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Ganji A, Saeedi M, Lloyd M, Xu J, Weichenthal S, Hatzopoulou M. Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:170075. [PMID: 38232822 DOI: 10.1016/j.scitotenv.2024.170075] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
An important challenge for studies of air pollution and health effects is the derivation of historical exposures. These generally entail some form of backcasting, which refers to a range of approaches that aim to project a current surface into the past. Accurate backcasting is conditional upon the availability of historical data for predictor variables and the ability to capture spatial and temporal trends in these variables. This study proposes a method to backcast traffic-related air pollution surfaces developed using land-use regression models by including temporal variability of traffic and emissions and trends in concentrations measured at reference stations. Nitrogen dioxide (NO2) concentrations collected in the City of Toronto using the Urban Scanner mobile platform were adjusted for historical trends captured at reference stations. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), a powerful tool for time series decomposition, was employed to isolate seasonal variations, annual trends, and abrupt changes in NO2 at reference stations, hence decomposing the signal. Exposure surfaces were generated for a period extending from 2006 to 2020, exhibiting decreases ranging from 10 to 50 % depending on the neighborhood, with an average of 20.46 % across the city. Yearly surfaces were intersected with mobility patterns of Torontonians extracted from travel survey data for 2006 and 2016, illustrating strong spatial gradients in the evolution of NO2 over time, with larger decreases along major roads and highways and in the central core. These findings demonstrate that air pollution improvements throughout the 14 years are inhomogeneous across space.
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Affiliation(s)
- Arman Ganji
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - Milad Saeedi
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - Marshall Lloyd
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada.
| | - Junshi Xu
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada.
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2
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Quinteros ME, Blazquez C, Ayala S, Kilby D, Cárdenas-R JP, Ossa X, Rosas-Diaz F, Stone EA, Blanco E, Delgado-Saborit JM, Harrison RM, Ruiz-Rudolph P. Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19473-19486. [PMID: 37976408 DOI: 10.1021/acs.est.3c00720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Biomass burning is common in much of the world, and in some areas, residential wood-burning has increased. However, air pollution resulting from biomass burning is an important public health problem. A sampling campaign was carried out between May 2017 and July 2018 in over 64 sites in four sessions, to develop a spatio-temporal land use regression (LUR) model for fine particulate matter (PM) and wood-burning tracers levoglucosan and soluble potassium (Ksol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m-3 for PM2.5, 0.607 (0.538) μg m-3 for levoglucosan, and 0.635 (0.489) μg m-3 for Ksol. LUR models for PM2.5, levoglucosan, and Ksol had a satisfactory performance (LOSOCV R2), explaining 88.8%, 87.4%, and 87.3% of the total variance, respectively. All models included sociodemographic predictors consistent with the pattern of use of wood-burning in homes. The models were applied to predict concentrations surfaces and to estimate exposures for an epidemiological study.
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Affiliation(s)
- María Elisa Quinteros
- Departamento de Salud Pública, Facultad de Ciencias de la Salud, Universidad de Talca, Avenida Lircay s/n, Talca, 3460000, Chile
- Programa Doctorado en Salud Pública, Instituto de Salud Poblacional, Facultad de Medicina, Universidad de Chile, Independencia 939, Santiago, 1025000, Chile
| | - Carola Blazquez
- Department of Engineering Sciences, Universidad Andres Bello, Quillota 980, Viña del Mar, 2531015, Chile
| | - Salvador Ayala
- Programa Doctorado en Salud Pública, Instituto de Salud Poblacional, Facultad de Medicina, Universidad de Chile, Independencia 939, Santiago, 1025000, Chile
- Departamento Agencia Nacional de Dispositivos Médicos, Innovación y Desarrollo, Instituto de Salud Pública de Chile, Marathon 1000, Ñuñoa, Santiago 0000000000, Chile
| | - Dylan Kilby
- School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109, United States
| | - Juan Pablo Cárdenas-R
- Departamento de Ingeniería en Obras Civiles, Universidad de La Frontera, Avenida Francisco Salazar 01145, Temuco, Chile
- Facultad de Arquitectura, Construcción y Medio Ambiente, Universidad Autónoma de Chile, Temuco 4810101, Chile
| | - Ximena Ossa
- Departamento de Salud Pública y Centro de Excelencia CIGES, Universidad de la Frontera, Caro Solar 115, Temuco, 4780000, Chile
| | - Felipe Rosas-Diaz
- Facultad de Ingeniería Civil, Universidad Autónoma de Nuevo León, San Nicolás de Los Garza 66451, Nuevo León, México
| | - Elizabeth A Stone
- Department of Chemistry and Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, Iowa 52242, United States
| | - Estela Blanco
- Programa Doctorado en Salud Pública, Instituto de Salud Poblacional, Facultad de Medicina, Universidad de Chile, Independencia 939, Santiago, 1025000, Chile
- Centro de Investigación en Sociedad y Salud and Núcleo Milenio de Sociomedicina, Universidad Mayor, Santiago, 7510041, Chile
| | - Juana-María Delgado-Saborit
- Perinatal Epidemiology, Environmental Health and Clinical Research, School of Medicine, Universitat Jaume I, Avinguda de Vicent Sos Baynat, s/n, 12071 Castelló de la Plana, Castellon Spain
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, SW7 2BX, United Kingdom
- Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston Birmingham B152TT, U.K
| | - Roy M Harrison
- Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston Birmingham B152TT, U.K
- Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, 21589, Saudi Arabia
| | - Pablo Ruiz-Rudolph
- * Programa de Epidemiología, Instituto de Salud Poblacional, Facultad de Medicina, Universidad de Chile, Independencia 939, Santiago 1025000, Chile
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Shen Y, de Hoogh K, Schmitz O, Clinton N, Tuxen-Bettman K, Brandt J, Christensen JH, Frohn LM, Geels C, Karssenberg D, Vermeulen R, Hoek G. Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression. ENVIRONMENT INTERNATIONAL 2022; 168:107485. [PMID: 36030744 DOI: 10.1016/j.envint.2022.107485] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/19/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.
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Affiliation(s)
- Youchen Shen
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Oliver Schmitz
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
| | | | | | - Jørgen Brandt
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | | | - Lise M Frohn
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | - Camilla Geels
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | - Derek Karssenberg
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
| | - Roel Vermeulen
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht University, Utrecht, the Netherlands
| | - Gerard Hoek
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
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Sheridan C, Klompmaker J, Cummins S, James P, Fecht D, Roscoe C. Associations of air pollution with COVID-19 positivity, hospitalisations, and mortality: Observational evidence from UK Biobank. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 308:119686. [PMID: 35779662 PMCID: PMC9243647 DOI: 10.1016/j.envpol.2022.119686] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 05/26/2023]
Abstract
Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March-December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006-2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM10 were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM2.5 and NO2 was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.
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Affiliation(s)
- Charlotte Sheridan
- London School of Hygiene & Tropical Medicine, Keppel St., London, WC1E 7HT, United Kingdom.
| | - Jochem Klompmaker
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, United States.
| | - Steven Cummins
- Population Health Innovation Lab, Department of Public Health, Environments and Society, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, Keppel St., London, United Kingdom.
| | - Peter James
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, United States; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, United States.
| | - Daniela Fecht
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Medicine, St Mary's Campus, Imperial College London, London, W2 1PG, United Kingdom.
| | - Charlotte Roscoe
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, United States; MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Medicine, St Mary's Campus, Imperial College London, London, W2 1PG, United Kingdom; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, United States.
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Fecht D, Chadeau-Hyam M, Owen R, Gregson J, Halliday BP, Lota AS, Gulliver J, Ware JS, Pennell DJ, Kelly FJ, Shah ASV, Miller MR, Newby DE, Prasad SK, Tayal U. Exposure to Elevated Nitrogen Dioxide Concentrations and Cardiac Remodeling in Patients With Dilated Cardiomyopathy. J Card Fail 2022; 28:924-934. [PMID: 35027315 PMCID: PMC9186493 DOI: 10.1016/j.cardfail.2021.11.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND Empirical evidence suggests a strong link between exposure to air pollution and heart failure incidence, hospitalizations, and mortality, but the biological basis of this remains unclear. We sought to determine the relationship between differential air pollution levels and changes in cardiac structure and function in patients with dilated cardiomyopathy. METHODS AND RESULTS We undertook a prospective longitudinal observational cohort study of patients in England with dilated cardiomyopathy (enrollment 2009-2015, n = 716, 66% male, 85% Caucasian) and conducted cross sectional analysis at the time of study enrollment. Annual average air pollution exposure estimates for nitrogen dioxide (NO2) and particulate matter with diameter of 2.5 µm or less (PM2.5) at enrolment were assigned to each residential postcode (on average 12 households). The relationship between air pollution and cardiac morphology was assessed using linear regression modelling. Greater ambient exposure to NO2 was associated with higher indexed left ventricular (LV) mass (4.3 g/m2 increase per interquartile range increase in NO2, 95% confidence interval 1.9-7.0 g/m2) and lower LV ejection fraction (-1.5% decrease per interquartile range increase in NO2, 95% confidence interval -2.7% to -0.2%), independent of age, sex, socioeconomic status, and clinical covariates. The associations were robust to adjustment for smoking status and geographical clustering by postcode area. The effect of air pollution on LV mass was greatest in women. These effects were specific to NO2 exposure. CONCLUSIONS Exposure to air pollution is associated with raised LV mass and lower LV ejection fraction, with the strongest effect in women. Although epidemiological associations between air pollution and heart failure have been established and supported by preclinical studies, our findings provide novel empirical evidence of cardiac remodeling and exposure to air pollution with important clinical and public health implications.
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Affiliation(s)
- Daniela Fecht
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Marc Chadeau-Hyam
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Ruth Owen
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - John Gregson
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Brian P Halliday
- Royal Brompton Hospital and Harefield NHS Trust, London, UK; National Heart Lung Institute, Imperial College London, London, UK
| | - Amrit S Lota
- Royal Brompton Hospital and Harefield NHS Trust, London, UK; National Heart Lung Institute, Imperial College London, London, UK
| | - John Gulliver
- Centre for Environmental Health and Sustainability & School of Geography, Geology and the Environment, University of Leicester, Leicester, UK
| | - James S Ware
- Royal Brompton Hospital and Harefield NHS Trust, London, UK; National Heart Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Dudley J Pennell
- Royal Brompton Hospital and Harefield NHS Trust, London, UK; National Heart Lung Institute, Imperial College London, London, UK
| | - Frank J Kelly
- NIHR Health Protection Unit in Environmental Exposures and Health, Imperial College London, London, UK
| | - Anoop S V Shah
- Department of Non-communicable Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Mark R Miller
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - David E Newby
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Sanjay K Prasad
- Royal Brompton Hospital and Harefield NHS Trust, London, UK; National Heart Lung Institute, Imperial College London, London, UK
| | - Upasana Tayal
- Royal Brompton Hospital and Harefield NHS Trust, London, UK; National Heart Lung Institute, Imperial College London, London, UK.
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Kirwa K, Szpiro AA, Sheppard L, Sampson PD, Wang M, Keller JP, Young MT, Kim SY, Larson TV, Kaufman JD. Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Curr Environ Health Rep 2021; 8:113-126. [PMID: 34086258 PMCID: PMC8278964 DOI: 10.1007/s40572-021-00310-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models. RECENT FINDINGS Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.
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Affiliation(s)
- Kipruto Kirwa
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA.
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Lianne Sheppard
- Departments of Biostatistics and Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
| | - Paul D Sampson
- Department of Statistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Meng Wang
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA
| | - Joshua P Keller
- Department of Statistics, Colorado State University, Fort Collins, CO, USA
| | - Michael T Young
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
| | - Sun-Young Kim
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Timothy V Larson
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Joel D Kaufman
- Departments of Environmental and Occupational Health Sciences, Epidemiology, and Medicine, University of Washington, Seattle, WA, USA
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Shairsingh KK, Brook JR, Mihele CM, Evans GJ. Characterizing long-term NO 2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements. ENVIRONMENTAL RESEARCH 2021; 196:111010. [PMID: 33716024 DOI: 10.1016/j.envres.2021.111010] [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: 05/13/2020] [Revised: 01/12/2021] [Accepted: 03/08/2021] [Indexed: 06/12/2023]
Abstract
A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO2) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO2 concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R2 = 0.71 for prediction of NO2 data in 2002 and 2004) uses daily average NO2 concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO2 levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO2 patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.
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Affiliation(s)
- Kerolyn K Shairsingh
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada.
| | - Jeffrey R Brook
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, M5T 3M7, Canada.
| | - Cristian M Mihele
- Environment and Climate Change Canada, North York, Ontario, M3H 5T4, Canada
| | - Greg J Evans
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada
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Long-term Exposure to Low Air Pollutant Concentrations and the Relationship with All-Cause Mortality and Stroke in Older Men. Epidemiology 2020; 30 Suppl 1:S82-S89. [PMID: 31181010 DOI: 10.1097/ede.0000000000001034] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Long-term air pollution exposure has been associated with increased risk of mortality and stroke. Less is known about the risk at lower concentrations. The association of long-term exposure to PM2.5, PM2.5 absorbance, NO2, and NOx with all-cause mortality and stroke was investigated in a cohort of men aged ≥ 65 years who lived in metropolitan Perth, Western Australia. METHODS Land use regression models were used to estimate long-term exposure to air pollutants at participant's home address (n = 11,627) over 16 years. Different metrics of exposure were assigned: baseline; year before the outcome event; and average exposure across follow-up period. The Mortality Register and Hospital Morbidity Data from the Western Australia Data Linkage System were used to ascertain mortality and stroke cases. Hazard ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazard models, adjusting for age, smoking, education, and body mass index for all-cause mortality. For fatal and hospitalized stroke, the models included variables controlled for all-cause mortality plus hypertension. RESULTS Fifty-four percent of all-participants died, 3% suffered a fatal stroke, and 14% were hospitalized stroke cases. PM2.5 absorbance increased the risk of all-cause mortality with adjusted HR of 1.12 (1.02-1.23) for baseline and average exposures, and 1.14 (1.02-1.24) for past-year exposure. There were no associations between PM2.5 absorbance, NO2, and NOx and stroke outcomes. However, PM2.5 was associated with reduced risks of fatal stroke. CONCLUSION Long-term exposure to PM2.5 absorbance was associated with all-cause mortality among older men exposed to low concentrations; and exposure to PM2.5 was associated with reduced risk of fatal stroke.
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Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Sabath MB, Choirat C, Koutrakis P, Lyapustin A, Wang Y, Mickley LJ, Schwartz J. Assessing NO 2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:1372-1384. [PMID: 31851499 PMCID: PMC7065654 DOI: 10.1021/acs.est.9b03358] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
NO2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO2 model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R2 of 0.788 overall, a spatial R2 of 0.844, and a temporal R2 of 0.729. The relationship between daily monitored and predicted NO2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO2 levels. This NO2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO2 in unmonitored areas. We found the highest NO2 levels along highways and in cities. We also observed that nationwide NO2 levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.
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Affiliation(s)
- Qian Di
- Research Center for Public Health, Tsinghua University, Beijing, China, 100084
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
- Corresponding author: Qian Di ()
| | - Heresh Amini
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States, 30322
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel, P.O.Box 653
| | - Rachel Silvern
- Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, United States, 02138
| | - James Kelly
- U.S. Environmental Protection Agency, Office of Air Quality Planning & Standards, Research Triangle Park, North Carolina, United States, 27711
| | - M. Benjamin Sabath
- Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02115
| | - Christine Choirat
- Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02115
| | - Petros Koutrakis
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
| | - Alexei Lyapustin
- NASA Goddard Space Flight Center, Greenbelt, Maryland, United States, 20771
| | - Yujie Wang
- University of Maryland, Baltimore County, Baltimore, Maryland, United States, 21250
| | - Loretta J. Mickley
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge Massachusetts, United States, 02138
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
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10
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Simon MC, Naumova EN, Levy JI, Brugge D, Durant JL. Ultrafine Particle Number Concentration Model for Estimating Retrospective and Prospective Long-Term Ambient Exposures in Urban Neighborhoods. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:1677-1686. [PMID: 31934748 PMCID: PMC8374642 DOI: 10.1021/acs.est.9b03369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Short-term exposure to ultrafine particles (UFP; <100 nm in diameter), which are present at high concentrations near busy roadways, is associated with markers of cardiovascular and respiratory disease risk. To date, few long-term studies (months to years) have been conducted due to the challenges of long-term exposure assignment. To address this, we modified hybrid land-use regression models of particle number concentrations (PNCs; a proxy for UFP) for two study areas in Boston (MA) by replacing the measured PNC term with an hourly model and adjusting for overprediction. The hourly PNC models used covariates for meteorology, traffic, and sulfur dioxide concentrations (a marker of secondary particle formation). We compared model performance against long-term PNC data collected continuously from 9 years before and up to 3 years after the model-development period. Model predictions captured the major temporal variations in the data and model performance remained relatively stable retrospectively and prospectively. The Pearson correlation of modeled versus measured hourly log-transformed PNC at a long-term monitoring site for 9 years prior was 0.74. Our results demonstrate that highly resolved spatial-temporal PNC models are capable of estimating ambient concentrations retrospectively and prospectively with generally good accuracy, giving us confidence in using these models in epidemiological studies.
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Affiliation(s)
- Matthew C Simon
- Department of Environmental Health , Boston University School of Public Health , 715 Albany Street , Boston , Massachusetts 02118 , United States
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
| | - Elena N Naumova
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
- Friedman School of Nutrition Science and Policy , Tufts University , 150 Harrison Avenue , Boston , Massachusetts 02111 , United States
| | - Jonathan I Levy
- Department of Environmental Health , Boston University School of Public Health , 715 Albany Street , Boston , Massachusetts 02118 , United States
| | - Doug Brugge
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
- Department of Public Health and Community Medicine , Tufts University , 136 Harrison Avenue , Boston , Massachusetts 02111 , United States
- Department of Community Medicine and Health Care , University of Connecticut , 195 Farmington Avenue , Farmington , Connecticut 06032 , United States
| | - John L Durant
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
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11
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Knibbs LD, van Donkelaar A, Martin RV, Bechle MJ, Brauer M, Cohen DD, Cowie CT, Dirgawati M, Guo Y, Hanigan IC, Johnston FH, Marks GB, Marshall JD, Pereira G, Jalaludin B, Heyworth JS, Morgan GG, Barnett AG. Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM 2.5 Exposure Assessment in Australia. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:12445-12455. [PMID: 30277062 DOI: 10.1021/acs.est.8b02328] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 μm, PM2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM2.5 ("SAT-PM2.5"). We aimed to determine the validity of such satellite-based LUR models for PM2.5 in Australia. We used global SAT-PM2.5 estimates (∼10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM2.5 predictor variable (and six others) explained the most spatial variability in PM2.5 (adjusted R2 = 0.63, RMSE (μg/m3 [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R2 = 0.52, RMSE: 1.15 [16%]). The evaluation R2 of the SAT-PM2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM2.5 exposure assessment in Australia.
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Affiliation(s)
- Luke D Knibbs
- Faculty of Medicine, School of Public Health , The University of Queensland , Herston , Queensland 4006 , Australia
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
| | - Aaron van Donkelaar
- Department of Physics and Atmospheric Science , Dalhousie University , Halifax , Nova Scotia B3H 4R2 , Canada
| | - Randall V Martin
- Department of Physics and Atmospheric Science , Dalhousie University , Halifax , Nova Scotia B3H 4R2 , Canada
- Smithsonian Astrophysical Observatory , Harvard-Smithsonian Center for Astrophysics , Cambridge , Massachusetts 02138 , United States
| | - Matthew J Bechle
- Department of Civil and Environmental Engineering , University of Washington , Seattle , Washington 98195 , United States
| | - Michael Brauer
- School of Population and Public Health , The University of British Columbia , Vancouver , British Columbia V6T 1Z3 , Canada
| | - David D Cohen
- Centre for Accelerator Science , Australian Nuclear Science and Technology Organisation , Locked Bag 2001 , Kirrawee DC, New South Wales 2232 , Australia
| | - Christine T Cowie
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- South Western Sydney Clinical School , The University of New South Wales , Liverpool , New South Wales 2170 , Australia
| | - Mila Dirgawati
- School of Population and Global Health , The University of Western Australia , Perth , Western Australia 6009 , Australia
- Environmental Engineering , Institut Teknologi Nasional , Bandung , Jawa Barat 40213 , Indonesia
| | - Yuming Guo
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- Department of Epidemiology and Biostatistics, School of Public Health and Preventive Medicine , Monash University , Melbourne , Victoria 3004 , Australia
| | - Ivan C Hanigan
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- School of Public Health , The University of Sydney , Sydney , New South Wales 2006 , Australia
| | - Fay H Johnston
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- Menzies Institute for Medical Research , The University of Tasmania , Hobart , Tasmania 7000 , Australia
| | - Guy B Marks
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- South Western Sydney Clinical School , The University of New South Wales , Liverpool , New South Wales 2170 , Australia
| | - Julian D Marshall
- Department of Civil and Environmental Engineering , University of Washington , Seattle , Washington 98195 , United States
| | - Gavin Pereira
- School of Public Health , Curtin University , Bentley , Washington 6102 , Australia
- Telethon Kids Institute , The University of Western Australia , Perth , Western Australia 6008 , Australia
| | - Bin Jalaludin
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- Population Health , South Western Sydney Local Health District , Liverpool , New South Wales 2170 , Australia
| | - Jane S Heyworth
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- School of Population and Global Health , The University of Western Australia , Perth , Western Australia 6009 , Australia
- Clean Air and Urban Landscapes Hub , National Environmental Science Programme , Melbourne , Victoria 3010 , Australia
| | - Geoffrey G Morgan
- Centre for Air Pollution , Energy and Health Research , Glebe , New South Wales 2037 , Australia
- School of Public Health , The University of Sydney , Sydney , New South Wales 2006 , Australia
| | - Adrian G Barnett
- School of Public Health and Social Work , Queensland University of Technology , Kelvin Grove , Queensland 4059 , Australia
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12
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de Hoogh K, Chen J, Gulliver J, Hoffmann B, Hertel O, Ketzel M, Bauwelinck M, van Donkelaar A, Hvidtfeldt UA, Katsouyanni K, Klompmaker J, Martin RV, Samoli E, Schwartz PE, Stafoggia M, Bellander T, Strak M, Wolf K, Vienneau D, Brunekreef B, Hoek G. Spatial PM 2.5, NO 2, O 3 and BC models for Western Europe - Evaluation of spatiotemporal stability. ENVIRONMENT INTERNATIONAL 2018; 120:81-92. [PMID: 30075373 DOI: 10.1016/j.envint.2018.07.036] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/17/2018] [Accepted: 07/25/2018] [Indexed: 05/22/2023]
Abstract
BACKGROUND In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe. METHODS We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013). RESULTS The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale. CONCLUSIONS We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
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Affiliation(s)
- Kees de Hoogh
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, Postfach, 4001 Basel, Switzerland.
| | - Jie Chen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands.
| | - John Gulliver
- School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK.
| | - Barbara Hoffmann
- Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
| | - Ole Hertel
- Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark.
| | - Matthias Ketzel
- Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark.
| | - Mariska Bauwelinck
- Interface Demography - Department of Sociology, Vrije Universiteit Brussel, Boulevard de la Plaine 2, 1050 Ixelles, Brussel, Belgium; Unit Health & Environment - Sciensano, Rue Juliette Wytsmanstraat 14, 1050, Brussels, Belgium.
| | - Aaron van Donkelaar
- Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada.
| | - Ulla A Hvidtfeldt
- Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark.
| | - Klea Katsouyanni
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece; Department Population Health Sciences, Department of Analytical, Environmental and Forensic Sciences, School of Population Health & Environmental Sciences, King's College Strand, London WC2R 2LS, UK.
| | - Jochem Klompmaker
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands; National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, Netherlands.
| | - Randal V Martin
- Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada; Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, United States of America.
| | - Evangelia Samoli
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece.
| | - Per E Schwartz
- Division of Environmental Medicine, Norwegian Institute of Public Health, PO Box 4404, Nydalen, N-0403 Oslo, Norway.
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Region Health Service/ASL, Roma 1, Via Cristoforo Colombo, 112 - 00147 Rome, Italy; Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
| | - Tom Bellander
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
| | - Maciej Strak
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands.
| | - Kathrin Wolf
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany.
| | - Danielle Vienneau
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, Postfach, 4001 Basel, Switzerland.
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands.
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands.
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13
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Contreras ZA, Heck JE, Lee PC, Cui X, Hobel CJ, Janzen C, Lurmann F, Ritz B. Prenatal air pollution exposure, smoking, and uterine vascular resistance. Environ Epidemiol 2018; 2:e017. [PMID: 30627692 PMCID: PMC6322670 DOI: 10.1097/ee9.0000000000000017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Accepted: 05/03/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Prenatal exposure to air pollution and smoking increases the risk of pregnancy complications and adverse birth outcomes, but pathophysiologic mechanisms are still debated. Few studies to date have examined the influence of air pollution on uterine vascular resistance and no studies have examined the independent impact of these exposures. We aimed to assess the impact of prenatal exposure to traffic-related air pollution and smoking on uterine vascular resistance. METHODS Our study included 566 pregnant women recruited between 1993 and 1996 in Los Angeles who completed visits at three gestational ages. Information on smoking was collected and uterine vascular resistance was measured at each visit by Doppler ultrasound. We calculated three resistance indices: the resistance index (RI), the pulsatility index (PI), and the systolic/diastolic (S/D) ratio. We estimated exposure to NO2 at the home address of the mother using a land use regression (LUR) model and to NOx using CALINE4 air dispersion modeling. We used generalized linear mixed models to estimate the effects of air pollution and smoking on uterine vascular resistance indices. RESULTS LUR-derived NO2 and CALINE4-derived NOx exposure increased the risk of high uterine artery resistance in late pregnancy. Smoking during pregnancy also increased the risk of higher uterine resistance and contributed to bilateral notching in mid-pregnancy. CONCLUSION Our results suggest that uterine vascular resistance is a mechanism underlying the association between smoking and air pollution, and adverse birth outcomes.
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Affiliation(s)
- Zuelma A. Contreras
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Julia E. Heck
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Pei-Chen Lee
- Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Xin Cui
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Calvin J. Hobel
- Department of Obstetrics, Gynecology and Pediatrics, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Carla Janzen
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Fred Lurmann
- Sonoma Technology, Inc., Petaluma, California, USA
| | - Beate Ritz
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
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14
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Knibbs LD, Coorey CP, Bechle MJ, Marshall JD, Hewson MG, Jalaludin B, Morgan GG, Barnett AG. Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia. ENVIRONMENTAL RESEARCH 2018; 163:16-25. [PMID: 29421169 DOI: 10.1016/j.envres.2018.01.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/05/2018] [Accepted: 01/30/2018] [Indexed: 06/08/2023]
Abstract
Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006-2011, could capture nitrogen dioxide (NO2) concentrations during 1990-2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO2 predictions: (1) 'do nothing' (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable 'year' in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006-2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R2 and mean-square error R2 (MSE-R2), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R2 = 31%) and 80% (2003; MSE-R2 = 78%) of spatial variability in NO2 in a given year, and 76% (MSE-R2 = 72%) averaged over 1990-2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO2 estimates for Australia during 1990-2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.
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Affiliation(s)
- Luke D Knibbs
- Faculty of Medicine, The University of Queensland, Herston, QLD 4006, Australia; Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia.
| | - Craig P Coorey
- Faculty of Medicine, The University of Queensland, Herston, QLD 4006, Australia
| | - Matthew J Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle 98195, WA, USA
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle 98195, WA, USA
| | - Michael G Hewson
- School of Education and the Arts, Central Queensland University, Rockhampton, QLD 4700, Australia
| | - Bin Jalaludin
- Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia; Population Health, South Western Sydney Local Health District, Liverpool, NSW 2170, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia; School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW 2052, Australia
| | - Geoff G Morgan
- Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia; University Centre for Rural Health, School of Public Health, The University of Sydney, Lismore, NSW 2480, Australia
| | - Adrian G Barnett
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia
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15
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Zhan Y, Luo Y, Deng X, Zhang K, Zhang M, Grieneisen ML, Di B. Satellite-Based Estimates of Daily NO 2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:4180-4189. [PMID: 29544242 DOI: 10.1021/acs.est.7b05669] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO2 concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R2 = 0.62 (RMSE = 13.3 μg/m3) for daily and R2 = 0.73 (RMSE = 6.5 μg/m3) for spatial predictions. The nationwide population-weighted multiyear average of NO2 was predicted to be 30.9 ± 11.7 μg/m3 (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 μg/m3/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 μg/m3/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO2 was predicted to be the highest in North China (40.3 ± 10.3 μg/m3) and lowest in Southwest China (24.9 ± 9.4 μg/m3). Approximately 25% of the population lived in nonattainment areas with annual-average NO2 > 40 μg/m3. A piecewise linear function with an abrupt point around 100 people/km2 characterized the relationship between the population density and the NO2, indicating a threshold of aggravated NO2 pollution due to urbanization. Leveraging the ground-level NO2 observations, this study fills the gap of statistically modeling nationwide NO2 in China, and provides essential data for epidemiological research and air quality management.
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Affiliation(s)
- Yu Zhan
- Department of Environmental Science and Engineering , Sichuan University , Chengdu , Sichuan 610065 , China
- Institute for Disaster Management and Reconstruction , Sichuan University , Chengdu , Sichuan 610200 , China
- Sino-German Centre for Water and Health Research , Sichuan University , Chengdu , Sichuan 610065 , China
| | - Yuzhou Luo
- Department of Land, Air, and Water Resources , University of California , Davis , California 95616 , United States
| | - Xunfei Deng
- Institute of Digital Agriculture , Zhejiang Academy of Agricultural Sciences , Hangzhou , Zhejiang 310021 , China
| | - Kaishan Zhang
- Department of Environmental Science and Engineering , Sichuan University , Chengdu , Sichuan 610065 , China
| | - Minghua Zhang
- Department of Land, Air, and Water Resources , University of California , Davis , California 95616 , United States
| | - Michael L Grieneisen
- Department of Land, Air, and Water Resources , University of California , Davis , California 95616 , United States
| | - Baofeng Di
- Department of Environmental Science and Engineering , Sichuan University , Chengdu , Sichuan 610065 , China
- Institute for Disaster Management and Reconstruction , Sichuan University , Chengdu , Sichuan 610200 , China
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16
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de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I. Modelling daily PM 2.5 concentrations at high spatio-temporal resolution across Switzerland. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 233:1147-1154. [PMID: 29037492 DOI: 10.1016/j.envpol.2017.10.025] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 09/04/2017] [Accepted: 10/06/2017] [Indexed: 05/27/2023]
Abstract
Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM2.5) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM2.5 monitoring data was supplemented by imputing PM2.5 concentrations at PM10 sites, using PM2.5/PM10 ratios at co-located sites. Daily PM2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM2.5 in cells with AOD but without PM2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM2.5 concentrations.
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Affiliation(s)
- Kees de Hoogh
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland.
| | - Harris Héritier
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Nino Künzli
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel
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17
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Dehbi HM, Blangiardo M, Gulliver J, Fecht D, de Hoogh K, Al-Kanaani Z, Tillin T, Hardy R, Chaturvedi N, Hansell AL. Air pollution and cardiovascular mortality with over 25years follow-up: A combined analysis of two British cohorts. ENVIRONMENT INTERNATIONAL 2017; 99:275-281. [PMID: 27939045 PMCID: PMC5292102 DOI: 10.1016/j.envint.2016.12.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 12/02/2016] [Accepted: 12/03/2016] [Indexed: 05/18/2023]
Abstract
BACKGROUND Adverse effects of air pollution on cardiovascular disease (CVD) mortality are well established. There are comparatively fewer studies in Europe, and in the UK particularly, than in North America. We examined associations in two British cohorts with >25years of follow-up. METHODS Annual average NO2, SO2 and black smoke (BS) air pollution exposure estimates for 1991 were obtained from land use regression models using contemporaneous monitoring data. From the European Study of Cohorts and Air Pollution (ESCAPE), air pollution estimates in 2010-11 were obtained for NO2, NOx, PM10, PMcoarse and PM2.5. The exposure estimates were assigned to place of residence 1989 for participants in a national birth cohort born in 1946, the MRC National Study of Health and Development (NSHD), and an adult multi-ethnic London cohort, Southall and Brent Revisited (SABRE) recruited 1988-91. The combined median follow-up was 26years. Single-pollutant competing risk models were employed, adjusting for individual risk factors. RESULTS Elevated non-significant hazard ratios for CVD mortality were seen with 1991 BS and SO2 and with ESCAPE PM10 and PM2.5 in fully adjusted linear models. Per 10μg/m3 increase HRs were 1.11 [95% CI: 0.76-1.61] for BS, 1.05 [95% CI: 0.91-1.22] for SO2, 1.16 [95% CI: 0.70-1.92] for PM10 and 1.30 [95% CI: 0.39-4.34] for PM2.5, with largest effects seen in the fourth quartile of BS and PM2.5 compared to the first with HR 1.24 [95% CI: 0.91-1.61] and 1.21 [95% CI: 0.88-1.66] respectively. There were no consistent associations with other ESCAPE pollutants, or with 1991 NO2. Modelling using Cox regression led to similar results. CONCLUSION Our results support a detrimental long-term effect for air pollutants on cardiovascular mortality.
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Affiliation(s)
- Hakim-Moulay Dehbi
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK.
| | - Marta Blangiardo
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - John Gulliver
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Daniela Fecht
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Kees de Hoogh
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK; Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Zaina Al-Kanaani
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Therese Tillin
- Institute of Cardiovascular Science, University College London, London, WC1E 7HB, UK
| | - Rebecca Hardy
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, WC1B 5JU, UK
| | - Nish Chaturvedi
- Institute of Cardiovascular Science, University College London, London, WC1E 7HB, UK
| | - Anna L Hansell
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK; Imperial College Healthcare NHS Trust, London, UK
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