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

Review on the synergistic mechanisms in harnessing rice residue-derived cellulose nanocrystals for sustainable water purification and wastewater treatment

The review explores the innovative use of rice residue for developing Cellulose nanocrystals and reinforcement applications of CNCs for wastewater treatment. Rice residue, rich in lignocellulose components like cellulose, hemicellulose, and lignin, presents a sustainable resource for biocomposite fabrication. The review highlights the significant challenges of managing rice residue, particularly the environmental impact of its open field burning, which contributes to severe air pollution and health risks. By examining recent advancements in the extraction of cellulose nanocrystals (CNCs) from rice residue, the review emphasizes their potential for enhancing water treatment technologies and contributing to Sustainable Development Goal 6 (Clean Water and Sanitation). The review provides a comprehensive analysis of the current state of research such as facts and challenges related to using CNCs for water treatment, and suggests future directions for developing eco-friendly, high-performance water filtration and its reinforcement perspectives, underscoring the importance of integrating waste valorization with sustainable practices.

Exploring the chemical composition and processes of submicron aerosols in Delhi using aerosol chemical speciation monitor driven factor analysis

Wintertime non-refractory submicron particulate matter (NR-PM1) species were measured in Delhi with an Aerodyne Aerosol Chemical Speciation Monitor (ACSM) during February-March 2018. The average NR-PM1 mass concentration throughout the study was 58.0 ± 42.6 µg m-3, where the contribution of organic aerosol (OA) was 69% of the total NR-PM1. In Delhi, chloride (10%) was the main inorganic contributor, followed by ammonium (8%), sulfate (7%), and nitrate (6%), contrasting with the prevalence of sulfate in most urban environments. Source apportionment analysis of the OA identified five major factors, including three primary contributors: hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-related OA (COA) and two secondary contributors: oxygenated primary OA (OPOA), and more-oxidized oxygenated OA (MO-OOA). A 19% rise in OPOA concentration was observed during high chloride episodes, suggesting the potential role of chloride in the atmospheric chemical transformation of OA. Traffic emissions significantly contribute to ambient OA, accounting for at least 41% of the total OA mass. Furthermore, the OA exhibited low oxidation levels regardless of its source. The f44:f43 analysis revealed slower atmospheric oxidization of OA compared to other urban locations worldwide. Further investigations, including chamber experiments tailored to the Delhi atmosphere, are necessary to elucidate the atmospheric oxidants and the genesis of secondary OA alongside primary emissions.

Impact of improved air quality during complete and partial lockdowns on surface energetics and atmospheric boundary layer

Long-term measurements of meteorological, radiation, and aerosol profiles at a rural location have been used to study (i) the differences in spatial and vertical variation of aerosols during complete and partial lockdowns (LDs), (ii) the impact of these LDs on surface energetics and atmospheric boundary layer (ABL) height, and (iii) underlying processes that explain the variations in the above-measured parameters during LDs from the climatology. Large reduction in aerosol optical depth (AOD) and lidar (aerosol) backscatter up to the ABL height during complete and partial LD periods relative to climatology is observed, indicating improved air-quality during these periods. The reduction, in fact, is more during the partial LD imposed in 2021 (46 % in AOD) than during the complete LD (40 %) in 2020 over most part of peninsular India, partly due to higher rainfall during LD period of 2021. The reduction in aerosols during the LD periods increased the shortwave radiation by 59.8 W m-2 and 76.9 W m-2 in 2020 and 2021, respectively, relative to climatology. Contrary to the expected increase in temperature and ABL height due to higher insolation, both decreased during the LD. On the other hand, the absolute humidity increased during the above period. To shed more light on the above observations, rainfall and turbulent heat fluxes during the above periods were examined. More rain events and higher rain amount are observed during the LD periods of both years, which increased the soil moisture and modified the portioning of net radiation into turbulent fluxes. It increased the latent heat flux considerably and thereby the absolute humidity. On the other hand, the sensible heat flux has decreased, which in turn reduced the temperature and also the ABL height. The present study highlights the complex interplay of natural and anthropogenic processes in modifying land-atmospheric interaction and ABL dynamics.

Janus Textile: Advancing Wearable Technology for Autonomous Sweat Management and Beyond

To alleviate the discomfort caused by excessive sweating, there is a growing emphasis on developing wearable textiles that can evacuate sweat autonomously. These advanced fabrics, unlike their absorbent and retention-prone predecessors, harness the Janus structure-distinguished by its asymmetric wettability-to facilitate one-way transport of liquid. This unique characteristic has significant potential in addressing issues related to excessive bodily moisture and propelling the realm of smart wearables. This review offers a comprehensive overview of the advancements in Janus-structured textiles within the wearable field, delving into the mechanisms behind their unidirectional liquid transport, which rely on chemical gradient and curvature gradient strategies, alongside the methodologies for achieving asymmetric wettability. It further spotlights the multifaceted applications of Janus-based textiles in wearables, including moisture and thermal management, wound care, and sweat analysis. In addition to examining existing hurdles, the review also explores avenues for future innovation, envisioning a new era of Janus textiles tailored for personalized comfort and health monitoring capabilities.

Exploring the impact of eco-enzyme diversity on the physicochemical characteristics of rice husk and sawdust compost

Management of agricultural waste generated by farm practices has emerged as a significant global problem. Also, handling raw animal manure in a hygienic and eco-friendly process appears to be tedious. The present lab scale study has been carried out to assess the effect of lab-synthesized eco-enzyme applications on the physio-chemical properties of compost prepared using rice husk and sawdust. The study portrays a maturation period of 150 days without eco-enzyme as compared to a maturation period of 120 days when using 10% eco-enzyme with water (V/V). The highest moisture content has been observed in compost from sawdust and rice husk with 20% eco-enzyme under the thermophilic stage. However, the highest organic carbon value of 36.75% was observed in rice husk compost with 10% eco-enzyme on the 15th day, whereas the minimum organic carbon value of 6.0% was observed in rice husk compost with 20% eco-enzyme on the 135th day. The minimum C/N ratio of 13% was observed in rice husk compost with 15% eco-enzyme. The data in the present study has been used for statistical analysis by applying one-way ANOVA, and the computed results in terms of F value for all eco-enzymes variations for sawdust and rice husk are less than the critical F value of 3.19 at a 5% level of significance for all analyzed parameters including pH, moisture content, organic, TKN, and C/N ratio. The current study's conclusions may inspire the creation of innovative and sustainable techniques for producing high-quality compost, promoting the more ecologically conscious and circular use of organic waste in horticultural and agricultural applications.

Unmasking the veil of PM2.5 pollution: A comprehensive analysis of health effects, economic losses, and environmental implications in North Indian States

This research paper analyses particulate matter (PM2.5) pollution data from five Indian states to assess health impacts, including premature deaths from lung cancer (LC), chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), stroke, and lower respiratory infections (LRI). Intake factors (iF), effect factors (EF), and characterization factors (CF) were used to quantify pollutant inhalation, health impact per unit intake, and potential health impact in Disability-Adjusted Life Years (DALYs), respectively. We found localities like Jahangirpuri in Delhi (DL) (3.25E-04-4.01E-04), Faridabad in Haryana (HR)(1.1E-04-9.59E-05), Bhiwadi in Rajasthan (RJ) (6.76E-05-9.73E-05), Ghaziabad in Uttar Pradesh (UP) (7.49E-05-1.32E-04) and Gobindgarh in Punjab (PB) (4.14E-05-5.16E-05) had much higher iFs than the previously reported pan- India value of 7.60E-06. Ghaziabad of UP recorded the highest EF of 4.02E+ 02 DALYs per kilogram PM2.5 inhalation in 2021. High iF and EF values resulted in alarmingly high CF values across all the monitoring stations of the states. The Jahangirpuri of DL recorded the highest CF of 9.88E+ 01 DALYs per kg of PM2.5. The combined five diseases PM2.5-induced mortality cases for five states were assessed to be 171,551 in 2019, which increased to 175,140 in 2022. UP had the largest share of combined mortality cases in 2022, where mortality cases due to stroke grew highest at 29.48 % during 2019-2022, followed by IHD (27.6 %), COPD (20.85 %), and LC (20.35 %). The study estimated economic loss from PM2.5 mortality for five diseases in five states from 2019 to 2022, ranging from 197,964 million US$ to 276,541 million US$. According to the research, the economic burden would still be worrisome if policy interventions can achieve a targeted 30 % PM2.5 reduction across five states by 2024. The study underscores the imperative need for a time-bound holistic approach to decimate air pollution in north Indian states.

Air pollution trend in Chandigarh during 2019-2022: status and influence of meteorological factors

The degradation of ambient air quality is a pressing global concern, and India, as a developing nation, has witnessed a rapid surge in industrial activities in recent decades. This surge has resulted in numerous Indian cities ranking among the world's most polluted urban areas. Chandigarh, strategically positioned within the Indo-Gangetic plains (IGP), has not escaped this distressing trend, experiencing a significant spike in air pollution levels. This study focuses on comprehending and addressing the air quality issues in Chandigarh, shedding light on the evolution of air pollution trends and their dependence on meteorological factors. Notably, the study reveals that, with the exception of O3, pollutant concentrations surge during the rice stubble burning season. These pollutants persist in the atmosphere for prolonged periods, exacerbating the situation during winter due to lower temperatures and heightened use of fossil fuels for heating by low-income households. In contrast, the wheat stubble burning period does not significantly impact pollutant concentrations. The study also identifies a spring peak in surface O3 concentrations, attributed to favorable high temperatures that promote the photochemical reactions responsible for this phenomenon, a distinctive feature in South Asia and the Himalayas. An examination of the connection between pollutant concentrations and meteorological parameters underscores that elevated pollutant levels, except for CO, are linked to lower relative humidity and temperatures. This suggests that current development patterns have contributed to the escalation of air pollution in Chandigarh, necessitating urgent interventions to preserve the city's aesthetics and the health of its residents. Furthermore, to model and monitor pollutant behavior in Chandigarh, more extensive and extended studies are imperative. Both short-term and long-term investigations into the environmental and health impacts of air pollutants, including primary and secondary pollutants, are of paramount importance. These endeavors are essential for the well-being of both the environment and the population of Chandigarh.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 μg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 μg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 μg m-3), which showed better output due to the frequent observation of PM2.5 >100 μg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 μg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning

Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM2.5) in the garden of one participant's house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 μg m-3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model's RMSE, MAE, and bias by 44-56, 38-48, and 41-52%, respectively.

Very high particulate pollution over northwest India captured by a high-density in situ sensor network

Exposure to particulate matter less than 2.5 µm in diameter (PM2.5) is a cause of concern in cities and major emission regions of northern India. An intensive field campaign involving the states of Punjab, Haryana and Delhi national capital region (NCR) was conducted in 2022 using 29 Compact and Useful PM2.5 Instrument with Gas sensors (CUPI-Gs). Continuous observations show that the PM2.5 in the region increased gradually from < 60 µg m-3 in 6-10 October to up to 500 µg m-3 on 5-9 November, which subsequently decreased to about 100 µg m-3 in 20-30 November. Two distinct plumes of PM2.5 over 500 µg m-3 are tracked from crop residue burning in Punjab to Delhi NCR on 2-3 November and 10-11 November with delays of 1 and 3 days, respectively. Experimental campaign demonstrates the advantages of source region observations to link agricultural waste burning and air pollution at local to regional scales.

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.

Variability of ambient black carbon concentration in the Central Himalaya and its assessment over the Hindu Kush Himalayan region

During 2015-2018, eight black carbon (BC) monitoring sites were established in Nepal and Bhutan to fill a significant data gap regarding BC measurement in Central Himalaya. This manuscript analyzes and presents data from these eight stations and one additional station on the Tibetan plateau (TP). Complex topography, varied emission sources, and atmospheric transport pathways significantly impacted the BC concentrations across these stations, with annual mean concentrations varying from 36 ng m^-3 to 45,737 ng m^-3. Higher annual mean concentrations (5609 ± 4515 ng m^-3) were recorded at low-altitude sites than in other locations, with seasonal concentrations highest in the winter (7316 ± 2541 ng m^-3). In contrast, the annual mean concentrations were lowest at high-altitude sites (376 ± 448 ng m^-3); the BC concentrations at these sites peaked during the pre-monsoon season (930 ± 685 ng m^-3). Potential source contributions to the total observed BC were analyzed using the absorption angstrom exponent (AAE). AAE analysis showed the dominance of biomass burning sources (>50 %), except in Kathmandu. By combining our data with previously published literature, we put our measurements in perspective by presenting a comprehensive assessment of BC concentrations and their variability over the Hindu Kush Himalayan (HKH) region. The BC levels in all three geographic regions, high, mid, and low altitude significantly influenced by the persistent seasonal meteorology. However, the mid-altitude stations were substantially affected by valley dynamics and urbanization. The low-altitude stations experienced high BC concentrations during the winter and post-monsoon seasons. Concentration weighted trajectory (CWT) and frequency analyses revealed the dominance of long-range transported pollution during winter over HKH, from west to east. South Asian sources remained significant during the monsoon season. During pre- and post-monsoon, the local, regional, and long-distance pollution varied depending on the location of the receptor site.

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.

Air quality trends in rural India: analysis of NO2 pollution using satellite measurements

India is a country with more than 67% of its population (947 million) residing in rural areas and 33% in urban areas (472 million) as of 2020. Therefore, health of the people living in rural India is very important for its future development plans, economy and growth. Here, we analyse the rural air quality using satellite measurements of NO₂ in India, as the sources of NO₂ are well connected to the industrial and economic uplift of a nation. Our analyses for the rural regions show distinct seasonal changes with the highest value (2.0 × 10¹⁵ molecules per cm²) in winter and the lowest in monsoon (1.5 × 10¹⁵ molecules per cm²) seasons. About 41% of the total NO₂ pollution in India is from its rural sources, but 59% of the urban sources were focused in the past studies. In addition, around 45% of the rural NO₂ pollution is due to road transport, whereas more than 90% of it in urban India comes from the power sector. Our assessment shows that the NO₂ exposure in rural regions is as serious as that in urban areas, indicating the need for more effective reduction of population exposure and protection of public health. Henceforth, this study reveals that rural India is gradually getting polluted from its nearby regions as well as from the new sources within. This is a big concern for the public health of the large rural population of India.

Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China

PM_2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM_2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM_2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM_2.5 were explored. The LTX model performed well with R² = 0.88, RMSE of 8.73 μg/m³ and MAE of 5.85 μg/m³. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM_2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM_2.5 values, which exhibit similar distribution trends to those of the observed PM_2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM_2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.

Spatio-Temporal Monitoring of Atmospheric Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of Stubble Burning a Case Study

The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.

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.

Comprehensive analysis of ambient air quality during second lockdown in national capital territory of Delhi

The lockdown imposed in Delhi, due to the second wave of the COVID-19 pandemic has led to significant gains in air quality. Under the lockdown, restrictions were imposed on movement of people, operation of industrial establishments and hospitality sector amongst others. In the study, Air Quality Index and concentration trends of six pollutants, i.e. PM2.5, PM10, NO2, SO2, CO, and O3 were analysed for National Capital Territory of Delhi, India for three periods in 2021 (pre-lockdown: 15 March to 16 April 2021, lockdown: 17 April to 31 May 2021 and post-lockdown: 01 June to 30 June). Data for corresponding periods in 2018-2020 was also analysed. Lockdown period saw 6 days in satisfactory AQI category as against 0 days in the same category during the pre-lockdown period. Average PM2.5, PM10, NO2 and SO2 concentrations reduced by 22%, 31%, 25% and 28% respectively during lockdown phase as compared to pre-lockdown phase, while O3 was seen to increase. Variation in meteorological parameters and correlation of pollutants has also been examined. The significant improvement arising due to curtailment of certain activities in the lockdown period indicates the importance of local emission control, and helps improve the understanding of the dynamics of air pollution, thus highlighting policy areas to regulatory bodies for effective control of air pollution.

Influence of meteorological parameters and air pollutants on the airborne pollen of city Chandigarh, India

Pollen, climatic variables and air pollutants coexist in nature with the potential to interact with one another and play a crucial role in increasing allergic diseases. The current study evaluates the influence of meteorological parameters and air pollutants on the airborne pollen in an urban city, Chandigarh, situated in the Indo-Gangetic Plains. Airborne pollen monitoring was done following Spanish Aerobiological Network guidelines and dynamics of daily total pollen and six most abundant taxa were studied from June 2018 to June 2020. Among meteorological parameters, temperature and wind were the most correlated and influential parameters to airborne pollen concentration. Annual Pollen Integral (APIn) of Cannabis sativa (r = 0.52), Parthenium hysterophorus (r = 0.27), Poaceae (r = 0.32) and total pollen concentration (r = 0.30) showed a statistically significant positive correlation with temperature. In contrast, precipitation and relative humidity negatively correlated with APIn of total pollen concentration, Eucalyptus sp. and Poaceae except for Parthenium hysterophorus and Celtis occidentalis. Similar results were found with Seasonal Pollen Integral (SPIn) of total pollen concentration, six major taxa and meteorological variables. Spearman correlation performed for NOx showed a significant positive correlation among APIn and SPIn of Celtis occidentalis and insignificant among APIn and SPIn of Eucalyptus sp. and Morus alba. In contrast, except for Eucalyptus sp., PM₁₀ and PM_2.5 were negatively correlated among APIn and SPIn of total pollen concentration and other major taxa. Spearman's correlation of APIn and SPIn for each pollen taxon, meteorological parameters and air pollutants suggests that each taxon has a different pattern in response to all parameters. The study findings suggest that pollen response must be examined at the taxon level, not the assemblage level, having long time-series data. This will help to compute future scenarios of changing environmental factors and comprehend the relationships and trends among meteorology, air pollutants and aerobiology.

The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city

Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases. The present paper is an attempt to study the twenty fog samples collected from the rooftop of an academic building of Guru Nanak Dev University, Amritsar, India from November 2017 to January 2018. Fog samples were analysed for various parameters viz. pH, electrical conductivity (EC), chloride (Cl-), nitrate (NO3 -) and sulphate (SO4 2-) levels. The pH, EC, and Cl-, NO3 - and SO4 2- levels in the fog samples were estimated as 6.3-7.9, 240-790 μS cm-1, 108-2025 μeq L-1, 105-836 μeq L-1 and 822-5642 μeq L-1, respectively. It was noticed that sulphate was the dominant anion in fog samples. The SO4 2- to NO3 - molar ratio in the fog was estimated as 7.6 which suggests the burning of fossil fuel as the major pollutant from vehicular exhausts. Multiple regression analysis was performed to evaluate the effect of PM2.5/PM10 ratio and relative humidity (RH) on visibility. A box-cox plot of power transformation produced better model fitting, employing a square root transformation of the visibility which indicated that the PM2.5/PM10 and RH have an exponential effect on visibility.

Visualization and analysis of mapping knowledge domains for the global transition towards clean cooking: a bibliometric review of research output from 1990 to 2020

Current statistics report that 2.6 billion households across the globe rely on polluting and inefficient cooking fuels and technologies, posing death-threatening health risks to people mainly from developing countries/regions. Several studies on clean cooking have been conducted with the emergence of international organizations such as the Clean Cooking Alliance to raise awareness. In the current study, a bibliometric tool, CiteSpace, was employed to analyze the 877 documents retrieved from the SCI-EXPANDED, SSCI, and A&HCI databases on clean cooking research from 1990 to 2020. The results reveal that interest in clean cooking research took a sharp rise in the last decade, especially after 2016. The research field has become increasingly interdisciplinary but has mostly centered on environment, energy, and health. The most productive countries/regions in this field are the USA, India, England, and China. The keyword and citation analyses indicate that research in this field mostly focuses on adverse impacts of household air pollution from unclean cooking fuels and technologies on the environment and public health particularly, in developing countries/regions. Also, the drivers and barriers to the large-scale adoption of clean cooking fuels and technologies have become a topic of interest in recent years. The three most studied clean cooking fuels among various regions are LPG, biogas, and electricity. This study synthesizes global research on clean cooking and may be beneficial to other researchers in understanding current trends in this field and serve as a guide for concentrating on the most important topics.

Interpreting the COVID effect on atmospheric constituents over the Indian region during the lockdown: chemistry, meteorology, and seasonality

Most of the published articles which document changes in atmospheric compositions during the various lockdown and unlock phases of COVID-19 pandemic have made a direct comparison to a reference point (which may be 1 year apart) for attribution of the COVID-mediated lockdown impact on atmospheric composition. In the present study, we offer a better attribution of the lockdown impacts by also considering the effect of meteorology and seasonality. We decrease the temporal distance between the impacted and reference points by considering the difference of adjacent periods first and then comparing the impacted point to the mean of several reference points in the previous years. Additionally, we conduct a multi-station analysis to get a holistic effect of the different climatic and emission regimes. In several places in eastern and coastal India, the seasonally induced changes already pointed to a decrease in PM concentrations based on the previous year data; hence, the actual decrease due to lockdown would be much less than that observed just on the basis of difference of concentrations between subsequent periods. In contrast, northern Indian stations would normally show an increase in PM concentration at the time of the year when lockdown was effected; hence, actual lockdown-induced change would be in surplus of the observed change. The impact of wind-borne transport of pollutants to the study sites dominates over the dilution effects. Box model simulations point to a VOC-sensitive composition.

Fine particulate matter pollution characteristics and source apportionment of Changchun atmosphere

In order to study the pollution characteristics and main sources of fine particulate matter in the atmosphere of the city of Changchun, PM_2.5 samples were collected during the four seasons in 2014, and representative months for each season are January, April, July, and October. Sample collection was carried out on 10 auto-monitoring stations in Changchun, and PM_2.5 mass concentration and its chemical components (including inorganic elements, organic carbon, elemental carbon, and water-soluble ions) were measured. The results show that the annual average mass concentration of PM_2.5 in Changchun in 2014 was about 66.77 μg/m³. Organic matter was the highest component in PM_2.5, followed by secondary inorganic ions (SNA), mineral dust (MIN), elemental carbon (EC), and trace elements (TE). Positive matrix factorization (PMF) results gave seven factors, namely, industrial, biomass and coal burning, industrial and soil dust, motor vehicle, soil and secondary ion, light industrial, and hybrid automotive and industrial sources in PM_2.5, with contributing values of 18.9%, 24.2%, 5.7%, 23.0%, 11.5%, 13.0%, and 3.6%, respectively.

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.

COVID-19 pandemic: What can we learn for better air quality and human health?

The COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.

Spatio-temporal variation and sensitivity analysis of aerosol particulate matter during the COVID-19 phase-wise lockdowns in Indian cities

At the pandemic of COVID-19, the movement of business and other non-essential activities were majorly restricted at the end of March 2020 in India and continued in different lockdown phases until June 2020. By categorically, studying sensitivity towards anthropogenic factors with other environmental implications in urban Indian cities during phase-wise lockdown scenarios will pave the way for a refined Clean Air Programme (CAP). In this study, the aerosol particulate matter variations between the lockdown phases in both spatial and temporal scales have been explored along with cities exceeding national ambient air quality (NAAQ) standards covering different geographical regions of India for their air quality level. The results of the spatial pattern of Copernicus Atmosphere Monitoring System (CAMS) near-real-time data showed a negative change both in Aerosol Optical Depth (AOD) (-0.2 to 0.1) and black carbon AOD (bcAOD) (-0.9 to -0.75). The changes were evident in successive phases of lockdown with an overall AOD reduction of about 70-90%. Southern urban cities showed a significant impact of mobile sources from temporal analysis than other cities. Principal Component Analysis (PCA) for effects of pollutants by anthropogenic factors (mobile and point source) and meteorological factors (wind speed, wind direction, solar radiation, relative humidity) revealed the two significant driving factors. PM reduction was about 50-70%, predominantly due to anthropogenic factors. The factor analysis revealed the influence of meteorological factors between the major urban cities (Delhi, Kolkata, Mumbai, Chennai, Bengaluru, and Hyderabad). Cities that exceed NAAQ standard performed well during phase-wise lockdowns, exceptional to cities in Gangetic plain. This study helps to frame region-specific strategic action plans for the CAP.

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

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

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.

Atmospheric Aerosols: Some Highlights and Highlighters, Past to Recent Years

The severe harmful impact of atmospheric aerosols over the environment leads to create the diverse human interests and concerns. Various progressive steps were taken by researchers and scientists to understand the fundamentals, such as nucleation and growth mechanisms, formalization of particle dynamics, characterization of the mechanisms for the particle-size dispensation, detection of chemical processes for atmospheric particle sources. The increase in population growth and different manmade activities have led to change in the environmental conditions causes to pollute the distinct vicinities. Different changes in the environment such as land use pattern, increased concentration of various greenhouse gases, and Industrial pollutants change the energy balance in our climatic conditions and affect the radiation budget of earth’ atmosphere. Such changes in climate and polluted environment leads to many health-related ailments to mankind. The present study outlines the recent research perspectives of atmospheric aerosols, their estimation through different modes, effects, and an overview of the current situations that need to be addressed before they become completely incorporated.

Source profiling of air pollution and its association with acute respiratory infections in the Himalayan-bound region of India

The studies related to air pollutants and their association with human health over the mountainous region are of utmost importance and are sparse especially over the Himalayan region of India. The linkages between various atmospheric variables and clinically validated data have been done using various datasets procured from satellite, model reanalysis, and surface observations during 2013-2017. Aerosol optical depth, air temperature, and wind speed are significantly related (p < 0.001) to the incidence of acute respiratory infections with its peak during winter. Model-derived particulate matter (PM_2.5) shows high contributions of black carbon, organic carbon, and sulfate during winter. The wind roses show the passage of winds from the south-west and southern side of the region. Back trajectory density plot along with bivariate polar plot analyses have shown that most of the winds coming from the western side are taking a southward direction before reaching the study area and may be bringing pollutants from the Indo-Gangetic Plain and other surrounding regions. Our study shows that the accumulation of pollutants in the Himalayan valley is owing to the meteorological stability with significant local emissions from burning of biomass and biofuels along with long-range and mid-range transport during the winter season that significantly correlated with the incidence of acute respiratory infections in the region.

Development of multifunctional food packaging films based on waste Garlic peel extract and Chitosan

A sustainable multifunctional food packaging composite film containing waste garlic peel extract (GPE) and Chitosan (CH) was prepared. This film exhibited antimicrobial potential towards Staphylococcus aureus and Klebsiella pneumoniae. GPE/CH films' morphological, physical, and functional properties were compared to those of CH film. Fourier transform infrared showed the interactions through hydrogen bonding between CH and GPE in the blends that improved the polymers' compatibility. Furthermore, X-ray diffraction analysis validated the compatibility between GPE and CH. GPE/CH films exhibited higher thickness and moisture content than the CH film. Remarkably, GPE/CH films showed lower water vapor barrier properties and higher ultra-violet protection and mechanical strength than CH film. Compact surfaces of the GPE infused CH films were unveiled through Scanning electron microscopy. GPE/CH film showed improved thermal stability after the addition of GPE. MTT method's cytotoxicity study manifested that the GPE/CH films are antioxidant and non-cytotoxic, implicating their biocompatibility and non-toxicity. The results suggest that GPE/CH films can find widespread commercial applications like food packaging materials, replacing the commonly used petrochemical plastics.

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.

The association between fine particulate matter and acute lower respiratory infections in Yancheng City, China

Due to the rapid economic development and acceleration of industrialization, most cities in China are experiencing severe air pollution. Exposure to fine particulate matter (PM_2.5) has been associated with acute lower respiratory tract infection (ALRI). To estimate associations between short-term exposure to PM_2.5 and ALRI hospitalization in Yancheng City, China. This was a 6-year time-series study from 2014 to 2019. Data on hospitalization were collected from four high-ranked general hospitals, including for community-acquired pneumonia (CAP), acute exacerbation of chronic bronchitis (AECB), acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and acute exacerbation of bronchiectasis (AEB), and the sum was termed total ALRIs. We obtained pollutant exposure data from five fixed monitoring stations. The association between PM_2.5 and ALRI hospitalization was estimated using the generalized linear model with quasi-Poisson regression. Two-pollutant models were applied to test the robustness of the observed correlations. Subgroup analyses included sex, age, and season. During the study period, a total of 43,283 cases of total ALRIs were recorded. The average annual mean PM_2.5 concentration was 45.4 ± 32.3 μg/m³. A 10-μg/m³ increase in PM_2.5 concentration (lag 0) was significantly associated with increases in hospitalizations for total ALRIs (at 0.73%; 95% CI: 0.40%, 1.06%), in CAP (at 0.80%; 95% CI: 0.02%, 1.57%), in for AECOPD (1.08%; 95% CI: 0.38%, 1.78%), and AECB (0.67%; 95% CI: 0.23%, 1.11%). The estimated effects for total ALRIs and AECB were relatively robust with adjustment for other air pollutants. Associations between PM_2.5 and total ALRIs were stronger in females, in the elderly, and in the cold season. PM_2.5 exposure was significantly associated with ALRI morbidity, and females and older people were more susceptible to PM_2.5 air pollution, especially in the cold season.

Assessment of PM10 and PM2.5 over Ghaziabad, an industrial city in the Indo-Gangetic Plain: spatio-temporal variability and associated health effects

This study examined the PM₁₀ and PM_2.5 concentration, associated mortality, and transport pathways in Ghaziabad which is an industrial city in the Indo-Gangetic Plain. To achieve this, PM (both PM₁₀ and PM_2.5) and meteorological parameters were measured from June 2018 to May 2019 at 2 locations and analyzed together with data from a 3rd location in Ghaziabad. The highest daily average PM₁₀ and PM_2.5 concentrations were ~ 1000 µg m^-3 and ~ 450 µg m^-3, respectively. At each of the three locations, the annual mean PM₁₀ concentrations were ~ 260 ± 150 µg m^-3 while the PM_2.5 concentrations were 140 ± 90 µg m^-3. Nonparametric Spearman rank correlation analysis between meteorological parameters and PM concentrations indicated that ventilation coefficient was anti-correlated with PM concentration during the post-monsoon and winter seasons (the most polluted seasons) with rank correlation values of approximately - 0.50. Multiple linear regression (MLR) revealed that the variability in local meteorological parameters account for ~ 50% variability (maximum) in PM₁₀ mass during the monsoon and PM_2.5 during the post-monsoon season. For long-range sources, cluster and concentrated weighted trajectory (CWT) analyses utilizing regional meteorology showed the impact of transported PM from sources in Arabian sea through western India in monsoon and from parts of South Asia through Northwestern IGP and neighboring cities in Uttar Pradesh in other seasons. Finally, mortality estimates show that the number of deaths attributable to ambient PM_2.5 in Ghaziabad were ~ 873 per million individuals which was ~ 70% higher than Delhi.

The impact of improved air-quality due to COVID-19 lockdown on surface meteorological parameters and planetary boundary layer over Gadanki, a tropical rural site in India

The nation-wide lockdowns imposed in India during March--May 2020 (in four phases) to curb the spread of the novel Corona virus, greatly enhanced the near-surface air-quality due to lowering of industrial, transport and human activities. The present study focuses on the changes in the vertical structure of aerosol concentration and how those changes impacted radiation balance, the planetary boundary layer (PBL) height and surface meteorological parameters. Instrumented tower and Ceilometer measurements made at Gadanki (13.45°N, 79.18°E), located in a rural environment, coupled with satellite-derived Aerosol Optical Depth (AOD) data have been used to understand the changes in lockdown period. Significant reduction in backscatter density during the lockdown compared to 2019 indicates that aerosol reduction during the lockdown is not only limited to the surface, rather observed in the entire PBL. Except for the fourth phase of lockdown during which several relaxations have been given for vehicular movement and other anthropogenic activities, the reduction in backscatter density is seen in all phases of lockdown. However, the reduction is prominently seen in the second and third phases. The AOD also reduced by 40% around Gadanki, comparable to that of in urban regions. Due to the reduction in aerosols during the lockdown period, the insolation increases by 60 Wm^-2, which is expected to increase the temperature. However, the increased loss of long-wave radiation (due to reduction in trapping gases) and more rain events during the lockdown period decreased the temperature by ~1 °C. Measurements also suggest that the most of net radiation is partitioned into the latent heat flux increasing the humidity and lowering the PBL height (due to reduced strength of thermals and sensible heat flux).

Effects of COVID-19 pandemic on the air quality of three megacities in India

COVID-19 pandemic compelled many countries in the world to go for a nationwide lockdown to prevent the spread of the coronavirus. India started the lockdown on 24 March 2020. We analyzed the air quality of three megacities of India, namely Mumbai, Delhi, and Kolkata, during the lockdown phase and compared it with the pre-lockdown and post-lockdown scenarios. We considered seven major air pollutants: PM_2.5, PM₁₀, NO₂, NH₃, SO₂, CO, and O₃. We analyzed the data acquired from 56 automatic air-monitoring stations (AAMS) under the Central Pollution Control Board (CPCB) spread across the megacities. The air pollution level in the eastern part of Mumbai and the western part of Delhi and Kolkata usually remains high. Delhi was the worst polluted megacity, followed by Kolkata and Mumbai. The stop of vehicular movements and industrial lockdown across the nation has substantial effects on the environment, especially in the atmosphere near the Earth's surface. Our analysis showed significant improvements in air quality during the period of lockdown (25 March to 14 April 2020) compared to the pre-lockdown phase (3 March to 23 March 2020) and the same time window of the previous year (25 March to 14 April 2019). The post-lockdown (15 April to 5 May) phase exhibited mixed results. We mapped the spatial pattern of these pollutants and the air quality index (AQI). According to CPCB, PM_2.5, PM₁₀, and CO are the major air pollutants in India that reduced by 47%, 41%, and 27% in Mumbai; 52%, 39%, and 13% in Delhi; and 49%, 37%, and 21% in Kolkata, respectively, in the lockdown phase. PM_2.5, PM₁₀, and NO₂ exhibited significant correlations across the three megacities. This study shows that occasional short-term lockdowns can effectively refresh the air in these megacities.

Transition to clean household energy through an application of integrated model: Ensuring sustainability for better health, climate and environment

Sustained use and adoption of clean cooking fuels have become an important concern for developing countries due to the enormous burden of diseases attributable to household air pollution (HAP). The transition and adoption of clean household energy involve various socio-economic, behavioral, and technological barriers at different community levels. Hence, the present paper aims to scrutinize the factors, key determinants, and other interventions among rural households that limit clean cookstoves' sustained uses. The study proposes an integrated model to enhance clean cooking fuel uptake and uses based on the available evidence. The health, climate and environmental factors were identified as the key to trigger the adoption of clean cooking fuel alternatives. The model comprises the integration of components for targeted clean fuel policy interventions and promotes green recovery. The elements include Knowledge, Housing characteristics, Awareness, Interventions, Willingness to pay, Adoption, Lower emissions and Gender Equality (THE KHAIWAL model) to ascertain the intervention focus regions. Integration of model components in policy implementation will promote clean household energy to reduce emissions, leading to improve quality of life, good health, women empowerment, better air quality and climate.

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.

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.

Directional Water Transfer Janus Nanofibrous Porous Membranes for Particulate Matter Filtration and Volatile Organic Compound Adsorption

In order to reduce the possible harm caused by air pollution, excellent personal protective materials are attracting more and more attention. Therefore, the research of multifunctional materials that can filter particulate matter (PM) and volatile organic compounds (VOCs) simultaneously is of great significance. In addition, in cold weather, water vapor in the exhaled gas condenses into small droplets inside the respirator causing uncomfortable feeling of dampness. Herein, we prepared several types of cyclodextrin-containing Janus nanofibrous porous membranes by electrospinning, which can efficiently filter PM of different sizes in the air, effectively adsorb VOCs, and orientate moisture from exhaled gas to the outside of the membranes to provide a dry and comfortable environment. These advantageous features, combined with the cheap price and easy availability of component materials and low respiratory resistance, highlight the great potential of these Janus nanofibrous porous membranes in the development of personal wearable air purifiers.

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

The present work deals with the seasonal variations in the contribution of sources to PM_2.5 and PM₁₀ in Delhi, India. Samples of PM_2.5 and PM₁₀ were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM_2.5 and PM₁₀ were 131 ± 79 μg m^-3 and 238 ± 106 μg m^-3, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM_2.5 and PM₁₀ were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM_2.5 and PM₁₀ as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM_2.5 and PM₁₀. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.

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.

Impact of lockdown during COVID-19 pandemic on the air quality of North Indian cities

The World Health Organization, which proclaimed the COVID-19 a pandemic in early March 2020, imposed a partial lockdown by the Government of India on 21 March 2020. The aim of this investigation was to measure the change in air pollutants, including particulate matter (PM_2.5 and PM₁₀) and gaseous pollutants (NO₂, CO and O₃) during COVID-19 lockdown (25th March to 14th April 2020) across four major polluted cities in North India. In all region, PM_2.5, PM₁₀, NO₂ and CO were significantly reduced while O₃ has been shown mixed variation with increased in Agra and decreased in all other stations during lockdown. PM_2.5 was reduced by ~20-50% and highly decreased in Noida. PM₁₀ was most significantly decreased by 49% in Delhi. NO₂ was reduced by ~10-70%, and high reduction was observed in Noida. Likewise, ~10-60% reduction was found in CO and most significantly decreased in Gurugram. However, an increased in O3 was observed in Agra by 98% while significantly reduced in other sites. Compared to the same timeframe in 2018-2019, PM_2.5 and PM₁₀ values for all sites were reduced by more than 40%.

Exceedances and trends of particulate matter (PM2.5) in five Indian megacities

Fine particulate matter (PM_2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM_2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014-2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m³ (2%-8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00-10:00 h) and the lowest level in late afternoon hours (~15:00-16:00 h). We found that the PM_2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20-380 μg/m³) suggest that not only is the annual mean PM_2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.

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

Columnar Aerosol Optical Depths (AOD) over an urban area (Chandigarh) and a rural area (Khera, Fatehgarh Sahib district) situated in the Indo-Gangetic Plains (IGP) of India were analysed to study their temporal heterogeneity in terms of interannual, seasonal and monthly variations. Over the last few decades, IGP has become one of the global hotspots of air pollution due to the increased anthropogenic activities such as traffic, industries, agricultural waste burning etc. Level-2 AODs (550 nm) were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard NASA's Terra and Aqua satellites, for a period of 14 years (2005-2018). The climatological mean Terra-MODIS (Aqua-MODIS) AOD over the urban location was ~0.497 ± 0.238 (0.474 ± 0.228), whereas over the rural location it was 0.542 ± 0.269 (0.534 ± 0.282). Linear trend analysis estimated an increase in annual mean Terra-MODIS (Aqua-MODIS) AOD at a rate of ~0.009 (0.013) per year over the urban site; whereas over the rural location the rate of increase was ~0.003 (0.004) per year. Results show that the observed increase is ~1.49% (2.41%) of climatological mean AOD over the urban location for Terra-MODIS (Aqua-MODIS), whereas, over the rural location, it was ~0.50% (0.67%). Using the HYSPLIT trajectory model, it was concluded that, during post-monsoon, the observed high AODs can be related to massive crop residue burning in the IGP region. These AOD trends can also be used to track the regional anthropogenic air-pollution changes. An empirical relation between AOD and PM₁₀ was established, which can be used to estimate PM₁₀ over the urban and rural areas of IGP (using MODIS AODs), complementing the sparse ground-based monitoring. Further, satellite-based air pollution data can be used for baseline assessment and understanding the impact of control policies such as National Clean Air Programme and to support formulate evidence-based pollution control strategies.

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.

Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India

Lockdown measures to contain COVID-19 pandemic has resulted in a considerable change in air pollution worldwide. We estimate the temporal and diurnal changes of the six criteria air pollutants, including particulate matter (PM2.5 and PM10) and gaseous pollutants (NO2, O3, CO, and SO2) during lockdown (25th March - 3rd May 2020) across regions of India using the observations from 134 real-time monitoring sites of Central Pollution Control Board (CPCB). Significant reduction in PM2.5, PM10, NO2, and CO has been found in all the regions during the lockdown. SO2 showed mixed behavior, with a slight increase at some sites but a comparatively significant decrease at other locations. O3 also showed a mixed variation with a mild increase in IGP and a decrease in the South. The absolute decrease in PM2.5, PM10, and NO2 was observed during peak morning traffic hours (08-10 Hrs) and late evening (20-24 Hrs), but the percentage reduction is almost constant throughout the day. A significant decrease in day-time O3 has been found over Indo Gangetic plain (IGP) and central India, whereas night-time O3 has increased over IGP due to less O3 loss. The most significant reduction (∼40-60%) was found in PM2.5 and PM10. The highest decrease in PM was found for the north-west and IGP followed by South and central regions. A considerable reduction (∼30-70%) in NO2 was found except for a few sites in the central region. A similar pattern was observed for CO having a ∼20-40% reduction. The reduction observed for PM2.5, PM10, NO2, and enhancement in O3 was proportional to the population density. Delhi's air quality has improved with a significant reduction in primary pollutants, however, an increase in O3 was observed. The changes reported during the lockdown are combined effect of changes in the emissions, meteorology, and atmospheric chemistry that requires detailed investigations.

Long-term (2008-2018) aerosol properties and radiative effect at high-altitude sites over western trans-Himalayas

Analysis of the climatology of aerosol properties is performed over Hanle (4500 m) and Merak (4310 m), two remote-background sites in the western trans-Himalayas, based on eleven years (2008-2018) of sun/sky radiometer (POM-01, Prede) measurements. The two sites present very similar atmospheric conditions and aerosol properties allowing us to examine them as continuous single-data series. The annual average aerosol optical depth at 500 nm (AOD₅₀₀) is 0.04 ± 0.03, associated with an Ångström exponent (AE_440-870) of 0.58 ± 0.35 and a single scattering albedo (SSA₅₀₀) of 0.95 ± 0.05. AOD₅₀₀ exhibits higher values in May (~0.07) and lower in winter (~0.03), while AE_400-870 minimizes in spring, indicating influence by coarse-mode dust aerosols, either emitted regionally or long-range transported. The de-convolution of AOD₅₀₀ into fine and coarse modes justifies the aerosol seasonality and sources, while the marginal diurnal variation in all aerosol properties reveals a weak influence from local sources, except for some few aerosol episodes. The aerosol-volume size distribution presents a mode value at ~10 μm with secondary peaks at accumulation (~ 2 μm) and fine modes (~0.03 μm) and low variability between the seasons. A classification of the aerosol types based on the fine-mode fraction (FMF) vs. SSA₅₀₀ relationship reveals the dominance of aerosols in the FMF range of 0.4-0.6, characterized as mixed (39%), followed by fine aerosols with high scattering efficiency (26%), while particles related to dust contribute ~21%, with low fractions of fine-absorbing aerosols (~13%). The aerosol radiative forcing (ARF) estimates reveal a small cooling effect at the top of the atmosphere (-1.3 Wm^-2), while at the surface, the ARF ranges from -2 Wm^-2 to -6 Wm^-2 on monthly basis. The monthly-mean atmospheric radiative forcing (~1 to 4 Wm^-2) leads to heating rates of 0.04 to 0.13 K day^-1. These ARF values are higher than the global averages and may cause climate implications over the trans-Himalayan region.