Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires
Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to...
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ftmichigantuniv:oai:digitalcommons.mtu.edu:michigantech-p-36649 2023-08-27T04:07:24+02:00 Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires Chen, Dong Billmire, Michael Loughner, Christopher P Bredder, Allison French, Nancy H Kim, Hyun Cheol Loboda, Tatiana V 2023-07-17T07:00:00Z https://digitalcommons.mtu.edu/michigantech-p/17350 https://doi.org/10.1016/j.scitotenv.2023.165594 unknown Digital Commons @ Michigan Tech https://digitalcommons.mtu.edu/michigantech-p/17350 doi:10.1016/j.scitotenv.2023.165594 https://doi.org/10.1016/j.scitotenv.2023.165594 Michigan Tech Publications Air pollution Alaska Biomass burning Boreal forests HYSPLIT PM(2.5) Remote sensing Wildfire text 2023 ftmichigantuniv https://doi.org/10.1016/j.scitotenv.2023.165594 2023-08-03T18:03:48Z Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires ... Text Arctic Human health Tundra Alaska Michigan Technological University: Digital Commons @ Michigan Tech Arctic Science of The Total Environment 898 165594 |
institution |
Open Polar |
collection |
Michigan Technological University: Digital Commons @ Michigan Tech |
op_collection_id |
ftmichigantuniv |
language |
unknown |
topic |
Air pollution Alaska Biomass burning Boreal forests HYSPLIT PM(2.5) Remote sensing Wildfire |
spellingShingle |
Air pollution Alaska Biomass burning Boreal forests HYSPLIT PM(2.5) Remote sensing Wildfire Chen, Dong Billmire, Michael Loughner, Christopher P Bredder, Allison French, Nancy H Kim, Hyun Cheol Loboda, Tatiana V Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires |
topic_facet |
Air pollution Alaska Biomass burning Boreal forests HYSPLIT PM(2.5) Remote sensing Wildfire |
description |
Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires ... |
format |
Text |
author |
Chen, Dong Billmire, Michael Loughner, Christopher P Bredder, Allison French, Nancy H Kim, Hyun Cheol Loboda, Tatiana V |
author_facet |
Chen, Dong Billmire, Michael Loughner, Christopher P Bredder, Allison French, Nancy H Kim, Hyun Cheol Loboda, Tatiana V |
author_sort |
Chen, Dong |
title |
Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires |
title_short |
Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires |
title_full |
Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires |
title_fullStr |
Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires |
title_full_unstemmed |
Simulating spatio-temporal dynamics of surface PM emitted from Alaskan wildfires |
title_sort |
simulating spatio-temporal dynamics of surface pm emitted from alaskan wildfires |
publisher |
Digital Commons @ Michigan Tech |
publishDate |
2023 |
url |
https://digitalcommons.mtu.edu/michigantech-p/17350 https://doi.org/10.1016/j.scitotenv.2023.165594 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Human health Tundra Alaska |
genre_facet |
Arctic Human health Tundra Alaska |
op_source |
Michigan Tech Publications |
op_relation |
https://digitalcommons.mtu.edu/michigantech-p/17350 doi:10.1016/j.scitotenv.2023.165594 https://doi.org/10.1016/j.scitotenv.2023.165594 |
op_doi |
https://doi.org/10.1016/j.scitotenv.2023.165594 |
container_title |
Science of The Total Environment |
container_volume |
898 |
container_start_page |
165594 |
_version_ |
1775348177329717248 |