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Mo S, Hu J, Yu C, Bao J, Shi Z, Zhou P, Yang Z, Luo S, Yin Z, Zhang Y. Short-term effects of fine particulate matter constituents on myocardial infarction death. J Environ Sci (China) 2023; 133:60-69. [PMID: 37451789 DOI: 10.1016/j.jes.2022.07.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 07/18/2023]
Abstract
Existing evidence suggested that short-term exposure to fine particulate matter (PM2.5) may increase the risk of death from myocardial infarction (MI), while PM2.5 constituents responsible for this association has not been determined. We collected 12,927 MI deaths from 32 counties in southern China during 2011-2013. County-level exposures of ambient PM2.5 and its 5 constituents (i.e., elemental carbon (EC), organic carbon (OC), sulfate (SO42-), ammonium (NH4+), and nitrate (NO3-)) were aggregated from gridded datasets predicted by Community Multiscale Air Quality Modeling System. We employed a space-time-stratified case-crossover design and conditional logistic regression models to quantify the association of MI mortality with short-term exposure to PM2.5 and its constituents across various lag days. Over the study period, the daily mean PM2.5 mass concentration was 77.8 (standard deviation (SD) = 72.7) µg/m3. We estimated an odds ratio of 1.038 (95% confidence interval (CI): 1.003-1.074), 1.038 (1.013-1.063) and 1.057 (1.023-1.097) for MI mortality associated with per interquartile range (IQR) increase in the 3-day moving-average exposure to PM2.5 (IQR = 76.3 µg/m3), EC (4.1 µg/m3) and OC (9.1 µg/m3), respectively. We did not identify significant association between MI death and exposure to water-soluble ions (SO42-, NH4+ and NO3-). Likelihood ratio tests supported no evident violations of linear assumptions for constituents-MI associations. Subgroup analyses showed stronger associations between MI death and EC/OC exposure in the elderly, males and cold months. Short-term exposure to PM2.5 constituents, particularly those carbonaceous aerosols, was associated with increased risks of MI mortality.
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Affiliation(s)
- Shaocai Mo
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Jianlin Hu
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan 430071, China
| | - Junzhe Bao
- Department of Epidemiology and Biostatistics, School of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Zhihao Shi
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Peixuan Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Zhiming Yang
- School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
| | - Siqi Luo
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Zhouxin Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China; Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan 430065, China.
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Remigio RV, He H, Raimann JG, Kotanko P, Maddux FW, Sapkota AR, Liang XZ, Puett R, He X, Sapkota A. Combined effects of air pollution and extreme heat events among ESKD patients within the Northeastern United States. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 812:152481. [PMID: 34921874 PMCID: PMC8962569 DOI: 10.1016/j.scitotenv.2021.152481] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/03/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Increasing number of studies have linked air pollution exposure with renal function decline and disease. However, there is a lack of data on its impact among end-stage kidney disease (ESKD) patients and its potential modifying effect from extreme heat events (EHE). METHODS Fresenius Kidney Care records from 28 selected northeastern US counties were used to pool daily all-cause mortality (ACM) and all-cause hospital admissions (ACHA) counts. County-level daily ambient PM2.5 and ozone (O3) were estimated using a high-resolution spatiotemporal coupled climate-air quality model and matched to ESKD patients based on ZIP codes of treatment sites. We used time-stratified case-crossover analyses to characterize acute exposures using individual and cumulative lag exposures for up to 3 days (Lag 0-3) by using a distributed lag nonlinear model framework. We used a nested model comparison hypothesis test to evaluate for interaction effects between air pollutants and EHE and stratification analyses to estimate effect measures modified by EHE days. RESULTS From 2001 to 2016, the sample population consisted of 43,338 ESKD patients. We recorded 5217 deaths and 78,433 hospital admissions. A 10-unit increase in PM2.5 concentration was associated with a 5% increase in ACM (rate ratio [RRLag0-3]: 1.05, 95% CI: 1.00-1.10) and same-day O3 (RRLag0: 1.02, 95% CI: 1.01-1.03) after adjusting for extreme heat exposures. Mortality models suggest evidence of interaction and effect measure modification, though not always simultaneously. ACM risk increased up to 8% when daily ozone concentrations exceeded National Ambient Air Quality Standards established by the United States, but the increases in risk were considerably higher during EHE days across lag periods. CONCLUSION Our findings suggest interdependent effects of EHE and air pollution among ESKD patients for all-cause mortality risks. National level assessments are needed to consider the ESKD population as a sensitive population and inform treatment protocols during extreme heat and degraded pollution episodes.
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Affiliation(s)
- Richard V Remigio
- Maryland Institute for Applied Environmental Health, University of Maryland School of Public Health, College Park, MD, USA
| | - Hao He
- Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
| | | | - Peter Kotanko
- Research Division, Renal Research Institute, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Amy Rebecca Sapkota
- Maryland Institute for Applied Environmental Health, University of Maryland School of Public Health, College Park, MD, USA
| | - Xin-Zhong Liang
- Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA; Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | - Robin Puett
- Maryland Institute for Applied Environmental Health, University of Maryland School of Public Health, College Park, MD, USA
| | - Xin He
- Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, USA
| | - Amir Sapkota
- Maryland Institute for Applied Environmental Health, University of Maryland School of Public Health, College Park, MD, USA.
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Habre R, Girguis M, Urman R, Fruin S, Lurmann F, Shafer M, Gorski P, Franklin M, McConnell R, Avol E, Gilliland F. Contribution of tailpipe and non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern California. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2021; 71:209-230. [PMID: 32990509 PMCID: PMC8112073 DOI: 10.1080/10962247.2020.1826366] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/21/2020] [Accepted: 09/09/2020] [Indexed: 05/19/2023]
Abstract
Exposure to traffic-related air pollution (TRAP) in the near-roadway environment is associated with multiple adverse health effects. To characterize the relative contribution of tailpipe and non-tailpipe TRAP sources to particulate matter (PM) in the quasi-ultrafine (PM0.2), fine (PM2.5) and coarse (PM2.5-10) size fractions and identify their spatial determinants in southern California (CA). Month-long integrated PM0.2, PM2.5 and PM2.5-10 samples (n = 461, 265 and 298, respectively) were collected across cool and warm seasons in 8 southern CA communities (2008-9). Concentrations of PM mass, elements, carbons and major ions were obtained. Enrichment ratios (ER) in PM0.2 and PM10 relative to PM2.5 were calculated for each element. The Positive Matrix Factorization model was used to resolve and estimate the relative contribution of TRAP sources to PM in three size fractions. Generalized additive models (GAMs) with bivariate loess smooths were used to understand the geographic variation of TRAP sources and identify their spatial determinants. EC, OC, and B had the highest median ER in PM0.2 relative to PM2.5. Six, seven and five sources (with characteristic species) were resolved in PM0.2, PM2.5 and PM2.5-10, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM0.2, PM2.5 and PM2.5-10 mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM0.2 mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM2.5. Others included fuel oil, biomass burning, secondary inorganic aerosol, sea salt, and crustal/soil. CALINE4 dispersion model nitrogen oxides, trucks and intersections were most correlated with TRAP sources. The influence of smaller roadways and intersections became more apparent once Long Beach was excluded. Non-tailpipe emissions constituted ~8%, 11% and 18% of PM0.2, PM2.5 and PM2.5-10, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures. Implications: Vehicle emissions result in a complex mix of air pollutants with both tailpipe and non-tailpipe components. As mobile source regulations lead to decreased tailpipe emissions, the relative contribution of non-tailpipe traffic emissions to near-roadway exposures is increasing. This study documents the presence of non-tailpipe abrasive vehicular emissions (AVE) from brake and tire wear, catalyst degradation and resuspended road dust in the quasi-ultrafine (PM0.2), fine and coarse particulate matter size fractions, with contributions reaching up to 30% in PM0.2 in some southern California communities. These findings have important exposure and policy implications given the high metal content of AVE and the efficiency of PM0.2 at reaching the alveolar region of the lungs and other organ systems once inhaled. This work also highlights important considerations for building models that can accurately predict tailpipe and non-tailpipe exposures for population health studies.
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Affiliation(s)
- Rima Habre
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | - Mariam Girguis
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | - Robert Urman
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | - Scott Fruin
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | | | - Martin Shafer
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI
- Environmental Chemistry & Technology Program, University of Wisconsin-Madison, Madison WI
| | - Patrick Gorski
- Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI
| | - Meredith Franklin
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | - Rob McConnell
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | - Ed Avol
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | - Frank Gilliland
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
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Li L, Girguis M, Lurmann F, Pavlovic N, McClure C, Franklin M, Wu J, Oman LD, Breton C, Gilliland F, Habre R. Ensemble-based deep learning for estimating PM 2.5 over California with multisource big data including wildfire smoke. ENVIRONMENT INTERNATIONAL 2020; 145:106143. [PMID: 32980736 PMCID: PMC7643812 DOI: 10.1016/j.envint.2020.106143] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/14/2020] [Accepted: 09/13/2020] [Indexed: 05/21/2023]
Abstract
INTRODUCTION Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use. METHODS Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model. RESULTS Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers. CONCLUSION Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.
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Affiliation(s)
- Lianfa Li
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China.
| | - Mariam Girguis
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Meredith Franklin
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jun Wu
- Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, USA
| | - Luke D Oman
- Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD, USA
| | - Carrie Breton
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Frank Gilliland
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Rima Habre
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.
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