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Forecasting PM2.5 concentrations using statistical modeling for Bengaluru and Delhi regions.

Akash Agarwal1, Manoranjan Sahu2,3,4

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Statistical models like ARIMA can forecast air quality in Indian cities. While effective for Bengaluru, models showed lower accuracy in Delhi, highlighting the need for more data to improve air pollution predictions.

Keywords:
ARIMAAir quality forecastingData analyticsMachine learning

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Area of Science:

  • Environmental Science
  • Data Science
  • Atmospheric Science

Background:

  • Air quality in Indian cities poses significant risks to public health and life expectancy.
  • Early warning systems for adverse air quality episodes are crucial for public health protection and governmental mitigation efforts.
  • Machine learning and statistical forecasting models offer cost-effective solutions for air quality prediction, especially in resource-limited areas.

Purpose of the Study:

  • To evaluate the effectiveness of statistical models for forecasting PM2.5 concentrations in Delhi and Bengaluru.
  • To compare the performance of Autoregressive (AR), Moving Average (MA), and Autoregressive Integrated Moving Average (ARIMA) models for short-term air quality prediction.
  • To assess the feasibility of these models for developing an early warning system for air pollution in Indian urban environments.

Main Methods:

  • Application of three statistical time-series models: AR, MA, and ARIMA.
  • Utilized historical PM2.5 concentration datasets for Delhi and Bengaluru.
  • Performed forecasting for 1-day-ahead and 7-day-ahead time frames.

Main Results:

  • ARIMA models demonstrated reasonable PM2.5 forecasting accuracy for Bengaluru, with Mean Absolute Percentage Errors (MAPE) as low as 5.62% for 1-day-ahead and 7.94% for 7-day-ahead forecasts.
  • Model performance significantly deteriorated in Delhi, yielding higher MAPEs (e.g., 23.53% for 1-day-ahead and 24.62% for 7-day-ahead with MA model).
  • The ARIMA model generally outperformed AR and MA models, but its accuracy varied depending on PM2.5 concentration levels.

Conclusions:

  • Statistical models, particularly ARIMA, show promise for air quality forecasting in Indian cities like Bengaluru.
  • The variability in model performance between Delhi and Bengaluru suggests that localized factors influence prediction accuracy.
  • Incorporating topographical and meteorological data is essential for developing more robust and accurate air quality forecasting models.