Public health impacts of the severe haze in Equatorial Asia in September-October 2015: A new tool for fire management to reduce downwind smoke exposure Shannon N. Koplitz Miriam E. Marlier Jonathan J. Buonocore Patrick S. Kim Tianjia Liu Melissa P. Sulprizio Ruth S. DeFries Daniel J. Jacob Joel Schwartz, Samuel S. Myers Haze in Jambi Province on Sumatra, Loretta J. Mickley, Harvard

The Guardian 15 September 2015 Major haze event engulfed much of Equatorial Asia in September-October Children on the way to school in Indonesia, October 2015

Source: IPCC AR5 Neutral Indonesia El Nino wet dry Australian Bureau of Meteorology Both El Nino and positive IOD phases suppress convection over Indonesia, leading to drought. Past haze events (1997 and 2006) also took place during El Nino and the positive phase of the IOD. Burning in 2015 was exacerbated by El Nino and positive Indian Ocean Dipole conditions. El Nino – Southern OscillationIndian Ocean Dipole

Smoke from open fires in Equatorial Asia affects one of the most densely populated regions in the world. Fire emissions (June-November 2006) Cities with population over 1 million people Tg C Jakarta Singapore Fires are used to clear land for oil palm and timber plantations, also to clear cropland after harvest.

What are the health consequences of haze episodes like the one in 2015 in Equatorial Asia? Is there anything that can be done in real-time to minimize these consequences?

Harvard, SEAS Columbia Harvard, HSPH Harvard, HUCE Ruth DeFries Miriam Marlier Joel Schwartz Loretta MickleyPatrick Kim Jonathan Buonocore Shannon Koplitz Daniel Jacob The SMOKE Collaboration Sam Myers

Prevailing winds can carry smoke to populated areas. Smoke from some regions can have a disproportionately large effect on human health. Kim et al., 2014 Fire emissions (  g m -2 s -1 ) Singapore El Nino, 2006 Neutral years July-November fire emissions and mean wind patterns

Diagnosis of regions where fire management would yield greatest benefit to human health. One particular goal of the SMOKE project: Build a model framework that can rapidly identify regions for fire management to target. Fire Emissions Smoke exposures in receptor regions Public Health Costs GEOS-Chem adjoint Health impacts model

GEOS-Chem did a good job calculating surface concentrations of smoke particles during the 2006 haze episode. GEOS-Chem is a 3-D global chemical transport model. Results shown are for nested simulation over Equatorial Asia at fine resolution. observations

The GEOS-Chem adjoint answers the question: What is the sensitivity of smoke exposure at a given receptor to burning in gridboxes across the domain? Steps: Choose receptors. Calculate sensitivity of smoke at that receptor to emissions across domain. Apply those sensitivities to emissions for a given event. (  g m -3 /  g m -2 s -1 ) The adjoint model Koplitz et al., in review For Indonesia and Malaysia, smoke exposures are population weighted.

Adjoint sensitivities for 2006, another El Nino year 2015 emissions in each grid box Smoke exposures at receptors in 2015 Use of adjoint archived sensitivities means that smoke exposures can be quantified rapidly. (  g m -3 /  g m -2 s -1 )  g m -2 s -1 ) (  g m -3 )

Koplitz et al., in review Resulting smoke exposures at receptor sites are sensitive to the fire emissions applied. GFAS, scaled by 3.4 GFAS, unscaled GFAS, scaled by 0.5 Site measurements Model results are monthly means.

Our model framework can identify which regions contribute the most to smoke exposures at receptor sites. Smoke exposure at receptors by province Jambi South Sumatra Jambi Koplitz et al., in review Excess deaths: Indonesia: 91,600 Malaysia: 6,500 Singapore: 22,200 Total > 100 thousand

Main results from project on Sept-Oct 2015 haze event. 1. Haze resulted in > 100,000 excess deaths in Indonesia, Malaysia, and Singapore. 2. Burning in Jambi and South Sumatra provinces accounted for most of the health impacts. 3. Burning on timber plantations contributed more than burning on palm oil plantations to smoke exposures. 4. Much of the smoke exposures arose from burning on degraded peatlands outside of plantations. Koplitz et al., in review

Going forward: We would like to apply GEOS-Chem to air quality problems in India. Aerosol optical depths AOD Sept-Oct-NovMar-Apr-May Aeronet sites GEOS-Chem forward model has difficulty capturing the observed AOD, especially in fall (and winter). There’s a need for improved emissions inventory.

Satellite data show increases in AOD during post-monsoon season. MEAN MEAN ( ) – ( ) Pre Monsoon - FMAM Post Monsoon - ONDJ Unpublished data from Pawan Gupta, NASA