Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India)
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View abstract on PubMed
Summary
This summary is machine-generated.Forecasting air quality in India
Area Of Science
- Environmental Science
- Data Science
- Public Health
Background
- Air pollution poses a significant threat in developing nations like India.
- Air Quality Index (AQI) forecasting enables proactive health measures.
Purpose Of The Study
- To forecast AQI for the SIDCUL industrial area in Haridwar City.
- Utilized a time series regression model for AQI prediction.
Main Methods
- Collected three years of post-COVID-19 AQI data.
- Applied time series decomposition and Augmented Dickey-Fuller test for stationarity.
- Selected a seasonal Autoregressive Integrated Moving Average (ARIMA) (0,1,0) (1,0,0)[12] model.
Main Results
- The chosen seasonal ARIMA model achieved minimum Akaike Information Criterion (253.143) and Mean Absolute Percentage Error (17.42%).
- AQI values were forecasted for the SIDCUL industrial area for the subsequent year.
Conclusions
- The seasonal ARIMA model is effective for forecasting non-stationary time series AQI data.
- The SIDCUL area is projected to experience moderate pollution, potentially impacting respiratory health.
- This model can be applied to other polluted regions for public and governmental preventive actions.
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