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

Rajarethinam, Jayanthi; Aik, Joel; Tian, Jing

doi:10.3390/ijerph17249345

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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

Jayanthi Rajarethinam

^1,*

Joel Aik

^1,2

and

Jing Tian

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

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

Author to whom correspondence should be addressed.

Int. J. Environ. Res. Public Health 2020, 17(24), 9345; https://doi.org/10.3390/ijerph17249345

Received: 16 October 2020 / Revised: 7 December 2020 / Accepted: 11 December 2020 / Published: 14 December 2020

(This article belongs to the Special Issue Environmental and Health Impacts of Fire: Primary and Secondary Effects in Local and Global Scale)

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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 PM₁₀ 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 PM₁₀ 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 PM₁₀. 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. View Full-Text

Keywords: air quality; forest fires; random forest model; vector autoregressive model

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Rajarethinam, J.; Aik, J.; Tian, J. 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. Int. J. Environ. Res. Public Health 2020, 17, 9345. https://doi.org/10.3390/ijerph17249345

AMA Style

Rajarethinam J, Aik J, Tian J. 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. International Journal of Environmental Research and Public Health. 2020; 17(24):9345. https://doi.org/10.3390/ijerph17249345

Chicago/Turabian Style

Rajarethinam, Jayanthi, Joel Aik, and 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" International Journal of Environmental Research and Public Health 17, no. 24: 9345. https://doi.org/10.3390/ijerph17249345

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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

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