Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction - Kavitha, S.; Manikandan, J. (Journal of Soft Computing Paradigm, 2021)
The climate change may be mitigated, and intra air quality assessment and local human well-being can benefit from a decrease in emission of pollutant content in the air. Monitoring the quality of the air around us is one way to do this. However, a location with various emission sources and short-term fluctuations in emissions in both time and space, and changes in winds, temperature, and precipitation creates a complex and variable pollution concentration field in the atmosphere. Therefore, based on the time and location where the sample is obtained, the measurement conducted are reflected in the monitoring results. This study aims to investigate one of India’s most polluted cities’ air quality measurements by greenhouse gas emissions. Using the Mann-Kendall and Sen’s slope estimators, the research piece gives a statistical trend analysis of several air contaminants based on previous pollution data from Mumbai, India’s air quality index station. In addition, future levels of air pollution may be correctly forecasted using an autoregressive integrated moving average model. This is followed by comparing different air quality standards and forecasts for future air pollution levels.