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

• Published in: Environmental monitoring and assessment Vol. 195; no. 4; p. 502
• Main Authors: Agarwal, Akash, Sahu, Manoranjan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2023; Springer Nature B.V
• Subjects: Air; Air quality; Air quality forecasting; Airborne particulates; Atmospheric models; Atmospheric Protection/Air Quality Control/Air Pollution; Autoregressive models; Autoregressive processes; Early warning systems; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental science; Forecasting; Life expectancy; Life span; Machine learning; Mathematical models; Meteorological parameters; Mitigation; Modelling; Monitoring/Environmental Analysis; Outdoor air quality; Parameters; Particulate matter; Statistical analysis; Statistical models; Data analytics; Air quality forecasting; ARIMA; Machine learning
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-023-11045-8

India is home to some of the most polluted cities on the planet. The worsening air quality in most of the cities has gone to an extent of causing severe impact on human health and life expectancy. An early warning system where people are alerted well before an adverse air quality episode can go a long way in preventing exposure to harmful air conditions. Having such system can also help the government to take better mitigation and preventive measures. Forecasting systems based on machine learning are gaining importance due to their cost-effectiveness and applicability to small towns and villages, where most complex models are not feasible due to resource constraints and limited data availability. This paper presents a study of air quality forecasting by application of statistical models. Three statistical models based on autoregression (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models were applied to the datasets of PM 2.5 concentrations of Delhi and Bengaluru, and forecasting was done for 1-day-ahead and 7-day-ahead time frames. All three models forecasted the PM 2.5 reasonably well for Bengaluru, but the model performance deteriorated for the Delhi region. The AR, MA, and ARIMA models achieved mean absolute percentage error (MAPE) of 10.82%, 7.94%, and 8.17% respectively for forecast of 7 days and MAPE of 7.35%, 5.62%, and 5.87% for 1-day-ahead forecasts for Bengaluru. For the Delhi region, the model gave an MAPE of 27.82%, 24.62%, and 27.32% for the AR, MA, and ARIMA models respectively in the 7-day-ahead forecast, and 24.48%, 23.53%, and 23.72% respectively for 1-day-ahead forecast. The analysis showed that ARIMA model performs better in comparison to the other models but performance varies with varying concentration regimes. Study indicates that other topographical and meteorological parameters need to be incorporated to develop better models and account for the effects of these parameters in the study.