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El Niño and health risks from landscape fire emissions in Southeast Asia.

Marlier ME, DeFries RS, Voulgarakis A, Kinney PL, Randerson JT, Shindell DT, Chen Y, Faluvegi G - Nat Clim Chang (2013)

Bottom Line: In this study, we combine satellite-derived fire estimates and atmospheric modeling to quantify health effects from fire emissions in Southeast Asia from 1997 to 2006.This region has large interannual variability in fire activity due to coupling between El Niño-induced droughts and anthropogenic land use change(2,3).We show that during strong El Niño years, fires contribute up to 200 μg/m(3) and 50 ppb in annual average fine particulate matter (PM2.5) and ozone (O3) surface concentrations near fire sources, respectively.

View Article: PubMed Central - PubMed

Affiliation: Department of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, 10964, USA.

ABSTRACT
Emissions from landscape fires affect both climate and air quality(1). In this study, we combine satellite-derived fire estimates and atmospheric modeling to quantify health effects from fire emissions in Southeast Asia from 1997 to 2006. This region has large interannual variability in fire activity due to coupling between El Niño-induced droughts and anthropogenic land use change(2,3). We show that during strong El Niño years, fires contribute up to 200 μg/m(3) and 50 ppb in annual average fine particulate matter (PM2.5) and ozone (O3) surface concentrations near fire sources, respectively. This corresponds to a fire contribution of 200 additional days per year that exceed the World Health Organization (WHO) 50 μg/m(3) 24-hour PM2.5 interim target (IT-2)(4) and an estimated 10,800 (6,800-14,300) person (~2%) annual increase in regional adult cardiovascular mortality. Our results indicate that reducing regional deforestation and degradation fires would improve public health along with widely established benefits from reducing carbon emissions, preserving biodiversity, and maintaining ecosystem services.

No MeSH data available.


Related in: MedlinePlus

Population exposure above World Health Organization (WHO) interim targetsa, Exposure over 50 μg/m3 24-hour PM2.5 interim target (IT-2). b, Exposure over 25 μg/m3 annual PM2.5 interim target (IT-2). c, Exposure over 80 ppb 8-hour maximum O3 interim target (IT-1). d, Fraction of population exposure above each WHO interim target that is attributable to fires. Each case is calculated with and without GFED3 fire emissions using GISS-E2-PUCCINI results, which was close to the average concentration estimate. Refer to Supplementary Table S1 for estimated health effects. Note the logarithmic scale for (a) and (c).
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Figure 3: Population exposure above World Health Organization (WHO) interim targetsa, Exposure over 50 μg/m3 24-hour PM2.5 interim target (IT-2). b, Exposure over 25 μg/m3 annual PM2.5 interim target (IT-2). c, Exposure over 80 ppb 8-hour maximum O3 interim target (IT-1). d, Fraction of population exposure above each WHO interim target that is attributable to fires. Each case is calculated with and without GFED3 fire emissions using GISS-E2-PUCCINI results, which was close to the average concentration estimate. Refer to Supplementary Table S1 for estimated health effects. Note the logarithmic scale for (a) and (c).

Mentions: We explored how modeled concentrations with and without fire emissions affect population exposure to WHO interim targets (Supplementary Table S1)4. Decadal exposure over these interim targets, along with the fraction of exposure due to fire, shows how the major influence of fires was not confined to the 1997–98 El Niño (Fig. 3). Interannual variability in exposure for both short- and long-term guidelines is dominated by the fire contribution of PM2.5 and O3; the WHO’s 25 μg/m3 annual PM2.5 interim target (IT-2) is never exceeded without including fire emissions.


El Niño and health risks from landscape fire emissions in Southeast Asia.

Marlier ME, DeFries RS, Voulgarakis A, Kinney PL, Randerson JT, Shindell DT, Chen Y, Faluvegi G - Nat Clim Chang (2013)

Population exposure above World Health Organization (WHO) interim targetsa, Exposure over 50 μg/m3 24-hour PM2.5 interim target (IT-2). b, Exposure over 25 μg/m3 annual PM2.5 interim target (IT-2). c, Exposure over 80 ppb 8-hour maximum O3 interim target (IT-1). d, Fraction of population exposure above each WHO interim target that is attributable to fires. Each case is calculated with and without GFED3 fire emissions using GISS-E2-PUCCINI results, which was close to the average concentration estimate. Refer to Supplementary Table S1 for estimated health effects. Note the logarithmic scale for (a) and (c).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219417&req=5

Figure 3: Population exposure above World Health Organization (WHO) interim targetsa, Exposure over 50 μg/m3 24-hour PM2.5 interim target (IT-2). b, Exposure over 25 μg/m3 annual PM2.5 interim target (IT-2). c, Exposure over 80 ppb 8-hour maximum O3 interim target (IT-1). d, Fraction of population exposure above each WHO interim target that is attributable to fires. Each case is calculated with and without GFED3 fire emissions using GISS-E2-PUCCINI results, which was close to the average concentration estimate. Refer to Supplementary Table S1 for estimated health effects. Note the logarithmic scale for (a) and (c).
Mentions: We explored how modeled concentrations with and without fire emissions affect population exposure to WHO interim targets (Supplementary Table S1)4. Decadal exposure over these interim targets, along with the fraction of exposure due to fire, shows how the major influence of fires was not confined to the 1997–98 El Niño (Fig. 3). Interannual variability in exposure for both short- and long-term guidelines is dominated by the fire contribution of PM2.5 and O3; the WHO’s 25 μg/m3 annual PM2.5 interim target (IT-2) is never exceeded without including fire emissions.

Bottom Line: In this study, we combine satellite-derived fire estimates and atmospheric modeling to quantify health effects from fire emissions in Southeast Asia from 1997 to 2006.This region has large interannual variability in fire activity due to coupling between El Niño-induced droughts and anthropogenic land use change(2,3).We show that during strong El Niño years, fires contribute up to 200 μg/m(3) and 50 ppb in annual average fine particulate matter (PM2.5) and ozone (O3) surface concentrations near fire sources, respectively.

View Article: PubMed Central - PubMed

Affiliation: Department of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, 10964, USA.

ABSTRACT
Emissions from landscape fires affect both climate and air quality(1). In this study, we combine satellite-derived fire estimates and atmospheric modeling to quantify health effects from fire emissions in Southeast Asia from 1997 to 2006. This region has large interannual variability in fire activity due to coupling between El Niño-induced droughts and anthropogenic land use change(2,3). We show that during strong El Niño years, fires contribute up to 200 μg/m(3) and 50 ppb in annual average fine particulate matter (PM2.5) and ozone (O3) surface concentrations near fire sources, respectively. This corresponds to a fire contribution of 200 additional days per year that exceed the World Health Organization (WHO) 50 μg/m(3) 24-hour PM2.5 interim target (IT-2)(4) and an estimated 10,800 (6,800-14,300) person (~2%) annual increase in regional adult cardiovascular mortality. Our results indicate that reducing regional deforestation and degradation fires would improve public health along with widely established benefits from reducing carbon emissions, preserving biodiversity, and maintaining ecosystem services.

No MeSH data available.


Related in: MedlinePlus