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Mejía C D, Faican G, Zalakeviciute R, Matovelle C, Bonilla S, Sobrino JA. Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador. Heliyon 2024; 10:e28152. [PMID: 38560184 PMCID: PMC10979269 DOI: 10.1016/j.heliyon.2024.e28152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 02/27/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.
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Affiliation(s)
- Danilo Mejía C
- Grupo CATOx – CEA de la Universidad de Cuenca, Campus Balzay, 010207 Cuenca, Ecuador
- Carrera de Ingeniería Ambiental de la Universidad de Cuenca, Campus Balzay, 010207 Cuenca, Ecuador
| | - Gina Faican
- Grupo CATOx – CEA de la Universidad de Cuenca, Campus Balzay, 010207 Cuenca, Ecuador
| | - Rasa Zalakeviciute
- Grupo de Biodiversidad Medio Ambiente y Salud (BIOMAS), Universidad de Las Americas, Quito - EC 170125, Ecuador
| | - Carlos Matovelle
- Carrera de Ingeniería Ambienta de la Universidad Católica de Cuenca, Ecuador
| | - Santiago Bonilla
- Research Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica, Machala y Sabanilla, 170301 Quito, Ecuador
| | - José A. Sobrino
- Gobal Change Unit (GCU), Image Processing Laboratory (IPL), University of Valencia, Spain
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Power MC, Bennett EE, Lynch KM, Stewart JD, Xu X, Park ES, Smith RL, Vizuete W, Margolis HG, Casanova R, Wallace R, Sheppard L, Ying Q, Serre ML, Szpiro AA, Chen JC, Liao D, Wellenius GA, van Donkelaar A, Yanosky JD, Whitsel E. Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS). Environ Health Perspect 2024; 132:17003. [PMID: 38226465 PMCID: PMC10790222 DOI: 10.1289/ehp12995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 11/17/2023] [Accepted: 12/05/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear. OBJECTIVES Our objective is to compare particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses. METHODS We assigned annual PM 2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM 2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region. RESULTS With a few exceptions, relative agreement of approach-specific PM 2.5 exposure estimates was high for PM 2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM 2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM 2.5 . There was no evidence of large differences in health effects associations with PM 2.5 among estimation approaches in analyses adjusted for climate region. CONCLUSIONS Different estimation approaches produced similar spatial patterns of PM 2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM 2.5 -health effects associations were similar among estimation approaches. PM 2.5 estimates and PM 2.5 -health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM 2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.
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Affiliation(s)
- Melinda C. Power
- Department of Epidemiology, Milken Institute School of Public Health, Washington, District of Columbia, USA
| | - Erin E. Bennett
- Department of Epidemiology, Milken Institute School of Public Health, Washington, District of Columbia, USA
| | - Katie M. Lynch
- Department of Epidemiology, Milken Institute School of Public Health, Washington, District of Columbia, USA
| | - James D. Stewart
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xiaohui Xu
- Department of Epidemiology and Biostatistics, Texas A&M Health Science Center School of Public Health, College Station, Texas, USA
| | - Eun Sug Park
- Texas A&M Transportation Institute, College Station, Texas, USA
| | - Richard L. Smith
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Will Vizuete
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Helene G. Margolis
- Department of Internal Medicine, School of Medicine, University of California at Davis, Sacramento, California, USA
| | - Ramon Casanova
- Department of Biostatics and Data Science, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA
| | - Robert Wallace
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA
- Department of Internal Medicine, College of Public Health, University of Iowa, Iowa City, Iowa, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, USA
- Department of Biostatistics, University of Washington School of Public Health, Seattle WA, USA
| | - Qi Ying
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas, USA
| | - Marc L. Serre
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adam A. Szpiro
- Department of Biostatistics, University of Washington School of Public Health, Seattle WA, USA
| | - Jiu-Chiuan Chen
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Duanping Liao
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Gregory A. Wellenius
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Aaron van Donkelaar
- Department of Energy, Environmental, and Chemical Engineering McKelvey School of Engineering, St. Louis, Missouri, USA
| | - Jeff D. Yanosky
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Eric Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Sheridan P, Chen C, Thompson CA, Benmarhnia T. Immortal Time Bias With Time-Varying Exposures in Environmental Epidemiology: A Case Study in Lung Cancer Survival. Am J Epidemiol 2023; 192:1754-1762. [PMID: 37400995 PMCID: PMC10558188 DOI: 10.1093/aje/kwad135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 01/19/2023] [Accepted: 06/04/2023] [Indexed: 07/05/2023] Open
Abstract
Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-μg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors.
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Affiliation(s)
- Paige Sheridan
- Correspondence to Dr. Paige Sheridan, Herbert Wertheim School of Public Health, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 (e-mail: )
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Chinatamby P, Jewaratnam J. A performance comparison study on PM 2.5 prediction at industrial areas using different training algorithms of feedforward-backpropagation neural network (FBNN). Chemosphere 2023; 317:137788. [PMID: 36642141 DOI: 10.1016/j.chemosphere.2023.137788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/16/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM2.5) in the atmosphere is fast increasing in Malaysia due to industrialization and urbanization. Prolonged exposure of PM2.5 can cause serious health effects to human. This research is aimed to identify the most reliable model to predict the PM2.5 pollution using multi-layered feedforward-backpropagation neural network (FBNN). Air quality and meteorological data were collected from Department of Environment (DOE) Malaysia. Six different training algorithms consisting of thirteen various training functions were trained and compared. FBNN model with the highest coefficient correlation (R2) and lowest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were selected as the best performing model. Levenberg Marquardt (trainlm) is the best performing algorithms compared to other algorithms with R2 value of 0.9834 and the lowest error values for RMSE (2.3981), MAE (1.7843) and MAPE (0.1063).
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Affiliation(s)
- Pavithra Chinatamby
- Center for Separation Science & Technology (CSST), Department of Chemical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Jegalakshimi Jewaratnam
- Center for Separation Science & Technology (CSST), Department of Chemical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
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Guan Y, Xiao Y, Chu C, Zhang N, Yu L. Trends and characteristics of ozone and nitrogen dioxide related health impacts in Chinese cities. Ecotoxicol Environ Saf 2022; 241:113808. [PMID: 35759982 DOI: 10.1016/j.ecoenv.2022.113808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/02/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
Ambient ozone pollution has been becoming severe and attributed to considerable health impacts in China. Nitrogen dioxide (NO2) is involved in atmospheric ozone production while also affecting public health directly. Joint control ozone and NO2 pollution would be of significance. This study quantitatively assessed the health impact attributed to ambient ozone and NO2 pollution in 338 Chinese cities from 2015 to 2020. The results reveal the generally opposite trends of ozone- and NO2-related health impacts in China. From 2015-2020, respiratory and chronic obstructive pulmonary disease (COPD) health impacts attributed to ozone in 338 cities increased by 65.30% and 63.98%. The NO2-attributed health impacts decreased by 24.80% and 24.62%. In 2020, the ozone- and NO2-related respiratory health impacts were 3.96 million DALYs (disability-adjusted life years) and 1.47 million DALYs. High health impacts are concentrated in big cities and city clusters. In 2020, the sum of ozone- and NO2-related respiratory health impacts in the top 20 cities was 0.98 million DALYs and 0.44 million DALYs, accounting for 24.70% and 30.24% of the 338 cities. The population attribution fraction analysis identified the increasing distributional consistency of ozone and NO2-related health impacts, emphasizing the necessity and possible efficiency of ozone-NO2 joint control. Emission source analysis based on gridded data provided a reference for understanding health impacts and developing targeted strategies.
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Affiliation(s)
- Yang Guan
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China; The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Yang Xiao
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China; The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Chengjun Chu
- Center of Environmental Status and Plan Assessment, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Nannan Zhang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
| | - Lei Yu
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China.
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Gariazzo C, Carlino G, Silibello C, Tinarelli G, Renzi M, Finardi S, Pepe N, Barbero D, Radice P, Marinaccio A, Forastiere F, Michelozzi P, Viegi G, Stafoggia M. Impact of different exposure models and spatial resolution on the long-term effects of air pollution. Environ Res 2021; 192:110351. [PMID: 33130163 DOI: 10.1016/j.envres.2020.110351] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 09/21/2020] [Accepted: 10/13/2020] [Indexed: 06/11/2023]
Abstract
Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 μm, PM10, and nitrogen dioxide, NO2) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO2 and PM10 were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km2) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO2 and PM10 concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO2 and PM10 regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO2 estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM10, with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 μg/m3 is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions.
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Affiliation(s)
- Claudio Gariazzo
- Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers' Compensation Authority (INAIL), Monte Porzio Catone (RM), Italy.
| | | | | | | | - Matteo Renzi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
| | | | | | | | | | - Alessandro Marinaccio
- Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers' Compensation Authority (INAIL), Monte Porzio Catone (RM), Italy
| | - Francesco Forastiere
- Institute for Biomedical Research and Innovation (IRIB), National Research Council, Palermo, Italy; Environmental Research Group, School of Public Health, Imperial College, London, UK
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
| | - Giovanni Viegi
- Institute for Biomedical Research and Innovation (IRIB), National Research Council, Palermo, Italy; Institute of Clinical Physiology (IFC), National Research Council, Pisa, Italy
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
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Leibel S, Nguyen M, Brick W, Parker J, Ilango S, Aguilera R, Gershunov A, Benmarhnia T. Increase in Pediatric Respiratory Visits Associated with Santa Ana Wind–Driven Wildfire Smoke and PM 2.5 Levels in San Diego County. Ann Am Thorac Soc 2020; 17:313-20. [DOI: 10.1513/annalsats.201902-150oc] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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Sheridan P, Ilango S, Bruckner TA, Wang Q, Basu R, Benmarhnia T. Ambient Fine Particulate Matter and Preterm Birth in California: Identification of Critical Exposure Windows. Am J Epidemiol 2019; 188:1608-1615. [PMID: 31107509 DOI: 10.1093/aje/kwz120] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 12/17/2022] Open
Abstract
Exposure to ambient fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5)) during pregnancy is associated with preterm birth (PTB), a leading cause of infant morbidity and mortality. Results from studies attempting to identify etiologically relevant exposure periods of vulnerability have been inconsistent, possibly because of failure to consider the time-to-event nature of the outcome and lagged exposure effects of PM2.5. In this study, we aimed to identify critical exposure windows for weekly PM2.5 exposure and PTB in California using California birth cohort data from 2005-2010. Associations were assessed using distributed-lag Cox proportional hazards models. We assessed effect-measure modification by race/ethnicity by calculating the weekly relative excess risk due to interaction. For a 10-μg/m3 increase in PM2.5 exposure over the entire period of gestation, PTB risk increased by 11% (hazard ratio = 1.11, 95% confidence interval: 1.09, 1.14). Gestational weeks 17-24 and 36 were associated with increased vulnerability to PM2.5 exposure. We find that non-Hispanic black mothers may be more susceptible to effects of PM2.5 exposure than non-Hispanic white mothers, particularly at the end of pregnancy. These findings extend our knowledge about the existence of specific exposure periods during pregnancy that have the greatest impact on preterm birth.
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Affiliation(s)
- Paige Sheridan
- Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, San Diego, California
- Division of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, California
| | - Sindana Ilango
- Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, San Diego, California
- Division of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, California
| | - Tim A Bruckner
- Department of Public Health and Planning, Policy and Design, University of California, Irvine, Irvine, California
| | - Qiong Wang
- School of Medicine, Yale University, New Haven, Connecticut
| | - Rupa Basu
- Air Toxicology and Epidemiology Branch, California Office of Environmental Health Hazard Assessment, Sacramento, California
| | - Tarik Benmarhnia
- Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, San Diego, California
- Scripps Institute of Oceanography, University of California, San Diego, San Diego, California
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Cowie CT, Garden F, Jegasothy E, Knibbs LD, Hanigan I, Morley D, Hansell A, Hoek G, Marks GB. Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO 2 for a birth cohort in Sydney, Australia. Environ Res 2019; 174:24-34. [PMID: 31026625 DOI: 10.1016/j.envres.2019.03.068] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 03/15/2019] [Accepted: 03/26/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO2) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model. METHODS Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO2 and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO2 concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC). RESULTS The NO2 LUR model predicted 84% of spatial variability in annual mean NO2 (RMSE: 1.2 ppb; cross-validated R2: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R2: 0.90). The annual average NO2 concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO2 cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO2 cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69. CONCLUSIONS Our LUR models explained a high degree of spatial variability in annual mean NO2 and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO2 for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.
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Affiliation(s)
- Christine T Cowie
- South West Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute of Medical Research, Sydney, Australia; Centre for Air Pollution, Energy & Health Research (CAR), Australia; Woolcock Institute of Medical Research, The University of Sydney, Australia.
| | - Frances Garden
- South West Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute of Medical Research, Sydney, Australia
| | - Edward Jegasothy
- Sydney School of Public Health, University of Sydney, Sydney, Australia
| | - Luke D Knibbs
- Centre for Air Pollution, Energy & Health Research (CAR), Australia; School of Public Health, The University of Queensland, Brisbane, Australia
| | - Ivan Hanigan
- Centre for Air Pollution, Energy & Health Research (CAR), Australia; University of Canberra, Canberra, Australia
| | - David Morley
- MRC-PHE Centre for Environment & Health, Imperial College London, London, UK
| | - Anna Hansell
- MRC-PHE Centre for Environment & Health, Imperial College London, London, UK
| | - Gerard Hoek
- Institute of Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Guy B Marks
- South West Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute of Medical Research, Sydney, Australia; Centre for Air Pollution, Energy & Health Research (CAR), Australia; Woolcock Institute of Medical Research, The University of Sydney, Australia
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Loizeau M, Buteau S, Chaix B, McElroy S, Counil E, Benmarhnia T. Does the air pollution model influence the evidence of socio-economic disparities in exposure and susceptibility? Environ Res 2018; 167:650-661. [PMID: 30241004 DOI: 10.1016/j.envres.2018.08.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Revised: 06/27/2018] [Accepted: 08/01/2018] [Indexed: 06/08/2023]
Abstract
Studies assessing socio-economic disparities in air pollution exposure and susceptibility are usually based on a single air pollution model. A time stratified case-crossover study was designed to assess the impact of the type of model on differential exposure and on the differential susceptibility in the relationship between ozone exposure and daily mortality by socio-economic strata (SES) in Montreal. Non-accidental deaths along with deaths from cardiovascular and respiratory causes on the island of Montreal for the period 1991-2002 were included as cases. Daily ozone concentration estimates at partictaipants' residence were obtained from the five following air pollution models: Average value (AV), Nearest station model (NS), Inverse-distance weighting interpolation (IDW), Land-use regression model with back-extrapolation (LUR-BE) and Bayesian maximum entropy model combined with a land-use regression (BME-LUR). The prevalence of a low household income (< 20,000/year) was used as socio-economic variable, divided into two categories as a proxy for deprivation. Multivariable conditional logistic regressions were used considering 3-day average concentrations. Multiplicative and additive interactions (using Relative Excess Risk due to Interaction) as well as Cochran's tests were calculated and results were compared across the different air pollution models. Heterogeneity of susceptibility and exposure according to socio-economic status (SES) were found. Ratio of exposure across SES groups means ranged from 0.75 [0.74-0.76] to 1.01 [1.00-1.02], respectively for the LUR-BE and the BME-LUR models. Ratio of mortality odds ratios ranged from 1.01 [0.96-1.05] to 1.02 [0.97-1.08], respectively for the IDW and LUR-BE models. Cochran's test of heterogeneity between the air pollution models showed important heterogeneity regarding the differential exposure by SES, but the air pollution model was not found to influence heterogeneity regarding the differential susceptibility. The study showed air pollution models can influence the assessment of disparities in exposure according to SES in Montreal but not that of disparities in susceptibility.
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Affiliation(s)
- Maxime Loizeau
- Department of Family Medicine and Public Health & Scripps Institution of Oceanography University of California, San Diego, CA, USA; EHESP School of Public Health, Rennes, France
| | - Stéphane Buteau
- Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Basile Chaix
- Inserm, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis team, Paris, France
| | - Sara McElroy
- Department of Family Medicine and Public Health & Scripps Institution of Oceanography University of California, San Diego, CA, USA
| | | | - Tarik Benmarhnia
- Department of Family Medicine and Public Health & Scripps Institution of Oceanography University of California, San Diego, CA, USA
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Buteau S, Goldberg MS, Burnett RT, Gasparrini A, Valois MF, Brophy JM, Crouse DL, Hatzopoulou M. Associations between ambient air pollution and daily mortality in a cohort of congestive heart failure: Case-crossover and nested case-control analyses using a distributed lag nonlinear model. Environ Int 2018; 113:313-324. [PMID: 29361317 DOI: 10.1016/j.envint.2018.01.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 01/09/2018] [Accepted: 01/09/2018] [Indexed: 06/07/2023]
Abstract
BACKGROUND Persons with congestive heart failure may be at higher risk of the acute effects related to daily fluctuations in ambient air pollution. To meet some of the limitations of previous studies using grouped-analysis, we developed a cohort study of persons with congestive heart failure to estimate whether daily non-accidental mortality were associated with spatially-resolved, daily exposures to ambient nitrogen dioxide (NO2) and ozone (O3), and whether these associations were modified according to a series of indicators potentially reflecting complications or worsening of health. METHODS We constructed the cohort from the linkage of administrative health databases. Daily exposure was assigned from different methods we developed previously to predict spatially-resolved, time-dependent concentrations of ambient NO2 (all year) and O3 (warm season) at participants' residences. We performed two distinct types of analyses: a case-crossover that contrasts the same person at different times, and a nested case-control that contrasts different persons at similar times. We modelled the effects of air pollution and weather (case-crossover only) on mortality using distributed lag nonlinear models over lags 0 to 3 days. We developed from administrative health data a series of indicators that may reflect the underlying construct of "declining health", and used interactions between these indicators and the cross-basis function for air pollutant to assess potential effect modification. RESULTS The magnitude of the cumulative as well as the lag-specific estimates of association differed in many instances according to the metric of exposure. Using the back-extrapolation method, which is our preferred exposure model, we found for the case-crossover design a cumulative mean percentage changes (MPC) in daily mortality per interquartile increment in NO2 (8.8 ppb) of 3.0% (95% CI: -0.4, 6.6%) and for O3 (16.5 ppb) 3.5% (95% CI: -4.5, 12.1). For O3 there was strong confounding by weather (unadjusted MPC = 7.1%; 95% CI: 1.7, 12.7%). For the nested case-control approach the cumulative MPC for NO2 in daily mortality was 2.9% (95% CI: -0.9, 6.9%) and for O3 7.3% (95% CI: 3.0, 11.9%). We found evidence of effect modification between daily mortality and cumulative NO2 and O3 according to the prescribed dose of furosemide in the nested case-control analysis, but not in the case-crossover analysis. CONCLUSIONS Mortality in congestive heart failure was associated with exposure to daily ambient NO2 and O3 predicted from a back-extrapolation method using a land use regression model from dense sampling surveys. The methods used to assess exposure can have considerable influence on the estimated acute health effects of the two air pollutants.
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Affiliation(s)
- Stephane Buteau
- Department of Medicine, McGill University, Montreal, Quebec, Canada; Institut national de sante publique du Quebec (INSPQ), Montreal, Quebec, Canada.
| | - Mark S Goldberg
- Department of Medicine, McGill University, Montreal, Quebec, Canada; Division of Clinical Epidemiology, Research Institute of the McGill University Hospital Centre, Montreal, Canada
| | | | - Antonio Gasparrini
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Marie-France Valois
- Department of Medicine, McGill University, Montreal, Quebec, Canada; Division of Clinical Epidemiology, Research Institute of the McGill University Hospital Centre, Montreal, Canada
| | - James M Brophy
- Department of Medicine, McGill University, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Dan L Crouse
- Department of Sociology, University of New Brunswick, Fredericton, New Brunswick, Canada; New Brunswick Institute for Research, Data, and Training, Fredericton, New Brunswick, Canada
| | - Marianne Hatzopoulou
- Department of Civil Engineering, University of Toronto, Toronto, Ontario, Canada
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Hanigan IC, Williamson GJ, Knibbs LD, Horsley J, Rolfe MI, Cope M, Barnett AG, Cowie CT, Heyworth JS, Serre ML, Jalaludin B, Morgan GG. Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research. Environ Sci Technol 2017; 51:12473-12480. [PMID: 28948787 DOI: 10.1021/acs.est.7b03035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Exposure to traffic related nitrogen dioxide (NO2) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO2. The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.
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Affiliation(s)
- Ivan C Hanigan
- Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research, University of Sydney , Sydney, Australia
- University of Canberra , Canberra, Australia
| | - Grant J Williamson
- Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research Sydney, Australia & School of Biological Sciences, University of Tasmania , Hobart, Australia
| | - Luke D Knibbs
- Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research Sydney, Australia & School of Public Health, The University of Queensland , Herston, Australia
| | - Joshua Horsley
- School of Public Health, University of Sydney , Sydney, Australia
| | - Margaret I Rolfe
- School of Public Health, University of Sydney , Sydney, Australia
| | - Martin Cope
- Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research Sydney, Australia & CSIRO, Melbourne, Australia
| | - Adrian G Barnett
- Institute of Health and Biomedical Innovation & School of Public Health and Social Work, Queensland University of Technology , Brisbane, Australia
| | - Christine T Cowie
- Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research, University of Sydney; South West Sydney Clinical School, University of NSW & Ingham Institute for Applied Medical Research , Sydney, Australia
| | - Jane S Heyworth
- Centre for Air Quality and Health Research and Evaluation, NESP Clean Air and Urban Landscapes, School of Population and Global Health, The University of Western Australia , Perth, Australia
| | - Marc L Serre
- University of North Carolina , Chapel Hill, United States
| | - Bin Jalaludin
- Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research, University of Sydney; South West Sydney Clinical School, University of NSW & Ingham Institute for Applied Medical Research , Sydney, Australia
| | - Geoffrey G Morgan
- Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research & University Centre for Rural Health, North Coast, School of Public Health, University of Sydney , Sydney, Australia
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