Correlation analysis of atmospheric pollutants and meteorological factors using statistical tools in Pune, Maharashtra

• Published in: E3S web of conferences 2023-01, Vol.391, p.1190
• Main Authors: Khedekar, Sneha, Thakare, Sunil
• Format: Article
• Language: English
• Subjects: air quality; meteorological; pearson correlation coefficient; pollutants; urbanisation
• ISSN: 2267-1242; 2267-1242
• DOI: 10.1051/e3sconf/202339101190

Air pollution has gotten worse due to the speeding up of urbanisation and industry, and the outlook for pollution control is not promising. A significant worldwide challenge that humanity is currently facing is climate change. India has suggested carbon neutrality and a carbon peak as ways to combat climate change. The intricate link and association between atmospheric contaminants and climatic variables that affect air quality, however, must be further elucidated. This work uses Pune's 2017–2021 high-resolution air pollution reanalysis open data set in conjunction with statistical techniques of the Pearson Correlation Coefficient (PCC) to compute and illustrate the design and analysis of environmental monitoring big data. The PCC is easy to use, immediately showed how contaminants and meteorological conditions relate to one another in time and space, and made environmental management agencies' jobs easier. The experimental results show that all contaminants are positively associated, with the exception of ozone, which is adversely connected. Pollutants are more influenced by meteorological factors than by temperature, which are all positively associated. Due to its strong negative relationship with the five pollutants, wind speed has a greater effect on the dispersion of pollutants.