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Jayanthi Rajarethinam
National Environment Agency
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8789-960X
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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.
Keywords
Air quality, forest fires, random forest model, vector autoregressive model
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Jayanthi Rajarethinam
National Environment Agency
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8789-960X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at International Journal of Environmental Research and Public Health.
Version 1
Posted 22 Jul, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Ecological Modeling
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at International Journal of Environmental Research and Public Health.
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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