Predictive Modeling of Air Quality in Indian Megacities: Time Series Analysis from 2010-2016 Using ARIMA and Seasonal Decomposition.
Keywords:
Air Quality, ARIMA, Seasonal Decomposition, Time Series Analysis, Indian Megacities, Predictive ModelingAbstract
This research paper presents a comprehensive study on the predictive modeling of air quality in Indian megacities, focusing on the period from 2010 to 2016. Utilizing time series analysis methods, specifically ARIMA (Autoregressive Integrated Moving Average) and Seasonal Decomposition, the study analyses air quality data from major Indian urban centers. The research aims to uncover patterns, trends, and seasonal variations in air pollutants, providing a nuanced understanding of air quality dynamics in the context of rapid urbanization and industrial growth in India. The findings reveal significant increases in key pollutants, including PM2.5, NO2, and SO2, with marked seasonal fluctuations, highlighting the impact of climatic conditions and urban activities. The paper also demonstrates the effective use of ARIMA models in forecasting future air quality scenarios, offering essential insights for policymakers and environmental agencies. The study's implications extend beyond academic interest, contributing to the development of targeted air quality management strategies and reinforcing the role of predictive analytics in environmental planning and public health protection in densely populated urban areas.
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