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The Influence of South East Asia Forest Fires on Ambient Particulate Matter Concentrations in Singapore: An Ecological Study Using Random Forest and Vector Autoregressive Models

Author

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  • Jayanthi Rajarethinam

    (Environmental Health Institute, National Environment Agency, 11 Biopolis Way, #06-05/08, Singapore 138667, Singapore)

  • Joel Aik

    (Environmental Health Institute, National Environment Agency, 11 Biopolis Way, #06-05/08, Singapore 138667, Singapore
    Pre-Hospital & Emergency Research Centre, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore)

  • Jing Tian

    (Institute of Systems Science, National University of Singapore, 29 Heng Mui Keng Terrace, Block C, D & E, Singapore 119620, Singapore)

Abstract

Haze, due to biomass burning, is a recurring problem in Southeast Asia (SEA). Exposure to atmospheric particulate matter (PM) remains an important public health concern. In this paper, we examined the long-term seasonality of PM 2.5 and PM 10 in Singapore. To study the association between forest fires in SEA and air quality in Singapore, we built two machine learning models, including the random forest (RF) model and the vector autoregressive (VAR) model, using a benchmark air quality dataset containing daily PM 2.5 and PM 10 from 2009 to 2018. Furthermore, we incorporated weather parameters as independent variables. We observed two annual peaks, one in the middle of the year and one at the end of the year for both PM 2.5 and PM 10 . Singapore was more affected by fires from Kalimantan compared to fires from other SEA countries. VAR models performed better than RF with Mean Absolute Percentage Error (MAPE) values being 0.8% and 6.1% lower for PM 2.5 and PM 10, respectively. The situation in Singapore can be reasonably anticipated with predictive models that incorporate information on forest fires and weather variations. Public communication of anticipated air quality at the national level benefits those at higher risk of experiencing poorer health due to poorer air quality.

Suggested Citation

  • Jayanthi Rajarethinam & Joel Aik & Jing Tian, 2020. "The Influence of South East Asia Forest Fires on Ambient Particulate Matter Concentrations in Singapore: An Ecological Study Using Random Forest and Vector Autoregressive Models," IJERPH, MDPI, vol. 17(24), pages 1-14, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9345-:d:461721
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    References listed on IDEAS

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    1. Miriam E. Marlier & Ruth S. DeFries & Apostolos Voulgarakis & Patrick L. Kinney & James T. Randerson & Drew T. Shindell & Yang Chen & Greg Faluvegi, 2013. "El Niño and health risks from landscape fire emissions in southeast Asia," Nature Climate Change, Nature, vol. 3(2), pages 131-136, February.
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    Cited by:

    1. Lianjun Cao & Xiaobing He & Sheng Chen & Luming Fang, 2023. "Assessing Forest Quality through Forest Growth Potential, an Index Based on Improved CatBoost Machine Learning," Sustainability, MDPI, vol. 15(11), pages 1-18, May.

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