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

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 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. 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. 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. 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.

Assessing Forest Quality through Forest Growth Potential, an Index Based on Improved CatBoost Machine Learning

“…The level of forest growth potential reflects the future forest quality, which is, to a certain extent, based on subcompartments. This study therefore produces relatively forward-looking and accurate guidance for improving forest quality, contributing to the goals of carbon peaking and carbon neutrality [9,10] and realizing the preliminary exploration and research into improving forest quality based on a CatBoost algorithm improved by subcompartments.…”