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Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China

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Abstract

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.

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Acknowledgements

The authors are thankful to the kind and precious suggestions of Prof. Dr. Muhammad Zaffar Hashmi.

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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China

    Ahmad Hasnain

  2. Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan

    Muhammad Zaffar Hashmi

  3. Department of Civil and Environmental Engineering, Michigan State University 1449 Engineering Research, East Lansing, MI, 48823, USA

    Muhammad Zaffar Hashmi

  4. Department of Environmental Health, Health Services Academy, Islamabad, Pakistan

    Muhammad Zaffar Hashmi

  5. School of Geography, Nanjing Normal University, Nanjing, 210023, China

    Sohaib Khan & Xiangqiang Min

  6. School of Information and Communication Engineering, Hainan University, Haikou, China

    Uzair Aslam Bhatti

  7. Xinjiang Key Laboratory of Oasis Ecology, College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, China

    Yin Yue

  8. Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, 330022, China

    Yufeng He

  9. School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, 330013, China

    Geng Wei

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  1. Ahmad Hasnain
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  7. Yufeng He
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Contributions

Ahmad Hasnain: conceptualization; methodology; data curation; formal analysis; writing—original draft; writing—review and editing; validation; visualization. Muhammad Zaffar Hashmi: supervision, conceptualization, resources, writing—review and editing. Sohaib Khan: validation, investigation, data curation, writing—review and editing. Uzair Aslam Bhatti: supervision, investigation, conceptualization, data curation, writing—review and editing. Xiangqiang Min: data curation, formal analysis, writing—review and editing. Yin Yue: data curation, validation, writing—review and editing. Yufeng He: data curation, writing—review and editing. Geng Wei: data curation; writing—review and editing; validation. All authors read and approved the final manuscript.

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Correspondence to Uzair Aslam Bhatti.

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Hasnain, A., Hashmi, M.Z., Khan, S. et al. Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China. Environ Monit Assess 196, 487 (2024). https://doi.org/10.1007/s10661-024-12644-9

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