Haze in Singapore – Source Attribution of Biomass Burning from Southeast Asia

Hansen, A. B.; Chong, Wei Ming; Kendall, Emma; Chew, Boon Ning; Gan, Christopher; Hort, Matthew C.; Lee, Shao-Yi; Witham, Claire

doi:10.5194/acp-2018-311

Cited by 4 publications

(10 citation statements)

References 16 publications

(31 reference statements)

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“…Haze was also shown to be worse in El Niňo years [26]. Previous study conducted in Singapore demonstrated that haze varies across year, season, and location and is in uenced by local and regional weather, climate, and regional burning [10]. A study on the Indonesia forest res reported aerosols being transmitted from Kalimantan to other countries in SEA, including Singapore [27].…”

Section: Introductionmentioning

confidence: 98%

“…Therefore, it is a substantial source of air pollution that affects local air quality as well as the air quality of distant places. Haze, generated by biomass burning, can have detrimental impacts on human health [4][5][6][7][8], climate, biodiversity, tourism and agricultural production [9] as well as degrade visibility [10].…”

Section: Introductionmentioning

confidence: 99%

“…Since 1960s, large res have occurred in Sumatra; however, the rst re event in Kalimantan happened in the 1980s [10]. In recent decades, biomass burning has become a recurring phenomenon in mainland Southeast Asia (SEA) and the islands of Sumatra and Borneo [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…A study on the Indonesia forest res reported aerosols being transmitted from Kalimantan to other countries in SEA, including Singapore [27]. Differences in haze concentrations and variation in the relative contributions from the various source regions are seen between monitoring stations across Singapore, on a seasonal as well as on an inter-annual timescale [10]. Even across small scales, such as in Singapore, variation in local meteorology can impact concentrations of particulate matter signi cantly, and it emphasises the importance of the scale of modelling both spatially and temporally.…”

Section: Introductionmentioning

confidence: 99%

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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

Rajarethinam

Aik

Tian

2020

Preprint

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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.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Rajarethinam

Aik

Tian

2020

Preprint

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“…Haze, generated by biomass burning, causes air pollution that affects local air quality as well as the air quality of distant places. Haze can have detrimental impacts on human health [ 4 , 5 , 6 , 7 , 8 ], climate, biodiversity, tourism and agricultural production [ 9 ] as well as degrade visibility [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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

Aik

Tian

2020

IJERPH

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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.

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Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World

O’Neill

Xian²,

Flemming

et al. 2022

Preprint

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Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke, often at unhealthy to hazardous levels leading to a range of short-term and long-term health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to adequately represent and include smoke and its consequences in atmospheric modeling systems to meet needs such as addressing the global climate carbon budget and informing and protecting the public during smoke episodes. Many scientific and technical challenges are associated with modeling the complex phenomenon of smoke. Variability in fire emissions estimates has an order of magnitude level of uncertainty, depending upon vegetation type, natural fuel heterogeneity, and fuel combustion processes. Quantifying fire emissions also vary from ground/vegetation-based methods to those based on remotely sensed fire radiative power data. These emission estimates are input into dispersion and air quality modeling systems, where their vertical allocation associated with plume rise, and temporal release parameterizations influence transport patterns, and, in turn affect chemical transformation and interaction with other sources. These processes lend another order of magnitude of variability to the downwind estimates of trace gases and aerosol concentrations. This chapter profiles many of the global and regional smoke prediction systems currently operational or quasi-operational in real time or near-real time. It is not an exhaustive list of systems, but rather is a profile of many of the systems in use to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. This chapter, and the systems described, reflect the needs of different agencies and regions, where the various systems are tailored to the best available science to address challenges of a region. Smoke forecasting requirements range from warning and informing the public about potential smoke impacts to planning burn activities for hazard reduction or resource benefit. Different agencies also have different mandates, and the lines blur between the missions of quasi-operational organizations (e.g. research institutions) and agencies with operational mandates. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble, as discussed in section 2. Regional and national systems are being developed independently and are discussed in sections 3-5 for Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization (WMO) effort (section 6) is bringing together global and regional systems and building the Vegetation Fire and Smoke Pollution Advisory and Assessment Systems (VFSP-WAS) to support countries with smoke issues and who lack resources.

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Haze in Singapore – Source Attribution of Biomass Burning from Southeast Asia

Cited by 4 publications

References 16 publications

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

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

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

Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World

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