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This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–2022 shows high accuracy (up to 80 %) and a very low false alarm ratio (∼ 20 %) from day 1 to day 5 of the forecasts, especially when the ambient air quality index (AQI) is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the National Capital Region (NCR)'s districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to the PM2.5 load in Delhi. During peak pollution events (stubble-burning periods or wintertime), however, the contribution from the main sources (farm fires in Punjab–Haryana or local sources within Delhi) could reach 65 %–69 %. According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios. [ABSTRACT FROM AUTHOR]

Open waste burning is a significant contributor to air pollution in urban cities. The contribution includes sources from local and centralized waste burning. This paper focuses on the air quality changes in the surrounding regions of the Bhalswa landfill in the north-west part of Delhi, India, where a fire broke out accidentally in the landfill from 20 April 2022 to 8 May 2022. Bhalswa landfill is one among the three major landfills in Delhi, and during the waste burning, PM2.5 shows a maximum average concentration of about 107.32 and 338 µg/m3 for PM10. The average PM2.5 concentration increment was around 45–55%, and for PM10, it was 40–50% during the fire. Apart from particulate matter, fumes generated during open waste burning emit gaseous and toxic substances. Dispersion of pollutants was less during low (< 3 m/s) and calm winds (< 1 m/s) and more during high winds (> 3 m/s). The correlation value (R2) of gaseous pollutants with PM2.5 was significant and exhibited a moderate correlation (R2 around 0.5–0.6). It is high time for residents, government authorities and various other stack holders to be aware and implement policies and adopt technologies for segregation and processing. Identifying potential reuse of waste in energy and volume reduction forms should be encouraged to avoid further accidents/incidents related to open waste burning. The study's outcomes indicate the potential changes that occurred in regional air quality during the open waste burning in a short period. [ABSTRACT FROM AUTHOR]

Accurate forecasting of air pollutant PM2.5(particulate matter with diameter less than 2.5 µm) is beneficial to society. However, the non-linear spatio-temporal correlations, multi-feasible forecast values and incomplete training data due to stochasticity make it challenging for discriminative deep learning approaches to forecasting PM2.5 data. In this paper, a generative modeling approach is proposed to overcome the challenges in forecasting PM2.5 data by considering it as an ill-posed inverse problem. To strengthen its applicability, the proposed approach is theoretically validated. Furthermore, based on the proposed generative modeling, an Autoencoder-based generative adversarial network (GAN) named Air-GAN is developed. Air-GAN combines a convolutional neural network- long short-term memory (CNN-LSTM) based Encoder with a conditional Wasserstein GAN (WGAN) to capture non-linear correlations in the data distribution via inverse mapping from the forecast distribution. The condition vector to conditional WGAN is the novelty in Air-GAN, which employs this inverse learning and allows the WGAN's Generator to generate accurate forecast estimates from noise distribution. The condition vector is composed of two elements: (1) the category label of the best correlated meteorological parameter with the PM2.5 data, assigned using an efficient classifier and (2) the output of the CNN-LSTM-based Encoder which is the latent representation of the forecast. The extensive evaluation of Air-GAN for predicting the real-time PM2.5 data of Delhi demonstrates its superior performance with an average inference error of 5.3 µg/m3, which achieves 31.7% improvement over the baseline approaches. The improved performance of Air-GAN demonstrates its efficiency to forecast stochastic PM2.5 data by generalizing to out-of-distribution data. [ABSTRACT FROM AUTHOR]

The quality of air that we breathe is one of the more serious environmental challenges that the government faces all around the world. It is a matter of concern for almost all developed and developing countries. The National Air Quality Index (NAQI) in India was first initiated and unveiled by the central government under the Swachh Bharat Abhiyan (Clean India Campaign). It was launched to spread cleanliness, and awareness to work towards a clean and healthy environment among all citizens living in India. This index is computed based on values obtained by monitoring eight types of pollutants that are known to commonly permeate around our immediate environment. These are particulate matter PM10; particulate matter PM2.5; nitrogen dioxide; sulfur dioxide; carbon monoxide; lead; ammonia; and ozone. Studies conducted have shown that almost 90% of particulate matters are produced from vehicular emissions, dust, debris on roads, and industries and from construction sites spanning across rural, semi-urban, and urban areas. While the State and Central governments have devised and implemented several schemes to keep air pollution levels under control, these alone have proved inadequate in cases such as the Delhi region of India. Internet of Things (IoT) offers a range of options that do extends into the domain of environmental management. Using an online monitoring system based on IoT technologies, users can stay informed on fluctuating levels of air pollution. In this paper, the design of a low-price pollution measurement kit working around a dust sensor, capable of transmitting data to a cloud service through a Wi-Fi module, is described. A system overview of urban route planning is also proposed. The proposed model can make users aware of pollutant concentrations at any point of time and can also act as useful input towards the design of the least polluted path prediction app. Hence, the proposed model can help travelers to plan a less polluted route in urban areas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is one of the biggest health calamities that the world has faced, which has infected millions of people and lead to hundreds of thousands of deaths all over the world. It has impacted the economic, social and health aspects of the countries to quite an extreme level. But an indirect positive impact can also be seen on the environment. In this paper, taking the example of Delhi, one of the most polluted cities of India, an analysis has been done to compare the levels of air pollutants (PM2.5, PM10, NOx and ozone) during the lockdown and the same period in the previous years. The study shows that the extent to which the industries, vehicles, power plants etc. release the air pollutants and severely impact the environment and human health. As during the lockdown when all such activities were either stopped or very much restricted, a reduction of almost 60% in the particulate matter pollution and up to 40% in the NOx pollution was observed while the ozone levels were reduced by 30-40% as compared to the same period during the previous two years. In the end, some suggestions have been made which can play some part to control air pollution once the lockdown is over. [ABSTRACT FROM AUTHOR]

The diurnal and seasonal variation of PM10, SO2, NO2, NH3 and water-soluble compounds were studied in Naraina industrial area; Delhi from January to December, 2017. It was observed that annual average concentrations of PM10, SO2, NO2, NH3, SO42−, NO3−, NH4+,Na+, K+, Ca2+, Mg2+, Cl− and F− in the day time were 227 ± 91, 9 ± 5, 59 ± 22, 65 ± 15, 17.45 ± 5.14, 17.60 ± 4.94, 8.66 ± 2.94, 4.05 ± 1.08, 3.46 ± 0.91, 10.38 ± 4.48, 3.15 ± 0.99, 43.06 ± 5.20 and 0.50 ± 0.12 µg m−3, respectively and night time were 320 ± 127, 14 ± 7, 82 ± 25, 83 ± 20, 22.64 ± 5.22, 21.66 ± 5.0, 11.81 ± 3.47, 3.29 ± 0.87, 3.02 ± 1.19, 7.55 ± 3.16, 2.49 ± 0.95, 31.86 ± 4.70 and 0.37 ± 0.12 µg m−3, respectively. PM10 and sometimes NO2 concentrations exceeded the Indian National Ambient Air Quality Standards. SO2, and NH3 concentrations were within the standard. The selected parameters varied from season to season. In the night time, selected parameters concentrations were high in comparison to day time might be due to formation of inorganic secondary particulate matters and low wind speed in the ambient air. [ABSTRACT FROM AUTHOR]

The paper presents the temporal variations of surface ozone (O) and its precursors (oxides of nitrogen (NO), carbon monoxide (CO), methane (CH) and non-methane hydrocarbons (NMHCs)) along with particulate matter (PM and PM) and their relationship with meteorology during January 2012 to December 2014 at an urban site of Delhi, India. The mean mixing ratio of surface O, NO, CO, CH and NMHCs were 29.5 ± 7.3 ppb, 34.7 ± 11.2 ppb, 1.82 ± 0.52 ppm, 3.07 ± 0.37 ppm and 0.53 ± 0.17 ppm, respectively. This study also comprises an analysis of the relation between UV irradiance and surface O. A relationship between the total oxidant concentrations (OX) and NO has been used to identify the regional background O values and the local levels of primary pollution. An attempt has been made to identify the existence of NO or NMHC sensitive regime by charting out relationships between O, NO and NMHCs. The respective high pollution periods of surface O and PM differ on a seasonal timescale. Linear regression analysis has been used to quantify the negative influence of the chemical constituents of PM (elemental carbon, NO , SO ) on O values. Statistical validation using bivariate correlation analysis, multiple linear regression (MLR) analysis and principal component analysis (PCA) strongly describes the intricate relationships among the aforesaid variables and meteorology. Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) analysis indicated upper Indo-Gangetic Plain (IGP) as a significant source region of O precursor gases contributing for O values at the study site. [ABSTRACT FROM AUTHOR]

Air pollution represents a significant fraction of the total mortality estimated by the World Health Organization (WHO) global burden of disease project (GBD). The present paper discusses the characteristics of trace gases (O, NO, NO, and CO) and particulate matter (PM and PM) in two Asian megacities, Delhi (India) and Beijing (China). A continuous measurement of trace gases and particulate matter are considered from 12 measuring sites in Beijing and 8 sites in Delhi. Over Beijing, the annual average of PM, PM, O, NO, and CO is, respectively, 85.3, 112.8, 58.7, and 53.4 μg/m and 1.4 mg/m, and, respectively, over Delhi 146.5, 264.3, 24.7,and 19.8 μg/m and 1.73 mg/m. From the spatial variations of pollutants, the concentrations of particulate matter and trace gases are observed to be much higher in the urban areas compared to the suburban areas. The higher average concentrations of PM and PM over Delhi and Beijing are observed during winter season compared with other seasons. The maximum diurnal variation of PM concentration is observed during winter season over Beijing and Delhi. The comparison of trace gases shows that the O concentrations during daytime are obviously higher compared with nighttime, and the highest diurnal variation of O is observed during summer. The concentrations of CO are highest during winter season, and higher concentrations are observed during nighttime compared to daytime. The O and CO show negative correlation over Beijing and Delhi. The negative correlation between O and NO is merely observed over Beijing, while CO and NO concentrations, in contrast, show positive correlation over Beijing. [ABSTRACT FROM AUTHOR]

We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM 2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µ m) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM 2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s -1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r>0.9). We evaluate modelled PM 2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM 2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM 2.5 during the severe episode. Delhi experiences some of the highest daily mean PM 2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM 2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM 2.5 levels during severe pollution episodes. [ABSTRACT FROM AUTHOR]

Every year at the onset of winter season (October–November), crop residue/parali/stubble burning starts in Punjab and Haryana, leading to heavy air pollution in Delhi, and adversely affecting human and environmental health. During this time, the combination of unfavourable meteorological conditions, additional emissions from stubble burning, and firework activities in this area causes the air quality to further deteriorate. In this study, we have attempted to understand the influence of parali and firecracker incidents on air pollutants' variability over Delhi during the last three years (2020 to 2022). For this purpose, daily average particulate matter and gaseous pollutants data were fetched from the Central Pollution Control Board (CPCB), and daily total fire counts and fire radiative power (FRP) data were retrieved from NASA's Fire Information for Resource Management System (FIRMS). A bigger area of severe burning is suggested by higher FRP values and higher fire counts in the middle of November in all the years considered. Three years satellite-based FIRMS data over Punjab and Haryana show the highest number of active fire counts in 2021 (n = 80,505) followed by 2020 (n = 75,428), and 2022 (n = 49,194). More than 90% parali burning incidents were observed in Punjab state only despite the considerable variability in numbers among the years. The significant effect of parali burning was seen on pollutant concentration variability. As the number of fire count increases or decreases in Punjab and Haryana, there is a corresponding increase or decrease in the particulate matter concentration with a time lag of few days (1 to 2 days). The trend in backward air mass trajectories suggests that the variable response time of pollutants' concentration is due to local and distant sources with different air mass speeds. Our estimates suggest that stubble burning contributes 50–75% increment in PM2.5 and 40 to 45% increase in PM10 concentration between October and November. A good positive correlation between PM2.5, PM10, NOX, and CO and fire counts (up to 0.8) suggests a strong influence of stubble burning on air quality over Delhi. Furthermore, the firecracker activities significantly increase the concentration of particulate matter with ~100% increment in PM2.5 and ~55% increment in PM10 mass concentrations for a relatively shorter period (1 to 2 days). [ABSTRACT FROM AUTHOR]

Studying the spatiotemporal variability of pollutants is necessary to identify the pollution hotspots with high health risk and enable the agencies to implement pollution abatement strategies in a targeted manner. Present study reports the spatio-temporal variability and health risk assessment (HRA) of PM2.5 (Particulate matter with aerodynamic diameter <2.5μm) and NO2 over IGP from 2019-2021. The HRA is expressed as passively smoked cigarettes (PSC) for four different health outcomes i.e., low birth weight (LBW), percentage decreased lung function (DLF) in school aged children, lung cancer (LC), and cardiovascular mortality (CM). The findings confirm very high PM2.5 and NO2 mass concentrations and high health risk over middle IGP and Delhi as compared to upper and lower IGP. Within Delhi, north Delhi region is the most polluted and at highest risk as compared to central and south Delhi. The health risk associated with PM2.5 over IGP is highest for DLF, equivalent to 21.63 PSCs daily, followed by CM (11.69), LBW (8.27) and LC (6.94). For NO2, the health risk is highest for DLF (3.09 PSCs) and CM (2.95), followed by LC (1.47) and LBW (1.04). PM2.5 and NO2 concentrations, along with the associated health risks, are highest during the post-monsoon and winter seasons and lowest during the monsoon season. [ABSTRACT FROM AUTHOR]

The enormity of air pollution has always been a matter of concern due to rapid development and urbanization over a long period. The increasing level of pollutants in ambient air in 2016-2017 has deteriorated the air quality of Delhi at an alarming rate. This brought us to focus our study on air quality in Delhi region. The prediction of future air quality has been carried out by analyzing the pollutants using data analysis techniques. In our previous study, we had analyzed the data from 2011-2015. Detailed analysis from 2009-2017 of air pollutants has been proposed in this extended paper along with the critical observation of 2016-2017 air pollutants trend in Delhi. Descriptive analysis and predictive analysis have been used to study the trends of various air pollutants like sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), suspended particulate matter (PM), ozone (O 3 ) carbon monoxide (CO), benzene, and forecast the future trend. We have observed through data analytics techniques that SO 2 is likely to increase by 1.24ug/m 3 , NO 2 is likely to increase by 16.77ug/m 3 , O3 is likely to increase by 6.11 mg/m 3 , benzene is likely to reduce by 1.33 mg/m 3 and NO 2 is predicted to reduce by 0.169 mg/m 3 in the coming years. [ABSTRACT FROM AUTHOR]

This study reports the seasonal variations in dry deposition of coarse and fine particles on the foliar surfaces of common indoor plants viz. Ficus elastica, Ficus lyrata, and Schefflera arboricola along with their biochemical changes in the urban households of metropolitan Delhi. The dustfall fluxes were significantly higher at the industrial site as compared to residential site and temporal variations indicated higher dustfall fluxes during the winter months as compared to other seasons. Seasonal concentrations of chlorophylls, pH, and soluble sugars were higher during monsoon season for the indoor plants whereas ascorbic acid content was higher in the winter season indicating more stress. The results of correlation plots revealed that the variations observed in the biochemical traits were interlinked with the changes in dustfall fluxes during different seasons, thereby reflecting the changes in air pollution stress faced by plant foliage. The air pollution tolerance index and anticipated performance index were evaluated to understand the overall tolerance of indoor plants toward air pollution which can be utilized in phytoremediation techniques. It was concluded that among the studied indoor plants, Ficus elastica and Ficus lyrata are better plant species than Schefflera arboricola for improving indoor air quality. The novelty of this work lies in studying the spatial and temporal variations in the deposition of indoor dust on foliar surfaces of plants in the households of a highly polluted metropolitan. Based on the biochemical parameters, the air pollution tolerance index (APTI) and anticipated performance index (API) have been calculated for the first time for Ficus elastica, Ficus lyrata, and Schefflera arboricola. The study strengthens the existing knowledge regarding the role of leaves as a platform to capture dust particles and role of indoor plants in improving air quality. The outcomes of this study can be applied in modern day concepts for urban areas such as nature-based solutions for air pollution mitigation, vertical greenery systems (VGS), green roofs, and outdoor green walls. [ABSTRACT FROM AUTHOR]

Globally, vehicular pollution is one of the greatest concerns in urban areas. Several studies on air pollution have been conducted using deterministic, statistical, and soft computing methods. However, there has been little research on how soft-computing methods like Artificial Neural Networks (ANN) can help us comprehend vehicular pollution's non-linear and highly complex dispersion. This study uses an ANN-based vehicular pollution model to investigate the effect of vehicular traffic on PM2.5 concentrations in built-up and open terrain-surrounding environments. Five distinct pollution models were developed for two locations in Delhi, considering PM2.5 pollutants, meteorological variables, traffic flow, and traffic composition into account. The results concluded that under open terrain conditions, the significance of the traffic variable in its association with PM2.5 is almost half the significance observed under built-up conditions. Also, in terms of PM2.5 reductions, the maximum reduction observed at Location-1 (built-up environment), and Location-2 (open terrain environment) is 1.85 and 2.44 times the percent reduction in traffic during peak hours, respectively. The study's findings have significant ramifications for the current practices of ignoring the contribution of traffic and the built environment to pollution and adopting measures like an odd-even rule and high fuel and parking prices to combat pollution. [ABSTRACT FROM AUTHOR]

Concentrations of ambient particulate matter (PM) depend on various factors including emissions of primary pollutants, meteorology and chemical transformations. New Delhi, India is the most polluted megacity in the world and routinely experiences extreme pollution episodes. As part of the Delhi Aerosol Supersite study, we measured online continuous PM1 (particulate matter of size less than 1 μm) concentrations and composition for over five years starting January 2017, using an Aerosol Chemical Speciation Monitor (ACSM). Here, we describe the development and application of machine learning models using random forest regression to estimate the concentrations, composition, sources and dynamics of PM in Delhi. These models estimate PM1 species concentrations based on meteorological parameters including ambient temperature, relative humidity, planetary boundary layer height, wind speed, wind direction, precipitation, agricultural burning fire counts, solar radiation and cloud cover. We used hour of day, day of week and month of year as proxies for time-dependent emissions (e.g., emissions from traffic during rush hours). We demonstrate the applicability of these models to capture temporal variability of the PM1 species, to understand the influence of individual factors via sensitivity analyses, and to separate impacts of the COVID-19 lockdowns and associated activity restrictions from impacts of other factors. Our models provide new insights into the factors influencing ambient PM1 in New Delhi, India, demonstrating the power of machine learning models in atmospheric science applications. Copyright © 2023 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Delhi is a commercial city and the people's living standard is also comparatively high. Due to that, different active security agencies hire security guards and deploy them in various areas to protect people and properties. These security guards generally perform their duties outside the premises or near the roadside, leading to their continuous exposure to different size fractions of particulate matter. The present study is an effort to analyze the security guards' exposure to different size particulate matter (PM2.5, PM1, PM0.5, and PM0.25) and toxic metals in different urban environments. The study showed that the PM2.5 concentrations were higher at all the selected locations, while the construction site depicted the highest concentration of all-size particles among all the identified locations. The ratio of PM1/PM2.5 was observed to be higher and indicated more contribution of PM1 size particles, specifically in commercial, construction, and roadside areas. After analyzing heavy metal presence in PM2.5 and PM1, out of 11 heavy metals, six metals (Fe, Cu, Pb, Cr, Mg, Zn) were found below the detection limit. Mg was found at higher concentrations in commercial, construction, and industrial areas in PM2.5 and PM1, while Zn was also found at higher concentrations in PM2.5 and PM1 at the roadside and residential sites. At the industrial site, the Cr concentration was observed highest in PM1, representing a serious concern to security guards of that area. [ABSTRACT FROM AUTHOR]

Megacities in Asia rank high in air pollution at the global scale. In many cities, ambient concentrations of fine particulate matter (PM 2.5 ) have been exceeding both the WHO interim targets as well as respective national air quality standards. This paper presents a systems analytical perspective on management options that could efficiently improve air quality at the urban scale, having Delhi as a case study. We employ the newly developed GAINS-City policy analysis framework, consisting of a bottom up emission calculation combined with atmospheric chemistry-transport calculation, to derive innovative insights into the current sources of pollution and their impacts on ambient PM 2.5 , both from emissions of primary PM as well as precursors of secondary inorganic and organic aerosols. We outline the likely future development of these sources, quantify the related ambient PM 2.5 concentrations and health impacts, and explore potential policy interventions that could effectively reduce environmental pollution and resulting health impacts in the coming years. The analysis demonstrates that effective improvement of Delhi's air quality requires collaboration with neighboring States and must involve sources that are less relevant in industrialized countries. At the same time, many of the policy interventions will have multiple co-benefits on development targets in Delhi and its neighboring States. Outcomes of this study, as well as the modelling tools used herein, are applicable to other urban areas and fast growing metropolitan zones in the emerging Asian regions. [ABSTRACT FROM AUTHOR]

Stubble burning is an emerging environmental issue in Northern India, which has severe implications for the air quality of the region. Although stubble burning occurs twice during a year, first during April–May and again in October–November due to paddy burning, the effects are severe during October–November months. This is exacerbated by the role of meteorological parameters and presence of inversion conditions in the atmosphere. The deterioration in the atmospheric quality can be attributed to the emissions from stubble burning which can be perceived from the changes observed in land use land cover (LULC) pattern, fire events, and sources of aerosol and gaseous pollutants. In addition, wind speed and wind direction also play a role in changing the concentration of pollutants and particulate matter over a specified area. The present study has been carried out for the states of Punjab, Haryana, Delhi, and western Uttar Pradesh to study the influence of stubble burning on the aerosol load of this region of Indo-Gangetic Plains (IGP). In this study, the aerosol level, smoke plume characteristics, long-range transport of pollutants, and affected areas during October–November from year 2016 to 2020 were examined over the Indo-Gangetic Plains (Northern India) region by the satellite observations. By MODIS-FIRMS (Moderate Resolution Imaging Spectroradiometer-Fire Information for Resource Management System) observations, it was revealed that there was an increase in stubble burning events with the highest number of events being observed during the year 2016 and then a decrease in the number of events in subsequent years from 2017 to 2020. MODIS observations revealed a strong AOD gradient from west to east. The prevailing north-westerly winds assist the spread of smoke plumes over Northern India during the peak burning season of October to November. The findings of this study might be used to expand on the atmospheric processes that occur over northern India during the post-monsoon season. The pollutant, smoke plume features, and impacted regions of biomass-burning aerosols in this region are critical for weather and climate research, especially given the rising trend in agricultural burning over the previous two decades. [ABSTRACT FROM AUTHOR]

Atmospheric ozone (O3) concentration is impacted by a number of factors, such as the amount of solar radiation, the composition of nitrogen oxides (NOx) and hydrocarbons, the transport of pollutants and the amount of particulate matter in the atmosphere. The oxidative potential of the atmosphere and the formation of secondary organic aerosols (SOAs) as a result of atmospheric oxidation are influenced by the prevalent O3 concentration. The formation of secondary aerosols from O3 depends on several meteorological, environmental and chemical factors. The relationship between PM2.5 and O3 in different urban environmental regimes of India is investigated in this study during the summer and winter seasons. A relationship between PM2.5 and O3 has been established for many meteorological and chemical variables, such as RH, WS, T and NOx, for the selected study locations. During the winter season, the correlation between PM2.5 and O3 was found to be negative for Delhi and Bengaluru, whereas it was positive in Ahmedabad. The city of Bengaluru was seen to have a positive correlation between PM2.5 and O3 during summer, coinciding with the transport of marine air masses with high RH and low wind speed (as evident from FLEXPART simulations), leading to the formation of SOAs. Further, O3 concentrations are predicted using a Recurrent Neural Network (RNN) model based on the relation obtained between PM2.5 and O3 for the summer season using NOx, T, RH, WS and PM2.5 as inputs. [ABSTRACT FROM AUTHOR]

Particulate matter in the atmosphere affects health, climate, visibility, and the overall environment. Trees can naturally aid in dry deposition of particles from the atmosphere onto leaf surface. The present study attempts to examine the relationship between tree cover, represented by leaf area index (LAI), and ambient particulate matter, PM10 (particulate matter having an aerodynamic diameter ≤ 10 µm). A robust statistical technique using a panel setup, considering spatiotemporal effects, is employed to investigate the relationship in Delhi, a semi-arid region in India. The result obtained from the fixed effects panel model indicates that a unit LAI reduces 2.39 µg of PM10 level. Panel analysis yielded two clusters of the study sites, one denoting the sites in vicinity of industrial areas and the other in proximity to the Delhi Ridge. The effects of seasonal variation on PM concentration are also studied. The results quantitatively emphasize the environmental benefits of tree cover in urban areas. [ABSTRACT FROM AUTHOR]

With ever-increasing global air pollution levels, researchers are exploring ways to forecast air pollutant concentrations to prevent the adverse effects of air pollution on humans. Powered by the data obtained from air pollution monitoring stations, we now have a chance to build sophisticated models to estimate the future concentration of various air pollutants. Previous researches show that deep learning models perform a better task of capturing the complex spatiotemporal dynamics from the data as compared to traditional statistical models. In this paper, a hybrid deep learning framework - AGCTCN (A ttention based G raph C onvolution and T emporal C onvolution N etwork), based on spatial attention, graph convolution and temporal convolution is presented for short-term forecasting of particulate matter (PM) levels. Specifically, quarter-hourly PM 10 and PM 2.5 data aggregated from 27 ground air pollution monitoring stations in Delhi, India is used for training the AGCTCN model and our model forecasts the concentrations of these pollutants at these stations for a particular horizon in the future. The forecasts generated by our model are compared and contrasted to those generated by other state-of-the-art models like GC-LSTM and ConvLSTM with respect to four widely used evaluation metrics namely RMSE (root mean squared error), MAE (mean absolute error), correlation coefficient, and R2 (coefficient of determination). Our model shows more than 80% improvement over the GC-LSTM model in terms of the R2 metric, performs the best task of estimating the peak PM concentrations and shows variability and correlation close to that of the observed data. A carefully conducted ablation study shows the effectiveness of the individual components of our architecture. The graph convolution layer improves the model accuracy by approximately 34% on average on the PM datasets. Compared to other models, the AGCTCN framework shows the best performance in short-term forecasting of particulate matter concentrations. • Spatiotemporal forecasting of particulate matter (PM2.5 and PM10) concentrations. • Attention based graph convolution and temporal convolution network. • An ablation study to measure the impact of each network component on the performance. • Performance evaluation of the model based on real world PM2.5 and PM10 datasets. [ABSTRACT FROM AUTHOR]

Delhi has been identified as one of the highly polluted cities in the world and recently associated with the highest population weighted PM2.5 concentration. However, the unavailability of the health risk estimations using long-term data for Indian cities has been pointed out as a hurdle in performing the correct assessment. The present work estimated deposition of particles in different regions of respiratory systems (head airway = 67% deposition for 2.5 µm particles; tracheo-bronchiolar (TB) = 73% deposition for 1.0 µm particles; alveolar (AL) = 17% deposition for 0.5 µm, 0.25 µm, and < 0.25 µm particles) using PM samples collected at a breathing height of 1.5 m near the major ring road in New Delhi (India). The calculated risk index (RI) varied considerably between winter (1.21 ± 0.26 to 1.33 ± 0.50) and pre-monsoon-southwest monsoon months (0.34 ± 0.08 to 0.96 ± 0.27). Respiratory deposition dose of nanosized particles (≤ 500 nm) in the alveoli region of the lung was found to be considerable (35%) indicating the need for understanding the role of these particles in posing health risk. Although the calculated values of risk metric for exposures of PM-associated metals indicated no risk to IIT Delhi population (hazard quotient < 1 and excess risk of getting cancer < 10−6–10−9), continuous monitoring for particles of different sizes at inhalation height are required for protecting human health. [ABSTRACT FROM AUTHOR]

The elevated levels of fine particulate matter (PM) and its associated health concerns are one of the major cause of apprehension for society as well as for policymakers in the region of Delhi-NCR. Atmospheric secondary aerosols are reported to hold one of the major fractions of PM. Considering its adverse effects and complex process of formation, the present article focuses on a critical review of its categorization based on formation mechanism, contribution to overall ambient aerosol load in the Delhi region, and other responsible factors. The formation of secondary aerosol is primarily governed by the abundance of precursor pollutants with variations in relative humidity, temperature, and solar radiation, which is evident in the seasons of post-monsoon and winter when there are higher emissions of NOx, SOx, NH3, VOCs, HCl, etc., which along with conducive meteorology, favor the increased rate of particle formation. The review reveals that secondary inorganic aerosols comprising of sulfate, nitrate, and ammonium contribute 24%, 15%, 21%, and 23% of PM10 during winter, summer, monsoon, and post-monsoon seasons, respectively, whereas the same is found to be higher in PM2.5, with 29%, 22%, 27%, and 28%. Thus, highlighting the need for comprehensive knowledge of secondary aerosol formation and its contributions in metropolitan areas such as Delhi, which will assist policymakers in determining the policies needed to curtail city-specific precursor factors, thereby reducing the ambient secondary aerosol concentration. [ABSTRACT FROM AUTHOR]

The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi. [ABSTRACT FROM AUTHOR]

Plants are now widely recognized for their potential role in improving the air quality by dispersion and deposition of atmospheric dust particles. However, suspended dust particles negatively affect plant growth and physiological development. The present study aims to assess the amount of dust accumulation on the leaf surface and to evaluate the effect of foliar dust on leaf gas exchange parameters, photosynthetic pigment, and metabolite content of five roadside herbaceous plant species (Amaranthus viridis, Achyranthes aspera, Acalypha indica, Parthenium hysterophorus, Trianthema portulacastrum). Two sites (site I and site II) were selected that differed in their surrounding anthropogenic activities and dust pollution levels. Results showed that the average amount of dust accumulated on the leaf surface was significantly greater in plants grown at the polluted site. Among the five species examined, the highest amount of foliar dust load was observed for A. aspera (0.49 mg cm−2). Dust accumulation caused substantial changes in plant physiology as indicated by the significant decline in chlorophyll content, photosynthetic rate, stomatal conductivity, and transpiration rate in plants grown at the polluted site. Moreover, an increase in antioxidant activity, total ascorbate, and metabolite content, responsible for maintaining plant defense, was higher in plants at polluted site. Biochemical response of the individual plants studied was variable, which suggests that different plants adopted different mechanisms to cope with the stress induced by dust particles. [ABSTRACT FROM AUTHOR]

Humans spend close to 90% of their time within the indoor environment. Deteriorating indoor air quality, especially high PM10, PM2.5 and PM1 is slowly becoming a major concern. A study was carried out, for two years, to characterize the spatiotemporal variation of PM in the indoor-outdoor environment across different residential setups (R1, R2, R3, and MC) in the Delhi region. The study established correlation between monthly variations of Indoor/Outdoor (I/O) ratios and meteorological factors. The results showed Spatio-temporal variation in the average mass concentrations of PM10 recorded peak values during the winter season (avg. 514± 72.15 µg/m³) and minimum concentration was observed during monsoon (avg. 91.41± 22.64 µg/m³) months. Among all the sites, the mixed cluster (MC), a residential cum commercial zone reported the highest particulate matter concentration (avg. 308.10 ±37.23 µg/m³) and while Residential area (R2) reported the least concentration (avg. 244.9± 27.65 µg/m³) within the indoor environment. The I/O ratios of particulate matter were observed to be highest in January (I/O ratio1.6) and lowest in June month (I/O ratio 0.8). PM10, PM2.5, and PM1 dynamics were found to be critically influenced by meteorological factors, regular household activities, and diverse building designs. The short- or long-term exposure of particulate pollutants (beyond the permissible limits) can increase the probability of acute health effects, so there is an utmost requirement to collect better and systematic information about actual exposure levels experienced in different urban residential environments. [ABSTRACT FROM AUTHOR]

Lockdown was imposed by the Indian government in the month of March 2020 as an early precaution to the COVID-19 pandemic which obstructed the socio-economic growth globally. The main aim of this study was to analyse the impact of lockdown (imposed in March and continued in April 2020) on the existing air quality in three megacities of India (Delhi, Mumbai and Kolkata) by assessing the trends of PM10 and NO2 concentrations. A comparison of the percentage reduction in concentrations of lockdown period with respect to same period in year 2019 and pre-lockdown period (February 14–March 24) was made. It was observed from the study that an overall decrease of pollutant concentrations was in the ranges of 30–60% and 52–80% of PM10 and NO2, respectively, in the three cities during lockdown in comparison with previous year and pre-lockdown period. The overall decrease in concentrations of pollutants at urban sites was greater than the background sites. Highest decline in concentrations of PM10 were observed in Kolkata city, followed by Mumbai and Delhi, while decline in NO2 was highest in Mumbai. Results also highlighted that capital city Delhi had the worst air quality amongst three cities, with particulate matter (PM10) being the dominant pollutant. Although COVID-19 has significantly affected the human life considering the mortality and morbidity, lockdowns imposed to control the pandemic had significantly improved the air quality in the selected study locations, although for the short amount of period. [ABSTRACT FROM AUTHOR]

The Indian megacity of Delhi suffers from some of the poorest air quality in the world. While ambient NO 2 and particulate matter (PM) concentrations have received considerable attention in the city, high ground-level ozone (O 3) concentrations are an often overlooked component of pollution. O 3 can lead to significant ecosystem damage and agricultural crop losses, and adversely affect human health. During October 2018, concentrations of speciated non-methane hydrocarbon volatile organic compounds (C 2 –C 13), oxygenated volatile organic compounds (o-VOCs), NO, NO 2 , HONO, CO, SO 2 , O 3 , and photolysis rates, were continuously measured at an urban site in Old Delhi. These observations were used to constrain a detailed chemical box model utilising the Master Chemical Mechanism v3.3.1. VOCs and NO x (NO + NO 2) were varied in the model to test their impact on local O 3 production rates, P(O3) , which revealed a VOC-limited chemical regime. When only NO x concentrations were reduced, a significant increase in P(O3) was observed; thus, VOC co-reduction approaches must also be considered in pollution abatement strategies. Of the VOCs examined in this work, mean morning P(O3) rates were most sensitive to monoaromatic compounds, followed by monoterpenes and alkenes, where halving their concentrations in the model led to a 15.6 %, 13.1 %, and 12.9 % reduction in P(O3) , respectively. P(O3) was not sensitive to direct changes in aerosol surface area but was very sensitive to changes in photolysis rates, which may be influenced by future changes in PM concentrations. VOC and NO x concentrations were divided into emission source sectors, as described by the Emissions Database for Global Atmospheric Research (EDGAR) v5.0 Global Air Pollutant Emissions and EDGAR v4.3.2_VOC_spec inventories, allowing for the impact of individual emission sources on P(O3) to be investigated. Reducing road transport emissions only, a common strategy in air pollution abatement strategies worldwide, was found to increase P(O3) , even when the source was removed in its entirety. Effective reduction in P(O3) was achieved by reducing road transport along with emissions from combustion for manufacturing and process emissions. Modelled P(O3) reduced by ∼ 20 ppb h -1 when these combined sources were halved. This study highlights the importance of reducing VOCs in parallel with NO x and PM in future pollution abatement strategies in Delhi. [ABSTRACT FROM AUTHOR]

The seasonal variation of particulate matter and its relationship with meteorological parameters were measured at five different residential sites in Delhi. Sampling was carried out for one year including all three seasons (summer, monsoon, and winter). The yearly average concentration of particulate matter (PM2.5) was 135.16 ± 41.34 µg/m3. The highest average values were observed in winter (208.44 ± 43.67 µg/m3) and the lowest during monsoon season (80.29 ± 39.47 µg/m3). The annual average concentration of PM2.5 was found to be the highest at the Mukherjee Nagar site (242.16 µg/m3) during the winter and lowest at (Jawaharlal Nehru University) JNU (35.65 µg/m3) during the monsoon season. The strongest correlation between PM mass and a meteorological parameter was a strong negative correlation with temperature (R2=0.55). All other parameters were weakly correlated (R2<0.2) with PM mass. [ABSTRACT FROM AUTHOR]

The present study aims to understand how increasing surface ozone and fine particulate matter concentrations affect wheat crop productivity under ambient conditions. A pot experiment was conducted spanning over a period of 117 days starting from December 2016 to April 2017 at one of the receptor locations in Delhi characterized with high levels of surface ozone and fine particulate matter. The study site recorded highest concentrations of PM1, PM2.5, PM10 and surface ozone of 159±77 μg m-3, 172±79 μg m-3, 280±108 μg m-3 and 335±18 μg m-3, respectively during the crop cycle indicating the high levels of air pollutants at the site. The crops were treated with ascorbic acid under different experimental setups. A large number of growth, biochemical and yield parameters were evaluated at the vegetative, reproductive and grain formation stage of the crop cycle. Results indicated that the chlorophyll content and harvest yield of crops grown under ambient conditions were ~23% and ~14% lower than those of crops grown under controlled environment. Furthermore, a ~13%, 5%, 15% and 10% decline in root length, plant height, number of tillers and number of leaves was observed in crops that were exposed to only surface ozone in comparison to crops exposed to only fine particulate matter under vegetative stage, respectively. Relative water content, chlorophyll content and air pollution tolerance index observed ~56%, 23% and 61% decline with fully exposed setup in comparison control setup in the vegetative stage, while ~57%, 23% and 44% decline was observed in the reproductive stage. Experiments also suggested that surface ozone had a more pronounced influence on overall productivity of wheat crops in comparison to fine particulate matter. [ABSTRACT FROM AUTHOR]

This study reports a very high-resolution (400 m grid-spacing) operational air quality forecasting system developed to alert residents of Delhi and the National Capital Region (NCR) about forthcoming acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real-time aerosol observations from in situ and space-borne platform in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to produce a 72-h forecast daily in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM2.5 observations improves the initial condition for surface PM2.5 by about 45 µg/m3 (about 50%).The accuracy of the forecast degrades slightly with lead time as mean bias increase from + 2.5 µg/m3 on the first day to − 17 µg/m3 on the third day of forecast. Our forecast is found to be very skillful both for PM2.5 concentration and unhealthy/ very unhealthy air quality index categories, and has been helping the decision-makers in Delhi make informed decisions. [ABSTRACT FROM AUTHOR]

The Indo-Gangetic Plain (IGP) experienced an intensive air pollution episode during November 2017. Weather Research and Forecasting model coupled to Chemistry (WRF-Chem), a coupled meteorology–chemistry model, was used to simulate this episode. In order to capture PM 2.5 peaks, we modified input chemical boundary conditions and biomass burning emissions. The Community Atmosphere Model with Chemistry (CAM-chem) and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) global models provided gaseous and aerosol chemical boundary conditions, respectively. We also incorporated Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points to fill in missing fire emissions in the Fire INventory from NCAR (FINN) and scaled by a factor of 7 for an 8 d period. Evaluations against various observations indicated the model captured the temporal trend very well although missed the peaks on 7, 8, and 10 November. Modeled aerosol composition in Delhi showed secondary inorganic aerosols (SIAs) and secondary organic aerosols (SOAs) comprised 30 % and 27 % of total PM 2.5 concentration, respectively, during November, with a modeled OC/BC ratio of 2.72. Back trajectories showed agricultural fires in Punjab were the major source for extremely polluted days in Delhi. Furthermore, high concentrations above the boundary layers in vertical profiles suggested either the plume rise in the model released the emissions too high or the model did not mix the smoke down fast enough. Results also showed long-range-transported dust did not affect Delhi's air quality during the episode. Spatial plots showed averaged aerosol optical depth (AOD) of 0.58 (±0.4) over November. The model AODs were biased high over central India and low over the eastern IGP, indicating improving emissions in the eastern IGP can significantly improve the air quality predictions. We also found high ozone concentrations over the domain, which indicates ozone should be considered in future air quality management strategies alongside particulate matter. [ABSTRACT FROM AUTHOR]

Ambient particulate matter concentrations in Delhi and its peripheral towns has been a matter of serious concern in the last decade. Understanding the changing nature of the chemical composition of particulates, their spatial and seasonal variability can be utilized for identifying probable sources. This study presents an extensive dataset of the chemical composition of PM2.5 and PM10 collected using speciation samplers, from 19 locations representing different activities and spread across Delhi–NCR during summer and winter seasons in the year 2016–17. Identification of contributing sources using chemical ratios as source indicators is attempted. A distinct seasonal variability in both PM2.5 and PM10 was observed, with winter maxima and summer minima. The fine fraction i.e. PM2.5 was dominated by organic matter (OM) with mean concentrations of 40.96±25.74 μg/m3 followed by Sulfate-Nitrate-Ammonium (SNA) ions (31.44±20.69 μg/m3) and Elemental Carbon (EC) (19.56±12.57 μg/m3); while the coarse fraction i.e. PM10 was dominated by OM (73.03±40.55 μg/m3) and SNA (47.25±30.56 μg/m3) along with significant contributions from crustal materials (40.85±18.89 μg/m3). The chemical ratios suggested mixed sources of PM with major contributions from vehicular emissions, re-suspended and/or construction dust, and fossil fuel combustion along with intermittent contributions from biomass and open waste burning. This analysis provides useful insights into the sources and processes affecting the particulate formation and underlines the need to control primary emissions as well as secondary precursors for air quality improvements in the region. The data generated under this campaign can also serve as an essential input for further evaluation using receptor modeling. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) was first noticed over Wuhan, China during December 2019. The spreading characteristics of COVID-19 infection from one person to another are resulting in a growing number of infected cases and created massive stresses across the world. The rapid dissemination of COVID-19 infection declares it as a pandemic. In India, till the mid of May 2020, there were around 75,048 confirm with 2440 number of death cases, specifically due to COVID-19. To break the chain of COVID-19, the Government of India had decided to implement the lockdown, first implemented on 23rd March 2020. Some of the significant benefits of lockdown resulted in the reduction in atmospheric pollutants of the cities across the world. The study is based on the fine and coarse particulate matters (PM) data corresponding to before lockdown and during lockdown periods. The efforts were made over Delhi (DEL) and one of the neighbouring cities, i.e. Gurgaon (GW) considering the duration 1st January 2020 to 15th May 2020 to understand the impact of lockdown on the particulate matter, i.e. PM (PM1.0, PM2.5 and PM10). Our investigation shows the decline in PM concentration during the lockdown period. The impact of COVID-19 pandemic resulted in the rapid increase in the number of COVID-19 cases in DEL, India, and the inhabitants of about 14 million people. The significance of the particulate matter, temperature (°C) and relative humidity (%) on the dispersal of COVID-19 virus and its association to the total number of cases (TC), active cases (AC), recovered cases (RC) and death cases (DC) with special reference to DEL were also discussed. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM) is a pollutant of concern not only because of its adverse effects on human health but because of its ability to reduce visibility and soil buildings and materials. It can be regarded as a suite of pollutants since PM covers a very wide range of particle sizes and also has a diverse chemical composition. Historically, much of the PM arose from coal burning and was measured as black smoke. However, in the second half of the twentieth century in developed countries, there was a reduction in black smoke emissions from coal burning and PM steadily became dominated by carbonaceous particles from road traffic exhaust and the secondary pollutants, ammonium salts and secondary organic carbon. This is exemplified by the composition of fine particles (referred to as PM2.5) as measured in London, Delhi and Beijing. Steadily, as control strategies have addressed the more tractable sources of emissions, so sources previously regarded as unconventional have emerged and have been seen to make a significant contribution to airborne PM concentrations. Among these are non-exhaust particles from road traffic, cooking aerosol and wood smoke. The particle size distribution of airborne PM is hugely diverse, ranging from newly formed particles of a few nanometres in diameter through to particles of tens of micrometres in diameter. There has been a great deal of interest in ultrafine (nano) particles because of suspicions of enhanced toxicity, and as traffic emissions decrease as a source, so regional nucleation processes have become much bigger relative contributors to particle number, but not mass. This article is part of a discussion meeting issue 'Air quality, past present and future'. [ABSTRACT FROM AUTHOR]

The Indian national capital, Delhi, routinely experiences some of the world's highest urban particulate matter concentrations. While fine particulate matter (PM 2.5) mass concentrations in Delhi are at least an order of magnitude higher than in many western cities, the particle number (PN) concentrations are not similarly elevated. Here we report on 1.25 years of highly time-resolved particle size distribution (PSD) data in the size range of 12–560 nm. We observed that the large number of accumulation mode particles – that constitute most of the PM 2.5 mass – also contributed substantially to the PN concentrations. The ultrafine particle (UFP; Dp<100 nm) fraction of PNs was higher during the traffic rush hours and for daytimes of warmer seasons, which is consistent with traffic and nucleation events being major sources of urban UFPs. UFP concentrations were found to be relatively lower during periods with some of the highest mass concentrations. Calculations based on measured PSDs and coagulation theory suggest UFP concentrations are suppressed by a rapid coagulation sink during polluted periods when large concentrations of particles in the accumulation mode result in high surface area concentrations. A smaller accumulation mode for warmer months results in an increased UFP fraction, likely owing to a comparatively smaller coagulation sink. We also see evidence suggestive of nucleation which may also contribute to the increased UFP proportions during the warmer seasons. Even though coagulation does not affect mass concentrations, it can significantly govern PN levels with important health and policy implications. Implications of a strong accumulation mode coagulation sink for future air quality control efforts in Delhi are that a reduction in mass concentration, especially in winter, may not produce a proportional reduction in PN concentrations. Strategies that only target accumulation mode particles (which constitute much of the fine PM 2.5 mass) may even lead to an increase in the UFP concentrations as the coagulation sink decreases. [ABSTRACT FROM AUTHOR]

PM10 samples were collected to characterize the seasonal and annual trends of carbonaceous content in PM10 at an urban site of megacity Delhi, India from January 2010 to December 2017. Organic carbon (OC) and elemental carbon (EC) concentrations were quantified by thermal-optical transmission (TOT) method of PM10 samples collected at Delhi. The average concentrations of PM10, OC, EC and TCA (total carbonaceous aerosol) were 222 ± 87 (range: 48.2-583.8 μg m−3), 25.6 ± 14.0 (range: 4.2-82.5 μg m−3), 8.7 ± 5.8 (range: 0.8-35.6 μg m−3) and 54.7 ± 30.6 μg m−3 (range: 8.4-175.2 μg m−3), respectively during entire sampling period. The average secondary organic carbon (SOC) concentration ranged from 2.5-9.1 μg m−3 in PM10, accounting from 14 to 28% of total OC mass concentration of PM10. Significant seasonal variations were recorded in concentrations of PM10, OC, EC and TCA with maxima during winter and minima during monsoon seasons. In the present study, the positive linear trend between OC and EC were recorded during winter (R2 = 0.53), summer (R2 = 0.59) and monsoon (R2 = 0.78) seasons. This behaviour suggests the contribution of similar sources and common atmospheric processes in both the fractions. OC/EC weight ratio suggested that vehicular emissions, fossil fuel combustion and biomass burning could be the major sources of carbonaceous aerosols of PM10 at the megacity Delhi, India. Trajectory analysis indicates that the air mass approches to the sampling site is mainly from Indo Gangetic plain (IGP) region (Uttar Pradesh, Haryana and Punjab etc.), Thar desert, Afghanistan, Pakistan and surrounding areas. [ABSTRACT FROM AUTHOR]

Phthalic acid esters (PAEs) are a group of chemical species, ubiquitously present in the environment and pose a serious risk to humans. In the present study, the average concentrations of PAEs in PM10 (particulate matter ≤ 10 µm) are reported at a densely populated site in Delhi. The average concentration of PAEs was reported to be 703.1 ± 36.2 ng m−3 with slightly higher concentrations in winter than in summer; suggesting that sources are relatively stable over the whole year. The average concentration of PAEs was 35.7 ± 30.5 ng m−3 in winter, 35.4 ± 27.0 ng m−3 in summer, 3.4 ± 1.5 ng m−3 in monsoon and 7.5 ± 5.2 ng m−3 in post-monsoon. Principal component analysis was performed, which suggested that emissions were mainly due to plasticizers, cosmetics and personal care products, municipal solid waste, thermal power stations, industrial wastewater, cement plants and coke ovens. [ABSTRACT FROM AUTHOR]

Haze-fog conditions over northern India are associated with visibility degradation and severe attenuation of solar radiation by airborne particles with various chemical compositions. PM samples have been collected in Delhi, India from December 2011 to November 2012 and analyzed for carbonaceous and inorganic species. PM measurements were made simultaneously such that PM could be estimated by difference. This study analyzes the temporal variation of PM and carbonaceous particles (CP), focusing on identification of the primary and secondary aerosol emissions, estimations of light extinction coefficient (b) and the contributions by the major PM chemical components. The annual mean concentrations of PM, organic carbon (OC), elemental carbon (EC) and PM were found to be 153.6 ± 59.8, 33.5 ± 15.9, 6.9 ± 3.9 and 91.1 ± 99.9 μg m, respectively. Total CP, secondary organic aerosols and major anions (e.g., SO and NO ) maximize during the post-monsoon and winter due to fossil fuel combustion and biomass burning. PM is more abundant during the pre-monsoon and post-monsoon. The OC/EC varies from 2.45 to 9.26 (mean of 5.18 ± 1.47), indicating the influence of multiple combustion sources. The b exhibits highest values (910 ± 280 and 1221 ± 371 Mm) in post-monsoon and winter and lowest in monsoon (363 ± 110 and 457 ± 133 Mm) as estimated via the original and revised IMPROVE algorithms, respectively. Organic matter (OM =1.6 × OC) accounts for ~39 % and ~48 % of the b, followed by (NH)SO (~21 % and ~24 %) and EC (~13 % and ~10 %), according to the original and revised algorithms, respectively. The b estimates via the two IMPROVE versions are highly correlated (R = 0.95, root mean square error = 38 % and mean bias error = 28 %) and are strongly related to visibility impairment ( r = −0.72), mostly associated with anthropogenic rather than natural PM contributions. Therefore, reduction of CP and precursor gas emissions represents an urgent opportunity for air quality improvement across Delhi. [ABSTRACT FROM AUTHOR]

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

Delhi is one of the most polluted cities in the world. The generation of aerosols in the lower atmosphere of the city is mainly due to a large amount of natural dust advection and sizable anthropogenic activities. The compositions of organic compounds in aerosols are highly variable in this region and need to be investigated thoroughly. Twenty-four-hour sampling to assess concentrations of n-alkanes (ng/m) in PM was carried out during January 2015 to June 2015 at Indira Gandhi Delhi Technical University for Women (IGDTUW) Campus, Delhi, India. The total average concentration of n-alkanes, 243.7 ± 5.5 ng/m, along with the diagnostic tools has been calculated. The values of CPI, CPI, and CPI for the whole range of n-alkanes series, petrogenic n-alkanes, and biogenic n-alkanes were 1.00, 1.02, and 1.04, respectively, and C were at C and C. Diagnostic indices and curves indicated that the dominant inputs of n-alkanes are from petrogenic emissions, with lower contribution from biogenic emissions. Significant seasonal variations were observed in average concentrations of n-alkanes, which is comparatively higher in winter (187.4 ± 4.3 ng/m) than during the summer season (56.3 ± 1.1 ng/m). [ABSTRACT FROM AUTHOR]

Open municipal solid waste (MSW)-burning is a major source of particulate matter emissions in developing world cities. Despite a legal ban, MSW-burning is observed ubiquitously in Indian cities with little being known about the factors shaping it. This study seeks to uncover social and infrastructural factors that affect MSW-burning at the neighborhood level. We couple physical assessments of the infrastructure provision and the MSW-burning incidences in three different neighborhoods of varying socio-economic status in Delhi, with an accompanying study of the social actors (interviews of waste handlers and households) to explore the extent to which, and potential reasons why, MSW-burning occurs. The observed differences in MSW-burning incidences range from 130 km−2 day−1 in low-income to 30 km−2 day−1 in the high-income areas. However, two high-income areas neighborhoods with functional infrastructure service also showed statistical differences in MSW-burning incidences. Our interviews revealed that, while the waste handlers were aware of the health risks associated with MSW-burning, it was not a high priority in the context of the other difficulties they faced. The awareness of the legal ban on MSW-burning was low among both waste handlers and households. In addition to providing infrastructure for waste pickup, informal restrictions from residents and neighborhood associations can play a significant role in restricting MSW-burning at the neighborhood scale. A more efficient management of MSW requires a combined effort that involves interplay of both social and infrastructural systems. [ABSTRACT FROM AUTHOR]

Background: Yamuna, a major tributary of Ganga, which flows through the national capital region of Delhi, is among the major polluted rivers in India. The accumulation of various effluents, toxic chemicals, heavy metals, and increased organic load in the Yamuna directly affects the organisms that thrive inside or around this river. It also makes it an ideal site for studying the impact of pollution on the river microflora, which are sentinels of the water quality. Results: In this study, the microbial community structure and functional diversity of the Yamuna river water was assessed from the New Delhi region. The community structure of Yamuna during pre-monsoon (June) was found to be significantly different from the post-monsoon (November) time, with Acinetobacter being the most abundant genus during June, and Aeromonas during November. The functional characterization revealed the higher abundance of Methyl-accepting chemotaxis protein in the river water, which could be important for the microbial chemosensory adaptation in the environment. A higher abundance of genes related to nitrogen and sulfur metabolism, metal tolerance, and xenobiotic degradation, and complete degradation pathways of aromatic compounds such as toluene, xylene, benzene and phenol were identified. Further, the results showed the presence of a pool of antibiotic resistance genes in the bacterial microbiome in the Yamuna alongside a large number of broad-spectrum antibiotics, such as carbapenemases and metallo-β-lactamases. Efflux mechanism of resistance was found to dominate among these microbes conferring multi-drug resistance. The Principal Coordinate Analysis of the taxonomic composition of the Yamuna River water with publicly available freshwater and sewage datasets revealed significant differences in the two Yamuna samples and a greater resemblance of pre-monsoon Yamuna sample to sewage sample owing to the higher pollution levels in Yamuna in the pre-monsoon time. Conclusion: The metagenomic study of the Yamuna river provides the first insights on the bacterial microbiome composition of this large polluted river, and also helps to understand the dynamics in the community structure and functions due to seasonal variations. The presence of antibiotic resistance genes and functional insights on the metabolic potential of a polluted river microbiome are likely to have several applications in health, biotechnology and bioremediation. [ABSTRACT FROM AUTHOR]