1
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Connolly R, Marlier ME, Garcia-Gonzales DA, Wilkins J, Su J, Bekker C, Jung J, Bonilla E, Burnett RT, Zhu Y, Jerrett M. Mortality attributable to PM 2.5 from wildland fires in California from 2008 to 2018. SCIENCE ADVANCES 2024; 10:eadl1252. [PMID: 38848356 PMCID: PMC11160451 DOI: 10.1126/sciadv.adl1252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 05/06/2024] [Indexed: 06/09/2024]
Abstract
In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM2.5), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM2.5 estimates. We used a concentration-response function for PM2.5, applying ZIP code-level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.
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Affiliation(s)
- Rachel Connolly
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Luskin Center for Innovation, University of California, Los Angeles, Los Angeles, CA, USA
| | - Miriam E. Marlier
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Diane A. Garcia-Gonzales
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joseph Wilkins
- Department of Earth, Environment and Equity, Howard University, Washington, DC, USA
| | - Jason Su
- Department of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Claire Bekker
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jihoon Jung
- Department of City and Regional Planning, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eimy Bonilla
- Department of Earth, Environment and Equity, Howard University, Washington, DC, USA
| | - Richard T. Burnett
- Institute of Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Population Studies Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Yifang Zhu
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael Jerrett
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
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2
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Dong Q, Li Y, Wei X, Jiao L, Wu L, Dong Z, An Y. A city-level dataset of heavy metal emissions into the atmosphere across China from 2015-2020. Sci Data 2024; 11:258. [PMID: 38424081 PMCID: PMC10904851 DOI: 10.1038/s41597-024-03089-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024] Open
Abstract
The absence of nationwide distribution data regarding heavy metal emissions into the atmosphere poses a significant constraint in environmental research and public health assessment. In response to the critical data deficiency, we have established a dataset covering Cr, Cd, As, and Pb emissions into the atmosphere (HMEAs, unit: ton) across 367 municipalities in China. Initially, we collected HMEAs data and covariates such as industrial emissions, vehicle emissions, meteorological variables, among other ten indicators. Following this, nine machine learning models, including Linear Regression (LR), Ridge, Bayesian Ridge (Bayesian), K-Neighbors Regressor (KNN), MLP Regressor (MLP), Random Forest Regressor (RF), LGBM Regressor (LGBM), Lasso, and ElasticNet, were assessed using coefficient of determination (R2), root-mean-square error (RMSE) and Mean Absolute Error (MAE) on the testing dataset. RF and LGBM models were chosen, due to their favorable predictive performance (R2: 0.58-0.84, lower RMSE/MAE), confirming their robustness in modelling. This dataset serves as a valuable resource for informing environmental policies, monitoring air quality, conducting environmental assessments, and facilitating academic research.
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Affiliation(s)
- Qi Dong
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
- Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan, Hunan, 411199, China
| | - Yue Li
- College of Computer Science, Nankai University, Tianjin, 300350, China
| | - Xinhua Wei
- College of Computer Science, Nankai University, Tianjin, 300350, China
| | - Le Jiao
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
| | - Lina Wu
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
| | - Zexin Dong
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
| | - Yi An
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China.
- Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan, Hunan, 411199, China.
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3
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Abstract
We review current knowledge on the trends and drivers of global wildfire activity, advances in the measurement of wildfire smoke exposure, and evidence on the health effects of this exposure. We describe methodological issues in estimating the causal effects of wildfire smoke exposures on health and quantify their importance, emphasizing the role of nonlinear and lagged effects. We conduct a systematic review and meta-analysis of the health effects of wildfire smoke exposure, finding positive impacts on all-cause mortality and respiratory hospitalizations but less consistent evidence on cardiovascular morbidity. We conclude by highlighting priority areas for future research, including leveraging recently developed spatially and temporally resolved wildfire-specific ambient air pollution data to improve estimates of the health effects of wildfire smoke exposure.
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Affiliation(s)
- Carlos F Gould
- Doerr School of Sustainability, Stanford University, Stanford, California, USA; ,
| | - Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University, Stanford, California, USA;
| | - Mary Johnson
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA; ,
| | - Juan Aguilera
- Center for Community Health Impact, The University of Texas Health Science Center at Houston School of Public Health, El Paso, Texas, USA;
| | - Marshall Burke
- Doerr School of Sustainability, Stanford University, Stanford, California, USA; ,
- Center on Food Security and the Environment, Stanford University, Stanford, California, USA;
- National Bureau of Economic Research, Boston, Massachusetts, USA
| | - Kari Nadeau
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA; ,
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4
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Chen AI, Ebisu K, Benmarhnia T, Basu R. Emergency department visits associated with wildfire smoke events in California, 2016-2019. ENVIRONMENTAL RESEARCH 2023; 238:117154. [PMID: 37716386 DOI: 10.1016/j.envres.2023.117154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/09/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023]
Abstract
Wildfire smoke has been associated with adverse respiratory outcomes, but the impacts of wildfire on other health outcomes and sensitive subpopulations are not fully understood. We examined associations between smoke events and emergency department visits (EDVs) for respiratory, cardiovascular, diabetes, and mental health outcomes in California during the wildfire season June-December 2016-2019. Daily, zip code tabulation area-level wildfire-specific fine particulate matter (PM2.5) concentrations were aggregated to air basins. A "smoke event" was defined as an air basin-day with a wildfire-specific PM2.5 concentration at or above the 98th percentile across all air basin-days (threshold = 13.5 μg/m3). We conducted a two-stage time-series analysis using quasi-Poisson regression considering lag effects and random effects meta-analysis. We also conducted analyses stratified by race/ethnicity, age, and sex to assess potential effect modification. Smoke events were associated with an increased risk of EDVs for all respiratory diseases at lag 1 [14.4%, 95% confidence interval (CI): (6.8, 22.5)], asthma at lag 0 [57.1% (44.5, 70.8)], and chronic lower respiratory disease at lag 0 [12.7% (6.2, 19.6)]. We also found positive associations with EDVs for all cardiovascular diseases at lag 10. Mixed results were observed for mental health outcomes. Stratified results revealed potential disparities by race/ethnicity. Short-term exposure to smoke events was associated with increased respiratory and schizophrenia EDVs. Cardiovascular impacts may be delayed compared to respiratory outcomes.
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Affiliation(s)
- Annie I Chen
- Air and Climate Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA, USA
| | - Keita Ebisu
- Air and Climate Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA, USA
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Rupa Basu
- Air and Climate Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA, USA.
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5
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Wei J, Wang J, Li Z, Kondragunta S, Anenberg S, Wang Y, Zhang H, Diner D, Hand J, Lyapustin A, Kahn R, Colarco P, da Silva A, Ichoku C. Long-term mortality burden trends attributed to black carbon and PM 2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. Lancet Planet Health 2023; 7:e963-e975. [PMID: 38056967 DOI: 10.1016/s2542-5196(23)00235-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. METHODS In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. FINDINGS Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. INTERPRETATION Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. FUNDING National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.
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Affiliation(s)
- Jing Wei
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA; Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA.
| | - Zhanqing Li
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Shobha Kondragunta
- Center for Satellite Applications and Research, NOAA National Environmental Satellite, Data, and Information Service, College Park, MD, USA
| | - Susan Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, DC, USA
| | - Yi Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - Huanxin Zhang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - David Diner
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jenny Hand
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
| | - Alexei Lyapustin
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Ralph Kahn
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Peter Colarco
- Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Arlindo da Silva
- Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Charles Ichoku
- Department of Geography and Environmental Systems, University of Maryland Baltimore County, Baltimore, MD, USA
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6
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Burke M, Childs ML, de la Cuesta B, Qiu M, Li J, Gould CF, Heft-Neal S, Wara M. The contribution of wildfire to PM 2.5 trends in the USA. Nature 2023; 622:761-766. [PMID: 37730996 DOI: 10.1038/s41586-023-06522-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/07/2023] [Indexed: 09/22/2023]
Abstract
Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy1,2, have led to public health benefits1-4. However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM2.5), a pollutant regulated under the Clean Air Act1, have stagnated or begun to reverse throughout much of the USA5. Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM2.5 concentrations are unregulated under current air pollution law6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm.
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Affiliation(s)
- Marshall Burke
- Doerr School of Sustainability, Stanford University, Stanford, CA, USA.
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
- National Bureau of Economic Research, Cambridge, MA, USA.
| | - Marissa L Childs
- Center for the Environment, Harvard University, Cambridge, MA, USA
| | - Brandon de la Cuesta
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Minghao Qiu
- Department of Earth System Science, Stanford University, Stanford, CA, USA
| | - Jessica Li
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Carlos F Gould
- Department of Earth System Science, Stanford University, Stanford, CA, USA
| | - Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Michael Wara
- Doerr School of Sustainability, Stanford University, Stanford, CA, USA
- Woods Institute of the Environment, Stanford University, Stanford, CA, USA
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7
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Considine EM, Hao J, deSouza P, Braun D, Reid CE, Nethery RC. Evaluation of Model-Based PM 2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:2031-2041. [PMID: 36693177 PMCID: PMC10288567 DOI: 10.1021/acs.est.2c06288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.
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Affiliation(s)
- Ellen M. Considine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Jiayuan Hao
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, University of Colorado Denver, Denver, Colorado, 80202, USA
- CU Population Center, University of Colorado Boulder, Boulder, Colorado, 80309, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, 02215, USA
| | - Colleen E. Reid
- CU Population Center, University of Colorado Boulder, Boulder, Colorado, 80309, USA
- Department of Geography, University of Colorado Boulder, Boulder, Colorado, 80302, USA
- Earth Lab, University of Colorado Boulder, Boulder, Colorado, 80303, USA
| | - Rachel C. Nethery
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
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8
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Adami G, Olivi P, Pontalti M, Benini C, Ramazzini L, Magnan B, Bertoldo E, Gatti D, Fassio A, Rossini M, Negri S. Association between acute exposure to environmental air pollution and fragility hip fractures. Bone 2023; 167:116619. [PMID: 36442796 DOI: 10.1016/j.bone.2022.116619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 10/03/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022]
Affiliation(s)
| | - Pietro Olivi
- Orthopedic Unit, University of Verona, Verona, Italy.
| | | | | | | | - Bruno Magnan
- Orthopedic Unit, University of Verona, Verona, Italy.
| | | | - Davide Gatti
- Rheumatology Unit, University of Verona, Verona, Italy.
| | - Angelo Fassio
- Rheumatology Unit, University of Verona, Verona, Italy
| | | | - Stefano Negri
- Orthopedic Unit, University of Verona, Verona, Italy.
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9
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Jiang X, Eum Y, Yoo EH. The impact of fire-specific PM 2.5 calibration on health effect analyses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159548. [PMID: 36270362 DOI: 10.1016/j.scitotenv.2022.159548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The quantification of PM2.5 concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as 'fire') is viable using the Community Multiscale Air Quality (CMAQ), although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM2.5 estimates. First, we calibrated CMAQ-based non-fire PM2.5 to ground PM2.5 observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM2.5 concentrations in the second stage by multiplying the calibrated non-fire PM2.5 obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM2.5 during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM2.5 better agreed with ground PM2.5 observations with a 10-fold cross-validated (CV) R2 of 0.79 compared to CMAQ-based PM2.5 estimates with R2 of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM2.5 and cardio-respiratory hospitalizations across the fire season: relative risk (RR) for cardiovascular disease = 1.074, 95% confidence interval (CI) = 1.021-1.130 in October; RR = 1.191, 95% CI = 1.099-1.291 in November; RR for respiratory disease = 1.078, 95% CI = 1.005-1.157 in October; RR = 1.153, 95% CI = 1.045-1.272 in November. However, the results were inconsistent when non-calibrated PM2.5 was used in the analysis. We found that calibration affected health effect assessments in the present study, but further research is needed to confirm our findings.
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Affiliation(s)
- Xiangyu Jiang
- Georgia Environmental Protection Division, Atlanta, GA 30354, USA.
| | - Youngseob Eum
- Department of Geography, State University of New York at Buffalo, Buffalo, NY 14261, USA
| | - Eun-Hye Yoo
- Department of Geography, State University of New York at Buffalo, Buffalo, NY 14261, USA
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10
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Aguilera R, Luo N, Basu R, Wu J, Clemesha R, Gershunov A, Benmarhnia T. A novel ensemble-based statistical approach to estimate daily wildfire-specific PM 2.5 in California (2006-2020). ENVIRONMENT INTERNATIONAL 2023; 171:107719. [PMID: 36592523 PMCID: PMC10191217 DOI: 10.1016/j.envint.2022.107719] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 05/20/2023]
Abstract
Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.
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Affiliation(s)
- Rosana Aguilera
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
| | - Nana Luo
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Rupa Basu
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA, USA
| | - Jun Wu
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA
| | - Rachel Clemesha
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Alexander Gershunov
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
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11
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Childs ML, Li J, Wen J, Heft-Neal S, Driscoll A, Wang S, Gould CF, Qiu M, Burney J, Burke M. Daily Local-Level Estimates of Ambient Wildfire Smoke PM 2.5 for the Contiguous US. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13607-13621. [PMID: 36134580 DOI: 10.1021/acs.est.2c02934] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Smoke from wildfires is a growing health risk across the US. Understanding the spatial and temporal patterns of such exposure and its population health impacts requires separating smoke-driven pollutants from non-smoke pollutants and a long time series to quantify patterns and measure health impacts. We develop a parsimonious and accurate machine learning model of daily wildfire-driven PM2.5 concentrations using a combination of ground, satellite, and reanalysis data sources that are easy to update. We apply our model across the contiguous US from 2006 to 2020, generating daily estimates of smoke PM2.5 over a 10 km-by-10 km grid and use these data to characterize levels and trends in smoke PM2.5. Smoke contributions to daily PM2.5 concentrations have increased by up to 5 μg/m3 in the Western US over the last decade, reversing decades of policy-driven improvements in overall air quality, with concentrations growing fastest for higher income populations and predominantly Hispanic populations. The number of people in locations with at least 1 day of smoke PM2.5 above 100 μg/m3 per year has increased 27-fold over the last decade, including nearly 25 million people in 2020 alone. Our data set can bolster efforts to comprehensively understand the drivers and societal impacts of trends and extremes in wildfire smoke.
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Affiliation(s)
- Marissa L Childs
- Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, California 94305, United States
| | - Jessica Li
- Center on Food Security and the Environment, Stanford University, Stanford, California 94305, United States
| | - Jeffrey Wen
- Department of Earth System Science, Stanford University, Stanford, California 94305, United States
| | - Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University, Stanford, California 94305, United States
| | - Anne Driscoll
- Center on Food Security and the Environment, Stanford University, Stanford, California 94305, United States
| | - Sherrie Wang
- Goldman School of Public Policy, UC Berkeley, Berkeley, California 94720, United States
| | - Carlos F Gould
- Department of Earth System Science, Stanford University, Stanford, California 94305, United States
| | - Minghao Qiu
- Department of Earth System Science, Stanford University, Stanford, California 94305, United States
| | - Jennifer Burney
- Global Policy School, UC San Diego, San Diego, California 92093, United States
| | - Marshall Burke
- Center on Food Security and the Environment, Stanford University, Stanford, California 94305, United States
- Department of Earth System Science, Stanford University, Stanford, California 94305, United States
- National Bureau of Economic Research, Cambridge, Massachusetts 02138, United States
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Burke M, Heft-Neal S, Li J, Driscoll A, Baylis P, Stigler M, Weill JA, Burney JA, Wen J, Childs ML, Gould CF. Exposures and behavioural responses to wildfire smoke. Nat Hum Behav 2022; 6:1351-1361. [PMID: 35798884 DOI: 10.1038/s41562-022-01396-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/18/2022] [Indexed: 11/10/2022]
Abstract
Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, we find that during large wildfire smoke events, individuals in wealthy locations increasingly search for information about air quality and health protection, stay at home more and are unhappier. Residents of lower-income neighbourhoods exhibit similar patterns in searches for air quality information but not for health protection, spend less time at home and have more muted sentiment responses. During smoke events, indoor particulate matter (PM2.5) concentrations often remain 3-4× above health-based guidelines and vary by 20× between neighbouring households. Our results suggest that policy reliance on self-protection to mitigate smoke health risks will have modest and unequal benefits.
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Affiliation(s)
- Marshall Burke
- Department of Earth System Science, Stanford University, Stanford, CA, USA.
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
- National Bureau of Economic Research, Cambridge, MA, USA.
| | - Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Jessica Li
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Anne Driscoll
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Patrick Baylis
- Department of Economics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthieu Stigler
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Joakim A Weill
- Department of Agricultural and Resource Economics, University of California, Davis, Davis, CA, USA
| | - Jennifer A Burney
- Global Policy School, University of California, San Diego, San Diego, CA, USA
| | - Jeff Wen
- Department of Earth System Science, Stanford University, Stanford, CA, USA
| | - Marissa L Childs
- Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA, USA
| | - Carlos F Gould
- Department of Earth System Science, Stanford University, Stanford, CA, USA
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13
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Swanson A, Holden ZA, Graham J, Warren DA, Noonan C, Landguth E. Daily 1 km terrain resolving maps of surface fine particulate matter for the western United States 2003-2021. Sci Data 2022; 9:466. [PMID: 35918383 PMCID: PMC9345996 DOI: 10.1038/s41597-022-01488-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 06/16/2022] [Indexed: 11/09/2022] Open
Abstract
We developed daily maps of surface fine particulate matter (PM2.5) for the western United States. We used geographically weighted regression fit to air quality station observations with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data, and meteorological data to produce daily 1-kilometer resolution PM2.5 concentration estimates from 2003-2020. To account for impacts of stagnant air and inversions, we included estimates of inversion strength based on meteorological conditions, and inversion potential based on human activities and local topography. Model accuracy based on cross-validation was R2 = 0.66. AOD data improve the model in summer and fall during periods of high wildfire activity while the stagnation terms capture the spatial and temporal dynamics of PM2.5 in mountain valleys, particularly during winter. These data can be used to explore exposure and health outcome impacts of PM2.5 across spatiotemporal domains particularly in the intermountain western United States where measurements from monitoring station data are sparse. Furthermore, these data may facilitate analyses of inversion impacts and local topography on exposure and health outcome studies.
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Affiliation(s)
- Alan Swanson
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | | | - Jon Graham
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
- Mathematical Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | - D Allen Warren
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | - Curtis Noonan
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | - Erin Landguth
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA.
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Heft-Neal S, Driscoll A, Yang W, Shaw G, Burke M. Associations between wildfire smoke exposure during pregnancy and risk of preterm birth in California. ENVIRONMENTAL RESEARCH 2022; 203:111872. [PMID: 34403668 DOI: 10.1016/j.envres.2021.111872] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/06/2021] [Accepted: 08/08/2021] [Indexed: 05/25/2023]
Abstract
There is limited population-scale evidence on the burden of exposure to wildfire smoke during pregnancy and its impacts on birth outcomes. In order to investigate this relationship, data on every singleton birth in California 2006-2012 were combined with satellite-based estimates of wildfire smoke plume boundaries and high-resolution gridded estimates of surface PM2.5 concentrations and a regression model was used to estimate associations with preterm birth risk. Results suggest that each additional day of exposure to any wildfire smoke during pregnancy was associated with an 0.49 % (95 % CI: 0.41-0.59 %) increase in risk of preterm birth (<37 weeks). At sample median smoke exposure (7 days) this translated to a 3.4 % increase in risk, relative to an unexposed mother. Estimates by trimester suggest stronger associations with exposure later in pregnancy and estimates by smoke intensity indicate that observed associations were driven by higher intensity smoke-days. Exposure to low intensity smoke-days had no association with preterm birth while an additional medium (smoke PM2.5 5-10 μg/m3) or high (smoke PM2.5 > 10 μg/m3) intensity smoke-day was associated with an 0.95 % (95 % CI: 0.47-1.42 %) and 0.82 % (95 % CI: 0.41-1.24 %) increase in preterm risk, respectively. In contrast to previous findings for other pollution types, neither exposure to smoke nor the relative impact of smoke on preterm birth differed by race/ethnicity or income in our sample. However, impacts differed greatly by baseline smoke exposure, with mothers in regions with infrequent smoke exposure experiencing substantially larger impacts from an additional smoke-day than mothers in regions where smoke is more common. We estimate 6,974 (95 % CI: 5,513-8,437) excess preterm births attributable to wildfire smoke exposure 2007-2012, accounting for 3.7 % of observed preterm births during this period. Our findings have important implications for understanding the costs of growing wildfire smoke exposure, and for understanding the benefits of smoke mitigation measures.
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Affiliation(s)
- Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
| | - Anne Driscoll
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Wei Yang
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Marshall Burke
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA; Department of Earth System Science, Stanford University, Stanford, CA, USA; National Bureau of Economic Research, Cambridge, MA, USA
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