Impact of Lockdown on Air Quality During COVID-19 Pandemic: A Case Study of India

It is crucial to study air quality and its impact on human health, as it can leave not only short-term effects but also have long-term effects, especially on people suffering from cardiovascular and lung diseases. During the COVID-19 pandemic, a major lockdown of almost 70 days in four different phases was announced in India. Due to this exercise, many visually observed a drastic change in air quality;however, actual quantifications were limited. Therefore, there is a need to quantify how air quality changed from before to during and post-lockdown scenarios. This study quantifies the COVID-19 India lockdown impact on air quality by analyzing the change in major air pollutants such as SO2, NO2, CO, O3, PM2.5, and PM10. The major objectives of this study are to quantify the change in major air pollutants across India during the lockdown and to identify their trends and respective hotspots. In order to achieve these objectives, air quality estimates are obtained from Sentinel 5P satellite, while PM2.5 and PM10 values are taken from Central Pollution Control Broad's ground monitoring stations. For temporal analysis, different time intervals starting from before the lockdown (i.e., March 1, 2020) till the end of the fourth lockdown (i.e., May 31, 2020) were analyzed across India. Results state that (1) There was a significant decline of − 48.11% and − 11.56% in concentrations of SO2 and NO2, respectively, after averaging values at their respective hotspots (2) A decrease of − 6.78% and − 0.42% was observed in O3 and CO concentration during the lockdown period in the year 2020 compared with the same period in the year 2019. (3) For PM2.5, Kolkata had the maximum drop of − 83.28%, while Bengaluru had the least drop of − 38.86%, whereas, for PM10, Kolkata had the maximum drop again of − 80.53%, while Delhi, on the other hand, had an increment of 13.42% at the end of the fourth lockdown. The results indicate the indirect benefit of the COVID-19 lockdown on air quality. It also provides a better understanding of hotspots and trends that can aid the government and the policy-makers to identify precautionary measures to reduce air pollution and prioritize hotspots. © 2022, Indian Society of Remote Sensing.

COVID-19 vaccine sentiment analysis using public opinions on Twitter.

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs.

An Approach to Detect COVID-19 Disease from CT Scan Images using CNN - VGG16 Model

As a result of the outbreak, an unusual virus spread event has occurred, threatening human safety worldwide. To prevent infections from spreading quickly, large numbers of people must be screened. Rapid Test and RT-PCR are common testing tool for regular testing that is used to test all covid affected users. However, the increasing number of false positives has paved the way for the investigation of alternative test methods for corona virus effected patients' chest X-rays have shown to be an effective alternate predictor for testing if an individual is affected with COVID-19 virus. However, consistency is, once again, dependent on radiological experience. A diagnostic decision support device that assists the physician in evaluating the victims' lung scans can alleviate the doctor's medical workload. Machine Learning Techniques, specifically Convolutional Neural Networks (CNN) VGG16 model is used to train dataset and use trained model to predict, have been developed in this project. Four distinct deep CNN architectures are tested on photographs of chest X-rays for treatment of COVID-19. The collection of data sets of covid 19 X-ray imageries and non-covid 19 X-ray imageries are used to train the model and test its accuracy. CNN-based architectures were discovered to be capable of diagnosing COVID-19 disease. © 2022 IEEE.