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

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

Assessment of persistent organic pollutants in soil and sediments from an urbanized flood plain area.

Polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs) and phenolic compounds (PCs) are persistent organic compounds. Contamination of these potentially toxic organic pollutants in soils and sediments is most studied environmental compartments. In recent past, studies were carried out on PAHs, OCPs and PCs in various soils and sediments in India. But, this is the first study on these pollutants in soils and sediments from an urbanized river flood plain area in Delhi, India. During 2018, a total of fifty-four samples including twenty-seven each of soil and sediment were collected and analyzed for thirteen priority PAHs, four OCPs and six PCs. The detected concentration of ∑PAHs, ∑OCPs and ∑PCs in soils ranged between 473 and 1132, 13 and 41, and 639 and 2112 µg/kg, respectively, while their concentrations in sediments ranged between 1685 and 4010, 4.2 and 47, and 553 and 20,983 µg/kg, respectively. PAHs with 4-aromatic rings were the dominant compounds, accounting for 51 and 76% of total PAHs in soils and sediments, respectively. The contribution of seven carcinogen PAHs (7CPAHs) in soils and sediments accounted for 43% and 61%, respectively, to ∑PAHs. Among OCPs, p, p'-DDT was the dominant compound in soils, while α-HCH was found to be dominated in sediments. The concentrations of ∑CPs (chlorophenols) were dominated over ∑NPs (nitrophenols) in both the matrices. Various diagnostic tools were applied for the identification of their possible sources in soil and sediments. The observed concentrations of PAHs, OCPs and PCs were more or less comparable with the recently reports from various locations around the world including India. Soil quality guidelines and consensus-based sediment quality guidelines were applied for the assessment of ecotoxicological health effect.

Impact of Shutdown due to COVID-19 Pandemic on Aerosol Characteristics in Kanpur, India.

BACKGROUND: Since March 2020, the number of confirmed COVID-19 positive cases have steadily risen in India. Various preventive measures have been taken to contain the spread of COVID-19. With restrictions on human activities, anthropogenic emissions driving air pollution levels have seen a reduction since March 23, 2020, when the government imposed the first nationwide shutdown. The landlocked Indo-Gangetic Plain (IGP) has many densely-populated cities, witnessing high levels of particulate matter due to both nature-driven and anthropogenic elements. Kanpur is an urban metropolis in the IGP with high aerosol loading, and this paper explores the impact of restricted anthropogenic activities on aerosol characteristics in Kanpur. OBJECTIVES: This study aims to investigate the change in aerosol optical depth level and its related parameters during the shutdown phases in Kanpur city compared to the same time periods in 2017-2019. METHODS: Aerosol optical properties such as aerosol optical depth (AOD) at 500 nm, Angstrom exponent (AE), fine mode fraction (FMF) of AOD at 500 nm and single scattering albedo (SSA) at 440 nm were obtained from the Aerosol Robotic Network (AERONET) station operating in Kanpur from the 1st March to the 30th April for 2017-2020. RESULTS: A significant decrease in aerosol loading was observed during the shutdown period compared to the pre-and partial shutdown periods in 2020 as well as during the same time periods of 2017-2019. Mean AOD, FMF and SSA were 0.37, 0.43 and 0.89, respectively, during the shutdown period in 2020. A 20-35% reduction in mean AOD levels was observed during the shutdown period in 2020 as compared to the same period in 2017-2019. CONCLUSIONS: The shutdown led to an improvement in air quality due to decreases in anthropogenic emissions. As fine particles, typically from urban and industrial emissions, dominate episodic air pollution events, this study can be further utilized by the scientific community and regulators to strengthen the emergency response action plan to check high pollution episodes in Kanpur city until cleaner technologies are in place. COMPETING INTERESTS: The authors declare no completing financial interests.

Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions.

Air pollution is an important issue, especially in megacities across the world. There are emission sources within and also in the regions around these cities, which cause fluctuations in air quality based on prevailing meteorological conditions. Short term air quality forecasting is used not to just possibly mitigate forthcoming high air pollution episodes, but also to plan for reduced exposures of residents. In this study, a model using Artificial Neural Networks (ANN) has been developed to forecast pollutant concentration of PM10, PM2.5, NO2, and O3 for the current day and subsequent 4 days in a highly polluted region (32 different locations in Delhi). The model has been trained using meteorological parameters and hourly pollution concentration data for the year 2018 and then used for generating air quality forecasts in real-time. It has also been equipped with Real Time Correction (RTC), to improve the quality of the forecasts by dynamically adjusting the forecasts based on the model performance during the past few days. The model without RTC performs decently, but with RTC the errors are further reduced in forecasted values. The utility of the model has been demonstrated in real-time and model validations were performed for the whole year of 2018 and also independently for 2019. The model shows very good performance for all the pollutants on several evaluation metrics. Coefficient of correlations for various pollutants varies from 0.79-0.88 to 0.49-0.68 between the Day0 to Day4 forecasts. Lowest deterioration of performance was observed for ozone over the four days of forecasts. Use of RTC further improves the model performance for all pollutants.

Effect of PM2.5 chemical constituents on atmospheric visibility impairment.

PM2.5 sampling was conducted at a curbside location in Delhi city for summer and winter seasons, to evaluate the effect of PM2.5 and its chemical components on the visibility impairment. The PM2.5 concentrations were observed to be higher than the National Ambient Air Quality Standards (NAAQS), indicating poor air quality. The chemical constituents of PM2.5 (the water-soluble ionic species SO42-, NO3-, Cl-, and NH4+, and carbonaceous species: organic carbon, elemental carbon) were analyzed to study their impact on visibility impairment by reconstructing the light extinction coefficient, bext. The visibility was found to be negatively correlated with PM2.5 and its components. The reconstructed bext showed that organic matter was the largest contributor to bext in both the seasons which may be attributed to combustion sources. In summer season, it was followed by elemental carbon and ammonium sulfate; however, in winter, major contributions were from ammonium nitrate and elemental carbon. Higher elemental carbon in both seasons may be attributed to traffic sources, while lower concentrations of nitrate during summer, may be attributed to volatility because of higher atmospheric temperatures. IMPLICATIONS: The chemical constituents of PM2.5 that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.