Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: Rural-urban contrast

Interdecadal variations of aerosol and its composition over the Fenwei Plain based on multi-source observations

Understanding the variations and potential source of air pollution is essential for implementing targeted mitigation actions. However, the distribution and long-term trends of Aerosol Optical Depth (AOD) and its components over the Fenwei Plain (FWP) have not been thoroughly investigated. Furthermore, the potential source contribution of AOD loading is still unclear. Thus, maximum synthesis and Mann-Kendall trend (MK) test with Sen's Slope methods are employed to reveal the spatiotemporal variation characteristics of AOD over the FWP. The Potential Source Contribution Function (PSCF) model was applied to analyze the potential source contribution of AOD over the FWP. Results demonstrated that the AOD in spatial pattern exhibited consistency with the topography. AOD over the FWP fluctuated annually from 2000 to 2020, with an increase in the previous decade followed by a gradual decline after 2011. There was a significant monthly variation in AOD with higher values in August (0.47 ± 0.21) and lower in November (0.29 ± 0.12). A positive AOD trend was confirmed from 2000 to 2010 yet a negative trend is identified from 2011 to 2020. The sulfate aerosol (AODSU) exhibited an increasing trend over an extended period. Clear-sky radiation shows a negative trend at the surface and the top of the atmosphere (TOA) from 2000 to 2010, which is consistent with the trend in AOD. The AOD in FWP was primarily influenced by local emissions, with contributions from northern and northwestern sources. This research offers an enhanced overarching comprehension of the distribution and regional climate effects of aerosols over the FWP.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about -8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (-0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

Black carbon over tropical Indian coast during the COVID-19 lockdown: inconspicuous role of coastal meteorology

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59' 26.5″ N, 80° 13' 51.8″ E) on the eastern coast of India during January-June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene.

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, PM_2.5, PM₁), trace gases (O₃, CO, NO, NO₂, NO_x, 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 PM_2.5 (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, PM_2.5 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.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R² = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R² = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Identification of prominent airborne pollen in a city situated in foot-hills of Himalayas, Chandigarh, India

Pollen allergy is considered one of the important critical thrust areas, as 20-30% of the world population suffers from allergic rhinitis. The increase in global surface temperature directly affects pollen physiological (e.g., pollen production) and morphological parameters and indirectly affects the distribution pattern, the allergenic potential of pollen, and plant species. Therefore, periodic sampling and pollen studies of a region have become necessary to assess the status of change in species and its morphological characteristics of different taxa. The current study is conducted to identify the airborne pollen based on studying their unique and distinctive morphological characters to serve as a reference pollen guide for future research. The airborne pollens were trapped using the Burkard volumetric sampler at three different locations in Chandigarh from 2018 to 2020 and analyzed under Leica DM5500B-Automated Upright Microscope System. The study investigated various pollen features such as shape, size, aperture type, and exine/surface pattern for taxonomic classification of plant groups. The majority of LM-analyzed pollen grains were prolate-spheroidal or oblate, whereas the aperture types were 3-zonocolporate, 3-colpate, and 3-zonocolporate. Exine patterns were predominantly psilate, reticulate, and straite and were easily discernible. Nonetheless, the vast majority of airborne pollen belonging to both arboreal and non-arboreal was quite small and fall into small pollen size classes, i.e., 10-24 μm. The exine pattern was readily apparent and were predominantly psilate, reticulate, and straight. The current study improved the knowledge on airborne pollen biodiversity, which will help to understand the regional distribution, long-range transport, and construct the current status of morphological features of species/taxa.

Preventable mortality attributable to exposure to air pollution at the rural district of Punjab, India

Air pollution has emerged as a leading global health risk in recent decades, where its health impacts are primarily focused on urban areas. In India, semi-urban and rural areas are also emerging as air pollution hotspots. As these regions are out of focus, the current study monitored air pollution (PM_2.5) at a rural district of Punjab in North India and attempted to study the associated health impacts. Hospital data of outpatient department (OPD), inpatient department (IPD) and all-cause mortality was correlated with change in PM_2.5 concentrations. PM_2.5 concentrations showed seasonal variations having relatively higher concentrations during post-monsoon and winter seasons. This rise in air pollution (annual average 92 µg/m³) was found to be mainly due to crop residue burning, including local meteorology. In comparison, hospital data shows that hospital visits and admissions were higher during monsoon. This shows that hospital admissions could not be directly linked to air pollution in rural areas as other factors such as short days during winters and self-medication, socio-economic factors and dependency on local unauthorised traditional healers may influence. The application of the AirQ + model for short-term health effects reveals that out of 2582 total deaths, preventable deaths ranging from 246 (WHO guidelines value - 10 µg/m³) to 159 (Indian NAAQS - 40 µg/m³) could be ascribed to air pollution exposure and specifically PM_2.5. However, these deaths are avoidable by developing strategies to minimise air pollution in rural areas. Hence, a comprehensive approach is needed to plan air pollution reduction strategies, including urban, semi-urban and rural areas.

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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

Can Forest Fires Be an Important Factor in the Reduction in Solar Power Production in India?

The wildfires over the central Indian Himalayan region have attracted the significant attention of environmental scientists. Despite their major and disastrous effects on the environment and air quality, studies on the forest fires’ impacts from a renewable energy point of view are lacking for this region. Therefore, for the first time, we examine the impact of massive forest fires on the reduction in solar energy production over the Indian subcontinent via remote sensing techniques. For this purpose, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO), the Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) in conjunction with radiative transfer model (RTM) simulation, in addition to 1-day aerosol forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The energy production during the first quarter of 2021 was found to reach 650 kWh/m2 and the revenue generated was about INR (Indian rupee) 79.5 million. During the study period, the total attenuation due to aerosols and clouds was estimated to be 116 and 63 kWh/m2 for global and beam horizontal irradiance (GHI and BHI), respectively. The financial loss due to the presence of aerosols was found to be INR 8 million, with the corresponding loss due to clouds reaching INR 14 million for the total Indian solar plant’s capacity potential (40 GW). This analysis of daily energy and financial losses can help the grid operators in planning and scheduling power generation and supply during the period of fires. The findings of the present study will drastically increase the awareness among the decision makers in India about the indirect effects of forest fires on renewable energy production, and help promote the reduction in carbon emissions and greenhouse gases in the air, along with the increase in mitigation processes and policies.

COVID-19 pandemic and sudden rise in crop residue burning in India: issues and prospects for sustainable crop residue management

The seasonal burning of crop residue significantly affects the environment, leading to poor air quality over Indo-Gangetic Plain (IGP) in India. Hence, there have been significant efforts to minimize crop residue burning through policy, innovations, and awareness measures. However, an abrupt increase in paddy residue burning was observed over IGP during 2020. Hence, the study explores the factors leading to this sharp rise. The business as usual trends analysis revealed that paddy crop residue burning activities increased significantly (60%) in 2020 compared to the previous year. The massive increase in crop residue burning consequently seems to be linked with the COVID-19 pandemic, which affected the farmer's income, including the poor compliance by the regulatory authorities. The study also highlights the issues and prospects for sustainable crop residue management and explores the solutions to minimize crop residue burning. There are few crops in India that have guaranteed minimum sale price and are also subsidized. These provisions encourage farmers to grow those particular crops, resulting in the generation of large amounts of crop residue from these specific crops. There have been several efforts by the Indian government, including based on recent court intervention. Still, there is no respite from burning activities and the occurrence of Delhi winter smog every year. Hence, the study emphasizes a need to adopt integrated approaches having in situ eco-friendly solutions, which enhances the farmer's income and focuses on employability, capacity building, awareness generation, and in situ economically viable solutions.

Understanding seasonal variation in ambient air quality and its relationship with crop residue burning activities in an agrarian state of India

In India, Indo-Gangetic Plains (IGP) is becoming the hotspot of air pollution due to increasing anthropogenic activities such as rapid industrial growth, infrastructure development, transportation activities, and seasonal practice of crop residue burning. In the current study, seasonal variation in ambient air quality for 14 parameters, i.e., particulate matter (PM), trace gases, and volatile organic compounds (VOCs), along with meteorological parameters, was studied in 21 districts of the Haryana state for year 2019, situated in IGP. To analyze spatial variation of pollutants, ambient air quality data of 23 continuous ambient air quality monitoring stations were divided into three zones based on ecology and cropping pattern. All the zones showed annual mean PM₁₀ and PM_2.5 concentrations much higher than national ambient air quality standards. Annual mean PM₁₀ concentration (±standard deviation) in Zones-1, 2, and 3 was 156±86, 174±93, and 143±74 μg m^-3, whereas for PM_2.5 was 71±44, 85±54, and 78±47 μg m^-3. The results showed a considerable seasonal variation in the concentration of all pollutants. Most of the pollutants peak in the post-monsoon season, followed by winters in which crop residue burning predominates in many parts of the Haryana. PM₁₀ concentrations increased by 65-112% and PM_2.5 concentrations increased by 131-147% in the post-monsoon season compared to monsoons. The post-monsoon season showed the highest concentration of PM₁₀, NO, and toluene (Zone-1); and PM_2.5, NH₃, CO, and benzene (Zone-2); whereas in winters, SO₂ (Zone-1); ethylbenzene, m,p-xylene, and xylene (Zone-2); and NO₂ and NOx (Zone-3) showed the maximum pollution levels. The O₃ concentration was highest in the pre-monsoon season (Zone-1). The satellite-based fire counts and PCA results show a significant influence of crop residue burning in the post-monsoon season and solid biomass burning in winters on Haryana's air quality. The study could help to understand seasonal variation in ambient air quality and the influence of factors such as crop residue burning in the IGP region, which could help to formulate season-specific control measures to improve regional air quality.

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, PM_2.5, 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., PM_10, PM_2.5, 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 PM_2.5/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.

Does airborne pollen influence COVID-19 outbreak?

The fast spread of SARS-CoV-2 presented a worldwide challenge to public health, economy, and educational system, affecting wellbeing of human society. With high transmission rates, there are increasing evidences of COVID-19 spread via bioaerosols from an infected person. The current review was conducted to examine airborne pollen impact on COVID-19 transmission and to identify the major gaps for post-pandemic research. The study used all key terms to identify revenant literature and observation were collated for the current research. Based on existing literature, there is a potential association between pollen bioaerosols and COVID-19. There are few studies focusing the impact of airborne pollen on SARS-CoV-2, which could be useful to advance future research. Allergic rhinitis and asthma patients were found to have pre-modified immune activation, which could help to provide protection against COVID-19. However, does airborne pollen acts as a potent carrier for SARS-CoV-2 transport, dispersal and its proliferation still require multidisciplinary research. Further, a clear conclusion cannot be drawn due to limited evidence and hence more research is needed to show how pollen bioaerosols could affect virus survivals. The small but growing literature review focuses on searching for every possible answer to provide additional security layers to overcome near future corona-like infectious diseases.

Appraisal of regional haze event and its relationship with PM2.5 concentration, crop residue burning and meteorology in Chandigarh, India

Air pollution affects not only the air quality in megacities but also in medium and small-sized cities due to rapid urbanization, industrialization, and other anthropogenic activities. From October 28, 2015 to November 3, 2015, the Indo-Gangetic Plains region, including Chandigarh encountered an episode of poor visibility during the daytime. The daily average PM_2.5 concentration reached 191 μg/m^3, and visibility reduced by ∼2.2 times in the Chandigarh region. PM_2.5 concentration was found around 4 times higher than a non-haze day and more than 3 times higher than National Ambient Air Quality Standards for 24 h. A significant correlation between PM_2.5 and CO (r: 0.87) during the haze period indicated similarity in their emission sources; which was attributed to the burning of solid organic matter. Further, satellite data and back-trajectory analysis of air masses showed large-scale rice stubble burning in the agricultural fields, adjoining to the city areas. The transboundary movement of air masses below 500 m and meteorological conditions played a major role in building the pollution load in the Chandigarh region. Moreover, the enhanced concentration of biomass burning tracers, i.e., organic carbon (∼3.8 times) and K⁺ ions (2∼ times) in PM_2.5 and acetonitrile (∼2.3 times) in ambient air was observed during the haze event. The study demonstrates how regional emissions and meteorological conditions can affect the air quality in a city; which can be useful for proper planning and mitigation policies to minimize high air pollution episodes.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 μg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

Impact of COVID-19 lockdown on ambient air quality in megacities of India and implication for air pollution control strategies

The impact of restrictions during various phases of COVID-19 lockdown on daily mean PM2.5 concentration in five Indian megacities (New Delhi, Chennai, Kolkata, Mumbai, and Hyderabad) was studied. The impact was studied for pre-lockdown (1st Mar-24th Mar 2020), lockdown (25th Mar-31st May 2020), and unlocking (1st Jun-31st Aug 2020) phases. The lockdown period comprises 4 lockdown phases with distinct measures, whereas the unlocking period had 3 phases. PM2.5 concentration reduced significantly in all megacities and met the national standards during the lockdown period. The maximum reduction in PM2.5 level was observed in Kolkata (62%), followed by Mumbai (49%), Chennai (34%), and New Delhi (26%) during the lockdown period. Comparatively, Hyderabad exhibited a smaller reduction in PM2.5 concentration, i.e., 10%. The average PM2.5 levels during the lockdown in the peak hour (i.e., 07:00-11:00 h) in New Delhi, Chennai, Kolkata, Mumbai, and Hyderabad decreased by 21.3%, 48.5%, 63.4%, 56.4%, and 23.8%, respectively, compared to those before lockdown period. During the unlocking period, except for Chennai, all megacities showed a reduction in average PM2.5 levels compared to concentrations in the lockdown period, but these reductions were mainly linked with monsoon rains in India. The current study provided an opportunity to study air pollution in the absence of major anthropogenic activities and during limited activities in monsoon season having an ecological design. The study reports a new baseline of PM2.5, except for monsoon, and explores this knowledge to plan future air pollution reduction strategies. The study also discusses how this new learning of knowledge could strengthen air pollution control policies for better air quality and sustainability.Graphical abstract.

Impact of the COVID-19 pandemic on clean fuel programmes in India and ensuring sustainability for household energy needs

Clean cooking energy strategies are critical for reducing air pollution, improving health, and achieving related Sustainable Development Goals. The recent COVID-19 lockdowns may impact the transition towards clean cooking fuels. The nationwide lockdown is likely to affect key factors such as energy access, income, transportation, etc., that play a role in decisions influencing household fuel use. The rural population already bears the burden of poverty and may not be able to afford and access clean cooking fuels during the lockdown. They are thus vulnerable to reversion to their traditional cooking methods using solid biomass fuels. The household air pollution caused due to the use of polluting fuels increases their susceptibility to non-communicable diseases, and thus may intensify the risk and severity of COVID-19 infection. Hence, there is an urgent need to expand sustainable energy solutions worldwide. The present study applies the DPSIR modeling framework to establish a set of comprehensive indicators for addressing the transition towards clean cooking fuels during the COVID-19 pandemic. The study also provides insights on various strategies adopted in India in response to the COVID-19 pandemic for maintaining continuity of delivering benefits under a clean cookstove program. The study offers future directions to ensure the transition towards cleaner fuels and sustainability.

Impact of COVID-19 lockdown on air quality in Chandigarh, India: Understanding the emission sources during controlled anthropogenic activities

The variation in ambient air quality during COVID-19 lockdown was studied in Chandigarh, located in the Indo-Gangetic plain of India. Total 14 air pollutants, including particulate matter (PM₁₀, PM_2.5), trace gases (NO₂, NO, NO_x, SO₂, O₃, NH₃, CO) and VOC's (benzene, toluene, o-xylene, m,p-xylene, ethylbenzene) were examined along with meteorological parameters. The study duration was divided into four parts, i.e., a) 21 days of before lockdown b) 21 days of the first phase of lockdown c) 19 days of the second phase of lockdown d) 14 days of the third phase of lockdown. The results showed significant reductions during the first and second phases for all pollutants. However, concentrations increased during the third phase. The concentrations of SO₂, O_3, and m,p-xylene kept on increasing throughout the study period, except for benzene, which continuously decreased. The percentage decrease in the concentrations during consecutive periods of lockdown were 28.8%, 23.4% and 1.1% for PM_2.5 and 36.8%, 22.8% and 2.4% for PM₁₀ respectively. The Principal Component Analysis (PCA) and characteristic ratios identified vehicular pollution as a primary source during different phases of lockdown. During the lockdown, residential sources showed a significant adverse impact on the air quality of the city. Regional atmospheric transfer of pollutants from coal-burning and stubble burning were identified as secondary sources of air pollution. The findings of the study offer the potential to plan air pollution reduction strategies in the extreme pollution episodes such as during crop residue burning period over Indo-Gangetic plain.

A high-resolution emission inventory of air pollutants from primary crop residue burning over Northern India based on VIIRS thermal anomalies

Emissions from the crop residue burning adversely affect the regional and global air quality including public health. In this study, a district-wise comprehensive emission inventory of key pollutants (PM_2.5, PM₁₀, CO, CO₂, SO₂, NOx, N₂O, NH₃, CH₄, NMVOC, EC, OC, PAH) emitted during primary crop residue burning was developed using activity data for the major agrarian states of north India for the agricultural year 2017-18. The emissions were scaled to the spatial resolution of 1 km grid to study the spatial distribution of crop residue burning activities using VIIRS Thermal anomalies datasets. An estimated 20.3 Mt and 9.6 Mt of crop residue were burned in Punjab and Haryana, resulting in an emission of 137.2 Gg and 56.9 Gg of PM_2.5 and 163.7 Gg and 72.1 of PM₁₀ Gg for respective states. The emissions of EC, OC, and PAHs were 8.6 Gg, 45.7 Gg, and 0.08 Gg in Punjab, whereas in Haryana emissions were 3.7 Gg, 17.7 Gg, and 0.03 Gg, respectively. The results show that rice and wheat crops were major contributor to residue burnt at the field (>90%) leading to the high load of atmospheric emissions in the IGP region. Further, CO₂ equivalent greenhouse gas emissions were 34.8 Tg and 17.3 Tg for Punjab and Haryana, respectively. Around 30000 and 8500 active fires were detected by VIIRS over the agricultural area of Punjab and Haryana during the studied year. The GIS-based bottom-up approach using gridded emission inventory shows pollutant distribution dominates over the south-western part of Punjab and north-western region of Haryana. The proximity of these regions to Delhi and transboundary movement of emissions towards Indo-Gangetic plains causes high air pollution episodes. The high-resolution inventory of various pollutants will be useful for regional air quality models to better predict and manage the hotspot of air pollution.

COVID-19 lockdown and its impact on tropospheric NO2 concentrations over India using satellite-based data

The World Health Organization has declared the COVID-19 pandemic a global public health emergency. Many countries of the world, including India, closed their borders and imposed a nationwide lockdown. In India, the lockdown was declared on March 24 for 21 days (March 25-April 14, 2020) and was later extended until May 3, 2020. During the lockdown, all major anthropogenic activities, which contribute to atmospheric pollution (such as industries, vehicles, and businesses), were restricted. The current study examines the impact of the lockdown on tropospheric NO2 concentrations. Satellite-based ozone monitoring instrument sensor data were analyzed in order to investigate the variations in tropospheric NO2 concentrations. The results showed that from March 1 to 21, 2020, the average tropospheric NO2 concentration was 214.4 ×1013 molecule cm-2 over India, and it subsequently decreased by 12.1% over the next four weeks. An increase of 0.8% in tropospheric NO2 concentrations was observed for the same period in 2019 and hence, the reduced tropospheric NO2 concentrations can be attributed to restricted anthropogenic activities during the lockdown. In the absence of significant activities, the contribution of various sources was estimated, and the emissions from biomass burning were identified as a major source of tropospheric NO2 during the lockdown. The findings of this study provide an opportunity to understand the mechanism of tropospheric NO2 emissions over India, in order to improve air quality modeling and management strategies.