (Φ > 1) were obtained, and the computed Φ was utilized to determine the total mixing ratios of the peroxy radical (PO2 ). The seasonal variation in j O 3 revealed that in the presence of intense solar radiation and NOx, VOCs, CO and NMVOCs in the mixing layer, O3 forms photochemically over the surface.

Characteristics, source apportionment and long-range transport of black carbon at a high-altitude urban centre in the Kashmir valley, North-western Himalaya.

Six years of data (2012-2017) at an urban site-Srinagar in the Northwest Himalaya were used to investigate temporal variability, meteorological influences, source apportionment and potential source regions of BC. The daily BC concentration varies from 0.56 to 40.16 µg/m3 with an inter-annual variation of 4.20-7.04 µg/m3 and is higher than majority of the Himalayan urban locations. High mean annual BC concentration (6.06 µg/m3) is attributed to the high BC observations during winter (8.60 µg/m3) and autumn (8.31 µg/m3) with a major contribution from Nov (13.88 µg/m3) to Dec (13.4 µg/m3). A considerable inter-month and inter-seasonal BC variability was observed owing to the large changes in synoptic meteorology. Low BC concentrations were observed in spring and summer (3.14 µg/m3 and 3.21 µg/m3), corresponding to high minimum temperatures (6.6 °C and 15.7 °C), wind speed (2.4 and 1.6 m/s), ventilation coefficient (2262 and 2616 m2/s), precipitation (316.7 mm and 173.3 mm) and low relative humidity (68% and 62%). However, during late autumn and winter, frequent temperature inversions, shallow PBL (173-1042 m), stagnant and dry weather conditions cause BC to accumulate in the valley. Through the observation period, two predominant diurnal BC peaks were observed at ⁓9:00 h (7.75 µg/m3) and ⁓21:00 h (6.67 µg/m3). Morning peak concentration in autumn (11.28 µg/m3) is ⁓2-2.5 times greater than spring (4.32 µg/m3) and summer (5.23 µg/m3), owing to the emission source peaks and diurnal boundary layer height. Diurnal BC concentration during autumn and winter is 65% and 60% higher than spring and summer respectively. During autumn and winter, biomass burning contributes approximately 50% of the BC concentration compared to only 10% during the summer. Air masses transport considerable BC from the Middle East and northern portions of South Asia, especially the Indo-Gangetic Plains, to Srinagar, with serious consequences for climate, human health, and the environment.

Process-based diagnostics of extreme pollution trail using numerical modelling during fatal second COVID-19 wave in the Indian capital.

The world's worst outbreak, the second COVID-19 wave, not only unleashed unprecedented devastation of human life, but also made an impact of lockdown in the Indian capital, New Delhi, in particulate matter (PM: PM2.5 and PM10) virtually ineffective during April to May 2021. The air quality remained not only unabated but also was marred by some unusual extreme pollution events. SAFAR-framework model simulations with different sensitivity experiments were conducted using the newly developed lockdown emission inventory to understand various processes responsible for these anomalies in PM. Model results well captured the magnitude and variations of the observed PM before and after the lockdown but significantly underestimated their levels in the initial period of lockdown followed by the first high pollution event when the mortality counts were at their peak (∼400 deaths/day). It is believed that an unaccounted emission source was playing a leading role after balancing off the impact of curtailed lockdown emissions. The model suggests that the unprecedented surge in PM10 (690 µg/m3) on May 23, 2021, though Delhi was still under lockdown, was associated with large-scale dust transport originating from the north west part of India combined with the thunderstorm. The rainfall and local dust lifting played decisive roles in other unusual events. Obtained results and the proposed interpretation are likely to enhance our understanding and envisaged to help policymakers to frame suitable strategies in such kinds of emergencies in the future.

Tracer-based characterization of source variations of ambient isoprene mixing ratios in a hillocky megacity, India, influenced by the local meteorology.

The ambient biogenic volatile organic compounds (BVOCs), mainly isoprene, are potentially involved in the formation of secondary pollutants, hence, they are significant in terms of air quality and climate. Although the largest sources of BVOCs are tropical regions, the measurements of isoprene in the Indian subcontinent are limited. We conducted the measurements of isoprene, benzene, and toluene at an urban site in a hillocky megacity of India using a high-sensitivity proton transfer reaction quadrupole mass spectrometer (PTR-QMS). The mixing ratios of isoprene were compared with those of aromatic compounds like benzene and toluene, which represent typical anthropogenic VOCs. Isoprene and isoprene/benzene (>5 ppbv ppbv-1) showed higher levels in the pre-monsoon months, most likely due to large emissions by urban vegetation during physiological activities in plants which was enhanced by the high ambient temperatures and solar radiation. While Benzene and toluene showed higher mixing ratios during winter, which were due to shallower boundary layer depths and transport of air masses from polluted Indo-Gangetic Plain during this season. The mixing ratios of VOCs show significant diurnal variation as a result of their different origins and the role of different meteorological parameters. The robust emission ratios of isoprene/benzene obtained from nighttime data were used to separate the non-anthropogenic and anthropogenic isoprene emissions. ∼30% enhancement observed in non-anthropogenic emissions to isoprene from winter to pre-monsoon season when temperatures and solar radiation were stronger, although traffic in the city. Isoprene/benzene ratio at lower temperatures (<25 °C) and solar radiation (<100 W m-2) was predominantly controlled by anthropogenic sources. Overall, toluene and isoprene are the most frequent species in terms of having the highest ozone-forming potential (OFP) values but biogenic isoprene became more important to ozone formation during the afternoon hours in the pre-monsoon months with high air temperatures (>25 °C).

Is Meteorology a Factor to COVID-19 Spread in a Tropical Climate?

It was speculated that fewer COVID-19 infections may emerge in tropical countries due to their hot climate, but India emerged as one of the leading hotspot. There is no concrete answer on the influence of meteorological parameters on COVID-19 even after more than a year of outbreak. The present study examines the impacts of Meteorological parameters during the summer and monsoon season of 2020, in different Indian mega cities having distinct climate and geography. The results indicate the sign of association, but it varies from one climatic zone to another. The principal component analysis revealed that humidity is strongly correlated with COVID-19 infections in hillocky city Pune (R = 0.70), dry Delhi (R = 0.50) and coastal Mumbai (R = 0.46), but comparatively weak correlation is found in arid climatic city of Ahmedabad. As against the expectations, no discernible correlation is found with temperature in any of the cities. As the virus in 2020 in India largely travelled with droplets, the association with absolute humidity in the dry regions has serious implications. Clarity in understanding the impact of seasonality will greatly help epidemiological research and in making strategies to control the pandemic in India and other tropical countries around the world.

Ambient ozone over mid-Brahmaputra Valley, India: effects of local emissions and atmospheric transport on the photostationary state.

This study presents the characteristics of ground level atmospheric ozone (O3) over the rural mid-Brahmaputra Valley region of the northeastern India. Ozone and oxides of nitrogen (NOx = NO + NO2) concentration data were obtained from continuous measurement of O3 and NOx housed at the MAPAN-AQM station at Tezpur University. The meteorological parameters were obtained from the same station. The diel, monthly, and seasonal variations of O3 were studied. The O3-NOx photostationary state (PS) was carefully examined and it was found that the net O3 concertation deviated substantially from the PS during the winter season. The deviation could be attributed to local biomass burning, biogenic VOC emission from forest and agriculture, and long-range transport of peroxyacyl nitrate (PAN). The long-range transport has been ascertained by examining the ventilation coefficients (VC), which correlated with the steep growth of net O3 concentrations in the morning hours. The HYSPLIT air mass back trajectories were used in concentration-weighted trajectory (CWT) analyses of O3 to assess the long-range regional transport of O3 precursors, which positively influenced local O3 concentrations.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Particulate pollution over an urban Himalayan site: Temporal variability, impact of meteorology and potential source regions.

Five-year (2013-2017) particulate matter (PM) data observed at an urban site, Srinagar, Kashmir Himalaya, India was used to examine the temporal variability, meteorological impacts and potential source regions of PM. The daily mean PM10 and PM2.5 concentration was 135 ± 112 µg/m3 and 87 ± 93 µg/m3 respectively with significant intra- and inter-daily variation. The annual PM10 and PM2.5 concentration was 2.0-3.2 and 1.7-2.8 times higher than the annual Indian National Ambient Air Quality Standards (PM10 = 60 µg/m3 and PM2.5 = 40 µg/m3). PM concentration shows a bimodal diurnal pattern with morning and evening peaks, which coincide with the increased anthropogenic activity and shallow planetary boundary layer (PBL). The combined effect of the low temperature, low wind speed, shallow and stable PBL and geomorphic setup of Kashmir valley leads to the accumulation of particulate pollution during autumn and winter and the converse meteorological conditions leads to dispersion, dilution and deposition during spring and summer. High precipitation rate (>15 mm/day) removes the coarse particles (PM10) more efficiently than fine particles (PM2.5), while as the moderate to high humid conditions (55-95%) leads to the accumulation and growth of more PM. It was observed that ~80% of the air masses arriving at the site during spring, autumn and winter are westerlies. Source contribution analysis revealed that highly potential source regions of PM at the site are neighboring Pakistan, Afghanistan, parts of Iran and Trans-Gangetic Plains, which could contribute high concentration of the PM10 (>250 µg/m3) and PM2.5 (>150 µg/m3) during autumn and winter. The high PM load observed at the site during autumn and winter, with major contribution from the anthropogenic source emissions like biomass and coal burning, fossil fuel combustion and suspension of road dust, is aggravated by the geomorphic and meteorological setup of the Kashmir valley.

Association of retreating monsoon and extreme air pollution in a megacity.

The world's top ranked mega city Delhi is known for deteriorated air quality. However, the analysis of air pollution data of 5 years (2014-2018) reveals that years 2016 and 2017, which were marked by an unusual delayed withdrawal of monsoon, witnessed an unprecedented extreme levels of toxic PM2.5 particles (≤2.5 µm in diameter) touching a peak level of ∼760 µg/m3 (24 hr average), immediately after the monsoon retreat, surpassing WHO standards by ∼30 time and Indian national standards by ∼12 times, jeopardising lives of its citizens. However, the normal monsoon withdrawal years do not show such extreme levels of pollution. The high resolution WRF-Chem model along with meteorological data are used in this work to understand that how the delayed monsoon withdrawal and associated vagarious anti-cyclonic circulation resulted in trapping externally generated pollutants ceaselessly under colder conditions, leading to historic air quality crisis in landlocked mega city in these selected years. The sensitivity analysis confirmed that when WRF-chem model forced the climatology of normal monsoon year (2015) to simulate the pollution scenario of 2016 and 2017 for the above time period, the crisis subsided. Present findings suggest that such unusual monsoon patterns are on the hook to spur extreme pollution events in recent time.

Impact of biomass induced black carbon particles in cascading COVID-19.

We explore the association of biomass-induced black carbon aerosolized virus with COVID-19 in one of the top-ranked polluted hot spot regions of the world, Delhi, at the time when other confounding factors were almost stable and the pandemic wave was on the declining stage. Delhi was worst affected by COVID-19. However, when it was fast returning back to normal after about 6 months with minimum fatalities, it suddenly encountered a reversal with a 10 fold increase in infection counts, coinciding with the onset of the stubble burning period in neighbouring states. We hereby report that the crop residue burning induced lethal aged Black carbon-rich particles which engulfs Delhi during the post-monsoon months of October-November are strongly associated with COVID-19 and largely responsible for the sudden surge. It is found that the virus efficacy is not necessarily related to any particulates but it is more of source-based toxicity of its component where the virus is piggybacking. We conclude that the aged biomass BC particles tend to aggregate and react with other compounds to grow in size, providing temporary habitat to viruses leading to the rapid increase in COVID-19 cases which declined after the crop burning stopped.

Influence of agricultural activities on atmospheric pollution during post-monsoon harvesting seasons at a rural location of Indo-Gangetic Plain.

The emissions from agricultural activities significantly impact the air quality at local (rural) and regional scales. The study monitored the near real-time concentrations of emission from agrarian activities, i.e., particulate matter (PM10, PM2.5, PM1), traces gases and VOCs, along with meteorological parameters in a rural area of Indo-Gangetic Plains (IGP). As different agricultural activities take place simultaneously in the region, sampling period was divided into three phases based on regional agricultural activities as HB (harvesting-burning) period, BTS (burning-tillage-sowing) period and PFS (pesticide-fertilizer spray) period. The highest mean concentration (± standard deviation) of particulate matter, i.e., PM10, PM2.5, PM1 was observed during HB period as 151.0 ± 52.3, 94.7 ± 32.9 and 41.0 ± 16.3 µgm-3 followed by PFS as 121.7 ± 49.1, 87.8 ± 35.5 and 39.7 ± 15.7 µgm-3 and BTS period as 92.5 ± 38.8, 63.5 ± 28.4, 26.6 ± 10.9 µgm-3 respectively. The mean concentration of NO (8.4 ± 3.4 ppb), SO2 (5.8 ± 1.2 ppb), CO (0.9 ± 0.3 ppm), O3 (12.5 ± 3.3 ppb) was also highest during harvesting-burning period. In the burning-tillage-sowing period, the mean concentration of NO2 (31.0 ± 2.9 ppb), benzene (2.8 ± 0.6 µgm-3) and o-xylene (2.1 ± 0.3 µgm-3) were highest. The data of crop residue burning fires showed that during HB period, around 34,683 active fires were there in the region (state of Punjab), whereas, in studied district, the number of fire counts were 635. During the HB period, around 70% of the air masses were originated within a 500 km area, whereas during the BTS and PFS period, 75% and 86% of air masses were originated from 500 km region, respectively. The ratio of PM2.5/PM10 during study period ranged from 0.63 to 0.72 and was observed highest during PFS period. The current study investigated the influence of agricultural activities on air quality during post-monsoon season in a rural area of Indo-Gangetic Plains to understand the impact of these activities on air quality in the region and plan mitigation strategies.

Establishing a link between fine particulate matter (PM2.5) zones and COVID -19 over India based on anthropogenic emission sources and air quality data.

The spread of coronavirus disease of 2019 (COVID-19) pandemic around the globe is affecting people. The majority of Indian urban complexes are reeling under high emissions of deadly fine particulate matter PM2.5 and resulting in poor air quality. These fine particles penetrate deep into the body and fuel inflammation in the lungs and respiratory tract, leading to the risk of having cardiovascular and respiratory problems, including a weak immune system. In the present study, we report the first national-scale study over India, which establishes a strong relationship between the PM2.5 emission load and COVID-19 infections and resulting deaths. We find a significant correlation (R2 = 0.66 & 0.60) between the states as well as districts having varied levels of PM2.5 emissions with corresponding COVID-19 positive cases respectively, and R2 = 0.61 between wavering air quality on a longer time scale and the number of COVID-19 related deaths till 5 November 2020. This study provides practical evidence that cities having pollution hotspot where fossil fuel emissions are dominating are highly susceptible to COVID-19 cases.