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, Joel Aik and Jing Tian
Additional contact information
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
Jing Tian: Institute of Systems Science, National University of Singapore, 29 Heng Mui Keng Terrace, Block C, D & E, Singapore 119620, Singapore

IJERPH, 2020, vol. 17, issue 24, 1-14

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.

Keywords: air quality; forest fires; random forest model; vector autoregressive model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/24/9345/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/24/9345/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:24:p:9345-:d:461721

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().