1
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Li C, Wang J, Zhang H, Diner DJ, Hasheminassab S, Janechek N. Improvement of Surface PM 2.5 Diurnal Variation Simulations in East Africa for the MAIA Satellite Mission. ACS ES&T AIR 2024; 1:223-233. [PMID: 38633207 PMCID: PMC11019548 DOI: 10.1021/acsestair.3c00008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 04/19/2024]
Abstract
The Multi-Angle Imager for Aerosols (MAIA), supported by NASA and the Italian Space Agency, is planned for launch into space in 2025. As part of its mission goal, outputs from a chemical transport model, the Unified Inputs for Weather Research and Forecasting Model coupled with Chemistry (UI-WRF-Chem), will be used together with satellite data and surface observations for estimating surface PM2.5. Here, we develop a method to improve UI-WRF-Chem with surface observations at the U.S. embassy in Ethiopia, one of MAIA's primary target areas in east Africa. The method inversely models the diurnal profile and amount of anthropogenic aerosol and trace gas emissions. Low-cost PurpleAir sensor data are used for validation after applying calibration functions obtained from the collocated data at the embassy. With the emission updates in UI-WRF-Chem, independent validation for February 2022 at several different PurpleAir sites shows an increase in the linear correlation coefficients from 0.1-0.7 to 0.6-0.9 between observations and simulations of the diurnal variation of surface PM2.5. Furthermore, even by using the emissions optimized for February 2021, the UI-WRF-Chem forecast for March 2022 is also improved. Annual update of monthly emissions via inverse modeling has the potential and is needed to improve MAIA's estimate of surface PM2.5.
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Affiliation(s)
- Chengzhe Li
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
| | - Jun Wang
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
| | - Huanxin Zhang
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
| | - David J. Diner
- Jet
Propulsion Laboratory, California Institute
of Technology, Pasadena, California 91109, United States
| | - Sina Hasheminassab
- Jet
Propulsion Laboratory, California Institute
of Technology, Pasadena, California 91109, United States
| | - Nathan Janechek
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
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2
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Cui Q, Jia Z, Liu Y, Wang Y, Li Y. 24-hour average PM2.5 concentration caused by aircraft in Chinese airports from Jan. 2006 to Dec. 2023. Sci Data 2024; 11:284. [PMID: 38461334 PMCID: PMC10925045 DOI: 10.1038/s41597-024-03110-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/01/2024] [Indexed: 03/11/2024] Open
Abstract
Since 2006, the rapid development of China's aviation industry has been accompanied by a significant increase in one of its emissions, namely, PM2.5, which poses a substantial threat to human health. However, little data is describing the PM2.5 concentration caused by aircraft activities. This study addresses this gap by initially computing the monthly PM2.5 emissions of the landing-take-off (LTO) stage from Jan. 2006 to Dec. 2023 for 175 Chinese airports, employing the modified BFFM2-FOA-FPM method. Subsequently, the study uses the Gaussian diffusion model to measure the 24-hour average PM2.5 concentration resulting from flight activities at each airport. This study mainly draws the following conclusions: Between 2006 and 2023, the highest recorded PM2.5 concentration data at all airports was observed in 2018, reaching 5.7985 micrograms per cubic meter, while the lowest point was recorded in 2022, at 2.0574 micrograms per cubic meter. Moreover, airports with higher emissions are predominantly located in densely populated and economically vibrant regions such as Beijing, Shanghai, Guangzhou, Chengdu, and Shenzhen.
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Affiliation(s)
- Qiang Cui
- School of Economics and Management, Southeast University, Nanjing, China.
| | - Zike Jia
- School of Economics and Management, Southeast University, Nanjing, China
| | - Yujie Liu
- School of Economics and Management, Southeast University, Nanjing, China
| | - Yu Wang
- School of Economics and Management, Civil Aviation Flight University of China, Guanghan, China.
| | - Ye Li
- School of Business Administration, Nanjing University of Finance and Economics, Nanjing, China.
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3
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Yang J, Wang G, Zhang C. Forecast of Fine Particles in Chengdu under Autumn-Winter Synoptic Conditions. TOXICS 2023; 11:777. [PMID: 37755787 PMCID: PMC10535754 DOI: 10.3390/toxics11090777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
We conducted an evaluation of the impact of meteorological factor forecasts on the prediction of fine particles in Chengdu, China, during autumn and winter, utilizing the European Cooperation in Science and Technology (COST)733 objective weather classification software and the Community Multiscale Air Quality model. This analysis was performed under four prevailing weather patterns. Fine particle pollution tended to occur under high-pressure rear, homogeneous-pressure, and low-pressure conditions; by contrast, fine particle concentrations were lower under high-pressure bottom conditions. The forecasts of fine particle concentrations were more accurate under high-pressure bottom conditions than under high-pressure rear and homogeneous-pressure conditions. Moreover, under all conditions, the 24 h forecast of fine particle concentrations were more accurate than the 48 and 72 h forecasts. Regarding meteorological factors, forecasts of 2 m relative humidity and 10 m wind speed were more accurate under high-pressure bottom conditions than high-pressure rear and homogeneous-pressure conditions. Moreover, 2 m relative humidity and 10 m wind speed were important factors for forecasting fine particles, whereas 2 m air temperature was not. Finally, the 24 h forecasts of meteorological factors were more accurate than the 48 and 72 h forecasts, consistent with the forecasting of fine particles.
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Affiliation(s)
- Jingchao Yang
- Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China;
- Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
| | - Ge Wang
- Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China;
- Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
| | - Chao Zhang
- Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
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4
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Wang J, Castro‐Garcia L, Jenerette GD, Chandler M, Ge C, Kucera D, Koutzoukis S, Zeng J. Resolving and Predicting Neighborhood Vulnerability to Urban Heat and Air Pollution: Insights From a Pilot Project of Community Science. GEOHEALTH 2022; 6:e2021GH000575. [PMID: 35509494 PMCID: PMC9055464 DOI: 10.1029/2021gh000575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/14/2022] [Accepted: 03/13/2022] [Indexed: 06/14/2023]
Abstract
Urban heat and air pollution, two environmental threats to urban residents, are studied via a community science project in Los Angeles, CA, USA. The data collected, for the first time, by community members, reveal the significance of both the large spatiotemporal variations of and the covariations between 2 m air temperature (2mT) and ozone (O3) concentration within the (4 km) neighborhood scale. This neighborhood variation was not exhibited in either daily satellite observations or operational model predictions, which makes the assessment of community health risks a challenge. Overall, the 2mT is much better predicted than O3 by the weather and research forecast model with atmospheric chemistry (WRF-Chem). For O3, diurnal variation is better predicted by WRF-Chem than spatial variation (i.e., underestimated by 50%). However, both WRF-chem and the surface observation show the overall consistency in describing statistically significant covariations between O3 and 2mT. In contrast, satellite-based land surface temperature at 1 km resolution is insufficient to capture air temperature variations at the neighborhood scale. Community engagement is augmented with interactive maps and apps that show the predictions in near real time and reveals the potential of green canopy to reduce air temperature and ozone; but different tree types and sizes may lead to different impacts on air temperature, which is not resolved by the WRF-Chem. These findings highlight the need for community science engagement to reveal otherwise impossible insights for models, observations, and real-time dissemination to understand, predict, and ultimately mitigate, urban neighborhood vulnerability to heat and air pollution.
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Affiliation(s)
- Jun Wang
- Center for Global & Regional Environmental Research and Iowa Technology InstituteThe University of IowaIowa CityIAUSA
- Department of Chemical and Biochemical EngineeringDepartment of Physics and AstronomyThe University of IowaIowa CityIAUSA
| | - Lorena Castro‐Garcia
- Center for Global & Regional Environmental Research and Iowa Technology InstituteThe University of IowaIowa CityIAUSA
| | - G. Darrel Jenerette
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
| | | | - Cui Ge
- Center for Global & Regional Environmental Research and Iowa Technology InstituteThe University of IowaIowa CityIAUSA
| | - Dion Kucera
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
| | - Sofia Koutzoukis
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
| | - Jing Zeng
- Center for Global & Regional Environmental Research and Iowa Technology InstituteThe University of IowaIowa CityIAUSA
- Department of Chemical and Biochemical EngineeringDepartment of Physics and AstronomyThe University of IowaIowa CityIAUSA
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5
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Bi J, Knowland KE, Keller CA, Liu Y. Combining Machine Learning and Numerical Simulation for High-Resolution PM 2.5 Concentration Forecast. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:1544-1556. [PMID: 35019267 DOI: 10.1021/acs.est.1c05578] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods rely on either chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA's Goddard Earth Observing System "Composition Forecasting" (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next 5 days at a 1 km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next 2 days had an overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following 3 forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with a validation normalized mean bias close to 0, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.
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Affiliation(s)
- Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, University of Washington, 4225 Roosevelt Way NE, Seattle, Washington 98105, United States
| | - K Emma Knowland
- NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, Maryland 20771, United States
- Universities Space Research Association/Goddard Earth Science Technology & Research (GESTAR), Columbia, Maryland 21046, United States
| | - Christoph A Keller
- NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, Maryland 20771, United States
- Universities Space Research Association/Goddard Earth Science Technology & Research (GESTAR), Columbia, Maryland 21046, United States
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, Georgia 30322, United States
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6
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Zhang H, Wang J, García LC, Zhou M, Ge C, Plessel T, Szykman J, Levy RC, Murphy B, Spero TL. Improving surface PM 2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas. JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES : JGR 2022; 127:1-19. [PMID: 38511152 PMCID: PMC10953817 DOI: 10.1029/2021jd035563] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/24/2021] [Indexed: 03/22/2024]
Abstract
This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi-satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multi chemical transport model (CTM) (GEOS-Chem, WRF-Chem and CMAQ) outputs and ground observations. In part I of the study, a multi-model ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non-rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125 - 300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non-rural areas, referred to as "extended ground truth" or EGT, for today. Lastly, we applied the KF technique to update the bias in the forecast for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three models for both today and next day show the best performance. Together, the two-part study develops a multi-model and multi-AOD bias correction technique that has the potential to improve PM2.5 forecasts in both rural and non-rural areas in near real time, and be readily implemented at state levels.
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Affiliation(s)
- Huanxin Zhang
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Lorena Castro García
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Meng Zhou
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
- Interdisciplinary Graduate Program in Geo-Informatics, The University of Iowa, Iowa City, IA, USA
| | - Cui Ge
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Todd Plessel
- General Dynamics Information Technology, RTP, NC, USA
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7
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Li J, Guan T, Guo Q, Geng G, Wang H, Guo F, Li J, Xue T. Exposure to landscape fire smoke reduced birthweight in low- and middle-income countries: findings from a siblings-matched case-control study. eLife 2021; 10:69298. [PMID: 34586064 PMCID: PMC8563002 DOI: 10.7554/elife.69298] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 09/26/2021] [Indexed: 01/20/2023] Open
Abstract
Background: Landscape fire smoke (LFS) has been associated with reduced birthweight, but evidence from low- and middle-income countries (LMICs) is rare. Methods: Here, we present a sibling-matched case–control study of 227,948 newborns to identify an association between fire-sourced fine particulate matter (PM2.5) and birthweight in 54 LMICs from 2000 to 2014. We selected mothers from the geocoded Demographic and Health Survey with at least two children and valid birthweight records. Newborns affiliated with the same mother were defined as a family group. Gestational exposure to LFS was assessed in each newborn using the concentration of fire-sourced PM2.5. We determined the associations of the within-group variations in LFS exposure with birthweight differences between matched siblings using a fixed-effects regression model. Additionally, we analyzed the binary outcomes of low birthweight (LBW) or very low birthweight (VLBW). Results: According to fully adjusted models, a 1 µg/m3 increase in the concentration of fire-sourced PM2.5 was significantly associated with a 2.17 g (95% confidence interval [CI] 0.56–3.77) reduction in birthweight, a 2.80% (95% CI 0.97–4.66) increase in LBW risk, and an 11.68% (95% CI 3.59–20.40) increase in VLBW risk. Conclusions: Our findings indicate that gestational exposure to LFS harms fetal health. Funding: PKU-Baidu Fund, National Natural Science Foundation of China, Peking University Health Science Centre, and CAMS Innovation Fund for Medical Sciences.
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Affiliation(s)
- Jiajianghui Li
- Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Tianjia Guan
- Department of Health Policy, School of Health Policy and Management, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Guo
- School of Energy and Environmental Engineering, University of Science and Technology, Beijing, China
| | - Guannan Geng
- School of Environment, Tsinghua University, Beijing, China
| | - Huiyu Wang
- Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Fuyu Guo
- Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Jiwei Li
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Tao Xue
- Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
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8
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Malings C, Knowland KE, Keller CA, Cohn SE. Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements. EARTH AND SPACE SCIENCE (HOBOKEN, N.J.) 2021; 8:e2021EA001743. [PMID: 34435082 PMCID: PMC8365697 DOI: 10.1029/2021ea001743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/03/2021] [Accepted: 05/27/2021] [Indexed: 05/19/2023]
Abstract
While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.
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Affiliation(s)
- C. Malings
- Goddard Space Flight CenterNASA Postdoctoral Program FellowGreenbeltMDUSA
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - K. E. Knowland
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - C. A. Keller
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - S. E. Cohn
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
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9
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O'Neill SM, Diao M, Raffuse S, Al-Hamdan M, Barik M, Jia Y, Reid S, Zou Y, Tong D, West JJ, Wilkins J, Marsha A, Freedman F, Vargo J, Larkin NK, Alvarado E, Loesche P. A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2021; 71:791-814. [PMID: 33630725 DOI: 10.1080/10962247.2021.1891994] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 01/11/2021] [Accepted: 02/10/2021] [Indexed: 06/12/2023]
Abstract
Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.
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Affiliation(s)
- Susan M O'Neill
- Pacific Northwest Research Station, US Department of Agriculture Forest Service, Seattle, WA, USA
| | - Minghui Diao
- Meteorology and Climate Science, San Jose State University, San Jose, CA, USA
| | - Sean Raffuse
- Air Quality Research Center, University of California Davis, Davis, CA, USA
| | - Mohammad Al-Hamdan
- National Space Science and Technology Center, Universities Space Research Association at NASA Marshall Space Flight Center, Huntsville, AL, USA
- National Center for Computational Hydroscience and Engineering (NCCHE) and Department of Civil Engineering and Department of Geology and Geological Engineering, University of Mississippi, Oxford, MS, USA
| | - Muhammad Barik
- Yara North America Inc., San Francisco Hub, San Francisco, CA, USA
| | - Yiqin Jia
- Assessment, Inventory & Modeling Division, Bay Area Air Quality Management District, San Francisco, CA, USA
| | - Steve Reid
- Assessment, Inventory & Modeling Division, Bay Area Air Quality Management District, San Francisco, CA, USA
| | - Yufei Zou
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Daniel Tong
- Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, USA
| | - J Jason West
- Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, NC, USA
| | - Joseph Wilkins
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
| | - Amy Marsha
- Pacific Northwest Research Station, US Department of Agriculture Forest Service, Seattle, WA, USA
| | - Frank Freedman
- Meteorology and Climate Science, San Jose State University, San Jose, CA, USA
| | - Jason Vargo
- Office of Health Equity, California Department of Public Health, Richmond, CA, USA
| | - Narasimhan K Larkin
- Pacific Northwest Research Station, US Department of Agriculture Forest Service, Seattle, WA, USA
| | - Ernesto Alvarado
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
| | - Patti Loesche
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
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10
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Open fire exposure increases the risk of pregnancy loss in South Asia. Nat Commun 2021; 12:3205. [PMID: 34050160 PMCID: PMC8163851 DOI: 10.1038/s41467-021-23529-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 04/30/2021] [Indexed: 12/27/2022] Open
Abstract
Interactions between climate change and anthropogenic activities result in increasing numbers of open fires, which have been shown to harm maternal health. However, few studies have examined the association between open fire and pregnancy loss. We conduct a self-comparison case-control study including 24,876 mothers from South Asia, the region with the heaviest pregnancy-loss burden in the world. Exposure is assessed using a chemical transport model as the concentrations of fire-sourced PM2.5 (i.e., fire PM2.5). The adjusted odds ratio (OR) of pregnancy loss for a 1-μg/m3 increment in averaged concentration of fire PM2.5 during pregnancy is estimated as 1.051 (95% confidence intervals [CI]: 1.035, 1.067). Because fire PM2.5 is more strongly linked with pregnancy loss than non-fire PM2.5 (OR: 1.014; 95% CI: 1.011, 1.016), it contributes to a non-neglectable fraction (13%) of PM2.5-associated pregnancy loss. Here, we show maternal health is threaten by gestational exposure to fire smoke in South Asia.
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