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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]

The Indian capital megacity of Delhi is reeling under deteriorating air quality, and control measures are not yielding any significant changes, mainly due to a poor understanding of sources of emissions; hence, priority option in mitigation planning is lacking. In this paper, we have made an attempt to develop a spatially resolved technological high-resolution gridded (∼0.4km×0.4km) emission inventory for eight major pollutants of the Delhi region where high-resolution activity data of all possible major and unattended minor sources are generated by organizing a mega-campaign involving hundreds of volunteers. It is for the first time that we are able to estimate the decadal growth in emissions of various pollutants by comparing newly developed 2020 emissions with SAFAR (System of Air Quality and Weather Forecasting and Research) emissions of 2010 using the identical methodology and quantum of activity data. The estimated annual emissions for PM 2.5 , PM 10 , CO, NOx , VOC, SO2 , BC, and OC over the Delhi National Capital Region (NCR) are estimated to be 123.8, 243.6, 799.0, 488.9, 730.0, 425.8, 33.6, and 20.3 Ggyr-1 , respectively, for the year 2020. The decadal growth (2010–2020) in PM 2.5 and PM 10 is found to be marginal at 31 % and 3 %, respectively. The maximum growth is found to be in the transport sector followed by the industrial and other sectors. Maximum decadal growth found for the pollutants BC, OC, and NOx is 57 %, 34 %, and 91 %, respectively. The decadal shift of sectorial emissions with changing policies is examined. The complete dataset is available on Zenodo at 10.5281/zenodo.7715595 (Sahu et al., 2023). [ABSTRACT FROM AUTHOR]

India is home to some of the most polluted cities on the planet. The worsening air quality in most of the cities has gone to an extent of causing severe impact on human health and life expectancy. An early warning system where people are alerted well before an adverse air quality episode can go a long way in preventing exposure to harmful air conditions. Having such system can also help the government to take better mitigation and preventive measures. Forecasting systems based on machine learning are gaining importance due to their cost-effectiveness and applicability to small towns and villages, where most complex models are not feasible due to resource constraints and limited data availability. This paper presents a study of air quality forecasting by application of statistical models. Three statistical models based on autoregression (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models were applied to the datasets of PM2.5 concentrations of Delhi and Bengaluru, and forecasting was done for 1-day-ahead and 7-day-ahead time frames. All three models forecasted the PM2.5 reasonably well for Bengaluru, but the model performance deteriorated for the Delhi region. The AR, MA, and ARIMA models achieved mean absolute percentage error (MAPE) of 10.82%, 7.94%, and 8.17% respectively for forecast of 7 days and MAPE of 7.35%, 5.62%, and 5.87% for 1-day-ahead forecasts for Bengaluru. For the Delhi region, the model gave an MAPE of 27.82%, 24.62%, and 27.32% for the AR, MA, and ARIMA models respectively in the 7-day-ahead forecast, and 24.48%, 23.53%, and 23.72% respectively for 1-day-ahead forecast. The analysis showed that ARIMA model performs better in comparison to the other models but performance varies with varying concentration regimes. Study indicates that other topographical and meteorological parameters need to be incorporated to develop better models and account for the effects of these parameters in the study. [ABSTRACT FROM AUTHOR]

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India's economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere's natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World. [ABSTRACT FROM AUTHOR]

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities. [ABSTRACT FROM AUTHOR]

In the present paper we attempted to explain the relationships among the landuse pattern, levels of air pollutants and bird diversity based on data from 5 sampling sites in Delhi. Five landuse categories -- percent built up area, tree cover, park area and barren area were recognized in the study area. The objective of this study is to find out the effects of landuse changes on air pollution and bird diversity and whether birds can serve as indicator of landuse changes and air pollutants. The levels of six air pollutants (PM10, PM2.5, NOX, SO2, Ozone and Benzene) from the monitoring stations were used. The bird diversity was assessed using conventional measures. All the sites showed remarkable differences with respect to each of the five landuse categories, air pollution levels, and bird diversity. The results suggest that landuse changes influence air pollution and bird diversity and some bird species can be used as indicator of landuse change and air pollution. [ABSTRACT FROM AUTHOR]

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 2021-22 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 AQI is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the NCR 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 PM2.5 load in Delhi. During peak pollution events (stubble-burning periods), however, the contribution from sources within Delhi (farm fires in Punjab-Haryana) 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]

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 paper intends to assess the impact of the odd-even scheme on the travel pattern of the daily commuters in Delhi. The objective of the paper is to assess the impact of the odd-even scheme on mode choice for daily work trips, shift in travel patterns - before, during, and post-implementation - of the odd-even scheme, and to understand people perception regarding the odd-even scheme. Based on the primary survey, the paper concludes that the odd- even scheme brought a significant impact in the travel pattern in terms of occupancy, travel cost, travel time, and modal shift, and statistically not so much on the air quality gain. It was observed that the scheme helped increase the occupancy rate in cars as well as ridership of buses and Delhi metro. The scheme had a huge impact on congestion, which was evident from both perception analysis and the change in travel time. The modal shift, with an improvement in public transport services and a reduction in car users, is one of the key successes of the scheme resulting in decrease in air pollution caused by private vehicles. To improve the outcome of the odd-even scheme on air pollution, two-wheelers should not be exempted going forward. [ABSTRACT FROM AUTHOR]

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Novel machine learning models (MLMs) using the seasonal indexing approach that captures the variation in air quality caused due to meteorological changes have been used to provide short-term, real-time forecasts of PM2.5 concentration for one of the most polluted air quality control regions (AQCR) in the capital city of Delhi. Two MLMs—multi-linear regression and random forest—have been developed for using time series data for 1-h and 24-h average PM2.5 concentration. Short-term, real-time forecasts have been made using the developed models. Various model performance evaluation indices indicate satisfactory model performance. R2 values for the hourly and daily models varied between 0.95 and 0.72 and between 0.76 and 0.68 for the 1st to 5th h/day, respectively. The lagged values of PM2.5 concentration (persistence) and the hourly and daily indices are the most influential variables for the forecasts for immediate time steps. In contrast, seasonal indices become more important with the forecasting time horizon. The developed models can be used for making short-term, real-time air quality forecasts and issuing a warning when the pollution levels go beyond acceptable limits. [ABSTRACT FROM AUTHOR]

Winter fog and severe aerosol loading in the boundary layer over northern India, particularly in the Indo-Gangetic Plain (IGP), disrupt the daily lives of millions of people in the region. To better understand the role of aerosol–radiation (AR) feedback on the occurrence, spatial extent, and persistence of winter fog, as well as the associated aqueous chemistry in fog in the IGP, several model simulations have been performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). While WRF-Chem was able to represent the fog formation for the 23–24 December 2017 fog event over the central IGP in comparison to station and satellite observations, the model underestimated PM 2.5 concentrations compared to the Central Pollution Control Board (CPCB) of India monitoring network. While evaluating aerosol composition for fog events in the IGP, we found that the WRF-Chem aerosol composition was quite different from measurements obtained during the Winter Fog Experiment (WiFEX) in Delhi, with secondary aerosols, particularly the chloride aerosol fraction, being strongly underpredicted (∼ 66.6 %). Missing emission sources (e.g., industry and residential burning of cow dung and trash) and aerosol and chemistry processes need to be investigated to improve model–observation agreement. By investigating a fog event on 23–24 December 2017 over the central IGP, we found that the aerosol–radiation feedback weakens turbulence, lowers the boundary layer height, and increases PM 2.5 concentrations and relative humidity (RH) within the boundary layer. Factors affecting the feedback include loss of aerosols through deposition of cloud droplets and internal mixing of absorbing and scattering aerosols. Aqueous-phase chemistry increases the PM 2.5 concentrations, which subsequently affect the aerosol–radiation feedback by both increased mass concentrations and aerosol sizes. With aerosol–radiation interaction and aqueous-phase chemistry, fog formation began 1–2 h earlier and caused a longer fog duration than when these processes were not included in the WRF-Chem simulation. The increase in RH in both experiments was found to be important for fog formation as it promoted the growth of aerosol size through water uptake, increasing the fog water content over the IGP. The results from this study suggest that the aerosol–radiation feedback and secondary aerosol formation play an important role in the air quality and the intensity and lifetime of fog over the IGP, yet other feedbacks, such as aerosol–cloud interactions, need to be quantified. [ABSTRACT FROM AUTHOR]

Air pollution has become a growing problem in megacities and large urban areas through out the globe, and transportation is recognized as the major source of air pollution in many cities, especially in developing countries. Contribution of automobiles is reported in the range of 40 to 80% of the total air pollution. The challenge facing megacities is how to reduce the adverse environmental impacts and other negative effects of transportation without giving up the benefits of mobility. The dilemma becomes most pressing under conditions of rapid urban growth, which is likely to increase travel demand significantly. The paper is aimed at understanding the problem of vehicular pollution vis-a-vis ambient air quality for a highly traffic affected megacity, Delhi, wherein, the contribution of transport sector was estimated to be as high as 72%. An effort has been made to review and evaluate the benefits (in terms of improved air quality) of the technological interventions/policies adopted for vehicular pollution control in Delhi. It also highlights the outcome of the efforts and suggests further improvements thereon. The importance of public participation and awareness are also discussed. The paper focuses on deriving the benefits of the implementation of management strategies, supported by scientific and technical data/interpretation, so that the people can realize and participate in the government's endeavor for clean city drive in a more effective manner. [ABSTRACT FROM AUTHOR]

Several cities in the developing world, of which the capital city of India, New Delhi, is an example, often experience air quality in which pollutant levels go way above the levels considered hazardous for human health. To bring down the air quality to within permissible limits quickly, the measures typically taken involve shutting down certain high-polluting activities for some time to enable the air quality to recover temporarily. This paper presents a first-ever model based on artificial neural networks to forecast the extent of reduction in air quality parameters that can be achieved and the time period within which a change can be experienced when the source of the emissions is cut off temporarily. The model is based on the extensive data on the extent of reduction in air quality parameters that occurred during the lockdown that was imposed during the COVID-19 pandemic. The non-linear autoregressive exogenous network-based model chosen for the purpose employs the hour since stopping of emissions, relative humidity, wind speed, wind direction, and ambient temperature as input parameters to predict the rate of change of PM 2.5 with respect to the concentration at the start of the stopping of the emissions. Air quality data from a key monitoring station in New Delhi was used to develop the model. The model predicted the rate of drop in PM 2.5 with an R and MSE of 0.0044 and 0.9736, respectively, while training and 0.0095 and 0.9583 while testing. The model was then tested with data from 19 other stations in New Delhi, and accuracy of the model was found to be exceptionally accurate, with the correlation between the measured and the predicted PM 2.5 levels ranging from 0.74 to 0.94 and the MSE ranging from 0.0110 to 1.0746. Thus, the model can be employed to determine the number of hours of temporary stoppage of emissions required for the PM 2.5 concentration to reach safe levels. The methodology of development of the model can be extrapolated to construct models tailored for use in other parts of the world as well. • A novel ANN -based model is reported to predict rate of drop of PM 2.5 • Model has been trained on extensive dataset from the covid lockdown in Delhi. • Duration and efficacy of emergency measures to reduce emissions can be estimated. • The model uses easy-to-measure meteorological parameters as inputs. • Model has been validated for 19 stations of various land-use types in Delhi. [ABSTRACT FROM AUTHOR]

Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon seasons, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx=NO+NO2). We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO 2) data, from S5P aboard TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emissions sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre-monsoon (February–May) and post-monsoon (October–February) seasons. According to this analysis, during the pre-monsoon season, the highest accumulated satellite TCNO 2 trajectories come from the east and north-west of Delhi. TCNO 2 is elevated within Delhi and the Indo-Gangetic Plain (IGP) to the east of city. The accumulated NOx emission trajectories indicate that the transport and industry sectors together account for more than 80 % of the total accumulated emissions, which are dominated by local sources (>70 %) under easterly winds and north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this season). During the post-monsoon season the highest accumulated satellite TCNO 2 trajectories are advected from Punjab and Haryana, where satellite TCNO 2 is elevated, indicating the potential for the long-range transport of agricultural burning emissions to Delhi. However, accumulated NOx emissions indicate local (70 %) emissions from the transport sector are the largest contributor to the total accumulated emissions. High local emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary layer, aid the build-up of emissions locally and along the trajectory path. This indicates the possibility that fire emissions datasets may not capture emissions from agricultural waste burning in the north-west sufficiently to accurately quantify their influence on Delhi air quality (AQ). Analysis of daily ground-based NO2 observations indicates that high-pollution episodes (>90 th percentile) occur predominantly in the post-monsoon season, and more than 75 % of high-pollution events are primarily caused by local sources. But there is also a considerable influence from non-local (30 %) emissions from the transport sector during the post-monsoon season. Overall, we find that in the post-monsoon season, there is substantial accumulation of high local NOx emissions from the transport sector (70 % of total emissions, 70 % local), alongside the import of NOx pollution into Delhi (30 % non-local). This work indicates that both high local NOx emissions from the transport sector and the advection of highly polluted air originating from outside Delhi are of concern for the population. As a result, air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport and agricultural waste burning sectors, particularly during the post-monsoon season. [ABSTRACT FROM AUTHOR]

Air quality is a matter of concern these days due to its adverse effect on human health. Multiple new air pollution monitoring and prediction stations are being developed in smart cities to tackle the issue. Recent advanced deep learning techniques show excellent performance for air quality predictions but need sufficient training data for model performance. The data insufficiency issue at a new station can be resolved using the proposed novel transfer learning-based framework to predict pollution concentration at the new station. The prediction ability at a new station can be significantly enhanced by this effective technology. The performance of the model is assessed on various stations in Delhi, India. [ABSTRACT FROM AUTHOR]

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena. [ABSTRACT FROM AUTHOR]

Air pollution is one of the major environmental problems in India, affecting health of thousands of ‘urban’ residents residing in mega cities. The need of the day is to evolve an ‘effective’ and ‘efficient’ air quality management plan (AQMP) encompassing the essential ‘key players’ and ‘stakeholders.’ This paper describes the formulation of an AQMP for mega cities like Delhi in India taking into account the aforementioned key ‘inputs.’ The AQMP formulation methodology is based on past studies of Longhurst et al., ( Atmospheric Environment, 30, 3975–3985, 1996); Longhurst & Elsom, ((1997). Air Pollution-II, Vol. 2 (pp. 525–532)) and Beatti et al., ( Atmospheric Environment, 35, 1479–1490, 2001). Further, the vulnerability analysis (VA) has been carried out to evaluate the stresses due to air pollution in the study area. The VA has given the vulnerability index (VI) of ‘medium to high’ and ‘low’ at urban roadways/intersections and residential areas, respectively. [ABSTRACT FROM AUTHOR]

A number of policy measures have been activated in India in order to control the levels of air pollutants such as particulate matter, sulphur dioxide (SO2) and nitrogen dioxide (NO2). Delhi, which is one of the most polluted cities in the world, is also going through the implementation phase of the control policies. Ambient air quality data monitored during 2000 to 2003, at 10 sites in Delhi, were analyzed to assess the impact of implementation of these measures, specifically fuel change in vehicles. This paper presents the impact of policy measures on ambient air quality levels and also the source apportionment. CO and NO2 concentration levels in ambient air are found to be associated with the mobile sources. The temporal variation of air quality data shows the significant effect of shift to CNG (Compressed Natural Gas) in vehicles. [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]

The impact of air pollution on people and the environment is a severe issue that has recently been the subject of extensive research. This study has looked at the factors that contribute to the seasonal and spatial variability of pollutant concentration over Delhi NCR from 2019 to 2021. Additionally, the causes of changes in air quality during the COVID-19's lockdown period in 2020 have been discussed, along with comparisons to the pre-lockdown year (2019) and the post-lockdown year (2021). Seven pollutant parameters, viz., (PM2.5, PM10, NOx, CO, SO2, NH3, and O3) were retrieved from the air quality monitoring stations spread over Delhi NCR. The results show a significant temporal (seasonal) and spatial variability in the air pollutants' concentration. The highest pollutant level was observed in winter and the lowest in summer seasons. The results suggest that the concentration of atmospheric pollutants was already lower (20–30%) before the implementation of the lockdown. Meteorology played an important role in emission reduction during the lockdown, in particular, and seasonal, in general. The results also suggest that Bhiwadi is not the most polluted city, as claimed in the World Air Quality Report 2022. The most polluted sites in terms of pollutant concentration were observed over Delhi in all the years considered. [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]

The nature and origin of organic aerosol in the atmosphere remain unclear. The gas–particle partitioning of semi-volatile organic compounds (SVOCs) that constitute primary organic aerosols (POAs) and the multigenerational chemical aging of SVOCs are particularly poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem (Weather Research and Forecasting model with Chemistry), can be a useful tool to describe emissions of POA and its chemical evolution. However, the evaluation of model uncertainty and the optimal model parameterization may be expensive to probe using only WRF-Chem simulations. Gaussian process emulators, trained on simulations from relatively few WRF-Chem simulations, are capable of reproducing model results and estimating the sources of model uncertainty within a defined range of model parameters. In this study, a WRF-Chem VBS parameterization is proposed; we then generate a perturbed parameter ensemble of 111 model runs, perturbing 10 parameters of the WRF-Chem model relating to organic aerosol emissions and the VBS oxidation reactions. This allowed us to cover the model's uncertainty space and to compare outputs from each run to aerosol mass spectrometer observations of organic aerosol concentrations and O:C ratios measured in New Delhi, India. The simulations spanned the organic aerosol concentrations measured with the aerosol mass spectrometer (AMS). However, they also highlighted potential structural errors in the model that may be related to unsuitable diurnal cycles in the emissions and/or failure to adequately represent the dynamics of the planetary boundary layer. While the structural errors prevented us from clearly identifying an optimized VBS approach in WRF-Chem, we were able to apply the emulator in the following two periods: the full period (1–29 May) and a subperiod period of 14:00–16:00 h LT (local time) on 1–29 May. The combination of emulator analysis and model evaluation metrics allowed us to identify plausible parameter combinations for the analyzed periods. We demonstrate that the methodology presented in this study can be used to determine the model uncertainty and to identify the appropriate parameter combination for the VBS approach and hence to provide valuable information to improve our understanding of OA production. [ABSTRACT FROM AUTHOR]

Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist. [ABSTRACT FROM AUTHOR]

Regional air quality monitoring, a critical component of sustainable development is realized through various air quality observation stations established across a region. Accurate forecasting of air quality data collected from these observation stations requires the modelling of spatial–temporal patterns in the data. Deep learning algorithms, known for their ability to capture layers of abstraction, can proficiently achieve spatial–temporal modeling. However, deterministic models that produces point forecast does not consider the underlying model uncertainty during prediction and are therefore less reliable for real-time applications. Probabilistic forecasting models that forecast prediction intervals rather than point estimates can overcome this through uncertainty quantification. The objective of the proposed study is three-fold: i) develop an efficient deterministic deep learning spatial–temporal neural network named DL-STNN for spatial–temporal air quality forecasting; ii) investigate different approaches to uncertainty quantification in deep learning models and integrate some of them, such as Monte-Carlo Dropout, Ensemble Averaging, Gaussian Process Regression, Quantile Regression, and Bayesian Inference, in tandem with DL-STNN to facilitate probabilistic forecasting; and iii) evaluate the developed deterministic and probabilistic models, using a real-world Delhi air quality dataset. The evaluation results show that, among the deterministic models, DL-STNN outperforms the baselines with 39.8% more accurate predictions and performs consistently across all seasons in Delhi. Furthermore, among the DL-STNN-based tandem models that performed probabilistic forecasting, Bayesian DL-STNN proved efficient. It does 13% more accurate point forecasting and has 20% higher suitability score than the other tandem models, indicating that Bayesian inference adapts DL-STNN more reliable for real-time applications. [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]

In November 2019, the Supreme Court of India issued a notification to all the states in the National Capital Region of Delhi to install smog towers for clean air and allocated INR 36 crores (~USD 5.2 million) for a pilot. Can we vacuum our air pollution problem using smog towers? The short answer is "no". Atmospheric science defines the air pollution problem as (a) a dynamic situation where the air is moving at various speeds with no boundaries and (b) a complex mixture of chemical compounds constantly forming and transforming into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it, and release into the same atmosphere simultaneously. In this paper, we outline the basics of atmospheric science to describe why the idea of vacuuming outdoor air pollution is unrealistic, and the long view on air quality management in Indian cities. [ABSTRACT FROM AUTHOR]

The rise in urbanization has led to an increase in dust pollution which is hazardous to the health of living beings. The role of roadside plant species in intercepting particulate matter and improving air quality is well reported. Hence, this study was carried out to determine the ability of various plant species to intercept atmospheric dust and withstand the abiotic stress triggered by dust deposition. In the present investigation, three sites (viz., control, commercial, and industrial) differing in anthropogenic activities and vegetation were selected. Sixteen plant species entailing both trees and shrubs that are commonly occurring at all three sites were selected to estimate their dust interception capacity (DIC). The impact of dust pollution on foliage biochemistry and their tolerance in winter and summer seasons were analyzed. Based on biochemical, biological, and socio-economic parameters, air pollution tolerance index (APTI) and anticipated performance index (API) were evaluated. Both dust load and DIC were found to be two times higher in winter than in the summer season. Terminalia arjuna, Ficus benghalensis, and Plumeria alba were the best dust accumulators, while Prosopis juliflora accumulated least. The highest DIC was observed at the industrial site, for Terminalia arjuna (0.025 mg/cm2/d) in winter and Plumeria alba (0.023 mg/cm2/d) in the summer season. Photosynthetic pigments showed a negative correlation with dust load, while pH, ascorbic acid, electrolytic leakage (E.L.), and proline content showed a positive correlation. In the present study, APTI and API values were highest for Ficus religiosa, Ficus benghalensis, Alstonia scholaris, Dalbergia sissoo, and Terminalia arjuna. Such plant species with wide canopy, large and rough leaf surface area with perforated veins are found to be more suitable and, hence, recommended for the development of greenery to improve air quality in urban areas like Delhi. [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]

This study investigates the air pollution-induced mortality rate during the second wave of COVID-19, which claimed several thousand lives in the capital city of India, New Delhi, even during a lockdown period. Delhi is a hotspot of unhealthy air quality. During the second wave of COVID-19 in 2021, surface ozone levels were observed to be higher, which had a direct impact on lung function, thereby making people more susceptible to COVID-19. The correlation coefficient between surface ozone concentration and mortality has been observed to be 0.74 at a 95% confidence level. This work focuses on the plausible impact and feedback of poor air quality induced by the burning of open-air funeral pyres due to the increased COVID-19 mortality rate in New Delhi, estimated by using an epidemiological model (AirQ+) of the World Health Organization. The mortality rate estimated quantitatively with the aid of AirQ+ is 1.27 excess deaths per 100,000 population due to surface ozone from pyre burning. The findings suggest transformational system goals before the resurgence of a subsequent wave. [ABSTRACT FROM AUTHOR]

Air pollution in Delhi is a serious environmental problem. In this paper, the effects of meteorological conditions on local air quality are quantitatively assessed by determining the relationships between concentrations of Suspended Particulate Matter (SPM), Respirable Suspended Particulate Matter (RSPM), sulphur dioxide (SO2) and nitrogen dioxide (NO2) with meteorological parameters on monthly basis, from 2000 to 2006. The multi-linear regression approach, used in the study, has facilitated analysis of the air quality-meteorology relationship and predicted the concentrations of SPM and RSPM within a scatter of ±18% and of SO2 and NO2 within a scatter of ±14%. [ABSTRACT FROM AUTHOR]

Atmospheric concentrations of polycyclic aromatic hydrocarbons (PAHs) in Delhi were evaluated to study particulate PAHs profiles during the different seasons of 2003. Samples of urban suspended particulate matter were collected during January 2003 to December 2003 at three locations (Okhla, Dhaulakuan and Daryaganj), using a high volume sampler provided with glass fiber filters. Samples were analyzed using the gas chromatography technique. The annual average concentrations of total PAHs were found as 1,049.3 ng/m3 at Okhla, 1,344.37 ng/m3 at Daryaganj, and 1,117.14 ng/m3 at Dhaulakuan. The seasonal average concentrations were found to be maximum in winter and minimum during the monsoon season. Principal Component Analysis (PCA) of the data was also carried out and the results indicate that diesel and gasoline driven vehicles are the principal sources of PAHs at all the three sites under investigation. Other sources might come from stationary combustion sources such as cooking fuel combustion and industrial emission. [ABSTRACT FROM AUTHOR]

This paper uses spectral methods to analyze changes in air quality at a single monitoring site in Delhi since 2000. Power spectral density calculations of daily concentration data for particulate matter (PM10), carbon monoxide (CO), oxides of nitrogen (NO x ) and oxides of sulfur (SO x ) reveal the presence of trends and periodic oscillations for all the pollutants. Singular Spectrum Analysis (SSA) is used to decompose daily data into statistically significant non-linear trends, seasonal cycles and other oscillations. Periods of sharp reductions were observed for both SO x and CO concentrations in 2001 and 2002, respectively. NO x concentration trends show a sustained rise from 2000 to 2004, followed by small decline thereafter. PM10 concentration trends remain essentially unchanged over the time period. All pollutants also show strong annual and biannual cycles. The observed trends in CO and NO x likely relate changes in Delhi''s vehicular traffic emissions. The sharp drop in both the trend and amplitude of the seasonal cycle of CO coincides with the switch to Compressed Natural Gas (CNG) as a fuel for Delhi''s public transport fleet. Observed changes in SO x and PM10 concentrations were most likely caused by sources unrelated to vehicular traffic. [Copyright &y& Elsevier]

High consumption of fossil fuels, increased greenhouse gas emissions and deterioration of local air quality have resulted from rapid industrialization and urbanization in India in the last decade. India has taken many initiatives to address these problems such as new automobile fuel policies, energy efficiency measures, promotion of renewable energy use, relocation of highly polluting industries, enforcing use of low emission fuels like CNG for public transport in large cities, and introducing metro rail networks in megacities. This paper highlights results of adopted policies in Delhi showing relationships between air quality and climate change and having sustainable development as the driving force with focus on environmental and social dimensions. [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]

Coronavirus disease (COVID-19) spread across the globe through the human transmission. The World Health Organization suggested social distancing to curb the community spread. After national social lockdown started in India, air quality improved drastically. This further hypothesized to influence the environment and human health, and this study is positively the first to weigh it using multiple indices. The calculated environmental indices are photochemical ozone creation potential (POCP), acidification potential (AP), and eutrophication potential (EP). The cancer risk, chronic health index (CHI), and acute health index (AHI) were considered to calculate the health risk. The spatial trend change in the air pollution reflecting on these indices are calculated for four Indian megacities Delhi, Bangalore, Hyderabad, and Kolkata. Temporal variation was accounted for monthly (2019 vs 2020), one-week and two-weeks period during the social lockdown. The results showed a significant decrease in environmental and health risk during the lockdown due to a corresponding decrement in air pollution. The decrease in the particulate matter was found to play a vital role in altering the air pollution mediated risks of interest. Delhi showed a maximum difference in POCP and Acute HI by recording a dip of 70.79% and 43.53% respectively in 2020 during lockdown. The maximum reduction in health risk indices was 41%, 31%, 17%, 19% for Delhi, Bangalore, Hyderabad, and Kolkata. Bangalore recorded the maximum decline in EP, Cancer risk, Chronic HI by 66.66%, 58.62%, and 58.76% in 2020 compared to 2019. A maximum fall in AP was seen in Kolkata by 57.23% in 2020 among all cities. The connection between these drop-in indices and the cause of air pollutants were well discussed. This present paper gives more in-depth insights into air pollution's effect on environmental and health parameters by connecting and converging various air pollution aspects into a single scale. This study also enlightens the importance of controlling air pollution to have a better environment and healthy life to attain sustainable development. Conceptual illustration of impact of COVID-19 enabled social lockdown on environmental and health risk indices in India. [Display omitted] • Detailed study on COVID-19 lockdown vs environmental and health indices in India. • Included three time periods & four megacities Delhi, Mumbai, Bangalore & Kolkata. • Noticeable reduction in particulate matter and indices during lockdown. • Maximum health risk reduction in Delhi (41%) and Bangalore (31%). • Bangalore recorded maximum reduction in EP, Cancer risk, Chronic HI. [ABSTRACT FROM AUTHOR]

Accelerated urbanization and industrialization have led to poor air quality, which threatens human health with various lung ailments. Monitoring, modeling, and forecasting air quality would be a prudent way to promote awareness and defend human beings from the adversities of air pollution. The air quality of a region is monitored through various air quality monitoring stations built in and around it. The air quality data collected from these stations are highly dynamic, nonlinear, and hold intensely stochastic spatiotemporal correlations in them. Deep learning algorithms that are capable of extracting a higher level of abstraction in data can efficiently capture the spatiotemporal features in it. In this paper, we propose a hierarchical deep learning model named DL-Air that embodies three components for air quality forecasting. The first component, the encoder, encodes all spatial relations in the data. The second component, STAA-LSTM, a proposed variant of LSTM, identifies all temporal relations and the level of association between the identified spatiotemporal relation and the forecast. Also, the STAA-LSTM predicts the future spatiotemporal relations in the latent space. The third component, decoder suitably decodes these relations to obtain the actual forecast. The proposed framework was extensively evaluated for forecasting the real-world air quality data of Delhi. DL-Air shows better performance with around 30% reduced RMSE and MAE, 37% reduced AAD, 11% improved R2 and 8% improved accuracy in AQI category prediction than the best performing baseline approaches. Also, the predictive performance of DL-Air is found to be consistent across all seasons in Delhi. Image 1 • Air pollution monitoring and forecasting is the primary step for air pollution control which is mandatory for sustainable development. • Precise forecasting of the stochastic and highly nonlinear air quality data imposes the need for extracting the spatiotemporal relations in them and their associations on the forecast. • A hierarchical deep learning framework named DL-Air is proposed for performing accurate forecasting of the air quality of Delhi, the capital city of India. • The STAA-LSTM proposed and incorporated in DL-Air estimates the impact of the extracted spatiotemporal features over the forecast in latent space. • DL-Air shows better predictive performance than the best performing baseline approaches. [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]

Covid-19 health pandemic resulted in nationwide lockdown in India. With the closure of almost everything from educational institutions to industries less no of vehicles came out on roads. As the vehicular emission is one of the reasons of higher rate of pollution in metro cities so this step of lockdown has shown positive impact on environment with decrease in pollution level. Despite various attempts made by the government over the last few years nothing proved fruitful but this forced decision has shown some great results. We have analysed the changes in the quality of air during lockdown period from 25th March to 30th April, 2020 over pre-lockdown period from 1st March to 24th March, 2020 of Delhi, Mumbai and Kolkata which are the polluted cities of India. Seven air pollutants PM2.5, PM10, O3, CO, SO2, NH3, and NO2 have been used to calculate Air Quality Index (AQI) to see the changes in quality of air. The results showed that due to lockdown concentration of various pollutants PM2.5, PM10 and NO2 has decreased significantly. There is a heavy reduction of 42.27%, 69.28% and 74.80% in concentration of NO2 in Delhi, Mumbai and Kolkata respectively. It has been deduced from change in AQI values that there is a significant improvement in quality of air during lockdown in Delhi, Mumbai and Kolkata. [ABSTRACT FROM AUTHOR]

Delhi has had the distinction of being one of the most polluted cities in the world, especially in the winter months from October—January. These months coincide with the religious festival of Diwali. It is argued that air quality gets worse in the aftermath of Diwali on account of firecrackers that get burned during the festival. We use hourly data on PM 2.5 particulate concentration from 2013 to 2017 to estimate the Diwali effect on air quality in Delhi. We improve on existing work by using the event study technique as well as a difference-in-difference regression framework to estimate the Diwali effect on air quality. The results suggest that Diwali leads to a small, but statistically significant increase in air pollution. The effect is different across locations within Delhi. To our knowledge, this is the first causal estimate of the contribution of Diwali firecracker burning to air pollution. [ABSTRACT FROM AUTHOR]

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications. [ABSTRACT FROM AUTHOR]

The use of alternative fuel is considered to be an effective measure to improve the urban air quality. Concerned over deteriorating air quality in Delhi, the Delhi government initiated different measures including stringent emission norms, improved fuel quality and above all introduction of cleaner fuel-CNG in public transport system. The entire city bus fleet was converted to CNG mode by 2002. The present study reports the comparative assessment of the status of air quality with respect to PM10 and PAH before and after the introduction of CNG in public transport system in Delhi. The study has been carried out for two different time periods: first in the year 1998 and second in the year 2004. Following the total conversion of public transport system to CNG in 2002, Post-CNG data indicate a sharp reduction of 51–74% in the PM10 concentration and 58–68% in the TPAH concentration as compared to the Pre-CNG data. [ABSTRACT FROM AUTHOR]

The study focuses on assessing the status of respiratory morbidity in Delhi over a four years period from 2000–2003. An attempt was made to investigate the role of important pollutants (SO2, NO2, SPM and RSPM) and various meteorological factors (temperature minimum & maximum, relative humidity at 0830 and 1730 hrs. and wind speed) in being responsible for respiratory admissions on account of COPD, asthma and emphysema. The study showed that winter months had greater exposure risk as pollutants often get trapped in the lower layers of atmosphere resulting in high concentrations. Statistical analysis revealed that two pollutants have significant positive correlation with the number of COPD cases viz., SPM ( r = 0.474; p < 0.01) and RSPM ( r = 0.353; p < 0.05), while a meteorological factor temperature (minimum) has a significant negative correlation ( r = −0.318; p < 0.05) with COPD. Stepwise multiple regression analysis was performed for COPD as dependent variable and R2 value of 0.33 was obtained indicating that SPM and RH(1730) were able to explain 33 percent variability in COPD. The partial correlation of SPM and RH(1730) on COPD was higher than any other combination and therefore they can be regarded as important contributing variables on COPD. [ABSTRACT FROM AUTHOR]

The levels of Suspended Particulate Matter (SPM) and heavy metals viz. Pb, Cd, Cr, Ni and Fe were measured. Aerosol samples from four different locations in Delhi were collected by High-volume samplers for a period of one year from July 1997 June 1998. Metal concentration was determined by Atomic Absorption Spectrometry. The annual average concentration of SPM in Delhi was found to be 416.34 ± 223 μg m-3. The atmospheric aerosol samples were highly enriched with elements viz. Pb and Cd, which originate from various human activities like transportation and industrial processes. Principal Component Analysis (PCA) showed vehicular traffic and industrial emission as the major contributors of metals. The annual average concentration of Pb did not exceed the national standard of 0.75 μg m-3. [ABSTRACT FROM AUTHOR]