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Ma X, Zou B, Deng J, Gao J, Longley I, Xiao S, Guo B, Wu Y, Xu T, Xu X, Yang X, Wang X, Tan Z, Wang Y, Morawska L, Salmond J. A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023. ENVIRONMENT INTERNATIONAL 2024; 183:108430. [PMID: 38219544 DOI: 10.1016/j.envint.2024.108430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/26/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
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Affiliation(s)
- Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China.
| | - Jun Deng
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Shaanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Jay Gao
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
| | - Ian Longley
- National Institute of Water and Atmospheric Research, Auckland 1010, New Zealand
| | - Shun Xiao
- School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yarui Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Tingting Xu
- School of Software Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
| | - Xin Xu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Xiaosha Yang
- Shandong Nova Fitness Co., Ltd., Baoji, Shaanxi 722404, China
| | - Xiaoqi Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Zelei Tan
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yifan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Jennifer Salmond
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
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Soppa V, Lucht S, Ogurtsova K, Buschka A, López-Vicente M, Guxens M, Weinhold K, Winkler U, Wiedensohler A, Held A, Lüchtrath S, Cyrys J, Kecorius S, Gastmeier P, Wiese-Posselt M, Hoffmann B. The Berlin-Brandenburg Air Study-A Methodological Study Paper of a Natural Experiment Investigating Health Effects Related to Changes in Airport-Related Exposures. Int J Public Health 2023; 68:1606096. [PMID: 38045993 PMCID: PMC10689260 DOI: 10.3389/ijph.2023.1606096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/19/2023] [Indexed: 12/05/2023] Open
Abstract
Objectives: This paper presents the study design of the Berlin-Brandenburg Air study (BEAR-study). We measure air quality in Berlin and Brandenburg before and after the relocation of aircraft (AC) traffic from Tegel (TXL) airport to the new Berlin-Brandenburg airport (BER) and investigate the association of AC-related ultrafine particles (UFP) with health outcomes in schoolchildren. Methods: The BEAR-study is a natural experiment examining schoolchildren attending schools near TXL and BER airports, and in control areas (CA) away from both airports and associated air corridors. Each child undergoes repeated school-based health-examinations. Total particle number concentration (PNC) and meteorological parameters are continuously monitored. Submicrometer particle number size distribution, equivalent black carbon, and gas-phase pollutants are collected from long-term air quality monitoring stations. Daily source-specific UFP concentrations are modeled. We will analyze short-term effects of UFP on respiratory, cardiovascular, and neurocognitive outcomes, as well as medium and long-term effects on lung growth and cognitive development. Results: We examined 1,070 children (as of 30 November 2022) from 16 schools in Berlin and Brandenburg. Conclusion: The BEAR study increases the understanding of how AC-related UFP affect children's health.
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Affiliation(s)
- Vanessa Soppa
- Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Lucht
- Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Cardinal Health Real-World Evidence and Insights, Dublin, OH, United States
| | - Katherine Ogurtsova
- Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Anna Buschka
- Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Mónica López-Vicente
- ISGlobal, Barcelona, Spain
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, Netherlands
| | - Mònica Guxens
- ISGlobal, Barcelona, Spain
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, Netherlands
| | - Kay Weinhold
- Leibniz Institute for Tropospheric Research, Leipzig, Germany
| | - Ulf Winkler
- Leibniz Institute for Tropospheric Research, Leipzig, Germany
| | | | - Andreas Held
- Environmental Chemistry and Air Research, Institute of Environmental Science and Technology, Technische Universität Berlin, Berlin, Germany
| | - Sabine Lüchtrath
- Environmental Chemistry and Air Research, Institute of Environmental Science and Technology, Technische Universität Berlin, Berlin, Germany
| | - Josef Cyrys
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Simonas Kecorius
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Petra Gastmeier
- Institute of Hygiene and Environmental Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Miriam Wiese-Posselt
- Institute of Hygiene and Environmental Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Barbara Hoffmann
- Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Abdillah SFI, Wang YF. Ambient ultrafine particle (PM 0.1): Sources, characteristics, measurements and exposure implications on human health. ENVIRONMENTAL RESEARCH 2023; 218:115061. [PMID: 36525995 DOI: 10.1016/j.envres.2022.115061] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/28/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
The problem of ultrafine particles (UFPs; PM0.1) has been prevalent since the past decades. In addition to become easily inhaled by human respiratory system due to their ultrafine diameter (<100 nm), ambient UFPs possess various physicochemical properties which make it more toxic. These properties vary based on the emission source profile. The current development of UFPs studies is hindered by the problem of expensive instruments and the inexistence of standardized measurement method. This review provides detailed insights on ambient UFPs sources, physicochemical properties, measurements, and estimation models development. Implications on health impacts due to short-term and long-term exposure of ambient UFPs are also presented alongside the development progress of potentially low-cost UFPs sensors which can be used for future UFPs studies references. Current challenge and future outlook of ambient UFPs research are also discussed in this review. Based on the review results, ambient UFPs may originate from primary and secondary sources which include anthropogenic and natural activities. In addition to that, it is confirmed from various chemical content analysis that UFPs carry heavy metals, PAHs, BCs which are toxic in its nature. Measurement of ambient UFPs may be performed through stationary and mobile methods for environmental profiling and exposure assessment purposes. UFPs PNC estimation model (LUR) developed from measurement data could be deployed to support future epidemiological study of ambient UFPs. Low-cost sensors such as bipolar ion and ionization sensor from common smoke detector device may be further developed as affordable instrument to monitor ambient UFPs. Recent studies indicate that short-term exposure of UFPs can be associated with HRV change and increased cardiopulmonary effects. On the other hand, long-term UFPs exposure have positive association with COPD, CVD, CHF, pre-term birth, asthma, and also acute myocardial infarction cases.
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Affiliation(s)
- Sultan F I Abdillah
- Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan, 32023, Taiwan; Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan, 32023, Taiwan
| | - Ya-Fen Wang
- Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan, 32023, Taiwan; Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan, 32023, Taiwan.
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Ma X, Gao J, Longley I, Zou B, Guo B, Xu X, Salmond J. Development of transferable neighborhood land use regression models for predicting intra-urban ambient nitrogen dioxide (NO 2) spatial variations. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:45903-45918. [PMID: 35150420 DOI: 10.1007/s11356-022-19141-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/05/2022] [Indexed: 06/14/2023]
Abstract
Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO2 LUR models with comparable predictive power based on only micro-scale predictor variables for modeling intra-urban ambient air pollution exposure. Taking Auckland metropolis, New Zealand, as a case study, the proposed method was applied to three neighborhoods (urban, central business district, and dominion road) and compared with the corresponding counterpart models developed using pools of (a) only macro-scale predictor variables and (b) a mixture of both micro- and macro-scale predictor variables (traditional method). The results showed that the models using only macro-scale variables achieved the lowest accuracy (R2: 0.388-0.484) and had the worst direct (R2: 0.0001-0.349) and indirect transferability (R2: 0.07-0.352). Those models using the traditional method had the highest model fitting R2 (0.629-0.966) with lower cross-validation R2 (0.495-0.941) and slightly better direct transferability (R2: 0.0003-0.386) but suffered poor model interpretability when indirectly transferred to new locations. Our proposed models had comparable model fitting R2 (0.601-0.966) and the best cross-validation R2 (0.514-0.941). They also had the strongest direct transferability (R2: 0.006-0.590) and moderate-to-good indirect transferability (R2: 0.072-0.850) with much better model interpretability. This study advances our knowledge of developing transferable LUR models for the very first time from the perspective of the scale of the predictor variables used in the model development and will significantly benefit the wider application of LUR approaches in epidemiological and environmental studies.
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Affiliation(s)
- Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Jay Gao
- School of Environment, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
| | - Ian Longley
- National Institute of Water and Atmospheric Research, Auckland, 1010, New Zealand
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, No. 932, South Lushan Road, Yuelu District, Changsha, 410083, Hunan, China.
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Xin Xu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an, 710061, China
| | - Jennifer Salmond
- School of Environment, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
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Ge Y, Fu Q, Yi M, Chao Y, Lei X, Xu X, Yang Z, Hu J, Kan H, Cai J. High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 816:151633. [PMID: 34785221 DOI: 10.1016/j.scitotenv.2021.151633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution. OBJECTIVE This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides. METHOD A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method. RESULT The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm-3. The 10-fold cross validation R2 reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NOx emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas. CONCLUSION We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.
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Affiliation(s)
- Yihui Ge
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Qingyan Fu
- Shanghai Environmental Monitoring Center, Shanghai 200233, China
| | - Min Yi
- Shanghai Environmental Monitoring Center, Shanghai 200233, China
| | - Yuan Chao
- Shanghai Environmental Monitoring Center, Shanghai 200233, China
| | - Xiaoning Lei
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Xueyi Xu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Zhenchun Yang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
| | - Jing Cai
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
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Lloyd M, Carter E, Diaz FG, Magara-Gomez KT, Hong KY, Baumgartner J, Herrera G VM, Weichenthal S. Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:12483-12492. [PMID: 34498865 DOI: 10.1021/acs.est.1c01412] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Outdoor ultrafine particles (UFP, <0.1 μm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health. In this study, we used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. We conducted a mobile monitoring campaign over 20 days in 2019. Regression models were trained on land use data and combined with predictions from convolutional neural networks (CNN) trained to predict UFP and BC concentrations using satellite and street-level images. The combined UFP model (R2 = 0.54) outperformed the CNN (R2 = 0.47) and land use regression (LUR) models (R2 = 0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R2 = 0.51 vs 0.43 and 0.45, respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models, suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. In general, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modeling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations.
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Affiliation(s)
- Marshall Lloyd
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
| | - Ellison Carter
- Department of Civil and Environmental Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins 80523, United States
| | - Florencio Guzman Diaz
- Department of Civil and Environmental Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins 80523, United States
| | | | - Kris Y Hong
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
| | - Jill Baumgartner
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
- Institute for Health and Social Policy, McGill University, Montreal H3A 1A2, Canada
| | - Víctor M Herrera G
- Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga 680006, Colombia
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
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Yang Z, Lin Y, Wang S, Liu X, Cullinan P, Chung KF, Zhang J. Urinary Amino-Polycyclic Aromatic Hydrocarbons in Urban Residents: Finding a Biomarker for Residential Exposure to Diesel Traffic. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:10569-10577. [PMID: 34264064 DOI: 10.1021/acs.est.1c01549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Despite substantial evidence of marked exposure to and ill-health effects from diesel exhaust (DE) emissions among occupational population (e.g., miners, truck drivers, and taxi drivers), it is less understood to what extent non-occupational population was exposed to DE among various combustion sources, largely due to the lack of biomarkers that would indicate specific exposure to DE. We evaluated whether urinary amino-polycyclic aromatic hydrocarbons (APAHs), such as major metabolites of DE-specific nitrated PAHs, can be used as DE exposure biomarkers in residential settings. We measured five urinary APAHs in 177 urine samples from 98 UK residents, 89 (91%) of them were London residents, and estimated their residential proximity to various traffic indicators (e.g., the road type, road length, traffic flow, and traffic volume). Participants living within 100 m of major roads exhibited increased levels of all five APAHs, among which 2-amino-fluorene (2-AFLU) reached statistical significance (p < 0.05). We estimated that a 10 m increase in the length of nearby major roads (<100 m) was associated with a 4.4% (95% CI of 1.1 to 7.6%) increase in 2-AFLU levels. Levels of 2-AFLU were significantly associated with the traffic flow of nearby buses and heavy-duty vehicles but not motorbikes, taxis, or coaches. We did not observe a significant association between distance to major roads or the sum of the major road length within 100 m with the other four biomarker concentrations. These results suggest the use of urinary 2-AFLU as a biomarker of DE exposure in urban residents.
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Affiliation(s)
- Zhenchun Yang
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu Province 215316, China
| | - Yan Lin
- Nicholas School of the Environment and Global Health Institute, Duke University, Durham, North Carolina 27708, United States
| | - Stella Wang
- Nicholas School of the Environment and Global Health Institute, Duke University, Durham, North Carolina 27708, United States
| | - Xing Liu
- Nicholas School of the Environment and Global Health Institute, Duke University, Durham, North Carolina 27708, United States
| | - Paul Cullinan
- National Heart & Lung Institute, Imperial College London, London SW7 2AZ, U.K
| | - Kian Fan Chung
- National Heart & Lung Institute, Imperial College London, London SW7 2AZ, U.K
| | - Junfeng Zhang
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu Province 215316, China
- Nicholas School of the Environment and Global Health Institute, Duke University, Durham, North Carolina 27708, United States
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Vlaanderen J, de Hoogh K, Hoek G, Peters A, Probst-Hensch N, Scalbert A, Melén E, Tonne C, de Wit GA, Chadeau-Hyam M, Katsouyanni K, Esko T, Jongsma KR, Vermeulen R. Developing the building blocks to elucidate the impact of the urban exposome on cardiometabolic-pulmonary disease: The EU EXPANSE project. Environ Epidemiol 2021; 5:e162. [PMID: 34414346 PMCID: PMC8367039 DOI: 10.1097/ee9.0000000000000162] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 06/01/2021] [Indexed: 12/30/2022] Open
Abstract
By 2030, more than 80% of Europe's population will live in an urban environment. The urban exposome, consisting of factors such as where we live and work, where and what we eat, our social network, and what chemical and physical hazards we are exposed to, provides important targets to improve population health. The EXPANSE (EXposome Powered tools for healthy living in urbAN SEttings) project will study the impact of the urban exposome on the major contributors to Europe's burden of disease: Cardio-Metabolic and Pulmonary Disease. EXPANSE will address one of the most pertinent questions for urban planners, policy makers, and European citizens: "How to maximize one's health in a modern urban environment?" EXPANSE will take the next step in exposome research by (1) bringing together exposome and health data of more than 55 million adult Europeans and OMICS information for more than 2 million Europeans; (2) perform personalized exposome assessment for 5,000 individuals in five urban regions; (3) applying ultra-high-resolution mass-spectrometry to screen for chemicals in 10,000 blood samples; (4) evaluating the evolution of the exposome and health through the life course; and (5) evaluating the impact of changes in the urban exposome on the burden of cardiometabolic and pulmonary disease. EXPANSE will translate its insights and innovations into research and dissemination tools that will be openly accessible via the EXPANSE toolbox. By applying innovative ethics-by-design throughout the project, the social and ethical acceptability of these tools will be safeguarded. EXPANSE is part of the European Human Exposome Network.
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Affiliation(s)
- Jelle Vlaanderen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Kees de Hoogh
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Swiss Tropical Health, Basel, Switzerland
- University of Basel, Switzerland
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Annette Peters
- Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Augustin Scalbert
- International Agency for Research on Cancer (IARC), Biomarkers Group, Lyon, France
| | - Erik Melén
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Cathryn Tonne
- Barcelona Institute for Global Health (ISGlobal), Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - G Ardine de Wit
- Department of health care innovation and evaluation, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Centre for Nutrition, Prevention and Healthcare. National Institute of Public Health and the Environment, Bilthoven, the Netherlands
| | - Marc Chadeau-Hyam
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Imperial College London, London, United Kingdom
| | - Klea Katsouyanni
- Imperial College London, London, United Kingdom
- National and Kapodistrian University of Athens, Athens, Greece
| | | | - Karin R Jongsma
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Department of health care innovation and evaluation, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Imperial College London, London, United Kingdom
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Zhang Y, Cheng H, Huang D, Fu C. High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM 2.5 Distribution in Beijing, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6143. [PMID: 34200158 PMCID: PMC8201188 DOI: 10.3390/ijerph18116143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/03/2022]
Abstract
PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.
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Affiliation(s)
| | - Hongguang Cheng
- School of Environment, Beijing Normal University, Beijing 100875, China; (Y.Z.); (D.H.); (C.F.)
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Xu X, Qin N, Yang Z, Liu Y, Cao S, Zou B, Jin L, Zhang Y, Duan X. Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 268:115951. [PMID: 33162219 DOI: 10.1016/j.envpol.2020.115951] [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: 08/03/2020] [Revised: 10/10/2020] [Accepted: 10/27/2020] [Indexed: 06/11/2023]
Abstract
Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM2.5), inhalable particulate matter (PM10), and nitrogen dioxide (NO2) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R2) for the LUR models ranged from 0.53-0.87 (PM2.5), 0.53-0.85 (PM10), and 0.33-0.67 (NO2). The performance of the daytime/nighttime LUR models for PM2.5 and PM10 was better than that of the full-day models according to the results of model adjusted R2 and validation R2. Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO2 to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM2.5/PM10 has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM2.5 and PM10. Nighttime could be a critical exposure period, due to high pollutant concentrations.
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Affiliation(s)
- Xiangyu Xu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Ning Qin
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Zhenchun Yang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom
| | - Yunwei Liu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Suzhen Cao
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, 410083, China
| | - Lan Jin
- Department of Surgery, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Yawei Zhang
- Department of Surgery, Yale School of Medicine, New Haven, CT, 06520, USA; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Xiaoli Duan
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China.
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