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A novel infectious coronavirus disease (COVID-19) identified in late 2019 has now been labelled as a global pandemic by World Health Organization (WHO). The COVID-19 outbreak has shown some positive impacts on the natural environment. In present work, India is taken as a case study to evaluate the effect of lockdown on air quality of three Indian cities. The variation in concentration of key air pollutants including P M 10 , P M 2.5 , N O 2 , S O 2 and O 3 during two phases, pre-lockdown and post-lockdown phases, was analysed. The concentration of P M 10 , P M 2.5 , N O 2 and S O 2 reduced by 55%, 49%, 60% and 19%, and 44%, 37%, 78% and 39% for Delhi and Mumbai, respectively, during post-lockdown phase. Overall, the findings in present study may provide confidence to the stakeholders involved in air quality policy development that a significant improvement in air quality can be achieved in future if better pollution control plans are strictly executed. [ABSTRACT FROM AUTHOR]

Owing to accurate future air quality estimates, need for detecting the anomalously high increase in concentration of pollutants cannot be adjourned. Plentiful approaches were proposed in the past to substantially determine the abnormal conditions, but most of the statistical approaches were computationally expensive and ignored the false alarm ratios. Thus, a hybrid of proximity- and clustering-based anomaly detection approaches to identify anomalies in the air quality data is suggested in this work. The Gaussian distribution property of the real-world data set is utilized further to segregate out anomalies. The results depicted twofold advantages of our approach, by efficient extraction of anomalies and with increased accuracy by reducing the number of false alarms. Specifically, the presence of NO2 concentration in air is investigated in this work, considering its constant increase over decades as well as its inevitable health risks. Furthermore, spatiotemporal segments with anomalously high NO2 concentrations for 14 residential, industrial, and commercial areas of five cities in India are extracted. To validate the results, a comparative analysis with existing approaches of anomaly detection and with two benchmark data sets is performed. Results showed that our method outperformed the existing methods of anomaly detection, when evaluated over metrics such as sensitivity, miss rate, and false alarms. Further, a detailed analysis of extracted anomalies and a detailed discussion about the factors responsible for such anomalies are presented in this work. This study is helpful in educating government and people about spatiotemporal, geographical, and economic conditions responsible for anomalously high NO2 concentrations in air. Implications: Using our methodology, days with extremely high concentration of any pollutant in air, at any particular location, can be extracted. The reasons for such extremely high pollutant concentration on particular days of a year can be studied and preventive measures can be taken by the government. Thus, by identification of causes of anomalies, future similar events can be avoided. This would also help in people's decision making in case such events occur in the future. [ABSTRACT FROM AUTHOR]

Deep learning models address air quality forecasting problems far more effectively and efficiently than the traditional machine learning models. Specifically, Long Short-Term Memory networks (LSTMs) constitute a significant breakthrough in understanding the complex sequential behavioral dependencies of the time series. Further, LSTM models justify well with the speed–accuracy tradeoff, among other deep learning models. However, there are several limitations of such deep learning models. Firstly, the addition of multiple hidden layers, on the one hand, improves the performance but, on the other hand, requires extensive hardware and computation capabilities. Secondly, most of the previous works that utilized LSTMs for air quality forecasting do not consider the issue of optimal hyperparameter calibration. While deciding the gradient, network learning parameters should be so fixed such that the model does not underfit or overfit. To address these issues, a stochastic optimization algorithm, mimicking the pattern of flocking birds, is utilized to find the most fitting solution in the parameter search space. Particle swarm optimization setup primarily models varying particles representing parameters to reach an optimum state. Furthermore, the Spatio-temporal instabilities of LSTM models are addressed in this work using preprocessing, segmentation and feature engineering to understand seasonal and trend characteristics along with the Spatio-temporal correlation of the time series. The proposed model is employed on the air quality dataset of 15 locations in India. A variety of experiments are performed to prove the superiority of the proposed method. Firstly, a comparison with traditional sequential models and deep learning models is done. Secondly, results are further evaluated over several existing benchmark dataset samples. Results suggest that the proposed method outperforms existing forecasting models when evaluated over a variety of performance metrics. • Complex spatio-temporal dependencies in the data renders model learning task difficult. • Long Short-Term Memory network are utilized to model complex sequential dependencies. • Optimal hyperparameters selection using swarm intelligence is done. [ABSTRACT FROM AUTHOR]

In present study, the variation in concentration of key air pollutants such as PM 2.5 , PM 10 , NO 2 , SO 2 and O 3 during the pre-lockdown and post-lockdown phase has been investigated. In addition, the monthly concentration of air pollutants in March, April and May of 2020 is also compared with that of 2019 to unfold the effect of restricted emissions under similar meteorological conditions. To evaluate the global impact of COVID-19 on the air quality, ground-based data from 162 monitoring stations from 12 cities across the globe are analysed for the first time. The concentration of PM 2.5 , PM 10 and NO 2 were reduced by 20–34%, 24–47% and 32–64%, respectively, due to restriction on anthropogenic emission sources during lockdown. However, a lower reduction in SO 2 was observed due to functional power plants. O 3 concentration was found to be increased due to the declined emission of NO. Nevertheless, the achieved improvements were temporary as the pollution level has gone up again in cities where lockdown was lifted. The study might assist the environmentalist, government and policymakers to curb down the air pollution in future by implementing the strategic lockdowns at the pollution hotspots with minimal economic loss. Unlabelled Image • Positive impacts of COVID-19 on environment are demonstrated. • Air quality data of 162 monitoring stations from 12 cities across the globe are assessed. • PM 2.5 , PM 10 and NO 2 have shown a significant reduction in lockdown phase as compared to pre-lockdown phase. • A notable improvement in air quality is observed during lockdown across the globe. • Achieved improvement in air quality is temporary as pollution level increased in cities where lockdown was lifted. [ABSTRACT FROM AUTHOR]