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

Abstract

Background In recent decades, haze, due to biomass burning, has become a recurring problem in Southeast Asia (SEA). Haze degrades air quality, thus, causing detrimental effects on human health. Exposure to atmospheric particulate matter (PM) remains an important public health concern.Methods In this paper, we examined the long-term seasonality of PM2.5 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 PM2.5 and PM10 from 2009 to 2018. Furthermore, we incorporated weather parameters as independent variables, to understand their effects on air quality.Results We observed two annual peaks, one in the middle of the year and one at the end of the year for both PM2.5 and PM10. Singapore was more affected by fires from Kalimantan compared to fires from other SEA countries. In our experimental results, VAR models performed better than RF with Mean Absolute Percentage Error (MAPE) values being 0.8% and 6.1% lower for PM2.5 and PM10, respectively.Conclusions Our study findings suggest that air quality in Singapore can be reasonably anticipated with predictive models that incorporate information on forest fires and weather variations. The public communication of anticipated air quality at the national level benefit who are at higher risk of experiencing poorer health due to poorer air quality.