2026-09-19T13:31:27Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1474812023-01-20T03:32:44Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Selin, Noelle Dlamini, Thandolwethu Massachusetts Institute of Technology. Institute for Data, Systems, and Society Technology and Policy Program 2023-01-19T19:53:16Z 2023-01-19T19:53:16Z 2022-09 2022-10-03T18:51:56.608Z https://hdl.handle.net/1721.1/147481 ASGM is the world’s largest source of anthropogenic Hg emissions and is common in Latin America, Sub-Saharan Africa, South Asia, and East Asia. However, the amount of mercury emitted from ASGM and contributing to global mercury emissions is subject to substantial uncertainty. Bottom-up studies have quantified sources of Hg, including ASGM, using data on underlying activities to estimate regional and global totals. In contrast, top-down studies have used atmospheric concentration measurements and models to constrain Hg emissions. However, no top-down estimates have yet been calculated for ASGM emissions. With GEOS-Chem’s global-scale chemical transport model for Hg, we investigate whether and how ASGM-related Hg emissions can be quantified from existing regional measurement sites for gaseous elemental mercury (GEM). By combining our top-down method with existing bottom-up data, we improve estimates of Hg emissions from ASGM activities, using Peru and the Madre de Dios region of South America as case studies. We find that quantitative constraints on ASGM emissions are better provided by information on the shape of the probability distribution of GEM concentrations, such as the interquartile range and the 95% range, suggesting possible design guidelines for monitoring networks. The model-based analysis offers insights into improving regional estimates of ASGM emissions. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Assessing the role of top-down techniques for improving regional estimates of artisanal and small-scale gold mining mercury emissions Thesis application/pdf Master Master of Science in Technology and Policy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="83bb71b7-465b-4185-81d3-ee48be88f472"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Assessing the role of top-down techniques for improving regional estimates of artisanal and small-scale gold mining mercury emissions</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2022-09</PublicationDate> <Authors> <Author> <DisplayName>Dlamini, Thandolwethu</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>ASGM is the world’s largest source of anthropogenic Hg emissions and is common in Latin America, Sub-Saharan Africa, South Asia, and East Asia. However, the amount of mercury emitted from ASGM and contributing to global mercury emissions is subject to substantial uncertainty. Bottom-up studies have quantified sources of Hg, including ASGM, using data on underlying activities to estimate regional and global totals. In contrast, top-down studies have used atmospheric concentration measurements and models to constrain Hg emissions. However, no top-down estimates have yet been calculated for ASGM emissions. With GEOS-Chem’s global-scale chemical transport model for Hg, we investigate whether and how ASGM-related Hg emissions can be quantified from existing regional measurement sites for gaseous elemental mercury (GEM). By combining our top-down method with existing bottom-up data, we improve estimates of Hg emissions from ASGM activities, using Peru and the Madre de Dios region of South America as case studies. We find that quantitative constraints on ASGM emissions are better provided by information on the shape of the probability distribution of GEM concentrations, such as the interquartile range and the 95% range, suggesting possible design guidelines for monitoring networks. The model-based analysis offers insights into improving regional estimates of ASGM emissions.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>