Air quality fluctuation monitoring and statistical data analysis during COVID-19 pandemic in Siliguri city, India

Introduction: Worldwide coronavirus created is a major problem for human health, food security, economy and many more. World Health Organisation (WHO) named this virus COVID-19. This virus is first detected in Wuhan, China in December 2019 and after that, it’s spreading over the world. Lockdown is healing the environmental condition because major Indian metropolitan cities are recovered from different pollutants. This study is to identify the air quality trend before, during and after the lockdown in Siliguri city, the third-largest city of West Bengal and this city is also a commercial and transportation hub. Materials and methods: The air quality data have been derived from West Bengal Pollution Control Board (WBPCB) and proceed in MS-Office and ArcGIS 10.4. The air pollutant and week air quality data have been used for monitoring the environmental situation. Results: In this study, results show that around 70%-90% of air quality is increased during strict lockdown but again air quality is decreased after lockdown gradually. The weekly air quality graph significantly changes during lockdown but after lockdown, the graph was increased. The highest air quality shows 347 before lockdown but during lockdown it’s decreased 25 on 23-24 May 2020. After lockdown public transport, industrial area and small scale industries are reopened and again the air quality increased. The highest air quality shows 353 on 14 January 2021 during unlock 8.0. Conclusion: This pandemic taught how anthropogenic activates, like urbanization, population pressure and industrial works were endangering the environment and some caution is essential for future livelihood.

Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

Air Quality in the Harbin-Changchun Metropolitan Area in Northeast China: Unique Episodes and New Trends

Because of the unique geographical, climate, and anthropogenic emission characteristics, it is meaningful to explore the air pollution in the Harbin-Changchun (HC) metropolitan area. In this study, the Air Quality Index (AQI) and the corresponding major pollutant were investigated for the HC cities, based on the air quality data derived from the China National Environmental Monitoring Center. The number of days with the air quality level of “good” gradually increased during recent years, pointing to an improvement of the air quality in HC. It was also found that ozone, a typical secondary pollutant, exhibited stronger inter-city correlations compared to typical primary pollutants such as carbon monoxide and nitrogen dioxide. In addition, for nearly all the HC cities, the concentrations of fine particulate matter (PM2.5) decreased substantially in 2020 compared to 2015. However, this was not the case for ozone, with the most significant increase of ozone observed for HC’s central city, Harbin. This study highlights the importance of ozone reduction for further improving HC’s air quality, and the importance of agricultural fire control for eliminating heavily-polluted and even off-the-charts PM2.5 episodes.

Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks

To prevent severe air pollution, it is important to analyze time-series air quality data, but this is often challenging as the time-series data is usually partially missing, especially when it is collected from multiple locations simultaneously. To solve this problem, various deep-learning-based missing value imputation models have been proposed. However, often they are barely interpretable, which makes it difficult to analyze the imputed data. Thus, we propose a novel deep learning-based imputation model that achieves high interpretability as well as shows great performance in missing value imputation for spatio-temporal data. We verify the effectiveness of our method through quantitative and qualitative results on a publicly available air-quality dataset.