Forecasting Air Pollution Concentrations and Binning Air Quality Index Values to Encourage Green Vehicles for Sustainability: A Data Science Approach
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Abstract
People don't get as much clean air as they used to because of pollution. Contaminated air is harmful since it can lead to respiratory and cardiovascular problems. The data science process is used to deal with this issue. This method assists in the systematic analysis of air pollutants that influence Air Quality Index (AQI) values. The primary objective of this research is to utilize data science in order to make long-term AQI predictions for the city of Hyderabad. To accomplish this goal, pre-COVID-19 and post-COVID-19 AQI data are combined into a dataset. The data science methodology is applied to solve this issue. Through this method, air pollutants that have an impact on the Air Quality Index (AQI) can be analyzed in a methodical fashion. The primary objective of this research is to utilize data science in order to forecast future AQI values for the city of Hyderabad. This is done by assembling a database of AQI readings from both before and after the onset of COVID-19. First, the data is cleaned, and then exploratory data analysis (EDA) is performed to better understand when and why varying air pollutants have changed over time. In addition to training the sophisticated forecasting model, the seasonal auto-regressive integrated moving average with exogenous factors (SARIMAX) is also trained with these trend and seasonality components. This model forecasts the amount of air pollution in the following three years. The severity of air pollution in a city is evaluated by aggregating the estimated AQI values across the AQI categories. Based on these results and how they can be interpreted, we want to encourage people to purchase environmentally friendly vehicles so that we can live in a sustainable manner.
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