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dc.contributor.authorBilsback, Kelsey
dc.contributor.authorKerry, Deanna
dc.contributor.authorCroft, Betty
dc.contributor.authorFord, Bonne
dc.contributor.authorJathar, Shantanu H.
dc.contributor.authorCarter, Ellison
dc.contributor.authorMartin, Randall V.
dc.contributor.authorPierce, Jeffrey R.
dc.coverage.spatialGlobal
dc.coverage.temporal2013
dc.date2020
dc.date.accessioned2020-06-18T18:36:13Z
dc.date.available2020-06-18T18:36:13Z
dc.descriptionThese data are analyzed results of five GEOS-Chem v12.6.0 simulations that were run to investigate the aerosol health and radiative impacts of four shipping-industry emission-control scenarios (see associated manuscript for details). The simulation was run for the globe in the year 2013 and the data were created in 2019 at Colorado State University in Fort Collins, Colorado, USA.
dc.description.abstractHistorically, cargo ships have been powered by low-grade fossil fuels, which emit particles and particle-precursor vapors that impact human health and climate. We used a global chemical-transport model with online aerosol microphysics (GEOS-Chem-TOMAS) to estimate the aerosol health and climate impacts of four emission-control policies: (1) 85% reduction in sulfur oxide (SOx) emissions (Sulf); (2) 85% reduction in SOx and black carbon (BC) emissions (Sulf-BC); (3) 85% reduction in SOx, BC, and organic aerosol (OA) emissions (Sulf-BC-OA); and (4) 85% reduction in SOx, BC, OA, and nitrogen oxide (NOx) emissions (Sulf-BC-OA-NOx). The SOx reductions reflect the 0.5% fuel-sulfur cap implemented by the International Maritime Organization (IMO) on January 1st, 2020. The other reductions represent realistic estimates of future emission-control policies. We estimate that these policies could reduce fine particulate matter (PM2.5)-attributable mortalities by 13,200 (Sulf) to 38,600 (Sulf-BC-OA-NOx) mortalities per year. These changes represent 0.3% and 0.8%, respectively, of annual PM2.5-attributable mortalities from anthropogenic sources. Comparing simulations, we estimate that adding the NOx cap has the greatest health benefit. In contrast to the health benefits, all scenarios lead to a simulated climate warming tendency. The combined aerosol direct radiative effect (DRE) and cloud-albedo indirect effects (AIE) are between 27 mW m-2 (Sulf) and 41 mW m-2 (Sulf-BC-OA-NOx). These changes are about 2.1% (Sulf) to 3.2% (Sulf-BC-OA-NOx) of the total anthropogenic aerosol radiative forcing. The emission control policies examined here yield larger relative changes in the aerosol radiative forcing (2.1-3.2%) than in health effects (0.3-0.8%), because most shipping emissions are distant from populated regions. Valuation of the impacts suggests that these emissions reductions could produce much larger marginal health benefits ($128-$374 billion annually) than the marginal climate costs ($12-$17 billion annually).
dc.description.sponsorshipResearch funding was provided by the Monfort Excellence Fund and by the Ocean Frontier Institute, through an award from the Canada First Research Excellence Fund.
dc.format.mediumTXT
dc.format.mediumCSV
dc.identifier.urihttps://hdl.handle.net/10217/208319
dc.identifier.urihttp://dx.doi.org/10.25675/10217/208319
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartofData - Colorado State University
dc.relation.isreferencedbyBilsback, K. R., Kerry, D., Croft, B., Ford, B., Jathar, S. H., Carter, E., Martin, R. V., and Pierce, J. R.: Beyond SOx reductions from shipping: Assessing the impact of NOx and carbonaceous-particle controls on human health and climate, Environ. Res. Lett., in press, https://doi.org/10.1088/1748-9326/abc718, 2020.
dc.subjectshipping
dc.subject0.5% fuel-sulfur cap
dc.subjectinternational maritime organization
dc.subjectIMO 2020
dc.subjectair pollution
dc.subjectaerosol direct effect
dc.subjectcloud-albedo indirect effect
dc.subjectPM2.5-attributable mortality
dc.titleDataset associated with "Beyond SOx reductions from shipping: assessing the impact of NOx and carbonaceous-particle controls on human health and climate”
dc.typeDataset


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