ABSTRACT
In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID‐19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21‐days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved.
ABSTRACT
In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID-19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21-days lockdown is implemented, then a reduction of 42 mu g m(-3) in PM10, 23 mu g m(-3) in PM2.5, 14 mu g m(-3) in NO2, 2 mu g m(-3) in SO2, and 0.7 mg m(-3) in CO can be achieved.
ABSTRACT
COVID-19 has affected the global economy like no other crisis in the history of mankind. It forced worldwide lockdown and economic shutdown to the point from where the recovery process has been very difficult. It has affected demand, supply, production and consumption in such a way that the entire economic development cycle has gone to its lowest levels. COVID-19 has also affected the social and economic sustainability structure which has led from one crisis to another and the developing countries have been the worst hit. Economic crisis resulted in unemployment leading to labour migrations, inevitable casualties and rising poverty etc. However, at a certain level, a few industries and organizations have shown resilience with better anticipation and survivability which may lead them to a quicker recovery. The current study aims at presenting a holistic view of organizational resilience which leads to the overall sustainable development. The study considers three aspects of organizational resilience as crisis anticipation, organizational robustness and recoverability. It assesses the impact of the aspects of resilience on social sustainability and economic sustainability. The study uses empirical analysis of primary data which is analysed to verify the hypothesized relationships by using a structural equation modelling approach. The study finds out that predicting the crisis and disruptions, building robustness and recoverability have a positive effect on both the social and economic aspects of sustainability. Findings of the study have their practical implications for industry, researchers and society.
ABSTRACT
Wastewater treatment plants (WWTPs) associated bioaerosols have emerged as one of the critical sustainability indicators, ensuring health and well-being of societies and cities. In this context, this review summarizes the various wastewater treatment technologies which have been studied with a focus of bioaerosols emissions, potential emission stages, available sampling strategies, survival and dispersion factors, dominant microbial species in bioaerosols, and possible control approaches. Literature review revealed that most of the studies were devoted to sampling, enumerating and identifying cultivable microbial species of bioaerosols, as well as measuring their concentrations. However, the role of treatment technologies and their operational factors are investigated in limited studies only. Moreover, few studies have been reported to investigate the presence and concentrations of air borne virus and fungi in WWTP, as compared to bacterial species. The common environmental factors, affecting the survival and dispersion of bioaerosols, are observed as relative humidity, temperature, wind speed, and solar illumination. Further, research studies on recent episodes of COVID-19 (SARS-CoV-2 virus) pandemic also revealed that continuous and effective surveillance on WWTPs associated bioaerosols may led to early sign for future pandemics. The evaluation of reported data is bit complicated, due to the variation in sampling approaches, ambient conditions, and site activities of each study. Therefore, such studies need a standardized methodology and improved guidance to help informed future policies, contextual research, and support a robust health-based risk assessment process. Based on this review, an integrated sampling and analysis framework is suggested for future WWTPs to ensure their sustainability at social and/or health associated aspects.