Identifying Optimal Statistical Distributions for Air Pollution Data of Agra
Keywords:
Air pollution, Statistical distribution, Air pollutant prediction.Abstract
High levels of air pollution pose significant risks to human health and contribute to environmental instability. Addressing these challenges requires effective tools, such as statistical air pollution modelling,which can forecast the return time of future high-level pollution episodes. This technique is also instrumental in supporting government agencies in enhancing air quality management, especially when air quality data is accurately analysed.In air pollution modelling, statistical distributions like Gamma, lognormal, and Weibull are more commonly used than other distribution models. The primary objective of this observational study is to identify the most suitable distributions for predicting air pollution levels in the city of Agra. Our findings indicate that the lognormal distribution best fits SO2 and NO2 levels, while the Weibull distribution is more appropriate for modelling PM10 and the Air Quality Index of Agra.
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