{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/49407"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/49407","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Constraining a historical black carbon emission inventory of the United States from 1960-2000","abstract":"In order to improve the accuracy of the black carbon (BC) historical emission inventory of the United States over the period 1960-2000, I studied the transport of global BC emissions and ambient air concentrations using the Community Atmosphere Model. I formulated the relationship between emissions and concentrations into matrices that allow reconstruction of emission for a time-varying ambient air concentration. When calculating the transport matrices from the model simulation result, I adjust the model meteorology according to the measured meteorology. This could avoid attributing model error to emission inaccuracy. With the transport matrices, I reconstructed the BC concentration in California and New Jersey where coefficient of haze data are available. The discrepancy between the modeled BC and the measurement identifies needed improvements to the emission inventory. However, BC concentrations simulated with different resolutions could vary by a factor of two. Therefore, I suggest that discrepancies in trends are more instructive than absolute discrepancies, especially for evaluating global model results. In the comparison, I examined magnitudes and seasonality of emissions. Emissions dominating BC concentrations in California and New Jersey have evolving seasonal pattern over 1960-2000. Heating Degree Days (HDDs) data are applied to estimate seasonal residential emissions. However, even the adjustment for HDDs does not fully explain the trend in seasonal variation of the measurement. Also, the mismatch of modeled BC and measurement in New Jersey reveals the inaccuracy of model meteorology in New Jersey. The magnitude of reconstructed emissions was decreasing throughout this period of time, while the original emission inventories peaked in the 1980s. This may indicate the shift of energy consumption between different technologies (shift to cleaner technology) which can cancel out the influence of energy consumption increase.","abstract_html":"In order to improve the accuracy of the black carbon (BC) historical emission inventory of the United States over the period 1960-2000, I studied the transport of global BC emissions and ambient air concentrations using the Community Atmosphere Model. I formulated the relationship between emissions and concentrations into matrices that allow reconstruction of emission for a time-varying ambient air concentration. When calculating the transport matrices from the model simulation result, I adjust the model meteorology according to the measured meteorology. This could avoid attributing model error to emission inaccuracy. With the transport matrices, I reconstructed the BC concentration in California and New Jersey where coefficient of haze data are available. The discrepancy between the modeled BC and the measurement identifies needed improvements to the emission inventory. However, BC concentrations simulated with different resolutions could vary by a factor of two. Therefore, I suggest that discrepancies in trends are more instructive than absolute discrepancies, especially for evaluating global model results. In the comparison, I examined magnitudes and seasonality of emissions. Emissions dominating BC concentrations in California and New Jersey have evolving seasonal pattern over 1960-2000. Heating Degree Days (HDDs) data are applied to estimate seasonal residential emissions. However, even the adjustment for HDDs does not fully explain the trend in seasonal variation of the measurement. Also, the mismatch of modeled BC and measurement in New Jersey reveals the inaccuracy of model meteorology in New Jersey. The magnitude of reconstructed emissions was decreasing throughout this period of time, while the original emission inventories peaked in the 1980s. This may indicate the shift of energy consumption between different technologies (shift to cleaner technology) which can cancel out the influence of energy consumption increase.","abstract_has_math":false,"creators":["Sun, Tianye"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Environ Engr in Civil Engr","degree_department":null,"school":null,"contributors":["Bond, Tami C."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-05-30T16:42:27Z","date_published":"2014-05-30T16:42:27Z","updated_at":"2026-07-22T22:25:38Z","subjects":["Black Carbon","Emission inventory"],"languages":["en"],"rights":["Copyright 2014 Tianye Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/49407","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bond, Tami C."]},{"key":"dc:creator","label":"Author","values":["Sun, Tianye"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-05-30T16:42:27Z","2014-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environ Engr in Civil Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Black Carbon","Emission inventory"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Tianye Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/49407"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In order to improve the accuracy of the black carbon (BC) historical emission inventory of the United States over the period 1960-2000, I studied the transport of global BC emissions and ambient air concentrations using the Community Atmosphere Model. I formulated the relationship between emissions and concentrations into matrices that allow reconstruction of emission for a time-varying ambient air concentration. When calculating the transport matrices from the model simulation result, I adjust the model meteorology according to the measured meteorology. This could avoid attributing model error to emission inaccuracy. With the transport matrices, I reconstructed the BC concentration in California and New Jersey where coefficient of haze data are available. The discrepancy between the modeled BC and the measurement identifies needed improvements to the emission inventory. However, BC concentrations simulated with different resolutions could vary by a factor of two. Therefore, I suggest that discrepancies in trends are more instructive than absolute discrepancies, especially for evaluating global model results. In the comparison, I examined magnitudes and seasonality of emissions. Emissions dominating BC concentrations in California and New Jersey have evolving seasonal pattern over 1960-2000. Heating Degree Days (HDDs) data are applied to estimate seasonal residential emissions. However, even the adjustment for HDDs does not fully explain the trend in seasonal variation of the measurement. Also, the mismatch of modeled BC and measurement in New Jersey reveals the inaccuracy of model meteorology in New Jersey. The magnitude of reconstructed emissions was decreasing throughout this period of time, while the original emission inventories peaked in the 1980s. This may indicate the shift of energy consumption between different technologies (shift to cleaner technology) which can cancel out the influence of energy consumption increase.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-05-02T21:07:08Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Sun_Tianye.docx: 376641 bytes, checksum: 7fe439db10a2d471dfac9535aea22054 (MD5) Sun_Tianye.pdf: 1120068 bytes, checksum: 8adad12680a64bc90f0a4ae4335fda12 (MD5)","Made available in DSpace on 2014-05-30T16:42:27Z (GMT). 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I formulated the relationship between emissions and concentrations into matrices that allow reconstruction of emission for a time-varying ambient air concentration. When calculating the transport matrices from the model simulation result, I adjust the model meteorology according to the measured meteorology. This could avoid attributing model error to emission inaccuracy. With the transport matrices, I reconstructed the BC concentration in California and New Jersey where coefficient of haze data are available. The discrepancy between the modeled BC and the measurement identifies needed improvements to the emission inventory. However, BC concentrations simulated with different resolutions could vary by a factor of two. Therefore, I suggest that discrepancies in trends are more instructive than absolute discrepancies, especially for evaluating global model results. In the comparison, I examined magnitudes and seasonality of emissions. Emissions dominating BC concentrations in California and New Jersey have evolving seasonal pattern over 1960-2000. Heating Degree Days (HDDs) data are applied to estimate seasonal residential emissions. However, even the adjustment for HDDs does not fully explain the trend in seasonal variation of the measurement. Also, the mismatch of modeled BC and measurement in New Jersey reveals the inaccuracy of model meteorology in New Jersey. The magnitude of reconstructed emissions was decreasing throughout this period of time, while the original emission inventories peaked in the 1980s. This may indicate the shift of energy consumption between different technologies (shift to cleaner technology) which can cancel out the influence of energy consumption increase.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-05-02T21:07:08Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Sun_Tianye.docx: 376641 bytes, checksum: 7fe439db10a2d471dfac9535aea22054 (MD5) Sun_Tianye.pdf: 1120068 bytes, checksum: 8adad12680a64bc90f0a4ae4335fda12 (MD5)","Made available in DSpace on 2014-05-30T16:42:27Z (GMT). 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