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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.

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Background Road and traffic accidents are uncertain and unpredictable incidents and their analysis requires the knowledge of the factors affecting them. Road and traffic accidents are defined by a set of variables which are mostly of discrete nature. The major problem in the analysis of accident data is its heterogeneous nature [1]. Thus heterogeneity must be considered during analysis of the data otherwise, some relationship between the data may remain hidden. Although, researchers used segmentation of the data to reduce this heterogeneity using some measures such as expert knowledge, but there is no guarantee that this will lead to an optimal segmentation which consists of homogeneous groups of road accidents [2]. Therefore, cluster analysis can assist the segmentation of road accidents. Cluster analysis which is an important data mining technique can be used as a preliminary task to achieve various goals. Karlaftis and Tarko [3] used cluster analysis to categorize the accident data into different categories and further analyzed cluster results using Negative Binomial (NB) to identify the impact of driver age on road accidents.

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Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. The association rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.