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Feng X, Tian Y, Zhang T, Xue Q, Song D, Huang F, Feng Y. High spatial-resolved source-specific exposure and risk in the city scale: Influence of spatial interrelationship between PM 2.5 sources and population on exposure. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171873. [PMID: 38521275 DOI: 10.1016/j.scitotenv.2024.171873] [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: 02/07/2024] [Revised: 03/05/2024] [Accepted: 03/20/2024] [Indexed: 03/25/2024]
Abstract
Research on High Spatial-Resolved Source-Specific Exposure and Risk (HSRSSER) was conducted based on multiple-year, multiple-site synchronous measurement of PM2.5-bound (particulate matter with aerodynamic diameter<2.5 μm) toxic components in a Chinese megacity. The developed HSRSSER model combined the Positive Matrix Factorization (PMF) and Land Use Regression (LUR) to predict high spatial-resolved source contributions, and estimated the source-specific exposure and risk by personal activity time- and population-weighting. A total of 287 PM2.5 samples were collected at ten sites in 2018-2020, and toxic species including heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs) and organophosphate esters (OPEs) were analyzed. The percentage non-cancer risk were in the order of traffic emission (48 %) > industrial emission (22 %) > coal combustion (12 %) > waste incineration (11 %) > resuspend dust (7 %) > OPE-related products (0 %) ≈ secondary particles (0 %). Similar orders were observed in cancer risk. For traffic emission, due to its higher source contributions and large population in central area, non-cancer and cancer risk fraction increased from 23 % to 48 % and 20 % to 46 % after exposure estimation; while for industrial emission, higher source contributions but small population in suburb area decreased the percentage non-cancer and cancer risk from 38 % to 22 % and 39 % to 24 %, respectively.
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Affiliation(s)
- Xinyao Feng
- State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Yingze Tian
- State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China.
| | - Tengfei Zhang
- State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Qianqian Xue
- State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Danlin Song
- Chengdu Research Academy of Environmental Sciences, Chengdu 610072, China
| | - Fengxia Huang
- Chengdu Research Academy of Environmental Sciences, Chengdu 610072, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
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Picciotto S, Huang S, Lurmann F, Pavlovic N, Ying Chang S, Mukherjee A, Goin DE, Sklar R, Noth E, Morello-Frosch R, Padula AM. Pregnancy exposure to PM 2.5 from wildland fire smoke and preterm birth in California. ENVIRONMENT INTERNATIONAL 2024; 186:108583. [PMID: 38521046 DOI: 10.1016/j.envint.2024.108583] [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: 09/27/2023] [Revised: 02/23/2024] [Accepted: 03/15/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Wildfires in the Western United States are a growing and significant source of air pollution that is eroding decades of progress in air pollution reduction. The effects on preterm birth during critical periods of pregnancy are unknown. METHODS We assessed associations between prenatal exposure to wildland fire smoke and risk of preterm birth (gestational age < 37 weeks). We assigned smoke exposure to geocoded residence at birth for all live singleton births in California conceived 2007-2018, using weekly average concentrations of particulate matter ≤ 2.5 µm (PM2.5) attributable to wildland fires from United States Environmental Protection Agency's Community Multiscale Air Quality Model. Logistic regression yielded odds ratio (OR) for preterm birth in relation to increases in average exposure across the whole pregnancy, each trimester, and each week of pregnancy. Models adjusted for season, age, education, race/ethnicity, medical insurance, and smoking of the birthing parent. RESULTS For the 5,155,026 births, higher wildland fire PM2.5 exposure averaged across pregnancy, or any trimester, was associated with higher odds of preterm birth. The OR for an increase of 1 µg/m3 of average wildland fire PM2.5 during pregnancy was 1.013 (95 % CI:1.008,1.017). Wildland fire PM2.5 during most weeks of pregnancy was associated with higher odds. Strongest estimates were observed in weeks in the second and third trimesters. A 10 µg/m3 increase in average wildland fire PM2·5 in gestational week 23 was associated with OR = 1.034; 95 % CI: 1.019, 1.049 for preterm birth. CONCLUSIONS Preterm birth is sensitive to wildland fire PM2.5; therefore, we must reduce exposure during pregnancy.
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Affiliation(s)
- Sally Picciotto
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | | | | | | | | | | | - Dana E Goin
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Rachel Sklar
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Elizabeth Noth
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Rachel Morello-Frosch
- Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, USA
| | - Amy M Padula
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
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LaPointe S, Lee JC, Nagy ZP, Shapiro DB, Chang HH, Wang Y, Russell AG, Hipp HS, Gaskins AJ. Ambient traffic related air pollution in relation to ovarian reserve and oocyte quality in young, healthy oocyte donors. ENVIRONMENT INTERNATIONAL 2024; 183:108382. [PMID: 38103346 PMCID: PMC10871039 DOI: 10.1016/j.envint.2023.108382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/30/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
Studies in mice and older, subfertile women have found that air pollution exposure may compromise female reproduction. Our objective was to evaluate the effects of air pollution on ovarian reserve and outcomes of ovarian stimulation among young, healthy females. We included 472 oocyte donors who underwent 781 ovarian stimulation cycles at a fertility clinic in Atlanta, Georgia, USA (2008-2019). Antral follicle count (AFC) was assessed with transvaginal ultrasonography and total and mature oocyte count was assessed following oocyte retrieval. Ovarian sensitivity index (OSI) was calculated as the total number of oocytes divided by total gonadotrophin dose × 1000. Daily ambient exposure to nitric oxide (NOx), carbon monoxide (CO), and particulate matter ≤ 2.5 (PM2.5) was estimated using a fused regional + line-source model for near-surface releases at a 250 m resolution based on residential address. Generalized estimating equations were used to evaluate the associations of an interquartile range (IQR) increase in pollutant exposure with outcomes adjusted for donor characteristics, census-level poverty, and meteorological factors. The median (IQR) age among oocyte donors was 25.0 (5.0) years, and 31% of the donors were racial/ethnic minorities. The median (IQR) exposure to NOx, CO, and PM2.5 in the 3 months prior to stimulation was 37.7 (32.0) ppb, 612 (317) ppb, and 9.8 (2.9) µg/m3, respectively. Ambient air pollution exposure in the 3 months before AFC was not associated with AFC. An IQR increase in PM2.5 in the 3 months before AFC and during stimulation was associated with -7.5% (95% CI -14.1, -0.4) and -6.4% (95% CI -11.0, -1.6) fewer mature oocytes, and a -1.9 (95% CI -3.2, -0.5) and -1.0 (95% CI -1.8, -0.2) lower OSI, respectively. Our results suggest that lowering the current 24-h PM2.5 standard in the US to 25 µg/m3 may still not adequately protect against the reprotoxic effects of short-term PM2.5 exposure.
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Affiliation(s)
- Sarah LaPointe
- Department of Epidemiology, Emory University Rollins School of Public Heath, Atlanta, GA, United States
| | - Jaqueline C Lee
- Division of Reproductive Endocrinology and Infertility, Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA, United States
| | - Zsolt P Nagy
- Reproductive Biology Associates, Sandy Springs, GA, United States
| | - Daniel B Shapiro
- Reproductive Biology Associates, Sandy Springs, GA, United States
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Yifeng Wang
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Heather S Hipp
- Division of Reproductive Endocrinology and Infertility, Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA, United States
| | - Audrey J Gaskins
- Department of Epidemiology, Emory University Rollins School of Public Heath, Atlanta, GA, United States.
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Gallagher CL, Holloway T, Tessum CW, Jackson CM, Heck C. Combining Satellite-Derived PM 2.5 Data and a Reduced-Form Air Quality Model to Support Air Quality Analysis in US Cities. GEOHEALTH 2023; 7:e2023GH000788. [PMID: 37181009 PMCID: PMC10169548 DOI: 10.1029/2023gh000788] [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: 01/28/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 05/16/2023]
Abstract
Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra-urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city-scale decision-making. To reduce InMAP's biases and increase its relevancy for urban-scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite-derived speciated PM2.5 from Washington University and ground-level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground-monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: -48%, pNO3: 8%, pNH4: 69%), but with city-specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city-scaling approach (15%-27%). The city-specific scaling method also improves the R 2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36-0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non-EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide -6%).
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Affiliation(s)
- Ciaran L. Gallagher
- Nelson Institute Center for Sustainability and the Global EnvironmentUniversity of Wisconsin—MadisonMadisonWIUSA
| | - Tracey Holloway
- Nelson Institute Center for Sustainability and the Global EnvironmentUniversity of Wisconsin—MadisonMadisonWIUSA
- Department of Atmospheric and Oceanic SciencesUniversity of Wisconsin—MadisonMadisonWIUSA
| | - Christopher W. Tessum
- Department of Civil and Environmental EngineeringUniversity of Illinois—Urbana‐ChampaignUrbanaILUSA
| | - Clara M. Jackson
- Nelson Institute Center for Sustainability and the Global EnvironmentUniversity of Wisconsin—MadisonMadisonWIUSA
| | - Colleen Heck
- Nelson Institute Center for Sustainability and the Global EnvironmentUniversity of Wisconsin—MadisonMadisonWIUSA
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Ebelt ST, D'Souza RR, Yu H, Scovronick N, Moss S, Chang HH. Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:377-385. [PMID: 35595966 PMCID: PMC9675877 DOI: 10.1038/s41370-022-00446-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 06/03/2023]
Abstract
BACKGROUND Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error. OBJECTIVE To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits. METHODS We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics. RESULTS Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging). SIGNIFICANCE The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies. IMPACT STATEMENT This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.
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Affiliation(s)
- Stefanie T Ebelt
- Gangarosa Department of Environmental Health, Emory University, Atlanta, GA, USA.
| | - Rohan R D'Souza
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Noah Scovronick
- Gangarosa Department of Environmental Health, Emory University, Atlanta, GA, USA
| | - Shannon Moss
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
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6
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Bui LT, Nguyen PH. Ground-level ozone in the Mekong Delta region: precursors, meteorological factors, and regional transport. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:23691-23713. [PMID: 36323970 DOI: 10.1007/s11356-022-23819-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
The Mekong Delta region (MDR), also known as Vietnam's rice bowl, produced a bountiful harvest of about 23.8 million tons in 2020, accounting for 55.7% of the country's total production, providing food security for 20% of the world population. With the rapid pace of industrialisation and urbanisation, the concentration of ozone in the lower atmosphere has risen to a level that reduces crop yields, especially rice, and is therefore the subject of research. This study aims to simulate the spatiotemporal distribution of ground-level ozone in the area and evaluate the impact of precursor emissions and meteorological factors on the spatiotemporal distributions of ozone concentrations. The study area was divided into seven zones, including six agro-ecological zones (AEZs) and one low-mountainous area, mainly to clarify the role of emissions in each AEZ. The simulation results showed that ground-level O3 in the MDR ranged from 40.39 to 52.13 µg/m3. In six agro-ecological zones, the average annual ground-level O3 concentration was relatively high and was the highest in zone 6 (CPZ) and zone 3 (LXZ) with values of 96.18 µg/m3 (exceeding 1.60 times the WHO Guidelines 2021) and 94.86 µg/m3 (exceeding 1.58 times the WHO Guidelines 2021), respectively. In each zone, the annual average O3 concentration tended to gradually increase from the inner delta to coastal areas. Two types of precursors, NOx and NMVOCs, are the main contributors to O3 pollution, with the largest contribution coming from zone 1 (FAZ) with 91.5 thousand tons of NOx/year and 455.2 thousand tons of NMVOCs/year. Among the meteorological factors considered, temperature (T), relative humidity (RH), and surface pressure (P) were the three main factors that contributed to the increase in ground-level ozone. The spatio-temporal distribution of ground-level O3 in the MDR was influenced by emission precursors from different zones as well as meteorological factors. The present results can help policy-makers formulate plans for agro-industrial development in the entire region.
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Affiliation(s)
- Long Ta Bui
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.
| | - Phong Hoang Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
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Hang Y, Meng X, Li T, Wang T, Cao J, Fu Q, Dey S, Li S, Huang K, Liang F, Kan H, Shi X, Liu Y. Assessment of long-term particulate nitrate air pollution and its health risk in China. iScience 2022; 25:104899. [PMID: 36039292 PMCID: PMC9418855 DOI: 10.1016/j.isci.2022.104899] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 06/26/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Air pollution is a major environmental and public health challenge in China and the Chinese government has implemented a series of strict air quality policies. However, particulate nitrate (NO3−) concentration remains high or even increases at monitoring sites despite the total PM2.5 concentration has decreased. Unfortunately, it has been difficult to estimate NO3− concentration across China due to the lack of a PM2.5 speciation monitoring network. Here, we use a machine learning model incorporating ground measurements and satellite data to characterize the spatiotemporal patterns of NO3−, thereby understanding the disease burden associated with long-term NO3− exposure in China. Our results show that existing air pollution control policies are effective, but increased NO3− of traffic emissions offset reduced NO3− of industrial emissions. In 2018, the national mean mortality burden attributable to NO3− was as high as 0.68 million, indicating that targeted regulations are needed to control NO3− pollution in China. We build a NO3− model using machine learning techniques incorporating satellite data We estimate spatiotemporal variations of NO3− concentration in China from 2005–2018 In 2018, the national mean mortality burden attributable to NO3− was about 0.68 million Targeted regulations on vehicle emissions are needed to control NO3− pollution in China
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Affiliation(s)
- Yun Hang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Xia Meng
- School of Public Health, Fudan University, Shanghai 200032, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Junji Cao
- Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing 100101, China
| | - Qingyan Fu
- State Ecologic Environmental Scientific Observation and Research Station at Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200235, China
| | - Sagnik Dey
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Shenshen Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100101, China
| | - Kan Huang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai 200032, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
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Sun H, Shin YM, Xia M, Ke S, Wan M, Yuan L, Guo Y, Archibald AT. Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990-2019: A Space-Time Bayesian Neural Network Downscaler. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7337-7349. [PMID: 34751030 DOI: 10.1021/acs.est.1c04797] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Long-term exposure to ambient ozone (O3) can lead to a series of chronic diseases and associated premature deaths, and thus population-level environmental health studies hanker after the high-resolution surface O3 concentration database. In response to this demand, we innovatively construct a space-time Bayesian neural network parametric regressor to fuse TOAR historical observations, CMIP6 multimodel simulation ensemble, population distributions, land cover properties, and emission inventories altogether and downscale to 10 km × 10 km spatial resolution with high methodological reliability (R2 = 0.89-0.97, RMSE = 1.97-3.42 ppbV), fair prediction accuracy (R2 = 0.69-0.77, RMSE = 5.63-7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R2 = 0.62-0.76, RMSE = 5.38-11.7 ppbV). Based on our predictions in 8-h maximum daily average metric, the rural-site surface O3 are 15.1±7.4 ppbV higher than urban globally averaged across 30 historical years during 1990-2019, with developing countries being of the most evident differences. The globe-wide urban surface O3 are climbing by 1.9±2.3 ppbV per decade, except for the decreasing trends in eastern United States. On the other hand, the global rural surface O3 tend to be relatively stable, except for the rising tendencies in China and India. Using CMIP6 model simulations directly without urban-rural differentiation will lead to underestimations of population O3 exposure by 2.0±0.8 ppbV averaged over each historical year. Our original Bayesian neural network framework contributes to the deep-learning-driven environmental studies methodologically by providing a brand-new feasible way to realize data fusion and downscaling, which maintains high interpretability by conforming to the principles of spatial statistics without compromising the prediction accuracy. Moreover, the 30-year highly spatial resolved monthly surface O3 database with multiple metrics fills in the literature gap for long-term surface O3 exposure tracing.
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Affiliation(s)
- Haitong Sun
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
- Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, U.K
| | - Youngsub Matthew Shin
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Mingtao Xia
- Department of Mathematics, University of California, Los Angeles, California 90095, United States
| | - Shengxian Ke
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Michelle Wan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Le Yuan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne Victoria 3004, Australia
| | - Alexander T Archibald
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
- National Centre for Atmospheric Science, Cambridge CB2 1EW, U.K
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9
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Dharmalingam S, Senthilkumar N, D'Souza RR, Hu Y, Chang HH, Ebelt S, Yu H, Kim CS, Rohr A. Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation. ENVIRONMENTAL RESEARCH 2022; 207:112207. [PMID: 34653409 DOI: 10.1016/j.envres.2021.112207] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 09/14/2021] [Accepted: 10/09/2021] [Indexed: 06/13/2023]
Abstract
Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), and three speciated PM2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO4)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO2 and PM2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO2, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.
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Affiliation(s)
- Selvaraj Dharmalingam
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Nirupama Senthilkumar
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Rohan Richard D'Souza
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yongtao Hu
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Stefanie Ebelt
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Haofei Yu
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USA.
| | - Chloe S Kim
- Electric Power Research Institute, Palo Alto, CA, USA
| | - Annette Rohr
- Electric Power Research Institute, Palo Alto, CA, USA
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Huang R, Li Z, Ivey CE, Zhai X, Shi G, Mulholland JA, Devlin R, Russell AG. Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2022; 276:119031. [PMID: 35814352 PMCID: PMC9262331 DOI: 10.1016/j.atmosenv.2022.119031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.
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Affiliation(s)
- Ran Huang
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Zongrun Li
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Cesunica E. Ivey
- Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, California, USA
| | - Xinxin Zhai
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, China
| | - James A. Mulholland
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Robert Devlin
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Armistead G. Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Correspondence:
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11
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Ren X, Mi Z, Cai T, Nolte CG, Georgopoulos PG. Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:3871-3883. [PMID: 35312316 PMCID: PMC9133919 DOI: 10.1021/acs.est.1c04076] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km2) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpolate, extrapolate, and capture concentration peaks. The Shapley value metric from coalitional game theory was applied to interpret the drivers of subgrid gradients. The flexibility (transferability) of the 2011-trained BEML model was further tested by evaluating its ability to estimate fine-scale concentrations for other years (2012-2017) without retraining. To demonstrate the feasibility of using the BEML approach to strictly "data-limited" situations, the model was applied to downscale CMAQ outputs for a future-year scenario-based simulation that considers effects of variations in meteorology associated with climate change.
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Affiliation(s)
- Xiang Ren
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA
| | - Zhongyuan Mi
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA
| | - Ting Cai
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA
| | - Christopher G. Nolte
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Panos G. Georgopoulos
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA
- Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway, NJ 08854, USA
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12
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Yu W, Li S, Ye T, Xu R, Song J, Guo Y. Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:37004. [PMID: 35254864 PMCID: PMC8901043 DOI: 10.1289/ehp9752] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 02/14/2022] [Accepted: 02/14/2022] [Indexed: 05/29/2023]
Abstract
BACKGROUND Accurate estimation of historical PM2.5 (particle matter with an aerodynamic diameter of less than 2.5μm) is critical and essential for environmental health risk assessment. OBJECTIVES The aim of this study was to develop a multiple-level stacked ensemble machine learning framework for improving the estimation of the daily ground-level PM2.5 concentrations. METHODS An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily PM2.5 concentrations. The framework has a three-stage structure: At the first stage, four base models [gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)] were used to generate a new data set of PM2.5 concentrations for training the next-stage learners. At the second stage, three meta-models [RF, XGBoost, and Generalized Linear Model (GLM)] were used to estimate PM2.5 concentrations using a combination of the original data set and the predictions from the first-stage models. At the third stage, a nonnegative least squares (NNLS) algorithm was employed to obtain the optimal weights for PM2.5 estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily PM2.5 at each 1km×1km grid cell from 2015 to 2019 across Italy. We evaluated the model performance by performing 10-fold cross-validation (CV) and compared it with five benchmark algorithms [GBM, SVM, RF, XGBoost, and Super Learner (SL)]. RESULTS The results revealed that the PM2.5 prediction performance of DEML [coefficients of determination (R2)=0.87 and root mean square error (RMSE)=5.38μg/m3] was superior to any benchmark models (with R2 of 0.51, 0.76, 0.83, 0.70, and 0.83 for GBM, SVM, RF, XGBoost, and SL approach, respectively). DEML displayed reliable performance in capturing the spatiotemporal variations of PM2.5 in Italy. DISCUSSION The proposed DEML framework achieved an outstanding performance in PM2.5 estimation, which could be used as a tool for more accurate environmental exposure assessment. https://doi.org/10.1289/EHP9752.
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Affiliation(s)
- Wenhua Yu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Tingting Ye
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Rongbin Xu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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13
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Gong C, Wang J, Bai Z, Rich DQ, Zhang Y. Maternal exposure to ambient PM 2.5 and term birth weight: A systematic review and meta-analysis of effect estimates. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 807:150744. [PMID: 34619220 DOI: 10.1016/j.scitotenv.2021.150744] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/18/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
Effect estimates of prenatal exposure to ambient PM2.5 on change in grams (β) of birth weight among term births (≥37 weeks of gestation; term birth weight, TBW) vary widely across studies. We present the first systematic review and meta-analysis of evidence regarding these associations. Sixty-two studies met the eligibility criteria for this review, and 31 studies were included in the meta-analysis. Random-effects meta-analysis was used to assess the quantitative relationships. Subgroup analyses were performed to gain insight into heterogeneity derived from exposure assessment methods (grouped by land use regression [LUR]-models, aerosol optical depth [AOD]-based models, interpolation/dispersion/Bayesian models, and data from monitoring stations), study regions, and concentrations of PM2.5 exposure. The overall pooled estimate involving 23,925,941 newborns showed that TBW was negatively associated with PM2.5 exposure (per 10 μg/m3 increment) during the entire pregnancy (β = -16.54 g), but with high heterogeneity (I2 = 95.6%). The effect estimate in the LUR-models subgroup (β = -16.77 g) was the closest to the overall estimate and with less heterogeneity (I2 = 18.3%) than in the other subgroups of AOD-based models (β = -41.58 g; I2 = 95.6%), interpolation/dispersion models (β = -10.78 g; I2 = 86.6%), and data from monitoring stations (β = -11.53 g; I2 = 97.3%). Even PM2.5 exposure levels of lower than 10 μg/m3 (the WHO air quality guideline value) had adverse effects on TBW. The LUR-models subgroup was the only subgroup that obtained similar significant of negative associations during the three trimesters as the overall trimester-specific analyses. In conclusion, TBW was negatively associated with maternal PM2.5 exposures during the entire pregnancy and each trimester. More studies based on relatively standardized exposure assessment methods need to be conducted to further understand the precise susceptible exposure time windows and potential mechanisms.
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Affiliation(s)
- Chen Gong
- Department of Family Planning, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Jianmei Wang
- Department of Family Planning, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zhipeng Bai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China; Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA
| | - David Q Rich
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Yujuan Zhang
- Department of Family Planning, The Second Hospital of Tianjin Medical University, Tianjin, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.
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14
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Bozigar M, Lawson AB, Pearce JL, Svendsen ER, Vena JE. Using Bayesian time-stratified case-crossover models to examine associations between air pollution and "asthma seasons" in a low air pollution environment. PLoS One 2021; 16:e0260264. [PMID: 34879071 PMCID: PMC8654232 DOI: 10.1371/journal.pone.0260264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 11/05/2021] [Indexed: 11/18/2022] Open
Abstract
Many areas of the United States have air pollution levels typically below Environmental Protection Agency (EPA) regulatory limits. Most health effects studies of air pollution use meteorological (e.g., warm/cool) or astronomical (e.g., solstice/equinox) definitions of seasons despite evidence suggesting temporally-misaligned intra-annual periods of relative asthma burden (i.e., “asthma seasons”). We introduce asthma seasons to elucidate whether air pollutants are associated with seasonal differences in asthma emergency department (ED) visits in a low air pollution environment. Within a Bayesian time-stratified case-crossover framework, we quantify seasonal associations between highly resolved estimates of six criteria air pollutants, two weather variables, and asthma ED visits among 66,092 children ages 5–19 living in South Carolina (SC) census tracts from 2005 to 2014. Results show that coarse particulates (particulate matter <10 μm and >2.5 μm: PM10-2.5) and nitrogen oxides (NOx) may contribute to asthma ED visits across years, but are particularly implicated in the highest-burden fall asthma season. Fine particulate matter (<2.5 μm: PM2.5) is only associated in the lowest-burden summer asthma season. Relatively cool and dry conditions in the summer asthma season and increased temperatures in the spring and fall asthma seasons are associated with increased ED visit odds. Few significant associations in the medium-burden winter and medium-high-burden spring asthma seasons suggest other ED visit drivers (e.g., viral infections) for each, respectively. Across rural and urban areas characterized by generally low air pollution levels, there are acute health effects associated with particulate matter, but only in the summer and fall asthma seasons and differing by PM size.
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Affiliation(s)
- Matthew Bozigar
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
- * E-mail:
| | - Andrew B. Lawson
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - John L. Pearce
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Erik R. Svendsen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - John E. Vena
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
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15
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Rafael S, Rodrigues V, Oliveira K, Coelho S, Lopes M. How to compute long-term averages for air quality assessment at urban areas? THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 795:148603. [PMID: 34328935 DOI: 10.1016/j.scitotenv.2021.148603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/02/2021] [Accepted: 06/18/2021] [Indexed: 06/13/2023]
Abstract
This paper proposes two innovative time-effective approaches to retrieve annual averaged concentrations for air quality assessment in the framework of the AQD. In addition, a traditional method (M1) was applied through numerical simulations for an entire year on an hourly basis to compare the performance of the proposed approaches. The first time-effective approach (M2) is based on the calculation of pollutant concentrations for the full year on an hourly basis through the combination of a set of numerical simulations for 4 typical days weighted by hourly factors obtained from air quality monitoring data. While the second time-effective approach (M3) considers the numerical simulation of pollutant concentrations for a set of typical meteorological conditions. For all the methods, air quality simulations were performed with the second-generation Gaussian model URBAIR. The three methods are applied over two distinct European urban areas, the Aveiro region in Portugal and Bristol in the United Kingdom, for the simulation of NO2 and PM10 annual concentrations. The main results highlight an underestimation of the NO2 annual concentrations by M2 and an overestimation of those concentrations by M3 for the Aveiro region, when compared to M1 as the reference method. While, for Bristol the main differences between methods were found for NO2 concentrations when using M3. M2 underestimates PM10 annual concentrations in the Aveiro Region, while M3 points out underestimation or overestimation of those concentrations for distinct areas of the domain. This study aims to foster the knowledge on air quality assessment under the European policy context, supporting air quality management and urban planning. The innovative nature of this study relies on the proposed time-effective tools, suitable for the fast simulation of complex urban areas applying high spatial resolution. Additionally, these modelling tools may provide key information on air quality to population, particularly where it is not readily available.
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Affiliation(s)
- S Rafael
- CESAM & Department of Environment and Planning, University of Aveiro, Aveiro, Portugal
| | - V Rodrigues
- CESAM & Department of Environment and Planning, University of Aveiro, Aveiro, Portugal.
| | - K Oliveira
- CESAM & Department of Environment and Planning, University of Aveiro, Aveiro, Portugal
| | - S Coelho
- CESAM & Department of Environment and Planning, University of Aveiro, Aveiro, Portugal
| | - M Lopes
- CESAM & Department of Environment and Planning, University of Aveiro, Aveiro, Portugal
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16
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Gong W, Reich BJ, Chang HH. Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA. ENVIRONMENTAL RESEARCH COMMUNICATIONS 2021; 3:101002. [PMID: 35694083 PMCID: PMC9187197 DOI: 10.1088/2515-7620/ac2f92] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO2, SO2, O3, PM10, and PM2.5 species EC, OC, NO3, NH4, SO4) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM2.5 species EC (R2 = 0.64), OC (R2 = 0.75), NH4 (R2 = 0.84), NO3 (R2 = 0.73), and SO4 (R2 = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM2.5 species and several gases at this spatial and temporal resolution.
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17
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Short-term exposure to fine particulate air pollution and emergency department visits for kidney diseases in the Atlanta metropolitan area. Environ Epidemiol 2021; 5:e164. [PMID: 34414347 PMCID: PMC8367053 DOI: 10.1097/ee9.0000000000000164] [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: 03/18/2021] [Accepted: 06/18/2021] [Indexed: 02/01/2023] Open
Abstract
Toxicological evidence has shown that fine particulate matter (PM2.5) may affect distant organs, including kidneys, over the short term. However, epidemiological evidence is limited. OBJECTIVES We investigated associations between short-term exposure to PM2.5, major PM2.5 components [elemental carbon (EC), organic carbon (OC), sulfate, and nitrate], and gaseous co-pollutants (O3, CO, SO2, NO2, and NOx) and emergency department (ED) visits for kidney diseases during 2002-2008 in Atlanta, Georgia. METHODS Log-linear time-series models were fitted to estimate the acute effects of air pollution, with single-day and unconstrained distributed lags, on rates of ED visits for kidney diseases [all renal diseases and acute renal failure (ARF)], controlling for meteorology (maximum air and dew-point temperatures) and time (season, day of week, holidays, and long-term time trend). RESULTS For all renal diseases, we observed positive associations for most air pollutants, particularly 8-day cumulative exposure to OC [rate ratio (RR) = 1.018, (95% confidence interval [CI]: 1.003, 1.034)] and EC [1.016 (1.000, 1.031)] per interquartile range increase exposure. For ARF, we observed positive associations particularly for 8-day exposure to OC [1.034 (1.005, 1.064)], EC [1.032 (1.002, 1.063)], nitrate [1.032 (0.996, 1.069)], and PM2.5 [1.026 (0.997, 1.057)] per interquartile range increase exposure. We also observed positive associations for most criteria gases. The RR estimates were generally higher for ARF than all renal diseases. CONCLUSIONS We observed positive associations between short-term exposure to fine particulate air pollution and kidney disease outcomes. This study adds to the growing epidemiological evidence that fine particles may impact distant organs (e.g., kidneys) over the short term.
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18
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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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Huang R, Lal R, Qin M, Hu Y, Russell AG, Odman MT, Afrin S, Garcia-Menendez F, O'Neill SM. Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2021; 71:815-829. [PMID: 33914671 DOI: 10.1080/10962247.2021.1924311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
Prescribed burning (PB) is a prominent source of PM2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM2.5 concentrations. Simulated PM2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R2 of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM2.5 fields to provide better estimates of hourly exposures to smoke from PB.Implications: Prescribed burning emissions are a major cause of elevated PM2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.
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Affiliation(s)
- Ran Huang
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Raj Lal
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Momei Qin
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yongtao Hu
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - M Talat Odman
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Sadia Afrin
- Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | - Fernando Garcia-Menendez
- Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | - Susan M O'Neill
- Pacific Northwest Research Station, US Forest Service, Seattle, WA, USA
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20
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Hunt SW, Winner DA, Wesson K, Kelly JT. Furthering a partnership: Air quality modeling and improving public health. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2021; 71:682-688. [PMID: 33443461 PMCID: PMC8318118 DOI: 10.1080/10962247.2021.1876180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/05/2021] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Abstract
Air pollution is one of the top five risk factors for population health globally. In recent years, advances in air pollution data and modeling have occurred simultaneously with advances in data and methods available for health studies. To realize the potential of such advances, the air quality modeling and public health communities should continue to strengthen their engagements and build effective interdisciplinary teams. These partnerships recognize the tight coupling between air quality and health data and methods and the value of expertise from multiple fields to ensure that this information is applied appropriately with a deep understanding of its capabilities and limitations. Building effective multidisciplinary teams takes a sustained commitment to engage with partners with different expertise to establish working partnerships and collaborations to better address public exposures to air pollution. Effective partnerships enable better targeting of research resources to answer important questions and provide essential information to protect public health.Implications: Air quality models are an effective tool that can be used to estimate air pollution exposure in epidemiologic studies and risk assessments. Working together in collaborative multidisciplinary teams will lead to greater advancements in understanding of air pollution impacts and in useful information informing actions to improve public health.
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Affiliation(s)
- Sherri W Hunt
- Immediate Office of the Assistant Administrator , Office of Research and Development, U.S. Environmental Protection Agency, Washington, USA
| | - Darrell A Winner
- Immediate Office of the Assistant Administrator , Office of Research and Development, U.S. Environmental Protection Agency, Washington, USA
| | - Karen Wesson
- Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, USA
| | - James T Kelly
- Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, USA
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21
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Shafran-Nathan R, Etzion Y, Broday DM. Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO 2 product. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 271:116334. [PMID: 33388684 DOI: 10.1016/j.envpol.2020.116334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 11/05/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO2) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.
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Affiliation(s)
| | - Yael Etzion
- Faculty of Civil and Environmental Engineering, Technion IIT, Haifa, 32000, Israel
| | - David M Broday
- Faculty of Civil and Environmental Engineering, Technion IIT, Haifa, 32000, Israel.
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22
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Xue T, Zheng Y, Geng G, Xiao Q, Meng X, Wang M, Li X, Wu N, Zhang Q, Zhu T. Estimating Spatiotemporal Variation in Ambient Ozone Exposure during 2013-2017 Using a Data-Fusion Model. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:14877-14888. [PMID: 33174716 DOI: 10.1021/acs.est.0c03098] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Since 2013, clean-air actions in China have reduced ambient concentrations of PM2.5. However, recent studies suggest that ground surface O3 concentrations increased over the same period. To understand the shift in air pollutants and to comprehensively evaluate their impacts on health, a spatiotemporal model for O3 is required for exposure assessment. This study presents a data-fusion algorithm for O3 estimation that combines in situ observations, satellite remote sensing measurements, and model results from the community multiscale air quality model. Performance of the algorithm for O3 estimation was evaluated by five-fold cross-validation. The estimates are highly correlated with the in situ observations of the maximum daily 8 h averaged O3 (R2 = 0.70). The mean modeling error (measured using the root-mean-squared error) is 26 μg/m3, which accounts for 29% of the mean level. We also found that satellite O3 played a key role to improve model performance, particularly during warm months. The estimates were further used to illustrate spatiotemporal variation in O3 during 2013-2017 for the whole country. In contrast to the reduced trend of PM2.5, we found that the population-weighted O3 mean increased from 86 μg/m3 in 2013 to 95 μg/m3 in 2017, with a rate of 2.07 (95% CI: 1.65, 2.48) μg/m3 per year at the national level. This increased trend in O3 suggests that it is becoming an important contributor to the burden of diseases attributable to air pollutants in China. The developed method and the results generated from this study can be used to support future health-related studies in China.
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Affiliation(s)
- 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, Beijing 100191, China
| | - Yixuan Zheng
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
- Center of Air Quality Simulation and System Analysis, Chinese Academy for Environmental Planning, Beijing 100012, China
| | - Guannan Geng
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Qingyang Xiao
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Xia Meng
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, & NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, New York 14214, United States
| | - Xin Li
- Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
| | - Nana Wu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Tong Zhu
- BIC-ESAT and SKL-ESPC, Peking University, Beijing 100871, China
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Li Y, Zhao X, Liao Q, Tao Y, Bai Y. Specific differences and responses to reductions for premature mortality attributable to ambient PM 2.5 in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 742:140643. [PMID: 32640394 DOI: 10.1016/j.scitotenv.2020.140643] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 06/25/2020] [Accepted: 06/28/2020] [Indexed: 06/11/2023]
Abstract
Although recent assessments have quantified the impact of ambient PM2.5 on public health in China, air quality managers would benefit from assessing specific differences in premature mortality and its responses to air quality improvement. Using PM2.5 data simulated by an observation-fused air quality model and an integrated exposure-response model for the full range of PM2.5, we determined the premature mortality attributable to ambient PM2.5 across mainland China in 2016. Overall, the total number of PM2.5-related deaths nationwide was 1.31 million, of which lung cancer, chronic obstructive pulmonary disease, ischemic heart disease, and stroke represented 0.13, 0.13, 0.42, and 0.62 million, respectively. Per capita PM2.5-related mortality in China was 95 per 100,000 person-years, and that of elderly people aged ≥75 years (1166 deaths per 100,000) was much higher than that of young people aged 25-44 years (11 deaths per 100,000). Additionally, there were significant spatial differences in premature deaths, which mainly occurred in regions with high PM2.5 levels or/and population density. Halving deaths across mainland China required an average of 63% reduction of PM2.5 nationwide and a decrease by 73% in high concentration regions exceeding 70 μg/m3 and 19% in low concentration locales below 10 μg/m3. Moreover, reducing PM2.5 to the WHO interim target I (IT-1) of 35 μg/m3 would only result in a 12.6% reduction in premature mortality, while a more exacting standard (reducing PM2.5 to 10 μg/m3) would avoid 73.0% of mortality. In particular, there is a large potential for reducing the high PM2.5-related mortality in heavily polluted locales. In conclusion, to further reduce premature mortality across mainland China, targets stricter than the IT-1 and tight policies to improve air quality and protect public health are necessary, especially for vulnerable groups such as the elderly and patients with cardio-cerebrovascular diseases, particularly in areas with high premature mortality.
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Affiliation(s)
- Yong Li
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Xiuge Zhao
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Qin Liao
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Yan Tao
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.
| | - Yun Bai
- National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China
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24
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Associations Between Ambient Air Pollutant Concentrations and Birth Weight: A Quantile Regression Analysis. Epidemiology 2020; 30:624-632. [PMID: 31386644 DOI: 10.1097/ede.0000000000001038] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
INTRODUCTION We investigated the extent to which associations of ambient air pollutant concentrations and birth weight varied across birth weight quantiles. METHODS We analyzed singleton births ≥27 weeks of gestation from 20-county metropolitan Atlanta with conception dates between January 1, 2002 and February 28, 2006 (N = 273,711). Trimester-specific and total pregnancy average concentrations for 10 pollutants, obtained from ground observations that were interpolated using 12-km Community Multiscale Air Quality model outputs, were assigned using maternal residence at delivery. We estimated associations between interquartile range width (IQRw) increases in pollutant concentrations and changes in birth weight using quantile regression. RESULTS Gestational age-adjusted associations were of greater magnitude at higher percentiles of the birth weight distribution. Pollutants with large vehicle source contributions (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon, and total PM2.5 mass), as well as PM2.5 sulfate and PM2.5 ammonium, were associated with birth weight decreases for the higher birth weight percentiles. For example, whereas the decrease in mean birthweight per IQRw increase in PM2.5 averaged over pregnancy was -7.8 g (95% confidence interval = -13.6, -2.0 g), the quantile-specific associations were: 10th percentile -2.4 g (-11.5, 6.7 g); 50th percentile -8.9 g (-15.7, -2.0g); and 90th percentile -19.3 g (-30.6, -7.9 g). Associations for the intermediate and high birth weight quantiles were not sensitive to gestational age adjustment. For some pollutants, we saw associations at the lowest quantile (10th percentile) when not adjusting for gestational age. CONCLUSIONS Associations between air pollution and reduced birth weight were of greater magnitude for newborns at relatively heavy birth weights.
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Chen H, Zhang Z, van Donkelaar A, Bai L, Martin RV, Lavigne E, Kwong JC, Burnett RT. Understanding the Joint Impacts of Fine Particulate Matter Concentration and Composition on the Incidence and Mortality of Cardiovascular Disease: A Component-Adjusted Approach. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:4388-4399. [PMID: 32101425 DOI: 10.1021/acs.est.9b06861] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Past health impact assessments of ambient fine particulate matter (particles with an aerodynamic diameter ≤2.5 μm; PM2.5) have generally considered mass concentration only, despite PM2.5 being a heterogeneous mixture. Given constant changes in the concentration and the composition of atmospheric aerosol, uncertainty exists as to whether the current focus on PM2.5 mass or individual components may fully characterize the health burden of PM2.5. We proposed a component-adjusted method that jointly estimates the health impacts of PM2.5 and its major components while allowing for a potential nonlinear PM2.5-outcome relationship. Using this method, we quantified the effects of PM2.5 on the risks of developing acute myocardial infarction (AMI) and dying from cardiovascular causes in comparison to three traditional approaches in the entire adult population across Ontario, Canada. We observed that PM2.5 was positively associated with AMI incidence and cardiovascular mortality with all four methods. Compared to the traditional approaches, however, the new component-adjusted approach demonstrated a significant improvement in explaining the health impacts of PM2.5, especially in the presence of a nonlinear PM2.5-outcome relationship. Using the new approach, we found that the effects of PM2.5 on AMI incidence and cardiovascular mortality may be 10% to 27% higher than what would be estimated from the conventional approaches examining PM2.5 alone.
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Affiliation(s)
- Hong Chen
- Environmental Health Science and Research Bureau, Health Canada, 101 Tunney's Pasture, Ottawa, Ontario K1A 0K9, Canada
- Public Health Ontario, Toronto, Ontario M5G 1V2, Canada
- ICES, Toronto, Ontario M4N 3M5, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Zilong Zhang
- Public Health Ontario, Toronto, Ontario M5G 1V2, Canada
- ICES, Toronto, Ontario M4N 3M5, Canada
| | - Aaron van Donkelaar
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada
- Department of Energy, Environmental & Chemical Engineering, Washington University, St. Louis, Missouri 63130, United States
| | - Li Bai
- ICES, Toronto, Ontario M4N 3M5, Canada
| | - Randall V Martin
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada
- Department of Energy, Environmental & Chemical Engineering, Washington University, St. Louis, Missouri 63130, United States
- Harvard-Smithsonian Centre for Astrophysics, Cambridge, Massachusetts 02138, United States
| | - Eric Lavigne
- Environmental Health Science and Research Bureau, Health Canada, 101 Tunney's Pasture, Ottawa, Ontario K1A 0K9, Canada
- School of Epidemiology & Public Health, University of Ottawa, Ottawa, Ontario K1G 5Z3, Canada
| | - Jeffrey C Kwong
- Public Health Ontario, Toronto, Ontario M5G 1V2, Canada
- ICES, Toronto, Ontario M4N 3M5, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Richard T Burnett
- Environmental Health Science and Research Bureau, Health Canada, 101 Tunney's Pasture, Ottawa, Ontario K1A 0K9, Canada
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Solomon PA, Vallano D, Lunden M, LaFranchi B, Blanchard CL, Shaw SL. Mobile-Platform Measurement of Air Pollutant Concentrations in California: Performance Assessment, Statistical Methods for Evaluating Spatial Variations, and Spatial Representativeness. ATMOSPHERIC MEASUREMENT TECHNIQUES 2020; 13:10.5194/amt-13-3277-2020. [PMID: 34497673 PMCID: PMC8422882 DOI: 10.5194/amt-13-3277-2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Mobile platform measurements provide new opportunities for characterizing spatial variations of air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc. mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study. On-road measurements of air quality were made during a series of sampling campaigns between May 2016 and September 2017 at high (i.e., 1-second [s]) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including non-urban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations of air pollutant concentrations over measurement periods as short as two weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments located within stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), methane (CH4) black carbon (BC), and particle number (PN) concentration with bias and precision ranging from <10 % for gases to <25 % for BC and PN at 1-s time resolution. The quality of the mobile measurements in the ambient environment is examined by comparisons with data from an adjacent (< 9 m) stationary regulatory air quality monitoring site and by paired collocated vehicle comparisons, both stationary and driving. The mobile measurements indicate that U.S. EPA classifications of two Los Angeles stationary regulatory monitors' scales of representation are appropriate. Paired time-synchronous mobile measurements are used to characterize the spatial scales of concentration variations when vehicles were separated by <1 to 10 kilometers (km). A data analysis approach is developed to characterize spatial variations while limiting the confounding influence of diurnal variability. The approach is illustrated using data from San Francisco, revealing 1-km scale differences in mean NO2 and O3 concentrations up to 117 % and 46 %, respectively, of mean values during a two-week sampling period. In San Francisco and Los Angeles, spatial variations up to factors of 6 to 8 occur at sampling scales of 100 - 300m, corresponding to 1-minute averages.
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Affiliation(s)
- Paul A. Solomon
- Independent consultant, Henderson, Nevada, 89052, USA, formerly with U.S. Environmental Protection Agency, Office of Research and Development, Las Vegas, NV, 89119, USA
| | - Dena Vallano
- U.S. Environmental Protection Agency, Region 9, Air and Radiation Division, 75 Hawthorne St, San Francisco, CA, 94105, USA
| | - Melissa Lunden
- Aclima, Inc., 10 Lombard St, Suite 200, San Francisco, CA, 94111, USA
| | - Brian LaFranchi
- Aclima, Inc., 10 Lombard St, Suite 200, San Francisco, CA, 94111, USA
| | | | - Stephanie L. Shaw
- Electric Power Research Institute, 3420 Hillview Ave, Palo Alto, CA, 94304, USA
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27
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Diao M, Holloway T, Choi S, O’Neill SM, Al-Hamdan MZ, van Donkelaar A, Martin RV, Jin X, Fiore AM, Henze DK, Lacey F, Kinney PL, Freedman F, Larkin NK, Zou Y, Kelly JT, Vaidyanathan A. Methods, availability, and applications of PM 2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2019; 69:1391-1414. [PMID: 31526242 PMCID: PMC7072999 DOI: 10.1080/10962247.2019.1668498] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 08/01/2019] [Accepted: 08/22/2019] [Indexed: 05/20/2023]
Abstract
Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.
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Affiliation(s)
- Minghui Diao
- San Jose State University, Department of Meteorology and Climate Science, One Washington Square, San Jose, California, USA, 95192-0104
| | - Tracey Holloway
- University of Wisconsin-Madison, Nelson Institute Center for Sustainability and the Global Environment (SAGE) and Department of Atmospheric and Oceanic Sciences, 201A Enzyme Institute, 1710 University Ave., Madison, Wisconsin, USA, 53726
| | - Seohyun Choi
- University of Wisconsin-Madison, Nelson Institute Center for Sustainability and the Global Environment (SAGE) and Department of Atmospheric and Oceanic Sciences, 201A Enzyme Institute, 1710 University Ave., Madison, Wisconsin, USA, 53726
| | - Susan M. O’Neill
- United States Department of Agriculture Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, 98103-8600
| | - Mohammad Z. Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, National Space Science and Technology Center, 320 Sparkman Dr., Huntsville, Alabama, USA, 35805
| | - Aaron van Donkelaar
- Dalhousie University, Department of Physics and Atmospheric Science, 6299 South St, Halifax, Nova Scotia, Canada, B3H 4R2
| | - Randall V. Martin
- Dalhousie University, Department of Physics and Atmospheric Science, 6299 South St, Halifax, Nova Scotia, Canada, B3H 4R2
- Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA, 02138
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA, 63130
| | - Xiaomeng Jin
- Columbia University, Department of Earth and Environmental Sciences and Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, New York, USA, 10964
| | - Arlene M. Fiore
- Columbia University, Department of Earth and Environmental Sciences and Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, New York, USA, 10964
| | - Daven K. Henze
- University of Colorado, Mechanical Engineering Department, 1111 Engineering Drive UCB 427, Boulder, CO, USA, 80309
| | - Forrest Lacey
- University of Colorado, Mechanical Engineering Department, 1111 Engineering Drive UCB 427, Boulder, CO, USA, 80309
- National Center for Atmospheric Research, Atmospheric Chemistry Observations and Modeling, 3450 Mitchell Ln, Boulder, CO, USA, 80301
| | - Patrick L. Kinney
- Boston University School of Public Health, Department of Environmental Health, 715 Albany Street, Talbot 4W, Boston, Massachusetts, USA, 02118
| | - Frank Freedman
- San Jose State University, Department of Meteorology and Climate Science, One Washington Square, San Jose, California, USA, 95192-0104
| | - Narasimhan K. Larkin
- United States Department of Agriculture Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, 98103-8600
| | - Yufei Zou
- University of Washington, School of Environmental and Forest Sciences, Anderson Hall, Seattle, WA, USA, 98195
| | - James T. Kelly
- Office of Air Quality Planning & Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA 27711
| | - Ambarish Vaidyanathan
- Asthma and Community Health Branch, Centers for Disease Control and Prevention, 1600 Clifton Road, Mail Stop E-19, Atlanta, Georgia, USA, 30333
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Boaz RM, Lawson AB, Pearce JL. Multivariate Air Pollution Prediction Modeling with partial Missingness. ENVIRONMETRICS 2019; 30:e2592. [PMID: 31983873 PMCID: PMC6980235 DOI: 10.1002/env.2592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 05/23/2019] [Indexed: 06/10/2023]
Abstract
Missing observations from air pollution monitoring networks have posed a longstanding problem for health investigators of air pollution. Growing interest in mixtures of air pollutants has further complicated this problem, as many new challenges have arisen that require development of novel methods. The objective of this study is to develop a methodology for multivariate prediction of air pollution. We focus specifically on tackling different forms of missing data, such as: spatial (sparse sites), outcome (pollutants not measured at some sites), and temporal (varieties of interrupted time series). To address these challenges, we develop a novel multivariate fusion framework, which leverages the observed inter-pollutant correlation structure to reduce error in the simultaneous prediction of multiple air pollutants. Our joint fusion model employs predictions from the Environmental Protection Agency's Community Multiscale Air Quality (CMAQ) model along with spatio-temporal error terms. We have implemented our models on both simulated data and a case study in South Carolina for 8 pollutants over a 28-day period in June 2006. We found that our model, which uses a multivariate correlated error in a Bayesian framework, showed promising predictive accuracy particularly for gaseous pollutants.
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Affiliation(s)
- R M Boaz
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - A B Lawson
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - J L Pearce
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
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29
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Senthilkumar N, Gilfether M, Metcalf F, Russell AG, Mulholland JA, Chang HH. Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16183314. [PMID: 31505818 PMCID: PMC6765984 DOI: 10.3390/ijerph16183314] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 09/02/2019] [Accepted: 09/04/2019] [Indexed: 11/24/2022]
Abstract
Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3−, NH4+, EC, OC, SO42−) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2.
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Affiliation(s)
- Niru Senthilkumar
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Mark Gilfether
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Francesca Metcalf
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - James A Mulholland
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
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Johnson Gaither C, Afrin S, Garcia-Menendez F, Odman MT, Huang R, Goodrick S, Ricardo da Silva A. African American Exposure to Prescribed Fire Smoke in Georgia, USA. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16173079. [PMID: 31450603 PMCID: PMC6747108 DOI: 10.3390/ijerph16173079] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 08/14/2019] [Accepted: 08/17/2019] [Indexed: 11/16/2022]
Abstract
Our project examines the association between percent African American and smoke pollution in the form of prescribed burn-sourced, fine particulate matter (PM2.5) in the U.S. state of Georgia for 2018. (1) Background: African Americans constitute 32.4% of Georgia's population, making it the largest racial/ethnic minority group in the state followed by Hispanic Americans at 9.8%. African Americans, Hispanic Americans, and lower wealth groups are more likely than most middle and upper income White Americans to be exposed to environmental pollutants. This is true because racial and ethnic minorities are more likely to live in urban areas where pollution is more concentrated. As a point of departure, we examine PM2.5 concentrations specific to prescribed fire smoke, which typically emanates from fires occurring in rural or peri-urban areas. Two objectives are specified: a) examine the association between percent African American and PM2.5 concentrations at the census tract level for Georgia, and b) identify emitters of PM2.5 concentrations that exceed National Ambient Air Quality Standards (NAAQS) for the 24-h average, i. e., >35 µg/m3. (2) Methods: For the first objective, we estimate a spatial Durbin error model (SDEM) where pollution concentration (PM2.5) estimates for 1683 census tracts are regressed on percent of the human population that is African American or Hispanic; lives in mobile homes; and is employed in agriculture and related occupations. Also included as controls are percent evergreen forest, percent mixed evergreen/deciduous forest, and variables denoting lagged explanatory and error variables, respectively. For the second objective, we merge parcel and prescribed burn permit data to identify landowners who conduct prescribed fires that produce smoke exceeding the NAAQS. (3) Results: Percent African American and mobile home dweller are positively related to PM2.5 concentrations; and government and non-industrial private landowners are the greatest contributors to exceedance levels (4) Conclusions: Reasons for higher PM2.5 concentrations in areas with higher African American and mobile home percent are not clear, although we suspect that neither group is a primary contributor to prescribed burn smoke but rather tend to live proximate to entities, both public and private, that are. Also, non-industrial private landowners who generated prescribed burn smoke exceeding NAAQS are wealthier than others, which suggests that African American and other environmental justice populations are less likely to contribute to exceedance levels in the state.
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Affiliation(s)
| | - Sadia Afrin
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27607, USA
| | - Fernando Garcia-Menendez
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27607, USA
| | - M Talat Odman
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Ran Huang
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Scott Goodrick
- USDA Forest Service, Southern Research Station, Athens, GA 30602, USA
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Huang R, Hu Y, Russell AG, Mulholland JA, Odman MT. The Impacts of Prescribed Fire on PM 2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E2312. [PMID: 31261860 PMCID: PMC6651061 DOI: 10.3390/ijerph16132312] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/21/2019] [Accepted: 06/26/2019] [Indexed: 11/18/2022]
Abstract
Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.
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Affiliation(s)
- Ran Huang
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Yongtao Hu
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - James A Mulholland
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - M Talat Odman
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
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Oyana TJ, Podila P, Relyea GE. Effects of childhood exposure to PM 2.5 in a Memphis pediatric asthma cohort. ENVIRONMENTAL MONITORING AND ASSESSMENT 2019; 191:330. [PMID: 31254117 DOI: 10.1007/s10661-019-7419-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 03/20/2019] [Indexed: 06/09/2023]
Abstract
The effects of childhood exposure to ambient air pollution and their influences on healthcare utilization and respiratory outcomes in Memphis pediatric asthma cohort are still unknown. This study seeks to (1) investigate individual-level associations between asthma and exposure measures in high asthma rate and low asthma rate areas and (2) determine factors that influence asthma at first year of a child's life, first 2 years, first 5 years, and during their childhood. Datasets include physician-diagnosed asthma patients, on-road and individual PM2.5 emissions, and high-resolution spatiotemporal PM2.5 estimates. Spatial analytical and logistic regression models were used to analyze the effects of childhood exposure on outcomes. Increased risk was associated with African American (AA) (odds ratio (OR) = 3.09, 95% confidence interval (CI) 2.80-3.41), aged < 5 years old (OR = 1.31, 95% 1.17-1.47), public insurance (OR = 2.80, 95% CI 2.60-3.01), a 2.5-km radius from on-road emission sources (OR = 3.06, 95% CI 2.84-3.30), and a 400-m radius from individual PM2.5 sources (OR = 1.33, 95% CI 1.25-1.41) among the cohort with residence in high asthma rate areas compared to low asthma rates areas. A significant interaction was observed between race and insurance with the odds of AA being approximately five times (OR = 4.68, 95% CI 2.23-9.85), public insurance being about three times (OR = 2.65, 95% CI 1.68-4.17), and children in their first 5 years of life have more hospital visits than other age groups. Findings from this study can guide efforts to minimize emissions, manage risk, and design interventions to reduce disease burden.
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Affiliation(s)
- Tonny J Oyana
- Department of Preventive Medicine, College of Medicine, The University of Tennessee Health Science Center, 66 North Pauline Street, Suite 651, Memphis, TN, 38163, USA.
| | | | - George E Relyea
- School of Public Health, The University of Memphis, Memphis, TN, USA
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O'Lenick CR, Wilhelmi OV, Michael R, Hayden MH, Baniassadi A, Wiedinmyer C, Monaghan AJ, Crank PJ, Sailor DJ. Urban heat and air pollution: A framework for integrating population vulnerability and indoor exposure in health risk analyses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 660:715-723. [PMID: 30743957 DOI: 10.1016/j.scitotenv.2019.01.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 12/15/2018] [Accepted: 01/01/2019] [Indexed: 05/07/2023]
Abstract
Urban growth and climate change will exacerbate extreme heat events and air pollution, posing considerable health challenges to urban populations. Although epidemiological studies have shown associations between health outcomes and exposures to ambient air pollution and extreme heat, the degree to which indoor exposures and social and behavioral factors may confound or modify these observed effects remains underexplored. To address this knowledge gap, we explore the linkages between vulnerability science and epidemiological conceptualizations of risk to propose a conceptual and analytical framework for characterizing current and future health risks to air pollution and extreme heat, indoors and outdoors. Our framework offers guidance for research on climatic variability, population vulnerability, the built environment, and health effects by illustrating how health data, spatially resolved ambient data, estimates of indoor conditions, and household-level vulnerability data can be integrated into an epidemiological model. We also describe an approach for characterizing population adaptive capacity and indoor exposure for use in population-based epidemiological models. Our framework and methods represent novel resources for the evaluation of health risks from extreme heat and air pollution, both indoors and outdoors.
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Affiliation(s)
- Cassandra R O'Lenick
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA.
| | - Olga V Wilhelmi
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Ryan Michael
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Mary H Hayden
- University of Colorado-Colorado Springs, Colorado Springs, CO, USA
| | - Amir Baniassadi
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA
| | | | | | - Peter J Crank
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA
| | - David J Sailor
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA
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Kelly JT, Jang CJ, Timin B, Gantt B, Reff A, Zhu Y, Long S, Hanna A. A System for Developing and Projecting PM 2.5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards. ATMOSPHERIC ENVIRONMENT: X 2019; 2:100019. [PMID: 31534416 PMCID: PMC6750759 DOI: 10.1016/j.aeaoa.2019.100019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.
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Affiliation(s)
- James T Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Carey J Jang
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brian Timin
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brett Gantt
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Adam Reff
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Yun Zhu
- College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China
| | - Shicheng Long
- College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China
| | - Adel Hanna
- Institute for the Environment, University of North Carolina at Chapel Hill, NC 27517 USA
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Zhai X, Mulholland JA, Friberg MD, Holmes HA, Russell AG, Hu Y. Spatial PM 2.5 mobile source impacts using a calibrated indicator method. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2019; 69:402-414. [PMID: 30499749 DOI: 10.1080/10962247.2018.1532468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 09/12/2018] [Accepted: 10/01/2018] [Indexed: 06/09/2023]
Abstract
Motor vehicles are major sources of fine particulate matter (PM2.5), and the PM2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NOx) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NOx estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.
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Affiliation(s)
- Xinxin Zhai
- a School of Civil and Environmental Engineering , Georgia Institute of Technology , Atlanta , GA, USA
| | - James A Mulholland
- a School of Civil and Environmental Engineering , Georgia Institute of Technology , Atlanta , GA, USA
| | - Mariel D Friberg
- a School of Civil and Environmental Engineering , Georgia Institute of Technology , Atlanta , GA, USA
| | - Heather A Holmes
- b Atmospheric Sciences Program, Department of Physics , University of Nevada , Reno, Reno, NV, USA
| | - Armistead G Russell
- a School of Civil and Environmental Engineering , Georgia Institute of Technology , Atlanta , GA, USA
| | - Yongtao Hu
- a School of Civil and Environmental Engineering , Georgia Institute of Technology , Atlanta , GA, USA
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Spatio-temporal variations and factors of a provincial PM 2.5 pollution in eastern China during 2013-2017 by geostatistics. Sci Rep 2019; 9:3613. [PMID: 30837622 PMCID: PMC6401087 DOI: 10.1038/s41598-019-40426-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 02/08/2019] [Indexed: 01/16/2023] Open
Abstract
Fine particulate matter (PM2.5) is a typical air pollutant and has adverse health effects across the world, especially in the rapidly developing China due to significant air pollution. The PM2.5 pollution varies with time and space, and is dominated by the locations owing to the differences in geographical conditions including topography and meteorology, the land use and the characteristics of urbanization and industrialization, all of which control the pollution formation by influencing the various sources and transport of PM2.5. To characterize these parameters and mechanisms, the 5-year PM2.5 pollution patterns of Jiangsu province in eastern China with high-resolution was investigated. The Kriging interpolation method of geostatistical analysis (GIS) and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were conducted to study the spatial and temporal distribution of air pollution at 110 sites from national air quality monitoring network covering 13 cities. The PM2.5 pollution of the studied region was obvious, although the annual average concentration decreased from previous 72 to recent 50 μg m−3. Evident temporal variations showed high PM2.5 level in winter and low in summer. Spatially, PM2.5 level was higher in northern (inland, heavy industry) than that in eastern (costal, plain) regions. Industrial sources contributed highest to the air pollution. Backward trajectory clustering and potential source contribution factor (PSCF) analysis indicated that the typical monsoon climate played an important role in the aerosol transport. In summer, the air mass in Jiangsu was mainly affected by the updraft from near region, which accounted for about 60% of the total number of trajectories, while in winter, the long-distance transport from the northwest had a significant impact on air pollution.
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Barry V, Klein M, Winquist A, Chang HH, Mulholland JA, Talbott EO, Rager JR, Tolbert PE, Sarnat SE. Characterization of the concentration-response curve for ambient ozone and acute respiratory morbidity in 5 US cities. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2019; 29:267-277. [PMID: 29915241 PMCID: PMC6301150 DOI: 10.1038/s41370-018-0048-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 03/26/2018] [Accepted: 04/08/2018] [Indexed: 05/27/2023]
Abstract
Although short-term exposure to ambient ozone (O3) can cause poor respiratory health outcomes, the shape of the concentration-response (C-R) between O3 and respiratory morbidity has not been widely investigated. We estimated the effect of daily O3 on emergency department (ED) visits for selected respiratory outcomes in 5 US cities under various model assumptions and assessed model fit. Population-weighted average 8-h maximum O3 concentrations were estimated in each city. Individual-level data on ED visits were obtained from hospitals or hospital associations. Poisson log-linear models were used to estimate city-specific associations between the daily number of respiratory ED visits and 3-day moving average O3 levels controlling for long-term trends and meteorology. Linear, linear-threshold, quadratic, cubic, categorical, and cubic spline O3 C-R models were considered. Using linear C-R models, O3 was significantly and positively associated with respiratory ED visits in each city with rate ratios of 1.02-1.07 per 25 ppb. Models suggested that O3-ED C-R shapes were linear until O3 concentrations of roughly 60 ppb at which point risk continued to increase linearly in some cities for certain outcomes while risk flattened in others. Assessing C-R shape is necessary to identify the most appropriate form of the exposure for each given study setting.
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Affiliation(s)
- Vaughn Barry
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
| | - Mitchel Klein
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Andrea Winquist
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - James A Mulholland
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Evelyn O Talbott
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Judith R Rager
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Paige E Tolbert
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Stefanie Ebelt Sarnat
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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Li J, Zhu Y, Kelly JT, Jang CJ, Wang S, Hanna A, Xing J, Lin CJ, Long S, Yu L. Health benefit assessment of PM 2.5 reduction in Pearl River Delta region of China using a model-monitor data fusion approach. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 233:489-498. [PMID: 30594114 PMCID: PMC7260885 DOI: 10.1016/j.jenvman.2018.12.060] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 11/29/2018] [Accepted: 12/19/2018] [Indexed: 05/22/2023]
Abstract
The Pearl River Delta (PRD), one of the most polluted and populous regions of China, experienced a 28% reduction in fine particulate matter (PM2.5) concentration between 2013 (47 μg/m3) and 2015 (34 μg/m3) under a stringent national policy known as the Air Pollution Prevention and Control Action Plan (hereafter Action Plan). In this study, the health and economic benefits associated with PM2.5 reductions in PRD during 2013-2015 were estimated using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) software. To create reliable gridded PM2.5 surfaces for BenMAP-CE calculations, a data fusion tool which incorporates the accuracy of monitoring data and the spatial coverage of predictions from the Community Multiscale Air Quality (CMAQ) model has been developed. The population-weighted average PM2.5 concentration over PRD was predicted to decline by 24%. PM2.5-related mortality was estimated to decrease by more than 3800 due to decreases in stroke (48%), ischemic heart disease (IHD) (35%), chronic obstructive pulmonary disease (COPD) (10%), and lung cancer (LC) (7%). A 13% reduction in PM2.5-related premature deaths from these four causes yielded a large economic benefit of about 1300 million US dollars. Our research suggests that the Action Plan played a major role in reducing emissions and additional measures should be implemented to further reduce PM2.5 pollution and protect public health in the future.
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Affiliation(s)
- Jiabin Li
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Yun Zhu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
| | - James T Kelly
- US EPA, Office Air Quality Planning & Standards, Research Triangle Park, NC 27711, USA
| | - Carey J Jang
- US EPA, Office Air Quality Planning & Standards, Research Triangle Park, NC 27711, USA
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Adel Hanna
- Institute for the Environment, University of North Carolina at Chapel Hill, NC 27517, USA
| | - Jia Xing
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Che-Jen Lin
- Department of Civil and Environmental Engineering, Lamar University, Beaumont, TX 77710, USA
| | - Shicheng Long
- Guangzhou Urban Environmental Cloud Information Technology R&D Co.ltd, Guangzhou 511400, China
| | - Lian Yu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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Impacts of gestational age uncertainty in estimating associations between preterm birth and ambient air pollution. Environ Epidemiol 2018; 2:e031. [PMID: 33210073 PMCID: PMC7660973 DOI: 10.1097/ee9.0000000000000031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 10/04/2018] [Indexed: 01/12/2023] Open
Abstract
Supplemental Digital Content is available in the text. Background: Previous epidemiologic studies utilizing birth records have shown heterogeneous associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB, gestational age <37 weeks). Uncertainty in gestational age at birth may contribute to this heterogeneity. Methods: We first examined disagreement between clinical and last menstrual period-based (LMP) determination of PTB from individual-level birth certificate data for the 20-county Atlanta metropolitan area during 2002 to 2006. We then estimated associations between five trimester-averaged pollutant exposures and PTB, defined using various methods based on the clinical or LMP gestational age. Finally, using a multiple imputation approach, we incorporated uncertainty in gestational age to quantify the impact of this variability on associations between pollutant exposures and PTB. Results: Odds ratios (OR) were most elevated when a more stringent definition of PTB was used. For example, defining PTB only when LMP and clinical diagnoses agree yielded an OR of 1.09 (95% confidence interval [CI] = 1.04, 1.14) per interquartile range increase in first trimester carbon monoxide exposure versus an OR of 1.04 (95% CI = 1.01, 1.08) when PTB was defined as either an LMP or clinical diagnosis. Accounting for outcome uncertainty resulted in wider CIs—between 7.4% and 43.8% wider than those assuming the PTB outcome is without error. Conclusions: Despite discrepancies in PTB derived using either the clinical or LMP gestational age estimates, our analyses demonstrated robust positive associations between PTB and ambient air pollution exposures even when gestational age uncertainty is present.
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Geng G, Murray NL, Chang HH, Liu Y. The sensitivity of satellite-based PM 2.5 estimates to its inputs: Implications to model development in data-poor regions. ENVIRONMENT INTERNATIONAL 2018; 121:550-560. [PMID: 30300813 DOI: 10.1016/j.envint.2018.09.051] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/26/2018] [Accepted: 09/26/2018] [Indexed: 06/08/2023]
Abstract
Exposure to fine particulate matter (PM2.5) has been associated with a wide range of negative health outcomes. The overwhelming majority of the epidemiological studies that helped establish such associations was conducted in regions with sufficient ground observations and other supporting data, i.e., the data-rich regions. However, air pollution health effects research in the data-poor regions, where pollution levels are often the highest, is still very limited due to the lack of high-quality exposure estimates. To improve our understanding of the desired input datasets for the application of satellite-based PM2.5 exposure models in data-poor areas, we applied a Bayesian ensemble model in the southeast U.S. that was selected as a representative data-rich region. We designed four groups of sensitivity tests to simulate various data-poor scenarios. The factors considered that would influence the model performance included the temporal sampling frequency of the monitors, the number of ground monitors, the accuracy of the chemical transport model simulation of PM2.5 concentrations, and different combinations of the additional predictors. While our full model achieved a 10-fold cross-validated (CV) R2 of 0.82, we found that when reducing the sampling frequency from the current 1-in-3 day to 1-in-9 day, the CV R2 decreased to 0.58, and the predictions could not capture the daily variations of PM2.5. Half of the current stations (i.e., 30 monitors) could still support a robust model with a CV R2 of 0.79. With 20 monitors, the CV R2 decreased from 0.71 to 0.55 when 100% additional random errors were added to the original CMAQ simulations. However, with a sufficient number of ground monitors (e.g., 30 monitors), our Bayesian ensemble model had the ability to tolerate CMAQ errors with only a slight decrease in CV R2 (from 0.79 to 0.75). With fewer than 15 monitors, our full model collapsed and failed to fit any covariates, while the models with only time-varying variables could still converge even with only five monitors left. A model without the land use parameters lacked fine spatial details in the prediction maps, but could still capture the daily variability of PM2.5 (CV R2 ≥ 0.67) and might support a study of the acute health effects of PM2.5 exposure.
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Affiliation(s)
- Guannan Geng
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Nancy L Murray
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
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41
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Pan A, Sarnat SE, Chang HH. Time-Series Analysis of Air Pollution and Health Accounting for Covariate-Dependent Overdispersion. Am J Epidemiol 2018; 187:2698-2704. [PMID: 30099479 DOI: 10.1093/aje/kwy170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 07/31/2018] [Indexed: 11/13/2022] Open
Abstract
Time-series studies are routinely used to estimate associations between adverse health outcomes and short-term exposures to ambient air pollutants. Use of the Poisson log-linear model with the assumption of constant overdispersion is the most common approach, particularly when estimating associations between daily air pollution concentrations and aggregated counts of adverse health events throughout a geographical region. We examined how the assumption of constant overdispersion plays a role in estimation of air pollution effects by comparing estimates derived from the standard approach with those estimated from covariate-dependent Bayesian generalized Poisson and negative binomial models that accounted for potential time-varying overdispersion. Through simulation studies, we found that while there was negligible bias in effect estimates, the standard quasi-Poisson approach can result in a larger standard error when the constant overdispersion assumption is violated. This was also observed in a time-series study of daily emergency department visits for respiratory diseases and ozone concentration in Atlanta, Georgia (1999-2009). Allowing for covariate-dependent overdispersion resulted in a reduction in the ozone effect standard error, while the ozone-associated relative risk remained robust to different model specifications. Our findings suggest that improved characterization of overdispersion in time-series modeling can result in more precise health effect estimates in studies of short-term environmental exposures.
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Affiliation(s)
- Anqi Pan
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Stefanie Ebelt Sarnat
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
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Krall JR, Chang HH, Waller LA, Mulholland JA, Winquist A, Talbott EO, Rager JR, Tolbert PE, Sarnat SE. A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities. ENVIRONMENT INTERNATIONAL 2018; 120:312-320. [PMID: 30107292 PMCID: PMC6218942 DOI: 10.1016/j.envint.2018.07.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/09/2018] [Accepted: 07/24/2018] [Indexed: 05/25/2023]
Abstract
Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.
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Affiliation(s)
- Jenna R Krall
- Department of Global and Community Health, George Mason University, 4400 University Drive, MS 5B7, Fairfax, VA 22030, United States.
| | - Howard H Chang
- Department of Biostatistics & Bioinformatics, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, United States.
| | - Lance A Waller
- Department of Biostatistics & Bioinformatics, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, United States.
| | - James A Mulholland
- School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive NW, Atlanta, GA 30332, United States.
| | - Andrea Winquist
- Department of Epidemiology, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, United States.
| | - Evelyn O Talbott
- Department of Epidemiology, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15261, United States.
| | - Judith R Rager
- Department of Epidemiology, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15261, United States.
| | - Paige E Tolbert
- Department of Environmental Health, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, United States.
| | - Stefanie Ebelt Sarnat
- Department of Environmental Health, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, United States.
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Geng G, Murray NL, Tong D, Fu JS, Hu X, Lee P, Meng X, Chang HH, Liu Y. Satellite-Based Daily PM 2.5 Estimates During Fire Seasons in Colorado. JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES : JGR 2018; 123:8159-8171. [PMID: 31289705 PMCID: PMC6615892 DOI: 10.1029/2018jd028573] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 07/09/2018] [Indexed: 05/04/2023]
Abstract
The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high-resolution PM2.5 exposure data over fire days. Satellite-based aerosol optical depth (AOD) data can provide additional information in ground PM2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5 concentrations over fire seasons (April to September) in Colorado for 2011-2014. Our model had a 10-fold cross-validated R 2 of 0.66 and root-mean-squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short-term and long-term epidemiological studies of wildfire PM2.5.
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Affiliation(s)
- Guannan Geng
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Nancy L Murray
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Daniel Tong
- NOAA Air Resources Laboratory, College Park, MD, USA
- Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA
- Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
| | - Joshua S Fu
- Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA
- Climate Change Science Institute and Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Xuefei Hu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Pius Lee
- NOAA Air Resources Laboratory, College Park, MD, USA
| | - Xia Meng
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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Friberg MD, Kahn RA, Limbacher JA, Appel KW, Mulholland JA. Constraining chemical transport PM 2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley. ATMOSPHERIC CHEMISTRY AND PHYSICS 2018; 18:12891-12913. [PMID: 30288162 PMCID: PMC6166888 DOI: 10.5194/acp-18-12891-2018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.
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Affiliation(s)
- Mariel D. Friberg
- NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
- School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Ralph A. Kahn
- NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
| | - James A. Limbacher
- NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
- Science Systems and Applications Inc., Lanham, MD 20706, USA
| | | | - James A. Mulholland
- School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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45
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Li Y, Tao Y. Daily PM10 concentration forecasting based on multiscale fusion support vector regression. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169555] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yong Li
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Lanzhou, China
| | - Yan Tao
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, Lanzhou, China
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46
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Koman PD, Hogan KA, Sampson N, Mandell R, Coombe CM, Tetteh MM, Hill-Ashford YR, Wilkins D, Zlatnik MG, Loch-Caruso R, Schulz AJ, Woodruff TJ. Examining Joint Effects of Air Pollution Exposure and Social Determinants of Health in Defining "At-Risk" Populations Under the Clean Air Act: Susceptibility of Pregnant Women to Hypertensive Disorders of Pregnancy. WORLD MEDICAL & HEALTH POLICY 2018; 10:7-54. [PMID: 30197817 PMCID: PMC6126379 DOI: 10.1002/wmh3.257] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Pregnant women are uniquely susceptible to adverse effects of air pollution exposure due to vulnerabilities and health consequences during pregnancy (e.g., hypertensive disorders of pregnancy [HDP]) compared to the general population. Because the Clean Air Act (CAA) creates a duty to protect at-risk groups, the regulatory assessment of at-risk populations has both policy and scientific foundations. Previously, pregnant women have not been specially protected in establishing the margin of safety for the ozone and particulate matter (PM) standards. Due to physiological changes, pregnant women can be at greater risk of adverse effects of air pollution and should be considered an at-risk population. Women with preexisting conditions, women experiencing poverty, and groups that suffer systematic discrimination may be particularly susceptible to cardiac effects of air pollutants during pregnancy. We rigorously reviewed 11 studies of over 1.3 million pregnant women in the United States to characterize the relationship between ozone or PM exposure and HDP. Findings were generally mixed, with a few studies reporting a joint association between ozone or PM and social determinants or pre-existing chronic health conditions related to HDP. Adequate evidence associates exposure to PM with an adverse effect of HDP among pregnant women not evident among non-gravid populations.
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Affiliation(s)
- Patricia D Koman
- University of Michigan School of Public Health, Environmental Health Sciences Department in Ann Arbor, Michigan
| | - Kelly A Hogan
- University of Michigan School of Public Health, Environmental Health Sciences Department in Ann Arbor, Michigan, and presently a research fellow in the Department of Biochemistry and Molecular Biology and the Robert and Arlene Kogod Center on Aging at Mayo Clinic, Rochester, Minnesota
| | - Natalie Sampson
- University of Michigan-Dearborn, Department of Health & Human Services in Dearborn, Michigan
| | - Rebecca Mandell
- Arbor Research Collaborative for Health in Ann Arbor, Michigan
| | - Chris M Coombe
- University of Michigan School of Public Health, Department of Health Behavior & Health Education in Ann Arbor, Michigan
| | - Myra M Tetteh
- University of Michigan School of Public Health, Department of Health Behavior & Health Education in Ann Arbor, Michigan
| | | | | | - Marya G Zlatnik
- University of California San Francisco, Department of Obstetrics, Gynecology and Reproductive Sciences in San Francisco, California
| | - Rita Loch-Caruso
- University of Michigan School of Public Health, Environmental Health Sciences Department and director of the Michigan Center on Lifestage Environmental Exposures and Disease and director of the Environmental Toxicology and Epidemiology Program in Ann Arbor, Michigan
| | - Amy J Schulz
- Department of Health Behavior and Health Education, associate director for the Center for Research on Ethnicity, Culture and Health, and co-lead for the Community Engagement Core for the Michigan Center on Lifestage Environmental Exposures and Disease at the University of Michigan School of Public Health
| | - Tracey J Woodruff
- University of California, San Francisco in the Department of Obstetrics, Gynecology, and Reproductive Sciences and Philip R. Lee Institute for Health Policy Studies and the director of the Program on Reproductive Health and the Environment in San Francisco, California
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47
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Abstract
Purpose of Review Epidemiological studies of health effects of long-term exposure to outdoor air pollution rely on different exposure assessment methods. This review discusses widely used methods with a special focus on new developments. Recent Findings New data and study designs have been applied, including satellite measurements of fine particles and nitrogen dioxide (NO2). The methods to apply satellite data for epidemiological studies are improving rapidly and have already contributed significantly to national-, continental- and global-scale models. Spatiotemporal models have been developed allowing more detailed temporal resolution compared to spatial models. The development of hybrid models combining dispersion models, satellite observations, land use and surface monitoring has improved models substantially. Mobile monitoring designs to develop models for long-term UFP exposure have been conducted. Summary Methods to assess long-term exposure to outdoor air pollution have improved significantly over the past decade. Application of satellite data and mobile monitoring designs is promising new methods.
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48
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Yu H, Russell A, Mulholland J, Huang Z. Using cell phone location to assess misclassification errors in air pollution exposure estimation. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 233:261-266. [PMID: 29096298 DOI: 10.1016/j.envpol.2017.10.077] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 05/26/2023]
Abstract
Air pollution epidemiologic and health impact studies often rely on home addresses to estimate individual subject's pollution exposure. In this study, we used detailed cell phone location data, the call detail record (CDR), to account for the impact of spatiotemporal subject mobility on estimates of ambient air pollutant exposure. This approach was applied on a sample with 9886 unique simcard IDs in Shenzhen, China, on one mid-week day in October 2013. Hourly ambient concentrations of six chosen pollutants were simulated by the Community Multi-scale Air Quality model fused with observational data, and matched with detailed location data for these IDs. The results were compared with exposure estimates using home addresses to assess potential exposure misclassification errors. We found the misclassifications errors are likely to be substantial when home location alone is applied. The CDR based approach indicates that the home based approach tends to over-estimate exposures for subjects with higher exposure levels and under-estimate exposures for those with lower exposure levels. Our results show that the cell phone location based approach can be used to assess exposure misclassification error and has the potential for improving exposure estimates in air pollution epidemiology studies.
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Affiliation(s)
- Haofei Yu
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA; Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA.
| | - Armistead Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - James Mulholland
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Zhijiong Huang
- School of Environmental Science and Engineering, South China University of Technology, Guangzhou, China
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49
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Ebisu K, Malig B, Hasheminassab S, Sioutas C, Basu R. Cause-specific stillbirth and exposure to chemical constituents and sources of fine particulate matter. ENVIRONMENTAL RESEARCH 2018; 160:358-364. [PMID: 29055831 DOI: 10.1016/j.envres.2017.10.015] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/05/2017] [Accepted: 10/07/2017] [Indexed: 06/07/2023]
Abstract
The stillbirth rate in the United States is relatively high, but limited evidence is available linking stillbirth with fine particulate matter (PM2.5), its chemical constituents and sources. In this study, we explored associations between cause-specific stillbirth and prenatal exposures to those pollutants with using live birth and stillbirth records from eight California locations during 2002-2009. ICD-10 codes were used to identify cause of stillbirth from stillbirth records. PM2.5 total mass and chemical constituents were collected from ambient monitors and PM2.5 sources were quantified using Positive Matrix Factorization. Conditional logistic regression was applied using a nested case-control study design (N = 32,262). We found that different causes of stillbirth were associated with different PM2.5 sources and/or chemical constituents. For stillbirths due to fetal growth, the odds ratio (OR) per interquartile range increase in gestational age-adjusted exposure to PM2.5 total mass was 1.23 (95% confidence interval (CI): 1.06, 1.44). Similar associations were found with resuspended soil (OR=1.25, 95% CI: 1.10, 1.42), and secondary ammonium sulfate (OR=1.45, 95% CI: 1.18, 1.78). No associations were found between any pollutants and stillbirths caused by maternal complications. This study highlighted the importance of investigating cause-specific stillbirth and the differential toxicity levels of specific PM2.5 sources and chemical constituents.
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Affiliation(s)
- Keita Ebisu
- Office of Environmental Health Hazard Assessment, California EPA, 1515 Clay Street, 16th floor, Oakland, CA 94612, USA.
| | - Brian Malig
- Office of Environmental Health Hazard Assessment, California EPA, 1515 Clay Street, 16th floor, Oakland, CA 94612, USA
| | - Sina Hasheminassab
- Department of Civil and Environmental Engineering, University of Southern California, 3620 South Vermont Avenue, Los Angeles, CA 90089, USA
| | - Constantinos Sioutas
- Department of Civil and Environmental Engineering, University of Southern California, 3620 South Vermont Avenue, Los Angeles, CA 90089, USA
| | - Rupa Basu
- Office of Environmental Health Hazard Assessment, California EPA, 1515 Clay Street, 16th floor, Oakland, CA 94612, USA
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50
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Vedal S, Han B, Xu J, Szpiro A, Bai Z. Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14121580. [PMID: 29244738 PMCID: PMC5750998 DOI: 10.3390/ijerph14121580] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 12/08/2017] [Accepted: 12/12/2017] [Indexed: 12/25/2022]
Abstract
No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.
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Affiliation(s)
- Sverre Vedal
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA 98105, USA.
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100112, China.
| | - Bin Han
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100112, China.
| | - Jia Xu
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA 98105, USA.
| | - Adam Szpiro
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA 98195, USA.
| | - Zhipeng Bai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100112, China.
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