Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts
The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM2.5 concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460225000044/type/journal_article |
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| author | Kyleen Liao Jatan Buch Kara D. Lamb Pierre Gentine |
| author_facet | Kyleen Liao Jatan Buch Kara D. Lamb Pierre Gentine |
| author_sort | Kyleen Liao |
| collection | DOAJ |
| description | The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM2.5 concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from prescribed fires, which is critical in planning the prescribed fires’ location and time, at hourly to daily time scales remains a challenging problem. In this paper, we introduce a spatio-temporal graph neural network (GNN)-based forecasting model for hourly PM2.5 predictions across California. Utilizing a two-step approach, we use our forecasting model to predict the net and ambient PM2.5 concentrations, which are used to estimate wildfire contributions. Integrating the GNN-based PM2.5 forecasting model with simulations of historically prescribed fires, we propose a novel framework to forecast their air quality impact. This framework determines that March is the optimal month for implementing prescribed fires in California and quantifies the potential air quality trade-offs involved in conducting more prescribed fires outside the peak of the fire season. |
| format | Article |
| id | doaj-art-dfd0c56277d54af4b785fd61080263bb |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-dfd0c56277d54af4b785fd61080263bb2025-02-12T07:42:22ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2025.4Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecastsKyleen Liao0https://orcid.org/0009-0005-0421-8335Jatan Buch1https://orcid.org/0000-0001-6672-6750Kara D. Lamb2Pierre Gentine3Department of Computer Science, Stanford University, Stanford, CA, USADepartment of Earth and Environmental Engineering, Columbia University, New York City, NY, USADepartment of Earth and Environmental Engineering, Columbia University, New York City, NY, USADepartment of Earth and Environmental Engineering, Columbia University, New York City, NY, USAThe increasing size and severity of wildfires across the western United States have generated dangerous levels of PM2.5 concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from prescribed fires, which is critical in planning the prescribed fires’ location and time, at hourly to daily time scales remains a challenging problem. In this paper, we introduce a spatio-temporal graph neural network (GNN)-based forecasting model for hourly PM2.5 predictions across California. Utilizing a two-step approach, we use our forecasting model to predict the net and ambient PM2.5 concentrations, which are used to estimate wildfire contributions. Integrating the GNN-based PM2.5 forecasting model with simulations of historically prescribed fires, we propose a novel framework to forecast their air quality impact. This framework determines that March is the optimal month for implementing prescribed fires in California and quantifies the potential air quality trade-offs involved in conducting more prescribed fires outside the peak of the fire season.https://www.cambridge.org/core/product/identifier/S2634460225000044/type/journal_articleair qualityforecastinggraph neural networkwildfires |
| spellingShingle | Kyleen Liao Jatan Buch Kara D. Lamb Pierre Gentine Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts Environmental Data Science air quality forecasting graph neural network wildfires |
| title | Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts |
| title_full | Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts |
| title_fullStr | Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts |
| title_full_unstemmed | Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts |
| title_short | Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts |
| title_sort | simulating the air quality impact of prescribed fires using graph neural network based pm2 5 forecasts |
| topic | air quality forecasting graph neural network wildfires |
| url | https://www.cambridge.org/core/product/identifier/S2634460225000044/type/journal_article |
| work_keys_str_mv | AT kyleenliao simulatingtheairqualityimpactofprescribedfiresusinggraphneuralnetworkbasedpm25forecasts AT jatanbuch simulatingtheairqualityimpactofprescribedfiresusinggraphneuralnetworkbasedpm25forecasts AT karadlamb simulatingtheairqualityimpactofprescribedfiresusinggraphneuralnetworkbasedpm25forecasts AT pierregentine simulatingtheairqualityimpactofprescribedfiresusinggraphneuralnetworkbasedpm25forecasts |