In recent years, due to rapid urbanization and industrialization, there is an increasing trend of air pollution that has brought the most alarming situation for the health of humans in some developing countries. Among various air pollutants, the most dangerous contaminants are CO (Carbon Monoxide), NO2 (Nitrogen Dioxide) as well as O3 (Ozone). This paper conducted a crucial analysis by utilizing the GEE cloud-based platform with Sentinel-5P TROPOMI satellite imagery of multi-temporal range to analyze the changes of NO2, CO, and O3 at a surface level during the nationwide lockdown in Pakistan due to COVID-19. As the whole country "shuts down", the sudden suspension of industrial activities and sparse vehicles on roads greatly reduced the air pollution. Our study found a notable reduction in NO2 (28.88%), CO (15.81%), and O3 (8.41%). The improvement in the air quality helps people who are suffering from respiratory and related diseases. Our study is helpful for the environmental department to design effective policies and take measures to improve and maintain the air quality.
A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers’ premises. The contribution of these female home-based workers (FHBWs) is significant to the country’s economic growth. FHBWs are often confronted with numerous occupational diseases due to a lack of awareness of occupational safety and health measures, and unhealthy living and working conditions. The informality of FHBWs prevents them from getting proper healthcare, safety, and other dispensations enjoyed by formal employees. Despite their undeniable importance, health issues of FHBWs are still overlooked. This study is an attempt to discover the frequent co-occurring occupational diseases encountered by FHBWs in Punjab, a province of Pakistan. Frequent itemset mining (FIM) or co-occurrence grouping is a technique of data science that identifies the associations among different entities in the data. Based on FIM, the D-GENE algorithm is applied in this study to efficiently discover frequent co-occurring diseases in the data obtained from the Punjab Home-based Workers Survey (2016). The far-reaching goal of the study is to bring awareness of the occupational health issues and safety risks to the health authorities as well as to the FHBWs.
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