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Chen S, Liu D, Huang L, Guo C, Gao X, Xu Z, Yang Z, Chen Y, Li M, Yang J. Global associations between long-term exposure to PM 2.5 constituents and health: A systematic review and meta-analysis of cohort studies. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134715. [PMID: 38838524 DOI: 10.1016/j.jhazmat.2024.134715] [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: 01/19/2024] [Revised: 05/10/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
Existing studies on the most impactful component remain controversial, hindering the optimization of future air quality standards that concerns particle composition. We aimed to summarize the health risk associated with PM2.5 components and identify those components with the greatest health risk. We performed a meta-analysis to quantify the combined health effects of PM2.5 components, and used the meta-smoothing to produce the pooled concentration-response (C-R) curves. Out of 8954 initial articles, 80 cohort studies met the inclusion criteria, including a total of 198.08 million population. The pooled C-R curves demonstrated approximately J-shaped association between total mortality and exposure to BC, and NO3-, but U-shaped and inverted U-shaped relationship withSO42- and OC, respectively. In addition, this study found that exposure to various elements, including BC,SO42-NO3-, NH4+, Zn, Ni, and Si, were significantly associated with an increased risk of total mortality, with Ni presenting the largest estimate. And exposure to NO3-, Zn, and Si was positively associated with an increased risk of respiratory mortality, while exposure to BC, SO42-, and NO3- showed a positive association with risk of cardiovascular mortality. For health outcome of morbidity, BC was notably associated with a higher incidence of asthma, type 2 diabetes and stroke. Subgroup analysis revealed a higher susceptibility to PM2.5 components in Asia compared to Europe and North America, and females showed a higher vulnerability. Given the significant health effects of PM2.5 components, governments are advised to introduce them in regional monitoring and air quality control guidelines. ENVIRONMENTAL IMPLICATION: PM2.5 is a complex mixture of chemical components from various sources, and each component has unique physicochemical properties and uncertain toxicity, posing significant threat to public health. This study systematically reviewed cohort studies on the association between long-term exposure to 13 PM2.5 components and the risk of morbidity and mortality. And we applied the meta-smoothing approach to establish the pooled concentration-response associations between PM2.5 components and mortality globally. Our findings will provide strong support for PM2.5 components monitoring and the improvement of air quality-related regulations. This will aid in helping to enhance health intervention strategies and mitigating public exposure to detrimental particulate matter.
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Affiliation(s)
- Sujuan Chen
- The Key Laboratory of Advanced Interdisciplinary Studies, The First Affiliated Hospital of Guangzhou Medical University, China; School of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Di Liu
- School of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Lin Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Cui Guo
- Department of Urban Planning and Design, Faculty of Architecture, the University of Hong Kong, Hong Kong SAR
| | - Xiaoke Gao
- School of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Zhiwei Xu
- School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, Australia
| | - Zhou Yang
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Yu Chen
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Mengmeng Li
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun Yang
- The Key Laboratory of Advanced Interdisciplinary Studies, The First Affiliated Hospital of Guangzhou Medical University, China; School of Public Health, Guangzhou Medical University, Guangzhou 511436, China.
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Craver A, Luo J, Kibriya MG, Randorf N, Bahl K, Connellan E, Powell J, Zakin P, Jones RR, Argos M, Ho J, Kim K, Daviglus ML, Greenland P, Ahsan H, Aschebrook-Kilfoy B. Air quality and cancer risk in the All of Us Research Program. Cancer Causes Control 2024; 35:749-760. [PMID: 38145439 PMCID: PMC11045436 DOI: 10.1007/s10552-023-01823-7] [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: 01/17/2023] [Accepted: 10/31/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION The NIH All of Us Research Program has enrolled over 544,000 participants across the US with unprecedented racial/ethnic diversity, offering opportunities to investigate myriad exposures and diseases. This paper aims to investigate the association between PM2.5 exposure and cancer risks. MATERIALS AND METHODS This work was performed on data from 409,876 All of Us Research Program participants using the All of Us Researcher Workbench. Cancer case ascertainment was performed using data from electronic health records and the self-reported Personal Medical History questionnaire. PM2.5 exposure was retrieved from NASA's Earth Observing System Data and Information Center and assigned using participants' 3-digit zip code prefixes. Multivariate logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Generalized additive models (GAMs) were used to investigate non-linear relationships. RESULTS A total of 33,387 participants and 46,176 prevalent cancer cases were ascertained from participant EHR data, while 20,297 cases were ascertained from self-reported survey data from 18,133 participants; 9,502 cancer cases were captured in both the EHR and survey data. Average PM2.5 level from 2007 to 2016 was 8.90 μg/m3 (min 2.56, max 15.05). In analysis of cancer cases from EHR, an increased odds for breast cancer (OR 1.17, 95% CI 1.09-1.25), endometrial cancer (OR 1.33, 95% CI 1.09-1.62) and ovarian cancer (OR 1.20, 95% CI 1.01-1.42) in the 4th quartile of exposure compared to the 1st. In GAM, higher PM2.5 concentration was associated with increased odds for blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer. CONCLUSIONS We found evidence of an association of PM2.5 with breast, ovarian, and endometrial cancers. There is little to no prior evidence in the literature on the impact of PM2.5 on risk of these cancers, warranting further investigation.
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Affiliation(s)
- Andrew Craver
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Jiajun Luo
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Muhammad G Kibriya
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Nina Randorf
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Kendall Bahl
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Elizabeth Connellan
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Johnny Powell
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Paul Zakin
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Rena R Jones
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Maria Argos
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL, USA
| | - Joyce Ho
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Karen Kim
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Philip Greenland
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Habibul Ahsan
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA
| | - Briseis Aschebrook-Kilfoy
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA.
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA.
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Sun N, Wu L, Zheng F, Liang D, Qi F, Song S, Peng J, Zhang Y, Mao H. Atmospheric environment characteristic of severe dust storms and its impact on sulfate formation in downstream city. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171128. [PMID: 38395168 DOI: 10.1016/j.scitotenv.2024.171128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/09/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
This study comprehensively investigated the impact of dust storms (DSs) on downstream cities, by selecting representative DS events. In this paper, we discussed the characteristics of meteorological conditions, air pollutants, PM2.5 components, and their influence on sulfate formation mechanisms. During DSs, strong winds, reaching speeds of up to 10 m/s, led to significant increases in PM10 and PM2.5, with maximum concentrations of 2684.5 and 429 μg/m3, respectively. Primary gaseous pollutants experienced substantial reductions, with decline rates of 48.1, 34.9, 36.8, and 9.0 % for SO2, NO2, NH3, and CO, respectively. Despite a notable increase in PM2.5 concentrations, only 7.6 % of the total mass of PM2.5 was attributed to ionic and carbonaceous components, a much lower value than observed before the DSs (77.3 %). Concentrations of Fe, Ti, and Mn exhibited increases by factors of 6.5-14.1, 10.4-17.0, and 1.6-4.7, respectively. In contrast to the significant decrease of >76.2 % in nitrogen oxidation ratio (NOR), sulfur oxidation ratio (SOR) remained at a relatively high level, displaying a strong positive correlation with high concentrations of Fe, Mn, and Ti. Quantitative analysis revealed an average increase of 0.187 and 0.045 μg/m3 in sulfate from natural sources and heterogeneous generation, respectively. The heterogeneous reaction on mineral dust was closely linked to atmospheric humidity, radiation intensity, the form of metal existence, and concentrations of it. High concentrations of titanium dioxide and iron‑manganese oxides in mineral dust promoted heterogeneous oxidation of SO2 through photocatalysis during the daytime and metal ion catalysis during the nighttime. This study establishes that the metal components in mineral dust promote heterogeneous sulfate formation, quantifies the yield of sulfate generated as a result, and provides possible mechanisms for heterogeneous sulfate formation.
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Affiliation(s)
- Naixiu Sun
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Lin Wu
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
| | - Fangyuan Zheng
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Danni Liang
- Tianjin Shuangyun Environmental Protection Technology Co., Ltd., Tianjin 300350, China
| | - FuYuan Qi
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Shaojie Song
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Jianfei Peng
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Yufen Zhang
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Hongjun Mao
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
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Zheng H, Li S, Jiang Y, Dong Z, Yin D, Zhao B, Wu Q, Liu K, Zhang S, Wu Y, Wen Y, Xing J, Henneman LRF, Kinney PL, Wang S, Hao J. Unpacking the factors contributing to changes in PM 2.5-associated mortality in China from 2013 to 2019. ENVIRONMENT INTERNATIONAL 2024; 184:108470. [PMID: 38324930 DOI: 10.1016/j.envint.2024.108470] [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: 06/27/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
From 2013 to 2019, a series of air pollution control actions significantly reduced PM2.5 pollution in China. Control actions included changes in activity levels, structural adjustment (SA) policy, energy and material saving (EMS) policy, and end-of-pipe (EOP) control in several sources, which have not been systematically studied in previous studies. Here, we integrate an emission inventory, a chemical transport model, a health impact assessment model, and a scenario analysis to quantify the contribution of each control action across a range of major emission sources to the changes in PM2.5 concentrations and associated mortality in China from 2013 to 2019. Assuming equal toxicity of PM2.5 from all the sources, we estimate that PM2.5-related mortality decreased from 2.52 (95 % confidence interval, 2.13-2.88) to 1.94 (1.62-2.24) million deaths. Anthropogenic emission reductions and declining baseline incidence rates significantly contributed to health benefits, but population aging partially offset their impact. Among the major sources, controls on power plants and industrial boilers were responsible for the highest reduction in PM2.5-related mortality (∼80 %), followed by industrial processes (∼40 %), residential combustion (∼40 %), and transportation (∼30 %). However, considering the potentially higher relative risks of power plant PM2.5, the adverse effects avoided by their control could be ∼2.4 times the current estimation. Our power plant sensitivity analyses indicate that future estimates of source-specific PM2.5 health effects should incorporate variations in individual source PM2.5 effect coefficients when available. As for the control actions, while activity levels increased for most sources, SA policy significantly reduced the emissions in residential combustion and industrial boilers, and EOP control dominated the contribution in health benefits in most sources except residential combustion. Considering the emission reduction potential by source and control actions in 2019, our results suggest that promoting clean energy in residential combustion and enforcing more stringent EOP control in the iron and steel industry should be prioritized in the future.
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Affiliation(s)
- Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Kaiyun Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ye Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yifan Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Lucas R F Henneman
- Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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Helon K, Wisłowska M, Kanecki K, Goryński P, Nitsch-Osuch A, Bonek K. Time Trend Analysis of Comorbidities in Ankylosing Spondylitis: A Population-Based Study from 53,142 Hospitalizations in Poland. J Clin Med 2024; 13:602. [PMID: 38276108 PMCID: PMC10816889 DOI: 10.3390/jcm13020602] [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: 11/20/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND (1) Influence of comorbidities on life expectancy and treatment outcomes is one of the main concerns of modern rheumatology, due to their rising prevalence and increasing impact on mortality and disability. The main objective of our study was to analyze the time trends and shifts in the comorbidity profile and mortality over 10 years in the Polish population with ankylosing spondylitis (AS). (2) Data from 2011-2020 years were acquired from the General Hospital Morbidity Study in the National Institute of Public Health-National Institute of Hygiene (NIH-PIB) as ICD-10 codes. Based on ICD10 codes, we calculated the percentage shares for comorbidities, with the relative risk ratios and odds ratios. We analyzed the hospitalization rates and mortality from the overlapping conditions. Also, we analyzed age and sex related differences in the clinical manifestations of AS patients. (3) Results: From 53,142 hospitalizations of patients with AS, we found that the male population presented higher rates of cardiovascular (2.7% vs. 1.3% p < 0.001) and pulmonary conditions (1.2% vs. 0.8% p < 0.025). Inflammatory bowel diseases were more common in the female population than in males (2.3% vs. 1.7%, p < 0.001). In the years 2011-2020, we observed a decline in the number of hospitalized patients due to cardiovascular (p < 0.001) and respiratory system conditions (p < 0.001), yet the relative risk and odd ratios remained high. In the years 2011-2020, 4056 patients received biological treatment (7%). The number of initiated biological therapies correlated negatively with the number of reported hospitalizations due to ischemic heart diseases (IHD) (p < 0.031, r = -0.8). Furthermore, in the logistic regression model, we found strong collinearity between cardiovascular and pulmonary comorbidities (VIF = 14; tolerance = 0.1); also, the number of reported IHD's correlated positively with the number of pulmonary infections (p < 0.031, r = 0.7) (4). CONCLUSIONS Cardiopulmonary comorbidities are a main factor associated with increased mortality in patients with AS, especially in hospitalized patients. The mortality rates among patients with AS admitted to hospital due to other conditions other than movement disorders exceed the populational risk. The number of biologically treated patients correlated negatively with hospital admissions due to IHD.
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Affiliation(s)
- Katarzyna Helon
- Department of Rheumatology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 02-637 Warsaw, Poland; (M.W.); (K.B.)
| | - Małgorzata Wisłowska
- Department of Rheumatology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 02-637 Warsaw, Poland; (M.W.); (K.B.)
| | - Krzysztof Kanecki
- National Institute of Public Health—National Institute of Hygiene, 00791 Warsaw, Poland; (K.K.); (P.G.); (A.N.-O.)
| | - Paweł Goryński
- National Institute of Public Health—National Institute of Hygiene, 00791 Warsaw, Poland; (K.K.); (P.G.); (A.N.-O.)
| | - Aneta Nitsch-Osuch
- National Institute of Public Health—National Institute of Hygiene, 00791 Warsaw, Poland; (K.K.); (P.G.); (A.N.-O.)
| | - Krzysztof Bonek
- Department of Rheumatology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 02-637 Warsaw, Poland; (M.W.); (K.B.)
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Li Z, Yim SHL, He X, Xia X, Ho KF, Yu JZ. High spatial resolution estimates of major PM 2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167932. [PMID: 37863225 DOI: 10.1016/j.scitotenv.2023.167932] [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: 09/05/2023] [Revised: 10/07/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023]
Abstract
Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85-0.96 and 0.62-0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.
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Affiliation(s)
- Zhiyuan Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
| | - Steve Hung Lam Yim
- Asian School of the Environment, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Earth Observatory of Singapore, Nanyang Technological University, Singapore
| | - Xiao He
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Xi Xia
- School of Public Health, Shaanxi University of Chinese Medicine, Xi'an, China
| | - Kin-Fai Ho
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
| | - Jian Zhen Yu
- Department of Chemistry and Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
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Saha PK, Presto AA, Robinson AL. Hyper-local to regional exposure contrast of source-resolved PM 2.5 components across the contiguous United States: implications for health assessment. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023:10.1038/s41370-023-00623-0. [PMID: 38110593 DOI: 10.1038/s41370-023-00623-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 12/20/2023]
Abstract
BACKGROUND Improved understanding of sources and processes that drive exposure contrast of fine particulate matter (PM2.5) is essential for designing and interpreting epidemiological study outcomes. OBJECTIVE We investigate the contribution of various sources and processes to PM2.5 exposure contrasts at different spatial scales across the continental United States. METHODS We consider three cases: exposure contrast within a metro area, nationwide exposure contrast with high spatial resolution, and nationwide exposure contrast with low spatial resolution. Using national empirical model estimates of source- and chemically specific PM2.5 concentration predictions, we quantified the contribution of various sources and processes to PM2.5 exposure contrasts in these three cases. RESULTS At the metro level (i.e., metropolitan statistical area; MSA), exposure contrasts of PM2.5 vary between -1.8 to 1.4 µg m-3 relative to the MSA-mean with about 50% of within-MSA exposure contrast of PM2.5 caused by cooking and mobile source primary PM2.5. For the national exposure contrast at low-resolution (i.e., using MSA-average mean concentrations), exposure contrasts (relative to the national mean: -3.9 to 3.2 µg m-3) are larger than within an MSA with ~80% of the variation due to secondary PM2.5. National exposure contrast at high resolution (census block) has the largest absolute range (relative to the national mean: -4.7 to 3.7 µg m-3) due to both regional and intra-urban contributions; on average, 65% of the national exposure contrast is due to secondary PM2.5 with the remaining from the primary PM2.5 (cooking and mobile source 26%, other 9%). IMPACT Our study provides a comprehensive analysis of the sources and processes that contribute to exposure contrasts of PM2.5 across different geographic areas in the US. For the first time on a national scale, we used high spatial resolution source-specific exposure estimates to identify the primary contributors to PM2.5 exposure contrasts. The study also highlights the advantages of different study designs for investigating the health impacts of specific PM2.5 components. The findings provide novel insights that can inform public health policies aimed at reducing PM2.5 exposure and advance the understanding of the epidemiological study outcomes.
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Affiliation(s)
- Provat K Saha
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
| | - Albert A Presto
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Allen L Robinson
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Department of Atmospheric Science, Colorado State University, Fort Collins, CO, 80523, USA.
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Wang Y, Li Q, Luo Z, Zhao J, Lv Z, Deng Q, Liu J, Ezzati M, Baumgartner J, Liu H, He K. Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing. COMMUNICATIONS EARTH & ENVIRONMENT 2023; 4:451. [PMID: 38130441 PMCID: PMC7615407 DOI: 10.1038/s43247-023-01119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023]
Abstract
With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78-0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31-0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 μg/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.
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Affiliation(s)
- Yongyue Wang
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiwei Li
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhenyu Luo
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Junchao Zhao
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhaofeng Lv
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiuju Deng
- Centre for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - Jing Liu
- Centre for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China
| | - Majid Ezzati
- School of Public Health, Imperial College London, London SW72AZ, UK
| | - Jill Baumgartner
- School of Population and Global Health, McGill University, Montréal, QC H3A0G4, Canada
| | - Huan Liu
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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9
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Wei J, Wang J, Li Z, Kondragunta S, Anenberg S, Wang Y, Zhang H, Diner D, Hand J, Lyapustin A, Kahn R, Colarco P, da Silva A, Ichoku C. Long-term mortality burden trends attributed to black carbon and PM 2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. Lancet Planet Health 2023; 7:e963-e975. [PMID: 38056967 DOI: 10.1016/s2542-5196(23)00235-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. METHODS In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. FINDINGS Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. INTERPRETATION Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. FUNDING National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.
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Affiliation(s)
- Jing Wei
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA; Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA.
| | - Zhanqing Li
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
| | - Shobha Kondragunta
- Center for Satellite Applications and Research, NOAA National Environmental Satellite, Data, and Information Service, College Park, MD, USA
| | - Susan Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, DC, USA
| | - Yi Wang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - Huanxin Zhang
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - David Diner
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jenny Hand
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
| | - Alexei Lyapustin
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Ralph Kahn
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Peter Colarco
- Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Arlindo da Silva
- Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Charles Ichoku
- Department of Geography and Environmental Systems, University of Maryland Baltimore County, Baltimore, MD, USA
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10
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Henneman L, Choirat C, Dedoussi I, Dominici F, Roberts J, Zigler C. Mortality risk from United States coal electricity generation. Science 2023; 382:941-946. [PMID: 37995235 PMCID: PMC10870829 DOI: 10.1126/science.adf4915] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 10/02/2023] [Indexed: 11/25/2023]
Abstract
Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.
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Affiliation(s)
- Lucas Henneman
- Department of Civil, Environmental, and Infrastructure Engineering, George Mason University Volgenau School of Engineering, Fairfax, VA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard Data Science Initiative, Harvard University, Boston, MA, USA
| | - Christine Choirat
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Irene Dedoussi
- Section Aircraft Noise and Climate Effects, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard Data Science Initiative, Harvard University, Boston, MA, USA
| | - Jessica Roberts
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Corwin Zigler
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard Data Science Initiative, Harvard University, Boston, MA, USA
- Department of Statistics and Data Sciences, University of Texas, Austin, TX, USA
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11
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Wu D, Zheng H, Li Q, Wang S, Zhao B, Jin L, Lyu R, Li S, Liu Y, Chen X, Zhang F, Wu Q, Liu T, Jiang J, Wang L, Li X, Chen J, Hao J. Achieving health-oriented air pollution control requires integrating unequal toxicities of industrial particles. Nat Commun 2023; 14:6491. [PMID: 37838777 PMCID: PMC10576764 DOI: 10.1038/s41467-023-42089-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 09/29/2023] [Indexed: 10/16/2023] Open
Abstract
Protecting human health from fine particulate matter (PM) pollution is the ambitious goal of clean air actions, but current control strategies largely ignore the role of source-specific PM toxicity. Here, we proposed health-oriented control strategies by integrating the unequal toxic potencies of the most polluting industrial PMs. Iron and steel industry (ISI)-emitted PM2.5 exhibit about one order of magnitude higher toxic potency than those of cement and power industries. Compared with the current mass-based control strategy (prioritizing implementation of ultralow emission standards in the power sector), the proposed health-oriented control strategy (priority control of the ISI sector) could generate 5.4 times higher reduction in population-weighted toxic potency-adjusted PM2.5 exposure among polluting industries in China. Furthermore, the marginal abatement cost per unit of toxic potency-adjusted mass of ISI-emitted PM2.5 is only a quarter of that of the other two sectors under ultralow emission scenarios. We highlight that a health-oriented air pollution control strategy is urgently required to achieve cost-effective reductions in particulate exposure risks.
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Affiliation(s)
- Di Wu
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Qing Li
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China.
- Shanghai Institute of Eco-Chongming (SIEC), 20 Cuiniao Road, Chenjia Town, Chongming District, Shanghai, 202162, China.
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
| | - Bin Zhao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Ling Jin
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Rui Lyu
- China Huaneng Clean Energy Research Institute, Beijing, 102209, China
| | - Shengyue Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yuzhe Liu
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Xiu Chen
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Fenfen Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Tonghao Liu
- China National Environmental Monitoring Center, Beijing, 100012, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Lin Wang
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Xiangdong Li
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jianmin Chen
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
- Shanghai Institute of Eco-Chongming (SIEC), 20 Cuiniao Road, Chenjia Town, Chongming District, Shanghai, 202162, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
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12
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Nan N, Yan Z, Zhang Y, Chen R, Qin G, Sang N. Overview of PM 2.5 and health outcomes: Focusing on components, sources, and pollutant mixture co-exposure. CHEMOSPHERE 2023; 323:138181. [PMID: 36806809 DOI: 10.1016/j.chemosphere.2023.138181] [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: 12/06/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
PM2.5 varies in source and composition over time and space as a complicated mixture. Consequently, the health effects caused by PM2.5 varies significantly over time and generally exhibit significant regional variations. According to numerous studies, a notable relationship exists between PM2.5 and the occurrence of many diseases, such as respiratory, cardiovascular, and nervous system diseases, as well as cancer. Therefore, a comprehensive understanding of the effect of PM2.5 on human health is critical. The toxic effects of various PM2.5 components, as well as the overall toxicity of PM2.5 are discussed in this review to provide a foundation for precise PM2.5 emission control. Furthermore, this review summarizes the synergistic effect of PM2.5 and other pollutants, which can be used to draft effective policies.
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Affiliation(s)
- Nan Nan
- College of Environment and Resource, Shanxi University, Taiyuan, Shanxi, 030006, PR China
| | - Zhipeng Yan
- College of Environment and Resource, Shanxi University, Taiyuan, Shanxi, 030006, PR China
| | - Yaru Zhang
- College of Environment and Resource, Shanxi University, Taiyuan, Shanxi, 030006, PR China
| | - Rui Chen
- Beijing Key Laboratory of Occupational Safety and Health, Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, Beijing, 100054, PR China; Beijing City University, Beijing, 11418, PR China.
| | - Guohua Qin
- College of Environment and Resource, Shanxi University, Taiyuan, Shanxi, 030006, PR China.
| | - Nan Sang
- College of Environment and Resource, Shanxi University, Taiyuan, Shanxi, 030006, PR China
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13
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Du P, Du H, Lu K, He MZ, Feng D, He M, Liu T, Hu J, Li T. Traffic-related PM 2.5 and its specific constituents on circulatory mortality: A nationwide modelling study in China. ENVIRONMENT INTERNATIONAL 2022; 170:107652. [PMID: 36446182 DOI: 10.1016/j.envint.2022.107652] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Short-term fine particulate matter (PM2.5) exposure and increased circulatory mortality have been well documented. However, there are inconsistent findings on mortality effects of traffic-related pollutants from the perspective of sources or constituents. Few studies have examined such associations using source and constituents simultaneously, and even less are based on large-scale, nationally representative data. We aimed to conduct a comprehensive analysis to investigate source- and constituent-specific mortality effects due to traffic-related PM2.5 pollution in China. METHODS We extracted daily mortality data in 280 counties from the China Disease Surveillance Points system (DSPs) from January 2013 to December 2018. Daily concentrations of traffic-related PM2.5 and specific constituents were simulated using the Community Multiscale Air Quality (CMAQ) model. The downscaling and adjustment methods were carried out to generate a refined exposure assessment. We estimated the circulatory mortality risk using a standard two-stage approach, combining generalized linear model (GLM) with a quasi-Poisson distribution and random-effects meta-analysis. RESULTS We observed that traffic-related PM2.5 and specific constituents were significantly associated with increased circulatory mortality. An increase of interquartile range of traffic-related PM2.5, elemental carbon (EC), organic carbon (OC), and nitrate (NO3-) were associated with elevated circulatory mortality risks of 1.80 % (95 % confidence interval, CI: 1.27, 2.33), 1.85 % (1.33, 2.37), 1.42 % (0.90, 1.94), and 1.10 % (0.55, 1.66) at 3-day moving average (lag 0-2 days), respectively. We also found relatively high associations between traffic-related PM2.5 and EC exposures and cardiovascular mortality, and OC exposure and cerebrovascular mortality. Moreover, our stratified analysis demonstrated such mortality risks tended to be stronger in males, individuals age 65 years or older, and during the cold season. CONCLUSION Our findings provided robust evidence on significant associations of traffic-related PM2.5 and specific constituents with circulatory mortality. Further emissions abatement from the transportation sector and corresponding pollutants should merit a particular focus in China.
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Affiliation(s)
- Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Hang Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Kailai Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Mike Z He
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Da Feng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Miao He
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Ting Liu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
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14
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Seasonal and Spatial Variations of PM10 and PM2.5 Oxidative Potential in Five Urban and Rural Sites across Lombardia Region, Italy. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19137778. [PMID: 35805434 PMCID: PMC9265313 DOI: 10.3390/ijerph19137778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 02/01/2023]
Abstract
Oxidative potential (OP) of particulate matter (PM) is gaining strong interest as a promising health exposure metric. This study investigated OP of a large set of PM10 and PM2.5 samples collected at five urban and background sites near Milan (Italy), one of the largest and most polluted urban areas in Europe, afflicted with high particle levels. OP responses from two acellular assays, based on ascorbic acid (AA) and dithiothreitol (DTT), were combined with atmospheric detailed composition to examine any possible feature in OP with PM size fraction, spatial and seasonal variations. A general association of volume-normalized OP with PM mass was found; this association may be related to the clear seasonality observed, whereby there was higher OP activity in wintertime at all investigated sites. Univariate correlations were used to link OP with the concentrations of the major chemical markers of vehicular and biomass burning emissions. Of the two assays, AA was particularly sensitive towards transition metals in coarse particles released from vehicular traffic. The results obtained confirm that the responses from the two assays and their relationship with atmospheric pollutants are assay- and location-dependent, and that their combination is therefore helpful to singling out the PM redox-active compounds driving its oxidative properties.
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15
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Pai S, Carter TS, Heald CL, Kroll JH. Updated World Health Organization Air Quality Guidelines Highlight the Importance of Non-anthropogenic PM 2.5. ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2022; 9:501-506. [PMID: 35719860 PMCID: PMC9202349 DOI: 10.1021/acs.estlett.2c00203] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 05/30/2023]
Abstract
The World Health Organization recently updated their air quality guideline for annual fine particulate matter (PM2.5) exposure from 10 to 5 μg m-3, citing global health considerations. We explore if this guideline is attainable across different regions of the world using a series of model sensitivity simulations for 2019. Our results indicate that >90% of the global population is exposed to PM2.5 concentrations that exceed the 5 μg m-3 guideline and that only a few sparsely populated regions (largely in boreal North America and Asia) experience annual average concentrations of <5 μg m-3. We find that even under an extreme abatement scenario, with no anthropogenic emissions, more than half of the world's population would still experience annual PM2.5 exposures above the 5 μg m-3 guideline (including >70% and >60% of the African and Asian populations, respectively), largely due to fires and natural dust. Our simulations demonstrate the large heterogeneity in PM2.5 composition across different regions and highlight how PM2.5 composition is sensitive to reductions in anthropogenic emissions. We thus suggest the use of speciated aerosol exposure guidelines to help facilitate region-specific air quality management decisions and improve health-burden estimates of fine aerosol exposure.
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Affiliation(s)
- Sidhant
J. Pai
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Therese S. Carter
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Colette L. Heald
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jesse H. Kroll
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
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