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Fan H, Zhao C, Yang Y, Yang X. Rapid reduction of air pollution and short-term exposure risks in China. J Environ Sci (China) 2025; 149:126-138. [PMID: 39181628 DOI: 10.1016/j.jes.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/05/2023] [Accepted: 11/06/2023] [Indexed: 08/27/2024]
Abstract
With the continuous control of anthropogenic emissions, China's air quality has improved significantly in recent years. Given this background, research on how the short-term exposure risks caused by air pollution in China have changed is insufficient. This study utilized hourly concentration data from ground observation stations and the official air quality guidelines of the Ministry of Ecology and Environment of China and the World Health Organization as standards to systematically investigate the spatiotemporal characteristics and short-term exposure risks of air pollution in China from 2015 to 2022. The results indicate that various atmospheric pollutants except for ozone showed a decreasing trend yearly. Nationwide, both single pollutant air pollution days (SAPDs) and multiple pollutant air pollution days (MAPDs) showed varying degrees of reduction within 15 and 25 days, respectively. SAPD was dominated mainly by excessive PM2.5 and PM10 pollutants, while MAPD was dominated mainly by excessive pollutant combinations, including PM2.5 + PM10, CO + PM2.5 + PM10, and SO2 + PM2.5 + PM10. As the concentration of atmospheric pollutants decreased, the total excess risk (ER) decreased yearly from 2015 to 2022, but there were significant regional differences. Now, the ER is less than 0.25% in southern China, in the range of 0.25%-0.5% in the North China Plain and some cities in the northeast, and higher than 1% in the northwest. Particulate matter is currently the primary pollutant posing short-term exposure risk in China, especially due to the impact of sandstorm weather. This study indicates that China's atmospheric cleaning action is significantly beneficial for reducing health risks.
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Affiliation(s)
- Hao Fan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Chuanfeng Zhao
- Laboratory for Climate and Ocean-Atmospheric Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China.
| | - Yikun Yang
- Laboratory for Climate and Ocean-Atmospheric Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Xingchuan Yang
- College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
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Libardi ADLC, Masselot P, Schneider R, Nightingale E, Milojevic A, Vanoli J, Mistry MN, Gasparrini A. High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003-2021) with multi-stage data reconstruction and ensemble machine learning methods. ATMOSPHERIC POLLUTION RESEARCH 2024; 15:102284. [PMID: 39175565 PMCID: PMC7616380 DOI: 10.1016/j.apr.2024.102284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) at a 1 km2 resolution over Great Britain for the period 2003-2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM2.5 using co-occurring PM10 values, gap-filling aerosol optical depth and columnar NO2 data obtained from satellite instruments, and finally the training of an ensemble model and the prediction of daily values across the whole geographical domain (2003-2021). Results show a good ensemble model performance, calculated through a ten-fold monitor-based cross-validation procedure, with an average R2 of 0.690 (range 0.611-0.792) for NO2, 0.704 (0.609-0.786) for PM10, and 0.802 (0.746-0.888) for PM2.5. Reconstructed pollution levels decreased markedly within the study period, with a stronger reduction in the latter eight years. The pollutants exhibited different spatial patterns, while NO2 rose in close proximity to high-traffic areas, PM demonstrated variation at a larger scale. The resulting 1 km2 spatially resolved daily datasets allow for linkage with health data across Great Britain over nearly two decades, thus contributing to extensive, extended, and detailed research on the long-and short-term health effects of air pollution.
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Affiliation(s)
- Arturo de la Cruz Libardi
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, WC1H 9SH, London, United Kingdom
| | - Pierre Masselot
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, WC1H 9SH, London, United Kingdom
| | - Rochelle Schneider
- Φ-lab (Phi-lab), European Space Agency (ESA), Frascati, Italy
- Forecast Department, European Centre for Medium-Range Weather Forecast (ECMWF), Reading, United Kingdom
| | - Emily Nightingale
- Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, Keppel Street, WC1E 7HT, London, United Kingdom
| | - Ai Milojevic
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, WC1H 9SH, London, United Kingdom
- Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine, Keppel Street, WC1E 7HT, London, United Kingdom
| | - Jacopo Vanoli
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, WC1H 9SH, London, United Kingdom
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Malcolm N. Mistry
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, WC1H 9SH, London, United Kingdom
- Department of Economics, Ca’ Foscari University of Venice, Italy
| | - Antonio Gasparrini
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, WC1H 9SH, London, United Kingdom
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Yang J, Peng Z, Sun J, Chen Z, Niu X, Xu H, Ho KF, Cao J, Shen Z. A review on advancements in atmospheric microplastics research: The pivotal role of machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:173966. [PMID: 38897457 DOI: 10.1016/j.scitotenv.2024.173966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/26/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
Microplastics (MPs), recognized as emerging pollutants, pose significant potential impacts on the environment and human health. The investigation into atmospheric MPs is nascent due to the absence of effective characterization methods, leaving their concentration, distribution, sources, and impacts on human health largely undefined with evidence still emerging. This review compiles the latest literature on the sources, distribution, environmental behaviors, and toxicological effects of atmospheric MPs. It delves into the methodologies for source identification, distribution patterns, and the contemporary approaches to assess the toxicological effects of atmospheric MPs. Significantly, this review emphasizes the role of Machine Learning (ML) and Artificial Intelligence (AI) technologies as novel and promising tools in enhancing the precision and depth of research into atmospheric MPs, including but not limited to the spatiotemporal dynamics, source apportionment, and potential health impacts of atmospheric MPs. The integration of these advanced technologies facilitates a more nuanced understanding of MPs' behavior and effects, marking a pivotal advancement in the field. This review aims to deliver an in-depth view of atmospheric MPs, enhancing knowledge and awareness of their environmental and human health impacts. It calls upon scholars to focus on the research of atmospheric MPs based on new technologies of ML and AI, improving the database as well as offering fresh perspectives on this critical issue.
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Affiliation(s)
- Jiaer Yang
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zezhi Peng
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jian Sun
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Zhiwen Chen
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xinyi Niu
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Hongmei Xu
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Kin-Fai Ho
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Junji Cao
- Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710049, China
| | - Zhenxing Shen
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
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Zhou S, Wang W, Zhu L, Qiao Q, Kang Y. Deep-learning architecture for PM 2.5 concentration prediction: A review. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 21:100400. [PMID: 38439920 PMCID: PMC10910069 DOI: 10.1016/j.ese.2024.100400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/06/2024]
Abstract
Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.
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Affiliation(s)
- Shiyun Zhou
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Wei Wang
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Long Zhu
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Qi Qiao
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yulin Kang
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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Daniels D, Berger Eberhardt A. Climate change, microplastics, and male infertility. Curr Opin Urol 2024; 34:366-370. [PMID: 38932480 DOI: 10.1097/mou.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
PURPOSE OF REVIEW Semen quality is on the decline. While the etiology is unknown, recent literature suggests there may be a relationship between climate change, environmental toxins and male fertility. This review relays new information regarding associations between our environment and male infertility. RECENT FINDINGS Several recent studies have documented a negative association between heat stress and spermatogenesis, which suggests that climate change may be a factor in declining in sperm counts. The influence of particle pollution on spermatogenesis has also been recently investigated, with studies demonstrating a negative association. Another possible factor are microplastics, which have been posited to reduce sperm production. Recent animal studies have shown that microplastic exposure alters both adult sperm production and prenatal male genital development. The relationship between endocrine disrupting chemicals and male fertility remains an area of active study, with recent animal and human studies suggesting an association between these chemicals and male fertility. SUMMARY The etiology of the decline in male fertility over the past decades is yet unknown. However, changes in our environment as seen with climate change and exposure to pollutants and endocrine disrupting chemicals are proposed mechanisms for this decline. Further studies are needed to investigate this association further.
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Affiliation(s)
| | - Alexandra Berger Eberhardt
- Member of the Faculty, Harvard Medical School, Associate Surgeon, Brigham and Women's Hospital Department of Urology, Boston, Massachusetts, USA
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Jiang S, Tong X, Yu K, Yin P, Shi S, Meng X, Chen R, Zhou M, Kan H, Niu Y, Li Y. Ambient particulate matter and chronic obstructive pulmonary disease mortality: a nationwide, individual-level, case-crossover study in China. EBioMedicine 2024; 107:105270. [PMID: 39137570 DOI: 10.1016/j.ebiom.2024.105270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Short-term exposure to particulate matter air pollution has been associated with the exacerbations of COPD, but its association with COPD mortality was not fully elucidated. We aimed to assess the association between short-term particulate matter exposure and the risk of COPD mortality in China using individual-level data. METHODS We derived 2.26 million COPD deaths from a national death registry database in Chinese mainland between 2013 and 2019. Exposures to fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) were assessed by satellite-based models of a 1 × 1 km resolution and assigned to each individual based on residential address. The associations of PM2.5 and PM2.5-10 with COPD mortality were examined using a time-stratified case-crossover design and conditional logistic regressions with distributed lag models. We further conducted stratified analyses by age, sex, education level, and season. FINDINGS Short-term exposures to both PM2.5 and PM2.5-10 were associated with increased risks of COPD mortality. These associations appeared and peaked on the concurrent day, attenuated and became nonsignificant after 5 or 7 days, respectively. The exposure-response curves were approximately linear without discernible thresholds. An interquartile range increase in PM2.5 and PM2.5-10 concentrations was associated with 4.23% (95% CI: 3.75%, 4.72%) and 2.67% (95% CI: 2.18%, 3.16%) higher risks of COPD mortality over lag 0-7 d, respectively. The associations of PM2.5 and PM2.5-10 attenuated slightly but were still significant in the mutual-adjustment models. A larger association of PM2.5-10 was observed in the warm season. INTERPRETATION This individual-level, nationwide, case-crossover study suggests that short-term exposure to PM2.5 and PM2.5-10 might act as one of the environmental risk factors for COPD mortality. FUNDING This study is supported by the National Key Research and Development Program of China (2023YFC3708304 and 2022YFC3702701), the National Natural Science Foundation of China (82304090 and 82030103), the 3-year Action Plan for Strengthening the Construction of the Public Health System in Shanghai (GWVI-11.2-YQ31), and the Science and Technology Commission of Shanghai Municipality (21TQ015).
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Affiliation(s)
- Shuo Jiang
- 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, China; Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xunliang Tong
- National Center for Chronic Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Kexin Yu
- 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, China
| | - Peng Yin
- National Center for Chronic Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Su Shi
- 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, China
| | - Xia Meng
- 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, China
| | - Renjie Chen
- 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, China
| | - Maigeng Zhou
- National Center for Chronic Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 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, China
| | - Yue Niu
- 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, China.
| | - Yanming Li
- Department of Respiratory and Critical Care Medicine, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical, Beijing, China.
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Herting MM, Bottenhorn KL, Cotter DL. Outdoor air pollution and brain development in childhood and adolescence. Trends Neurosci 2024; 47:593-607. [PMID: 39054161 PMCID: PMC11324378 DOI: 10.1016/j.tins.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/26/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024]
Abstract
Exposure to outdoor air pollution has been linked to adverse health effects, including potential widespread impacts on the CNS. Ongoing brain development may render children and adolescents especially vulnerable to neurotoxic effects of air pollution. While mechanisms remain unclear, promising advances in human neuroimaging can help elucidate both sensitive periods and neurobiological consequences of exposure to air pollution. Herein we review the potential influences of air pollution exposure on neurodevelopment, drawing from animal toxicology and human neuroimaging studies. Due to ongoing cellular and system-level changes during childhood and adolescence, the developing brain may be more sensitive to pollutants' neurotoxic effects, as a function of both timing and duration, with relevance to cognition and mental health. Building on these foundations, the emerging field of environmental neuroscience is poised to further decipher which air toxicants are most harmful and to whom.
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Affiliation(s)
- Megan M Herting
- Department of Populations and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Katherine L Bottenhorn
- Department of Populations and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Department of Psychology, Florida International University, Miami, FL, USA
| | - Devyn L Cotter
- Department of Populations and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
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Li S, Wang S, Wu Q, Zhao B, Jiang Y, Zheng H, Wen Y, Zhang S, Wu Y, Hao J. Integrated Benefits of Synergistically Reducing Air Pollutants and Carbon Dioxide in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39086301 DOI: 10.1021/acs.est.4c00599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
China's advancements in addressing air pollution and reducing CO2 emissions offer valuable lessons for collaborative strategies to achieve diverse environmental objectives. Previous studies have assessed the mutual benefits of climate policies and air pollution control measures on one another, lacking an integrated assessment of the benefits of synergistic control attributed to refined measures. Here, we comprehensively used coupled emission inventory and response models to evaluate the integrated benefits and synergy degrees of various measures in reducing air pollutants and CO2 in China during 2013-2021. Results indicated that the implemented measures yielded integrated benefits value at 6.7 (2.4-12.6) trillion Chinese Yuan. The top five contributors, accounting for 55%, included promoting non-thermal power, implementing end-of-pipe control technologies in power plants and iron and steel industry, replacing residential scattered coal, and saving building energy. Measures demonstrating high synergies and integrated benefits per unit of reduction (e.g., green traffic promotion) yielded low benefits mainly due to their low application, which are expected to gain greater implementation and prioritization in the future. Our findings provide insights into the effectiveness and limitations of strategies aimed at joint control. By ranking these measures based on their benefits and synergy, we offer valuable guidance for policy development in China and other nations with similar needs.
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Affiliation(s)
- Shengyue Li
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Shuxiao Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Qingru Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Bin Zhao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Yueqi Jiang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Haotian Zheng
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
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Zhu Y, Wu Y, Cheng J, Liang H, Chang Q, Lin F, Li D, Zhou X, Chen X, Pan P, Liu H, Guo Y, Zhang Y. Ambient air pollution, lifestyle, and genetic predisposition on all-cause and cause-specific mortality: A prospective cohort study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:173120. [PMID: 38750765 DOI: 10.1016/j.scitotenv.2024.173120] [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: 01/20/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Although it is widely acknowledged that long-term exposure to ambient air pollution is closely related to the risk of mortality, there were inconsistencies in terms of cause-specific mortality and it is still unknown whether lifestyle and genetic susceptibility could modify the association. METHODS This population-based prospective cohort study involved 461,112 participants from the UK Biobank. The land-use regression model was used to estimate the concentrations of particulate matter (PM2.5, PMcoarse, PM10), and nitrogen oxides (NO2 and NOx). The association between air pollution and mortality was evaluated using Cox proportional hazard models. Furthermore, a lifestyle score incorporated with smoking status, physical activity, alcohol consumption, and diet behaviors, and polygenic risk score using 12 genetic variants, were developed to assess the modifying effect of air pollution on mortality outcomes. RESULTS During a median follow-up of 14.0 years, 33,903 deaths were recorded, including 17,083 (2835; 14,248), 6970, 2429, and 1287 deaths due to cancer (lung cancer, non-lung cancer), cardiovascular disease (CVD), respiratory and digestive disease, respectively. Each interquartile range (IQR) increase in PM2.5, NO2 and NOx was associated with 7 %, 6 % and 5 % higher risk of all-cause mortality, respectively. Specifically, for cause-specific mortality, each IQR increase in PM2.5, NO2 and NOx was also linked to mortality due to cancer (lung cancer and non-lung cancer), CVD, respiratory and digestive disease. Furthermore, additive and multiplicative interactions were identified between high ambient air pollution and unhealthy lifestyle on mortality. In addition, associations between air pollution and mortality were modified by lifestyle behaviors. CONCLUSION Long-term exposure to air pollutants increased the risk of all-cause and cause-specific mortality, which was modified by lifestyle behaviors. In addition, we also revealed a synergistically detrimental effect between air pollution and an unhealthy lifestyle, suggesting the significance of joint air pollution management and adherence to a healthy lifestyle on public health.
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Affiliation(s)
- Yiqun Zhu
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Yao Wu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jun Cheng
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Huaying Liang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Qinyu Chang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Fengyu Lin
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Dianwu Li
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Xin Zhou
- Department of Dermatology, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China
| | - Xiang Chen
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia; Department of Dermatology, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, Hunan, China
| | - Pinhua Pan
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, Hunan, China
| | - Hong Liu
- Department of Dermatology, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, Hunan, China
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yan Zhang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China.
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10
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Chen G, Guo Y, Yue X, Xu R, Yu W, Ye T, Tong S, Gasparrini A, Bell ML, Armstrong B, Schwartz J, Jaakkola JJK, Lavigne E, Saldiva PHN, Kan H, Royé D, Urban A, Vicedo-Cabrera AM, Tobias A, Forsberg B, Sera F, Lei Y, Abramson MJ, Li S. All-cause, cardiovascular, and respiratory mortality and wildfire-related ozone: a multicountry two-stage time series analysis. Lancet Planet Health 2024; 8:e452-e462. [PMID: 38969473 DOI: 10.1016/s2542-5196(24)00117-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Wildfire activity is an important source of tropospheric ozone (O3) pollution. However, no study to date has systematically examined the associations of wildfire-related O3 exposure with mortality globally. METHODS We did a multicountry two-stage time series analysis. From the Multi-City Multi-Country (MCC) Collaborative Research Network, data on daily all-cause, cardiovascular, and respiratory deaths were obtained from 749 locations in 43 countries or areas, representing overlapping periods from Jan 1, 2000, to Dec 31, 2016. We estimated the daily concentration of wildfire-related O3 in study locations using a chemical transport model, and then calibrated and downscaled O3 estimates to a resolution of 0·25° × 0·25° (approximately 28 km2 at the equator). Using a random-effects meta-analysis, we examined the associations of short-term wildfire-related O3 exposure (lag period of 0-2 days) with daily mortality, first at the location level and then pooled at the country, regional, and global levels. Annual excess mortality fraction in each location attributable to wildfire-related O3 was calculated with pooled effect estimates and used to obtain excess mortality fractions at country, regional, and global levels. FINDINGS Between 2000 and 2016, the highest maximum daily wildfire-related O3 concentrations (≥30 μg/m3) were observed in locations in South America, central America, and southeastern Asia, and the country of South Africa. Across all locations, an increase of 1 μg/m3 in the mean daily concentration of wildfire-related O3 during lag 0-2 days was associated with increases of 0·55% (95% CI 0·29 to 0·80) in daily all-cause mortality, 0·44% (-0·10 to 0·99) in daily cardiovascular mortality, and 0·82% (0·18 to 1·47) in daily respiratory mortality. The associations of daily mortality rates with wildfire-related O3 exposure showed substantial geographical heterogeneity at the country and regional levels. Across all locations, estimated annual excess mortality fractions of 0·58% (95% CI 0·31 to 0·85; 31 606 deaths [95% CI 17 038 to 46 027]) for all-cause mortality, 0·41% (-0·10 to 0·91; 5249 [-1244 to 11 620]) for cardiovascular mortality, and 0·86% (0·18 to 1·51; 4657 [999 to 8206]) for respiratory mortality were attributable to short-term exposure to wildfire-related O3. INTERPRETATION In this study, we observed an increase in all-cause and respiratory mortality associated with short-term wildfire-related O3 exposure. Effective risk and smoke management strategies should be implemented to protect the public from the impacts of wildfires. FUNDING Australian Research Council and the Australian National Health and Medical Research Council.
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Affiliation(s)
- Gongbo Chen
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Xu Yue
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
| | - Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Wenhua Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Tingting Ye
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Shilu Tong
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Antonio Gasparrini
- Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA; School of Health Policy and Management, College of Health Sciences, Korea University, Seoul, South Korea
| | - Ben Armstrong
- Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Joel Schwartz
- Department of Environmental Health, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jouni J K Jaakkola
- Center for Environmental and Respiratory Health Research, University of Oulu, Oulu, Finland; Medical Research Center Oulu, OuluUniversity Hospital and University of Oulu, Oulu, Finland; Finnish Meteorological Institute, Helsinki, Finland
| | - Eric Lavigne
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada; Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | | | - Haidong Kan
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China
| | - Dominic Royé
- Department of Geography, University of Santiago de Compostela, Santiago de Compostela, Spain; CIBER Epidemiology and Public Health, Madrid, Spain
| | - Aleš Urban
- Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Ana Maria Vicedo-Cabrera
- Institute of Social and Preventive Medicine and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Aurelio Tobias
- Institute of Environmental Assessment and Water Research, Spanish Council for Scientific Research, Barcelona, Spain; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Bertil Forsberg
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Francesco Sera
- Department of Statistics, Computer Science and Applications "G Parenti", University of Florence, Florence, Italy
| | - Yadong Lei
- State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing, China
| | - Michael J Abramson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
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11
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Fayyad R, Josey K, Gandhi P, Rua M, Visaria A, Bates B, Setoguchi S, Nethery RC. Air pollution and serious bleeding events in high-risk older adults. ENVIRONMENTAL RESEARCH 2024; 251:118628. [PMID: 38460663 PMCID: PMC11144089 DOI: 10.1016/j.envres.2024.118628] [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: 10/24/2023] [Revised: 02/18/2024] [Accepted: 03/04/2024] [Indexed: 03/11/2024]
Abstract
IMPORTANCE Despite biological plausibility, very few epidemiologic studies have investigated the risks of clinically significant bleeding events due to particulate air pollution. OBJECTIVE To measure the independent and synergistic effects of PM2.5 exposure and anticoagulant use on serious bleeding events. DESIGN Retrospective cohort study (2008-2016). SETTING Nationwide Medicare population. PARTICIPANTS A 50% random sample of Medicare Part D-eligible Fee-for-Service beneficiaries at high risk for cardiovascular and thromboembolic events. EXPOSURES Fine particulate matter (PM2.5) and anticoagulant drugs (apixaban, dabigatran, edoxaban, rivaroxaban, or warfarin). MAIN OUTCOMES AND MEASURES The outcomes were acute hospitalizations for gastrointestinal bleeding, intracranial bleeding, or epistaxis. Hazard ratios and 95% CIs for PM2.5 exposure were estimated by fitting inverse probability weighted marginal structural Cox proportional hazards models. The relative excess risk due to interaction was used to assess additive-scale interaction between PM2.5 exposure and anticoagulant use. RESULTS The study cohort included 1.86 million high-risk older adults (mean age 77, 60% male, 87% White, 8% Black, 30% anticoagulant users, mean PM2.5 exposure 8.81 μg/m3). A 10 μg/m3 increase in PM2.5 was associated with a 48% (95% CI: 45%-52%), 58% (95% CI: 49%-68%) and 55% (95% CI: 37%-76%) increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis, respectively. Significant additive interaction between PM2.5 exposure and anticoagulant use was observed for gastrointestinal and intracranial bleeding. CONCLUSIONS Among older adults at high risk for cardiovascular and thromboembolic events, increasing PM2.5 exposure was significantly associated with increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis. In addition, PM2.5 exposure and anticoagulant use may act together to increase risks of severe gastrointestinal and intracranial bleeding. Thus, clinicians may recommend that high-risk individuals limit their outdoor air pollution exposure during periods of increased PM2.5 concentrations. Our findings may inform environmental policies to protect the health of vulnerable populations.
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Affiliation(s)
- Rindala Fayyad
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA, 02115, USA
| | - Kevin Josey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA, 02115, USA
| | - Poonam Gandhi
- Rutgers University Institute for Health, Healthcare Policy, and Aging Research, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Melanie Rua
- Rutgers University Institute for Health, Healthcare Policy, and Aging Research, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Aayush Visaria
- Rutgers University Institute for Health, Healthcare Policy, and Aging Research, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, 08901, USA; Department of Medicine, Rutgers Robert Wood Johnson Medical School, One Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Benjamin Bates
- Rutgers University Institute for Health, Healthcare Policy, and Aging Research, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, 08901, USA; Department of Medicine, Rutgers Robert Wood Johnson Medical School, One Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Soko Setoguchi
- Rutgers University Institute for Health, Healthcare Policy, and Aging Research, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, 08901, USA; Department of Medicine, Rutgers Robert Wood Johnson Medical School, One Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA.
| | - Rachel C Nethery
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA, 02115, USA.
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12
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Shen S, Li C, van Donkelaar A, Jacobs N, Wang C, Martin RV. Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T AIR 2024; 1:332-345. [PMID: 38751607 PMCID: PMC11092969 DOI: 10.1021/acsestair.3c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 05/18/2024]
Abstract
Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).
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Affiliation(s)
- Siyuan Shen
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Chi Li
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Aaron van Donkelaar
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Nathan Jacobs
- Department
of Computer Science and Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United
States
| | - Chenguang Wang
- Department
of Computer Science and Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United
States
| | - Randall V. Martin
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department
of Computer Science and Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United
States
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13
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Yu W, Song J, Li S, Guo Y. Is model-estimated PM 2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 348:123852. [PMID: 38531468 DOI: 10.1016/j.envpol.2024.123852] [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: 11/25/2023] [Revised: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 03/28/2024]
Abstract
Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.
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Affiliation(s)
- Wenhua Yu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.
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14
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Singer M. Is Pollution the Primary Driver of Infectious Syndemics? Pathogens 2024; 13:370. [PMID: 38787222 PMCID: PMC11124193 DOI: 10.3390/pathogens13050370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 04/13/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Syndemics, the adverse interaction of two or more coterminous diseases or other negative health conditions, have probably existed since human settlement, plant and animal domestication, urbanization, and the growth of social inequality beginning about 10-12,000 years ago. These dramatic changes in human social evolution significantly increased opportunities for the spread of zoonotic infectious diseases in denser human communities with increased sanitation challenges. In light of a growing body of research that indicates that anthropogenic air pollution causes numerous threats to health and is taking a far greater toll on human life and wellbeing than had been reported, this paper proposes the possibility that air pollution is now the primary driver of infectious disease syndemics. In support of this assertion, this paper reviews the growth and health impacts of air pollution, the relationship of air pollution to the development and spread of infectious diseases, and reported cases of air pollution-driven infectious disease syndemics, and presents public health recommendations for leveraging the biosocial insight of syndemic theory in responding to infectious disease.
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Affiliation(s)
- Merrill Singer
- Anthropology, Storrs Campus, University of Connecticut, Storrs, CT 06269, USA
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15
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Rajagopalan S, Vergara-Martel A, Zhong J, Khraishah H, Kosiborod M, Neeland IJ, Dazard JE, Chen Z, Munzel T, Brook RD, Nieuwenhuijsen M, Hovmand P, Al-Kindi S. The Urban Environment and Cardiometabolic Health. Circulation 2024; 149:1298-1314. [PMID: 38620080 DOI: 10.1161/circulationaha.123.067461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Urban environments contribute substantially to the rising burden of cardiometabolic diseases worldwide. Cities are complex adaptive systems that continually exchange resources, shaping exposures relevant to human health such as air pollution, noise, and chemical exposures. In addition, urban infrastructure and provisioning systems influence multiple domains of health risk, including behaviors, psychological stress, pollution, and nutrition through various pathways (eg, physical inactivity, air pollution, noise, heat stress, food systems, the availability of green space, and contaminant exposures). Beyond cardiometabolic health, city design may also affect climate change through energy and material consumption that share many of the same drivers with cardiometabolic diseases. Integrated spatial planning focusing on developing sustainable compact cities could simultaneously create heart-healthy and environmentally healthy city designs. This article reviews current evidence on the associations between the urban exposome (totality of exposures a person experiences, including environmental, occupational, lifestyle, social, and psychological factors) and cardiometabolic diseases within a systems science framework, and examines urban planning principles (eg, connectivity, density, diversity of land use, destination accessibility, and distance to transit). We highlight critical knowledge gaps regarding built-environment feature thresholds for optimizing cardiometabolic health outcomes. Last, we discuss emerging models and metrics to align urban development with the dual goals of mitigating cardiometabolic diseases while reducing climate change through cross-sector collaboration, governance, and community engagement. This review demonstrates that cities represent crucial settings for implementing policies and interventions to simultaneously tackle the global epidemics of cardiovascular disease and climate change.
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Affiliation(s)
- Sanjay Rajagopalan
- Harrington Heart and Vascular Institute, University Hospitals and Case Western Reserve School of Medicine, Cleveland, OH (S.R., A.V.-M., J.Z., I.J.N., J.-E.D., Z.C.)
| | - Armando Vergara-Martel
- Harrington Heart and Vascular Institute, University Hospitals and Case Western Reserve School of Medicine, Cleveland, OH (S.R., A.V.-M., J.Z., I.J.N., J.-E.D., Z.C.)
| | - Jeffrey Zhong
- Harrington Heart and Vascular Institute, University Hospitals and Case Western Reserve School of Medicine, Cleveland, OH (S.R., A.V.-M., J.Z., I.J.N., J.-E.D., Z.C.)
| | - Haitham Khraishah
- Division of Cardiovascular Medicine, University of Maryland School of Medicine, Baltimore, MD (H.K.)
| | | | - Ian J Neeland
- Harrington Heart and Vascular Institute, University Hospitals and Case Western Reserve School of Medicine, Cleveland, OH (S.R., A.V.-M., J.Z., I.J.N., J.-E.D., Z.C.)
| | - Jean-Eudes Dazard
- Harrington Heart and Vascular Institute, University Hospitals and Case Western Reserve School of Medicine, Cleveland, OH (S.R., A.V.-M., J.Z., I.J.N., J.-E.D., Z.C.)
| | - Zhuo Chen
- Harrington Heart and Vascular Institute, University Hospitals and Case Western Reserve School of Medicine, Cleveland, OH (S.R., A.V.-M., J.Z., I.J.N., J.-E.D., Z.C.)
| | - Thomas Munzel
- Department of Cardiology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany (T.M.)
- German Centre for Cardiovascular Research, Partner Site Rhine Main (T.M.)
| | - Robert D Brook
- Division of Cardiovascular Diseases, Department of Internal Medicine, Wayne State University, Detroit, MI (R.D.B.)
| | | | - Peter Hovmand
- Center for Community Health Integration, Case Western Reserve University, Cleveland, OH (P.H.)
| | - Sadeer Al-Kindi
- DeBakey Heart and Vascular Center, Houston Methodist, TX (S.A.-K.)
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16
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Borchers-Arriagada N, Vander Hoorn S, Cope M, Morgan G, Hanigan I, Williamson G, Johnston FH. The mortality burden attributable to wood heater smoke particulate matter (PM 2.5) in Australia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:171069. [PMID: 38395157 DOI: 10.1016/j.scitotenv.2024.171069] [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: 09/25/2023] [Revised: 02/16/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024]
Abstract
Air pollution is the leading environmental risk factor for mortality worldwide. In Australia, residential wood heating is the single largest source of pollution in many regions of the country. Estimates around the world and in some limited locations across Australia have shown that the health burden attributable to wood heating PM2.5 is considerable, and that there is great potential to reduce this burden. Here, we aimed to calculate the mortality burden attributable to wood heating emissions (WHE)-related PM2.5 throughout Australia and estimate the potential health benefits of reducing WHE-related air pollution, by replacing wood heaters with cleaner heating technologies. In summary, we used a four-stage process to (1) compile a nationwide WHE inventory, (2) generate annual exposure estimates of WHE-PM2.5, (3) estimate the annual mortality burden attributable to wood heater use across Australia for the year 2015, and (4) assess the potential health benefits of replacing existing wood heaters with cleaner heating technologies. We estimated that population weighted WHE-PM2.5 exposure across Australia for 2015 ranged between 0.62 μg/m3 and 1.35 μg/m3, with differing exposures across State/Territories. We estimated a considerable mortality burden attributable to WHE-PM2.5 ranging between 558 (95 % CI, 364-738) and 1555 (95 % CI, 1180-1740) deaths annually, depending on the scenario assessed. We calculated that replacing 50 % of the current wood heater stock, with zero or lower emission technologies could produce relevant health benefits, of between $AUD 1.61 and $AUD 1.93 billion per year (303-364 attributable deaths). These findings provide a preliminary and likely conservative assessment of the health burden of wood heater smoke across Australia, and an estimation of the potential benefits from replacing the current wood heater stock with cleaner technologies. The results presented here underscore the magnitude of the health burden attributable to wood heating in Australia.
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Affiliation(s)
- Nicolas Borchers-Arriagada
- Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia; Centre for Safe Air, NHMRC Centre for Research Excellence, 17 Liverpool Street, Hobart, Tasmania, Australia.
| | - Stephen Vander Hoorn
- School of Population and Global Health, The University of Western Australia, Western Australia, Australia
| | - Martin Cope
- CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia; Centre for Safe Air, NHMRC Centre for Research Excellence, 17 Liverpool Street, Hobart, Tasmania, Australia
| | - Geoffrey Morgan
- Sydney School of Public Health, University Centre for Rural Health, Faculty of Medicine and Health, University of Sydney, New South Wales, Australia; Health Environments and Lives (HEAL) National Research Network, Australia; Centre for Safe Air, NHMRC Centre for Research Excellence, 17 Liverpool Street, Hobart, Tasmania, Australia
| | - Ivan Hanigan
- WHO Collaborating Centre for Climate Change and Health Impact Assessment, School of Population Health, Curtin University, Western Australia, Australia; Health Environments and Lives (HEAL) National Research Network, Australia; Centre for Safe Air, NHMRC Centre for Research Excellence, 17 Liverpool Street, Hobart, Tasmania, Australia
| | - Grant Williamson
- School of Natural Sciences, University of Tasmania, Tasmania, Australia
| | - Fay H Johnston
- Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia; Health Environments and Lives (HEAL) National Research Network, Australia; Centre for Safe Air, NHMRC Centre for Research Excellence, 17 Liverpool Street, Hobart, Tasmania, Australia
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17
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Landguth EL, Knudson J, Graham J, Orr A, Coyle EA, Smith P, Semmens EO, Noonan C. Seasonal extreme temperatures and short-term fine particulate matter increases pediatric respiratory healthcare encounters in a sparsely populated region of the intermountain western United States. Environ Health 2024; 23:40. [PMID: 38622704 PMCID: PMC11017546 DOI: 10.1186/s12940-024-01082-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 04/10/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important. METHODS We explored short-term exposure to air pollution on children's respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season. RESULTS For asthma, increases of 1 µg/m3 in PM2.5 exposure 7-13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6-12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4-10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods. CONCLUSIONS Delayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.
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Affiliation(s)
- Erin L Landguth
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA.
| | - Jonathon Knudson
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | - Jon Graham
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
- Mathematical Sciences, University of Montana, Missoula, USA
| | - Ava Orr
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | - Emily A Coyle
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | - Paul Smith
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
- Pediatric Pulmonology, Community Medical Center, Missoula, MT, USA
| | - Erin O Semmens
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
| | - Curtis Noonan
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA
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18
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Hayden L, Lightner JM, Strausborger S, Franks TJ, Watson NL, Lewin-Smith MR. Particle analysis of surgical lung biopsies from deployed and non-deployed US service members during the Global War on Terrorism. PLoS One 2024; 19:e0301868. [PMID: 38603724 PMCID: PMC11008878 DOI: 10.1371/journal.pone.0301868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 03/22/2024] [Indexed: 04/13/2024] Open
Abstract
The role that inhaled particulate matter plays in the development of post-deployment lung disease among US service members deployed to Southwest Asia during the Global War on Terrorism has been difficult to define. There is a persistent gap in data addressing the relationship between relatively short-term (months to a few years) exposures to high levels of particulate matter during deployment and the subsequent development of adverse pulmonary outcomes. Surgical lung biopsies from deployed service members and veterans (DSMs) and non-deployed service members and veterans (NDSMs) who develop lung diseases can be analyzed to potentially identify residual deployment-specific particles and develop associations with pulmonary pathological diagnoses. We examined 52 surgical lung biopsies from 25 DSMs and 27 NDSMs using field emission scanning electron microscopy (FE-SEM) with energy dispersive x-ray spectroscopy (EDS) to identify any between-group differences in the number and composition of retained inorganic particles, then compared the particle analysis results with the original histopathologic diagnoses. We recorded a higher number of total particles in biopsies from DSMs than from NDSMs, and this difference was mainly accounted for by geologic clays (illite, kaolinite), feldspars, quartz/silica, and titanium-rich silicate mixtures. Biopsies from DSMs deployed to other Southwest Asia regions (SWA-Other) had higher particle counts than those from DSMs primarily deployed to Iraq or Afghanistan, due mainly to illite. Distinct deployment-specific particles were not identified. Particles did not qualitatively associate with country of deployment. The individual diagnoses of the DSMs and NDSMs were not associated with elevated levels of total particles, metals, cerium oxide, or titanium dioxide particles. These results support the examination of particle-related lung disease in DSMs in the context of comparison groups, such as NDSMs, to assist in determining the strength of associations between specific pulmonary pathology diagnoses and deployment-specific inorganic particulate matter exposure.
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Affiliation(s)
- Leslie Hayden
- Institute for Functional Materials and Devices, Lehigh University, Bethlehem, Pennsylvania, United States of America
| | - James M. Lightner
- Environmental Microscopy Laboratory, Joint Pathology Center, Silver Spring, Maryland, United States of America
| | - Stacy Strausborger
- Environmental Microscopy Laboratory, Joint Pathology Center, Silver Spring, Maryland, United States of America
| | - Teri J. Franks
- Pulmonary and Mediastinal Pathology, Joint Pathology Center, Silver Spring, Maryland, United States of America
| | - Nora L. Watson
- Department of Research Programs, Walter Reed National Military Medical Center, Bethesda, Maryland, United States of America
| | - Michael R. Lewin-Smith
- Environmental Pathology, Joint Pathology Center, Silver Spring, Maryland, United States of America
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19
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Yu W, Huang W, Gasparrini A, Sera F, Schneider A, Breitner S, Kyselý J, Schwartz J, Madureira J, Gaio V, Guo YL, Xu R, Chen G, Yang Z, Wen B, Wu Y, Zanobetti A, Kan H, Song J, Li S, Guo Y. Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities. Int J Epidemiol 2024; 53:dyae066. [PMID: 38725299 PMCID: PMC11082424 DOI: 10.1093/ije/dyae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 04/13/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.
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Affiliation(s)
- Wenhua Yu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Wenzhong Huang
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Antonio Gasparrini
- Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Francesco Sera
- Department of Statistics, Computer Science and Applications ‘G. Parenti’, University of Florence, Florence, Italy
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Susanne Breitner
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Jan Kyselý
- Department of Climatology, Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic
- Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Joana Madureira
- Department of Environmental Health, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal
- EPIUnit—Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
| | - Vânia Gaio
- Department of Epidemiology, Instituto Nacional de Saúde Dr Ricardo Jorge, Lisboa, Portugal
| | - Yue Leon Guo
- Department of Environmental and Occupational Medicine, National Taiwan University (NTU) College of Medicine and NTU Hospital, Taipei, Taiwan
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan
- Institute of Environmental and Occupational Health Sciences, NTU College of Public Health, Taipei, Taiwan
| | - Rongbin Xu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Gongbo Chen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Zhengyu Yang
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Bo Wen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yao Wu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Haidong Kan
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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20
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Chen ZY, Petetin H, Méndez Turrubiates RF, Achebak H, Pérez García-Pando C, Ballester J. Population exposure to multiple air pollutants and its compound episodes in Europe. Nat Commun 2024; 15:2094. [PMID: 38480711 PMCID: PMC10937992 DOI: 10.1038/s41467-024-46103-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data. Here we analyze daily ambient PM2.5, PM10, NO2 and O3 concentrations at a 0.1-degree resolution during 2003-2019 across 1426 contiguous regions in 35 European countries, representing 543 million people. We find that PM10 levels decline by 2.72% annually, followed by NO2 (2.45%) and PM2.5 (1.72%). In contrast, O3 increase by 0.58% in southern Europe, leading to a surge in unclean air days. Despite air quality advances, 86.3% of Europeans experience at least one compound event day per year, especially for PM2.5-NO2 and PM2.5-O3. We highlight the improvements in air quality control but emphasize the need for targeted measures addressing specific pollutants and their compound events, particularly amidst rising temperatures.
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Affiliation(s)
- Zhao-Yue Chen
- ISGlobal, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
| | | | | | - Hicham Achebak
- ISGlobal, Barcelona, Spain
- Inserm, France Cohortes, Paris, France
| | - Carlos Pérez García-Pando
- Barcelona Supercomputing Center, Barcelona, Spain
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain
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21
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Rajagopalan S, Brook RD, Salerno PRVO, Bourges-Sevenier B, Landrigan P, Nieuwenhuijsen MJ, Munzel T, Deo SV, Al-Kindi S. Air pollution exposure and cardiometabolic risk. Lancet Diabetes Endocrinol 2024; 12:196-208. [PMID: 38310921 PMCID: PMC11264310 DOI: 10.1016/s2213-8587(23)00361-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/15/2023] [Accepted: 11/23/2023] [Indexed: 02/06/2024]
Abstract
The Global Burden of Disease assessment estimates that 20% of global type 2 diabetes cases are related to chronic exposure to particulate matter (PM) with a diameter of 2·5 μm or less (PM2·5). With 99% of the global population residing in areas where air pollution levels are above current WHO air quality guidelines, and increasing concern in regard to the common drivers of air pollution and climate change, there is a compelling need to understand the connection between air pollution and cardiometabolic disease, and pathways to address this preventable risk factor. This Review provides an up to date summary of the epidemiological evidence and mechanistic underpinnings linking air pollution with cardiometabolic risk. We also outline approaches to improve awareness, and discuss personal-level, community, governmental, and policy interventions to help mitigate the growing global public health risk of air pollution exposure.
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Affiliation(s)
- Sanjay Rajagopalan
- University Hospitals, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
| | - Robert D Brook
- Division of Cardiovascular Diseases, Department of Internal Medicine, Wayne State University, Detroit, MI, USA
| | - Pedro R V O Salerno
- University Hospitals, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | | | - Philip Landrigan
- Program for Global Public Health and the Common Good, Boston College, Boston, MA, USA; Centre Scientifique de Monaco, Monaco
| | | | - Thomas Munzel
- Department of Cardiology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany; German Center of Cardiovascular Research, Partner-Site Rhine-Main, Germany
| | - Salil V Deo
- Louis Stokes Cleveland VA Medical Center, Case Western Reserve School of Medicine, Cleveland, OH, USA
| | - Sadeer Al-Kindi
- DeBakey Heart and Vascular Center, Houston Methodist, Houston, TX, USA
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22
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Münzel T, Daiber A, Hahad O. [Air pollution, noise and hypertension : Partners in crime]. Herz 2024; 49:124-133. [PMID: 38321170 DOI: 10.1007/s00059-024-05234-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 02/08/2024]
Abstract
Air pollution and traffic noise are two important environmental risk factors that endanger health in urban societies and often act together as "partners in crime". Although air pollution and noise often co-occur in urban environments, they have typically been studied separately, with numerous studies documenting consistent effects of individual exposure on blood pressure. In the following review article, we examine the epidemiology of air pollution and noise, especially regarding the cardiovascular risk factor arterial hypertension and the underlying pathophysiology. Both environmental stressors have been shown to lead to endothelial dysfunction, oxidative stress, pronounced vascular inflammation, disruption of circadian rhythms and activation of the autonomic nervous system, all of which promote the development of hypertension and cardiovascular diseases. From a societal and political perspective, there is an urgent need to point out the potential dangers of air pollution and traffic noise in the American Heart Association (AHA)/American College of Cardiology (ACC) prevention guidelines and the European Society of Cardiology (ESC) guidelines on prevention. Therefore, an essential goal for the future is to raise awareness of environmental risk factors as important and, in particular, preventable risk factors for cardiovascular diseases.
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Affiliation(s)
- T Münzel
- Zentrum für Kardiologie, Kardiologie I, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland.
| | - A Daiber
- Zentrum für Kardiologie, Kardiologie I, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland
| | - O Hahad
- Zentrum für Kardiologie, Kardiologie I, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland
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23
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Yu W, Xu R, Ye T, Abramson MJ, Morawska L, Jalaludin B, Johnston FH, Henderson SB, Knibbs LD, Morgan GG, Lavigne E, Heyworth J, Hales S, Marks GB, Woodward A, Bell ML, Samet JM, Song J, Li S, Guo Y. Estimates of global mortality burden associated with short-term exposure to fine particulate matter (PM 2·5). Lancet Planet Health 2024; 8:e146-e155. [PMID: 38453380 DOI: 10.1016/s2542-5196(24)00003-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 11/27/2023] [Accepted: 01/12/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND The acute health effects of short-term (hours to days) exposure to fine particulate matter (PM2·5) have been well documented; however, the global mortality burden attributable to this exposure has not been estimated. We aimed to estimate the global, regional, and urban mortality burden associated with short-term exposure to PM2·5 and the spatiotemporal variations in this burden from 2000 to 2019. METHODS We combined estimated global daily PM2·5 concentrations, annual population counts, country-level mortality rates, and epidemiologically derived exposure-response functions to estimate the mortality attributable to short-term PM2·5 exposure from 2000 to 2019, in the continental regions and in 13 189 urban centres worldwide at a spatial resolution of 0·1° × 0·1°. We tested the robustness of our mortality estimates with different theoretical minimum risk exposure levels, lag effects, and exposure-response functions. FINDINGS Approximately 1 million (95% CI 690 000-1·3 million) premature deaths per year from 2000 to 2019 were attributable to short-term PM2·5 exposure, representing 2·08% (1·41-2·75) of total global deaths or 17 (11-22) premature deaths per 100 000 population. Annually, 0·23 million (0·15 million-0·30 million) deaths attributable to short-term PM2·5 exposure were in urban areas, constituting 22·74% of the total global deaths attributable to this cause and accounting for 2·30% (1·56-3·05) of total global deaths in urban areas. The sensitivity analyses showed that our worldwide estimates of mortality attributed to short-term PM2·5 exposure were robust. INTERPRETATION Short-term exposure to PM2·5 contributes a substantial global mortality burden, particularly in Asia and Africa, as well as in global urban areas. Our results highlight the importance of mitigation strategies to reduce short-term exposure to air pollution and its adverse effects on human health. FUNDING Australian Research Council and the Australian National Health and Medical Research Council.
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Affiliation(s)
- Wenhua Yu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Rongbin Xu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Tingting Ye
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Michael J Abramson
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Bin Jalaludin
- School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Fay H Johnston
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Sarah B Henderson
- Environmental Health Services, BC Centre for Disease Control, Vancouver, BC, Canada
| | - Luke D Knibbs
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Public Health Unit, Sydney Local Health District, Sydney, NSW, Australia
| | - Geoffrey G Morgan
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; University Centre for Rural Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Eric Lavigne
- Air Health Science Division, Health Canada, Ottawa, ON, Canada
| | - Jane Heyworth
- School of Population and Global Health, The University of Western Australia, Crawley, WA, Australia
| | - Simon Hales
- Department of Public Health, University of Otago, Wellington South, New Zealand
| | - Guy B Marks
- Medicine & Health, University of New South Wales, Sydney, NSW, Australia
| | - Alistair Woodward
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
| | | | - Jiangning Song
- Monash Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
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24
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Liu Y, Zhang Y, Yu P, Ye T, Zhang Y, Xu R, Li S, Guo Y. Applying traffic camera and deep learning-based image analysis to predict PM 2.5 concentrations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169233. [PMID: 38097087 DOI: 10.1016/j.scitotenv.2023.169233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/24/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Air pollution has caused a significant burden in terms of mortality and mobility worldwide. However, the current coverage of air quality monitoring networks is still limited. OBJECTIVE This study aims to apply a novel approach to convert the existing traffic cameras into sensors measuring particulate matter with a diameter of 2.5 μm or less (PM2.5) so that the coverage of PM2.5 monitoring could be expanded without extra cost. METHODS In our study, the traffic camera images were collected at a rate of 4 images/h and the corresponding hourly PM2.5 concentration was collected from the reference grade PM2.5 station 3 km away. A customized neural network model was trained to obtain the PM2.5 concentration from images followed by a random forest model to predict the hourly PM2.5 concentration. The saliency maps and the feature importance were utilized to interpret the neural network. RESULTS Proposed novel approach has a high prediction performance to predict hourly PM2.5 from traffic camera images, with a root mean square error (RMSE) of 0.76 μg/m3 and a coefficient of determination (R2) of 0.98. The saliency map shows neural network focuses on unobstructed far-end road surfaces while the random forest feature importance highlights the first quarter image's significance. The model performance is robust whether weather conditions are controlled or not. CONCLUSION Our study provided a practical approach to converting the existing traffic cameras into PM2.5 sensors. The deep learning method based on the Resnet architecture in our study can broaden the coverage of PM2.5 monitoring with no additional infrastructure needed.
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Affiliation(s)
- Yanming Liu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuxi Zhang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; School of Life and Environmental Sciences, the University of Sydney, Sydney, NSW 2006, Australia
| | - Pei Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Tingting Ye
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yiwen Zhang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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25
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Fung PL, Savadkoohi M, Zaidan MA, Niemi JV, Timonen H, Pandolfi M, Alastuey A, Querol X, Hussein T, Petäjä T. Constructing transferable and interpretable machine learning models for black carbon concentrations. ENVIRONMENT INTERNATIONAL 2024; 184:108449. [PMID: 38286044 DOI: 10.1016/j.envint.2024.108449] [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: 11/08/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.
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Affiliation(s)
- Pak Lun Fung
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland; Helsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland.
| | - Marjan Savadkoohi
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain; Department of Mining, Industrial and ICT Engineering (EMIT), Manresa School of Engineering (EPSEM), Universitat Politècnica de Catalunya (UPC), Manresa 08242, Spain.
| | - Martha Arbayani Zaidan
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland; Helsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland; Department of Computer Science, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland.
| | - Jarkko V Niemi
- Helsinki Region Environmental Services Authority (HSY), Helsinki FI-00066, Finland.
| | - Hilkka Timonen
- Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki FI-00560, Finland.
| | - Marco Pandolfi
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain.
| | - Andrés Alastuey
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain.
| | - Xavier Querol
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain.
| | - Tareq Hussein
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland; Environmental and Atmospheric Research Laboratory (EARL), Department of Physics, School of Science, Amman 11942, Jordan.
| | - Tuukka Petäjä
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland.
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26
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Hertzog L, Morgan GG, Yuen C, Gopi K, Pereira GF, Johnston FH, Cope M, Chaston TB, Vyas A, Vardoulakis S, Hanigan IC. Mortality burden attributable to exceptional PM 2.5 air pollution events in Australian cities: A health impact assessment. Heliyon 2024; 10:e24532. [PMID: 38298653 PMCID: PMC10828683 DOI: 10.1016/j.heliyon.2024.e24532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/29/2023] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Background People living in Australian cities face increased mortality risks from exposure to extreme air pollution events due to bushfires and dust storms. However, the burden of mortality attributable to exceptional PM2.5 levels has not been well characterised. We assessed the burden of mortality due to PM2.5 pollution events in Australian capital cities between 2001 and 2020. Methods For this health impact assessment, we obtained data on daily counts of deaths for all non-accidental causes and ages from the Australian National Vital Statistics Register. Daily concentrations of PM2.5 were estimated at a 5 km grid cell, using a Random Forest statistical model of data from air pollution monitoring sites combined with a range of satellite and land use-related data. We calculated the exceptional PM2.5 levels for each extreme pollution exposure day using the deviation from a seasonal and trend loess decomposition model. The burden of mortality was examined using a relative risk concentration-response function suggested in the literature. Findings Over the 20-year study period, we estimated 1454 (95 % CI 987, 1920) deaths in the major Australian cities attributable to exceptional PM2.5 exposure levels. The mortality burden due to PM2.5 exposure on extreme pollution days was considerable. Variations were observed across Australia. Despite relatively low daily PM2.5 levels compared to global averages, all Australian cities have extreme pollution exposure days, with PM2.5 concentrations exceeding the World Health Organisation Air Quality Guideline standard for 24-h exposure. Our analysis results indicate that nearly one-third of deaths from extreme air pollution exposure can be prevented with a 5 % reduction in PM2.5 levels on days with exceptional pollution. Interpretation Exposure to exceptional PM2.5 events was associated with an increased mortality burden in Australia's cities. Policies and coordinated action are needed to manage the health risks of extreme air pollution events due to bushfires and dust storms under climate change.
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Affiliation(s)
- Lucas Hertzog
- Curtin School of Population Health, Curtin University, WA, 6102, Australia
- WHO Collaborating Centre for Climate Change and Health Impact Assessment, WA, 6102, Australia
- Healthy Environments and Lives (HEAL) National Research Network, Australia
| | - Geoffrey G. Morgan
- Healthy Environments and Lives (HEAL) National Research Network, Australia
- School of Public Health, University of Sydney, Camperdown, NSW, 2006, Australia
- Centre for Safe Air, NHMRC CRE, Australia
- University Centre for Rural Health, University of Sydney, Lismore, NSW, 2480, Australia
| | - Cassandra Yuen
- Curtin School of Population Health, Curtin University, WA, 6102, Australia
- School of Public Health, University of Sydney, Camperdown, NSW, 2006, Australia
| | - Karthik Gopi
- School of Public Health, University of Sydney, Camperdown, NSW, 2006, Australia
- University Centre for Rural Health, University of Sydney, Lismore, NSW, 2480, Australia
| | - Gavin F. Pereira
- Curtin School of Population Health, Curtin University, WA, 6102, Australia
- EnAble Institute, Curtin University, WA, 6102, Australia
| | - Fay H. Johnston
- Centre for Safe Air, NHMRC CRE, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Martin Cope
- CSIRO Land and Water Flagship, Melbourne, Australia
| | | | - Aditya Vyas
- Curtin School of Population Health, Curtin University, WA, 6102, Australia
- WHO Collaborating Centre for Climate Change and Health Impact Assessment, WA, 6102, Australia
| | - Sotiris Vardoulakis
- Healthy Environments and Lives (HEAL) National Research Network, Australia
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, 2061, Australia
| | - Ivan C. Hanigan
- Curtin School of Population Health, Curtin University, WA, 6102, Australia
- WHO Collaborating Centre for Climate Change and Health Impact Assessment, WA, 6102, Australia
- Healthy Environments and Lives (HEAL) National Research Network, Australia
- Centre for Safe Air, NHMRC CRE, Australia
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Navaratnam AMD, Williams H, Sharp SJ, Woodcock J, Khreis H. Systematic review and meta-analysis on the impact of COVID-19 related restrictions on air quality in low- and middle-income countries. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168110. [PMID: 37884141 DOI: 10.1016/j.scitotenv.2023.168110] [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: 08/09/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND Low- and middle-income countries (LMIC) are disproportionately affected by air pollution and its health burden, representing a global inequity. The COVID-19 pandemic provided a unique opportunity to investigate the impact of unprecedented lockdown measures on air pollutant concentrations globally. We aim to quantify air pollutant concentration changes across LMIC settings as a result of COVID-19 restrictions. METHODS Searches for this systematic review and meta-analysis were carried out across five databases on 30th March 2022; MEDLINE, Embase, Web of Science, Scopus and Transport Research Information Documentation. Modelling and observational studies were included, as long as the estimates reflected city or town level data and were taken exclusively in pre-lockdown and lockdown periods. Mean percentage changes per pollutant were calculated and meta-analyses were carried out to calculate mean difference in measured ground-level observed concentrations for each pollutant (PROSPERO CRD42022326924). FINDINGS Of the 2982 manuscripts from initial searches, 256 manuscripts were included providing 3818 percentage changes of all pollutants. No studies included any countries from Sub-Saharan Africa and 34 % and 39.4 % of studies were from China and India, respectively. There was a mean percentage change of -37.4 %, -21.7 %, -54.6 %, -39.1 %, -48.9 %, 16.9 %, -34.9 %, -30.6 % and - 14.7 % for black carbon (BC), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), ozone (O3), particulate matter 10 (PM10) and 2.5 (PM2.5) and sulphur dioxide (SO2), respectively. Meta-analysis included 100 manuscripts, providing 908 mean concentration differences, which showed significant reduction in mean concentration in all study settings for BC (-0.46 μg/m3, PI -0.85; -0.08), CO (-0.25 mg/m3, PI -0.44; -0.03), NO2 (-19.41 μg/m3, PI -31.14; -7.68) and NOx (-22.32 μg/m3, PI -40.94; -3.70). INTERPRETATION The findings of this systematic review and meta-analysis quantify and confirm the trends reported across the globe in air pollutant concentration, including increases in O3. Despite the majority of global urban growth occurring in LMIC, there are distinct geographical gaps in air pollution data and, where it is available, differing approaches to analysis and reporting.
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Affiliation(s)
| | - Harry Williams
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - James Woodcock
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Haneen Khreis
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
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28
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Bai Y, Liu M. Multi-scale spatiotemporal trends and corresponding disparities of PM 2.5 exposure in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 340:122857. [PMID: 37925009 DOI: 10.1016/j.envpol.2023.122857] [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: 05/26/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 11/06/2023]
Abstract
Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.
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Affiliation(s)
- Yu Bai
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Menghang Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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29
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Liu R, Ma Z, Gasparrini A, de la Cruz A, Bi J, Chen K. Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980-2019. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21605-21615. [PMID: 38085698 DOI: 10.1021/acs.est.3c05424] [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: 12/27/2023]
Abstract
Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71-0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32-0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment.
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Affiliation(s)
- Riyang Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, People's Republic of China
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
- Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, Connecticut 06520, United States
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, People's Republic of China
| | - Antonio Gasparrini
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London WC1H 9SH, United Kingdom
| | - Arturo de la Cruz
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London WC1H 9SH, United Kingdom
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, People's Republic of China
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
- Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, Connecticut 06520, United States
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30
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Wei J, Li Z, Lyapustin A, Wang J, Dubovik O, Schwartz J, Sun L, Li C, Liu S, Zhu T. First close insight into global daily gapless 1 km PM 2.5 pollution, variability, and health impact. Nat Commun 2023; 14:8349. [PMID: 38102117 PMCID: PMC10724144 DOI: 10.1038/s41467-023-43862-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
Here we retrieve global daily 1 km gapless PM2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth's populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.
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Affiliation(s)
- Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Zhanqing Li
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Alexei Lyapustin
- Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, The University of Iowa, Iowa City, IA, USA
| | - Oleg Dubovik
- Laboratoire d'Optique Atmosphérique, Université de Lille, CNRS, Lille, France
| | - Joel Schwartz
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Lin Sun
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China
| | - Chi Li
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Song Liu
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Tong Zhu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
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Dai H, Liu Y, Wang J, Ren J, Gao Y, Dong Z, Zhao B. Large-scale spatiotemporal deep learning predicting urban residential indoor PM 2.5 concentration. ENVIRONMENT INTERNATIONAL 2023; 182:108343. [PMID: 38029622 DOI: 10.1016/j.envint.2023.108343] [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: 09/06/2023] [Revised: 11/09/2023] [Accepted: 11/20/2023] [Indexed: 12/01/2023]
Abstract
Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of daily average urban residential PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95 % prediction interval coverage of 85 %. To demonstrate the application process, this model was applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. The validity of the model at the population level can be further bolstered, making it valuable for assessing and managing indoor air pollution-related health risks.
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Affiliation(s)
- Hui Dai
- Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Yumeng Liu
- Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Jianghao Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jun Ren
- Shenzhen Institute of Building Research Co. Ltd, China
| | - Yao Gao
- Shenzhen Institute of Building Research Co. Ltd, China
| | - Zhaomin Dong
- School of Space and Environment, Beihang University, Beijing 100191, China.
| | - Bin Zhao
- Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
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Saveleva L, Sima M, Klema J, Krejčík Z, Vartiainen P, Sitnikova V, Belaya I, Malm T, Jalava PI, Rössner P, Kanninen KM. Transcriptomic alterations in the olfactory bulb induced by exposure to air pollution: Identification of potential biomarkers and insights into olfactory system function. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2023; 104:104316. [PMID: 37981204 DOI: 10.1016/j.etap.2023.104316] [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: 08/15/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
This study evaluated how exposure to the ubiquitous air pollution component, ultrafine particles (UFPs), alters the olfactory bulb (OB) transcriptome. The study utilised a whole-body inhalation chamber to simulate real-life conditions and focused on UFPs due to their high translocation and deposition ability in OBs as well as their prevalence in ambient air. Female C57BL/6J mice were exposed to clean air or to freshly generated combustion derived UFPs for two weeks, after which OBs were dissected and mRNA transcripts were investigated using RNA sequencing analysis. For the first time, transcriptomics was applied to determine changes in mRNA expression levels occurring after subacute exposure to UFPs in the OBs. We found forty-five newly described mRNAs to be involved in air pollution-induced responses, including genes involved in odorant binding, synaptic regulation, and myelination signalling pathway, providing new gene candidates for future research. This study provides new insights for the environmental science and neuroscience fields and nominates future research directions.
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Affiliation(s)
- Liudmila Saveleva
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Michal Sima
- Department of Nanotoxicology and Molecular Epidemiology, Institute of Experimental Medicine CAS, Vídeňská 1083, Prague 142 20, Czech Republic
| | - Jiri Klema
- Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, Prague 160 00, Czech Republic
| | - Zdeněk Krejčík
- Department of Genetic Toxicology and Epigenetics, Institute of Experimental Medicine CAS, Vídeňská 1083, Prague 142 20, Czech Republic
| | - Petra Vartiainen
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland
| | - Valeriia Sitnikova
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Irina Belaya
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tarja Malm
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Pasi I Jalava
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland
| | - Pavel Rössner
- Department of Nanotoxicology and Molecular Epidemiology, Institute of Experimental Medicine CAS, Vídeňská 1083, Prague 142 20, Czech Republic
| | - Katja M Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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Landguth EL, Knudson J, Graham J, Orr A, Coyle EA, Smith P, Semmens EO, Noonan C. Seasonal extreme temperatures and short-term fine particulate matter increases child respiratory hospitalizations in a sparsely populated region of the intermountain western United States. RESEARCH SQUARE 2023:rs.3.rs-3438033. [PMID: 37886498 PMCID: PMC10602161 DOI: 10.21203/rs.3.rs-3438033/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Background Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Few studies have evaluated these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health. Methods We explored short-term exposure to air pollution on childhood respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated hospitalizations. The main outcome measure included all respiratory-related hospital admissions for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for all individuals aged 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis and distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 15 prior-days modified by temperature or season. Results Short-term exposure increases of 1 μg/m3 in PM2.5 were associated with elevated odds of all three respiratory hospital admission categories. PM2.5 was associated with the largest increased odds of hospitalizations for asthma at lag 7-13 days [1.87(1.17-2.97)], for LRTI at lag 6-12 days [2.18(1.20-3.97)], and for URTI at a cumulative lag of 13 days [1.29(1.07-1.57)]. The impact of PM2.5 varied by temperature and season for each respiratory outcome scenario. For asthma, PM2.5 was associated most strongly during colder temperatures [3.11(1.40-6.89)] and the winter season [3.26(1.07-9.95)]. Also in colder temperatures, PM2.5 was associated with increased odds of LRTI hospitalization [2.61(1.15-5.94)], but no seasonal effect was observed. Finally, 13 days of cumulative PM2.5 prior to admissions date was associated with the greatest increased odds of URTI hospitalization during summer days [3.35(1.85-6.04)] and hotter temperatures [1.71(1.31-2.22)]. Conclusions Children's respiratory-related hospital admissions were associated with short-term exposure to PM2.5. PM2.5 associations with asthma and LRTI hospitalizations were strongest during cold periods, whereas associations with URTI were largest during hot periods. Classification environmental public health, fine particulate matter air pollution, respiratory infections.
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Barros B, Oliveira M, Morais S. Continent-based systematic review of the short-term health impacts of wildfire emissions. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2023; 26:387-415. [PMID: 37469022 DOI: 10.1080/10937404.2023.2236548] [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: 07/21/2023]
Abstract
This review systematically gathers and provides an analysis of pollutants levels emitted from wildfire (WF) and their impact on short-term health effects of affected populations. The available literature was searched according to Population, Exposure, Comparator, Outcome, and Study design (PECOS) database defined by the World Health Organization (WHO) and a meta-analysis was conducted whenever possible. Data obtained through PECOS characterized information from the USA, Europe, Australia, and some Asian countries; South American countries were seldom characterized, and no data were available for Africa and Russia. Extremely high levels of pollutants, mostly of fine fraction of particulate matter (PM) and ozone, were associated with intense WF emissions in North America, Oceania, and Asia and reported to exceed several-fold the WHO guidelines. Adverse health outcomes include emergency department visits and hospital admissions for cardiorespiratory diseases as well as mortality. Despite the heterogeneity among exposure and health assessment methods, all-cause mortality, and specific-cause mortality were significantly associated with WF emissions in most of the reports. Globally, a significant association was found for all-cause respiratory outcomes including asthma, but mixed results were noted for cardiovascular-related effects. For the latter, estimates were only significant several days after WF emissions, suggesting a more delayed impact on the heart. Different research gaps are presented, including the need for the application of standardized protocols for assessment of both exposure and adverse health risks. Mitigation actions also need to be strengthened, including dedicated efforts to communicate with the affected populations, to engage them for adoption of protective behaviors and measures.
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Affiliation(s)
- Bela Barros
- REQUIMTE/LAQV, Instituto Superior de Engenharia Do Porto, Instituto Politécnico Do Porto, Porto, Portugal
| | - Marta Oliveira
- REQUIMTE/LAQV, Instituto Superior de Engenharia Do Porto, Instituto Politécnico Do Porto, Porto, Portugal
| | - Simone Morais
- REQUIMTE/LAQV, Instituto Superior de Engenharia Do Porto, Instituto Politécnico Do Porto, Porto, Portugal
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Liu X, Yi G, Zhou X, Zhang T, Bie X, Li J, Tan H. Spatio-temporal variations of PM 2.5 and O 3 in China during 2013-2021: Impact factor analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 334:122189. [PMID: 37451585 DOI: 10.1016/j.envpol.2023.122189] [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: 01/05/2023] [Revised: 06/13/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
Fine particulate matter (PM2.5) and ozone (O3) pollution are regarded as significant secondary air pollutants. The PM2.5 in most regions in China declined, and the decreasing rate in January was lower than the annual average. However, O3 concentration showed a steady increasing trend in most regions, and the increasing rate in July was slightly higher than the annual average. In particular, the annual average PM2.5 concentration and excess rate showed an increasing trend on the northern slope of the Tianshan Mountains. Conversely, O3 concentrations had shown a consistent increasing trend, exceeding the annual average limit of 100 μg/m3. Surface pressure exhibited positive correlations with PM2.5 in winter and O3 in summer across urban agglomerations. Moreover, soil temperature at different depths explained over 30% of the variations in PM2.5 and O3 in the Chengdu-Chongqing, Beijing-Tianjin-Hebei, and Lanzhou-Xining urban agglomerations. In winter, relative humidity demonstrated a positive correlation with urban agglomerations in northeast and northwest China, regions characterized by dry climates. During the COVID-19 period, the impacts of meteorological factors and soil temperature on PM2.5 and O3 differed significantly compared to preceding and subsequent periods. Notably, during the winter of 2020, the Harbin-Changchuan urban agglomeration exhibited a notable transition, as O3 and PM2.5 concentrations shifted from a strong negative correlation to a robust positive correlation. This remarkable shift, with deviations explained up to 60%, represents a unique phenomenon worth emphasizing in the study's findings.
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Affiliation(s)
- Xian Liu
- College of Earth Science, Chengdu University of Technology, Chengdu, 610059, China
| | - Guihua Yi
- College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, 610059, China.
| | - Xiaobing Zhou
- Geological Engineering Department, Montana Technological University, Butte, MT, 59701, USA
| | - Tingbin Zhang
- College of Earth Science, Chengdu University of Technology, Chengdu, 610059, China; State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, 610059, China
| | - Xiaojuan Bie
- College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, 610059, China
| | - Jingji Li
- State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, 610059, China; College of Ecological Environment, Chengdu University of Technology, Chengdu, 610059, China
| | - Huizhi Tan
- College of Earth Science, Chengdu University of Technology, Chengdu, 610059, China
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36
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Yu W, Nakisa B, Ali E, Loke SW, Stevanovic S, Guo Y. Sensor-based indoor air temperature prediction using deep ensemble machine learning: An Australian urban environment case study. URBAN CLIMATE 2023; 51:101599. [DOI: 10.1016/j.uclim.2023.101599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Wang P, Sun S, Lam S, Lockwood WW. New insights into the biology and development of lung cancer in never smokers-implications for early detection and treatment. J Transl Med 2023; 21:585. [PMID: 37653450 PMCID: PMC10472682 DOI: 10.1186/s12967-023-04430-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/10/2023] [Indexed: 09/02/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths worldwide. Despite never smokers comprising between 10 and 25% of all cases, lung cancer in never smokers (LCNS) is relatively under characterized from an etiological and biological perspective. The application of multi-omics techniques on large patient cohorts has significantly advanced the current understanding of LCNS tumor biology. By synthesizing the findings of multi-omics studies on LCNS from a clinical perspective, we can directly translate knowledge regarding tumor biology into implications for patient care. Primarily focused on never smokers with lung adenocarcinoma, this review details the predominance of driver mutations, particularly in East Asian patients, as well as the frequency and importance of germline variants in LCNS. The mutational patterns present in LCNS tumors are thoroughly explored, highlighting the high abundance of the APOBEC signature. Moreover, this review recognizes the spectrum of immune profiles present in LCNS tumors and posits how it can be translated to treatment selection. The recurring and novel insights from multi-omics studies on LCNS tumor biology have a wide range of clinical implications. Risk factors such as exposure to outdoor air pollution, second hand smoke, and potentially diet have a genomic imprint in LCNS at varying degrees, and although they do not encompass all LCNS cases, they can be leveraged to stratify risk. Germline variants similarly contribute to a notable proportion of LCNS, which warrants detailed documentation of family history of lung cancer among never smokers and demonstrates value in developing testing for pathogenic variants in never smokers for early detection in the future. Molecular driver subtypes and specific co-mutations and mutational signatures have prognostic value in LCNS and can guide treatment selection. LCNS tumors with no known driver alterations tend to be stem-like and genes contributing to this state may serve as potential therapeutic targets. Overall, the comprehensive findings of multi-omics studies exert a wide influence on clinical management and future research directions in the realm of LCNS.
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Affiliation(s)
- Peiyao Wang
- Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, BC, Canada
- Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC, Canada
| | - Sophie Sun
- Department of Medical Oncology, British Columbia Cancer Agency Vancouver, Vancouver, BC, Canada
| | - Stephen Lam
- Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, BC, Canada
| | - William W Lockwood
- Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, BC, Canada.
- Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC, Canada.
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
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38
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Rentschler J, Leonova N. Global air pollution exposure and poverty. Nat Commun 2023; 14:4432. [PMID: 37481598 PMCID: PMC10363163 DOI: 10.1038/s41467-023-39797-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/29/2023] [Indexed: 07/24/2023] Open
Abstract
Air pollution is one of the leading causes of health complications and mortality worldwide, especially affecting lower-income groups, who tend to be more exposed and vulnerable. This study documents the relationship between ambient air pollution exposure and poverty in 211 countries and territories. Using the World Health Organization's (WHO) 2021 revised fine particulate matter (PM2.5) thresholds, we show that globally, 7.3 billion people are directly exposed to unsafe average annual PM2.5 concentrations, 80 percent of whom live in low- and middle-income countries. Moreover, 716 million of the world's lowest income people (living on less than $1.90 per day) live in areas with unsafe levels of air pollution, especially in Sub-Saharan Africa. Air pollution levels are particularly high in lower-middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. These findings are based on high-resolution air pollution and population maps with global coverage, as well as subnational poverty estimates based on harmonized household surveys.
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39
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Gao Y, Huang W, Xu R, Gasevic D, Liu Y, Yu W, Yu P, Yue X, Zhou G, Zhang Y, Liu H, Song J, Guo Y, Li S. Association between long-term exposure to wildfire-related PM 2.5 and mortality: A longitudinal analysis of the UK Biobank. JOURNAL OF HAZARDOUS MATERIALS 2023; 457:131779. [PMID: 37307727 DOI: 10.1016/j.jhazmat.2023.131779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/26/2023] [Accepted: 06/03/2023] [Indexed: 06/14/2023]
Abstract
Little is known about the associations between long-term exposure to wildfire-related fine particulate matter (PM2.5) and mortality. We aimed to explore theses associations using the data from the UK Biobank cohort. Long-term wildfire-related PM2.5 exposure was defined as the 3-year cumulative concentrations of wildfire-related PM2.5 within a 10-km buffer surrounding the residential address for each individual. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using the time-varying Cox regression model. We included 492,394 participants aged between 38 and 73 years. We found that after adjusting for potential covariates, a 10 μg/m3 increase of wildfire-related PM2.5 exposure was associated with a 0.4% higher risk of all-cause mortality (HR = 1.004 [95% CI: 1.001, 1.006]) and nonaccidental mortality (HR = 1.004 [95% CI: 1.002, 1.006]), and a 0.5% higher risk of neoplasm mortality (HR = 1.005 [95% CI: 1.002, 1.008]). However, no significant associations were observed between wildfire-related PM2.5 exposure and mortality from cardiovascular, respiratory, and mental diseases. Additionally, no significant modification effects of a series of modifiers were observed. Targeted health protection strategies should be adopted in response to wildfire-related PM2.5 exposure, in order to reduce the risk of premature mortality.
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Affiliation(s)
- Yuan Gao
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Wenzhong Huang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Rongbin Xu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Danijela Gasevic
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Yanming Liu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Wenhua Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Pei Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Xu Yue
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
| | - Guowei Zhou
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Yan Zhang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Hong Liu
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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40
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van Zandwijk N, Marshall HM, Fong KM. Lung cancer: Removing toxic dust from our environment. Respirology 2023; 28:511-512. [PMID: 37105901 DOI: 10.1111/resp.14512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 04/29/2023]
Affiliation(s)
- Nico van Zandwijk
- Department of Cell and Molecular Therapies, Royal Prince Alfred Hospital/Sydney Local Health District, Camperdown, New South Wales, Australia
- Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Henry M Marshall
- The Prince Charles Hospital and Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Kwun M Fong
- The Prince Charles Hospital and Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
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41
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Deng S, Lau J, Wang Z, Wargocki P. Associations between illness-related absences and ventilation and indoor PM 2.5 in elementary schools of the Midwestern United States. ENVIRONMENT INTERNATIONAL 2023; 176:107944. [PMID: 37216835 DOI: 10.1016/j.envint.2023.107944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/30/2023] [Accepted: 04/19/2023] [Indexed: 05/24/2023]
Abstract
This study monitored indoor environmental data in 144 classrooms in 31 schools in the Midwestern United States for two consecutive days every fall, winter, and spring during a two-year period; 3,105 pupils attended classrooms where the measurements were conducted. All classrooms were ventilated with mechanical systems that had recirculation; there were no operable exterior windows or doors. The daily absence rate at the student level and demographic data at the classroom level were collected. The overall mean ventilation rate, using outdoor air, was 5.5 L/s per person (the corresponding mean carbon dioxide concentrations were < 2,000 ppm), and the mean indoor PM2.5 was 3.6 μg/m3. The annual illness-related absence rate at the classroom level was extracted from the student-level absence data and regressed on measured indoor environmental parameters. Significant associations were found. Every 1 L/s per person increase in ventilation rate was associated with a 5.59 decrease in days with absences per year. This corresponds to a 0.15% increase in the annual daily attendance rate. Every additional 1 μg/m3 of indoor PM2.5 was associated with a 7.37 increase in days with absences per year. This corresponds to a 0.19% decrease in the annual daily attendance rate. No other relationships were significant. Present results agree with the previously demonstrated benefits of reduced absence rates when classroom ventilation is improved and provide additional evidence on the potential benefits of reducing indoor inhalable particles. Overall, reduced absence rates are expected to provide socioeconomic benefits and benefits for academic achievements, while higher ventilation rates and reduced particle levels will also contribute to reduced health risks, including those related to airborne respiratory pathogens.
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Affiliation(s)
- Shihan Deng
- Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, 727 South Jingming Road, Chenggong District, Kunming, Yunnan, 650500, China; Durham School of Architectural Engineering and Construction, University of Nebraska - Lincoln, 1110 S. 67th Street, Omaha, NE, 68182-0816, United States
| | - Josephine Lau
- Durham School of Architectural Engineering and Construction, University of Nebraska - Lincoln, 1110 S. 67th Street, Omaha, NE, 68182-0816, United States.
| | - Zhihao Wang
- Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, 727 South Jingming Road, Chenggong District, Kunming, Yunnan, 650500, China
| | - Pawel Wargocki
- Department of Environmental and Resource Engineering (DTU Sustain), Technical University of Denmark, Koppels Allé, 402, 2800 Kgs. Lyngby, Denmark
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