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A systematic literature review on indoor PM2.5 concentrations and personal exposure in urban residential buildings. Heliyon 2022; 8:e10174. [PMID: 36061003 PMCID: PMC9434053 DOI: 10.1016/j.heliyon.2022.e10174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/15/2022] [Accepted: 07/29/2022] [Indexed: 12/01/2022] Open
Abstract
Particulate matter with an aerodynamic diameter less than 2.5μm (PM2.5) is currently a major air pollutant that has been raising public attention. Studies have found that short/long-term exposure to PM2.5 lead detrimental health effects. Since people in most region of the world spend a large proportion of time in dwellings, personal exposure to PM2.5 in home microenvironment should be carefully investigated. The objective of this review is to investigate and summary studies in terms of personal exposure to indoor PM2.5 pollutants from the literature between 2000 and 2021. Factors from both outdoor and indoor environment that have impact on indoor PM2.5 levels were explicated. Exposure studies were verified relating to individual activity pattern and exposure models. It was found that abundant investigations in terms of personal exposure to indoor PM2.5 is affected by factors including concentration level, exposure duration and personal diversity. Personal exposure models, including microenvironment model, mathematical model, stochastic model and other simulation models of particle deposition in different regions of human airway are reviewed. Further studies joining indoor measurement and simulation of PM2.5 concentration and estimation of deposition in human respiratory tract are necessary for individual health protection.
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Hu Y, Yao M, Liu Y, Zhao B. Personal exposure to ambient PM 2.5, PM 10, O 3, NO 2, and SO 2 for different populations in 31 Chinese provinces. ENVIRONMENT INTERNATIONAL 2020; 144:106018. [PMID: 32771828 DOI: 10.1016/j.envint.2020.106018] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 07/04/2020] [Accepted: 07/27/2020] [Indexed: 05/06/2023]
Abstract
Most epidemiological studies usually employ ambient air pollutant concentrations as a proxy of personal exposure to air pollutants originating outdoors, which could lead to a biased estimation of health effects. Herein, we modeled infiltration and exposure factors as the modifications of personal exposure to ambient PM2.5, PM10, O3, NO2, and SO2 for all seasons, genders, and ages in 31 Chinese provinces. The annual average exposure factors of PM10, PM2.5, O3, NO2, and SO2 were 0.42 ± 0.13 (arithmetic mean ± standard deviation), 0.68 ± 0.14, 0.34 ± 0.12, 0.50 ± 0.14, and 0.40 ± 0.13, respectively. We observed significant age, gender, seasonal, and geographical differences in infiltration and exposure factors for all studied ambient air pollutants. These factors were higher in southern China than in the north, and they were the highest in summer and the lowest in winter. The exposure factor of minors (age < 18 years) was significantly lower than that of adults (age ≥ 18 years, P < 0.01). Adult males had higher exposure factors than females (P < 0.01). Epidemiological studies utilizing outdoor concentrations of air pollutants could overestimate personal exposure to these pollutants. The present study could help in reducing the bias in the estimation of the health effects of air pollutants.
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Affiliation(s)
- Ying Hu
- Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Mingyao Yao
- 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
| | - 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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Chen XC, Ward TJ, Cao JJ, Lee SC, Chow JC, Lau GNC, Yim SHL, Ho KF. Determinants of personal exposure to fine particulate matter (PM 2.5) in adult subjects in Hong Kong. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 628-629:1165-1177. [PMID: 30045539 DOI: 10.1016/j.scitotenv.2018.02.049] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 01/31/2018] [Accepted: 02/05/2018] [Indexed: 06/08/2023]
Abstract
Personal monitoring for fine particulate matter (PM2.5) was conducted for adults (48 subjects, 18-63years of age) in Hong Kong during the summer and winter of 2014-2015. All filters were analyzed for PM2.5 mass and constituents (including carbonaceous aerosols, water-soluble ions, and elements). We found that season (p=0.02) and occupation (p<0.001) were significant factors affecting the strength of the personal-ambient PM2.5 associations. We applied mixed-effects models to investigate the determinants of personal exposure to PM2.5 mass and constituents, along with within- and between-individual variance components. Ambient PM2.5 was the dominant predictor of (R2=0.12-0.59, p<0.01) and the largest contributor (>37.3%) to personal exposures for PM2.5 mass and most components. For all subjects, a one-unit (2.72μg/m3) increase in ambient PM2.5 was associated with a 0.75μg/m3 (95% CI: 0.59-0.94μg/m3) increase in personal PM2.5 exposure. The adjusted mixed-effects models included information extracted from individual's activity diaries as covariates. The results showed that season, occupation, time indoors at home, in transit, and cleaning were significant determinants for PM2.5 components in personal exposure (R2β=0.06-0.63, p<0.05), contributing to 3.0-70.4% of the variability. For one-hour extra time spent at home, in transit, and cleaning an average increase of 1.7-3.6% (ammonium, sulfate, nitrate, sulfur), 2.7-12.3% (elemental carbon, ammonium, titanium, iron), and 8.7-19.4% (ammonium, magnesium ions, vanadium) in components of personal PM2.5 were observed, respectively. In this research, the within-individual variance component dominated the total variability for all investigated exposure data except PM2.5 and EC. Results from this study indicate that performing long-term personal monitoring is needed for examining the associations of mass and constituents of personal PM2.5 with health outcomes in epidemiological studies by describing the impacts of individual-specific data on personal exposures.
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Affiliation(s)
- Xiao-Cui Chen
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
| | - Tony J Ward
- School of Public and Community Health Sciences, University of Montana, Missoula, MT, USA
| | - Jun-Ji Cao
- Key Laboratory of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China; Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, China
| | - Shun-Cheng Lee
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Judith C Chow
- Key Laboratory of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China; Division of Atmospheric Sciences, Desert Research Institute, NV 89512-1095, USA
| | - Gabriel N C Lau
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
| | - Steve H L Yim
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
| | - Kin-Fai Ho
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China; The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China; Key Laboratory of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China.
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Jafta N, Barregard L, Jeena PM, Naidoo RN. Indoor air quality of low and middle income urban households in Durban, South Africa. ENVIRONMENTAL RESEARCH 2017; 156:47-56. [PMID: 28319817 DOI: 10.1016/j.envres.2017.03.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 03/03/2017] [Accepted: 03/04/2017] [Indexed: 06/06/2023]
Abstract
INTRODUCTION Elevated levels of indoor air pollutants may cause cardiopulmonary disease such as lower respiratory infection, chronic obstructive lung disease and lung cancer, but the association with tuberculosis (TB) is unclear. So far the risk estimates of TB infection or/and disease due to indoor air pollution (IAP) exposure are based on self-reported exposures rather than direct measurements of IAP, and these exposures have not been validated. OBJECTIVE The aim of this paper was to characterize and develop predictive models for concentrations of three air pollutants (PM10, NO2 and SO2) in homes of children participating in a childhood TB study. METHODS Children younger than 15 years living within the eThekwini Municipality in South Africa were recruited for a childhood TB case control study. The homes of these children (n=246) were assessed using a walkthrough checklist, and in 114 of them monitoring of three indoor pollutants was also performed (sampling period: 24h for PM10, and 2-3 weeks for NO2 and SO2). Linear regression models were used to predict PM10 and NO2 concentrations from household characteristics, and these models were validated using leave out one cross validation (LOOCV). SO2 concentrations were not modeled as concentrations were very low. RESULTS Mean indoor concentrations of PM10 (n=105), NO2 (n=82) and SO2 (n=82) were 64μg/m3 (range 6.6-241); 19μg/m3 (range 4.5-55) and 0.6μg/m3 (range 0.005-3.4) respectively with the distributions for all three pollutants being skewed to the right. Spearman correlations showed weak positive correlations between the three pollutants. The largest contributors to the PM10 predictive model were type of housing structure (formal or informal), number of smokers in the household, and type of primary fuel used in the household. The NO2 predictive model was influenced mostly by the primary fuel type and by distance from the major roadway. The coefficients of determination (R2) for the models were 0.41 for PM10 and 0.31 for NO2. Spearman correlations were significant between measured vs. predicted PM10 and NO2 with coefficients of 0.66 and 0.55 respectively. CONCLUSION Indoor PM10 levels were relatively high in these households. Both PM10 and NO2 can be modeled with a reasonable validity and these predictive models can decrease the necessary number of direct measurements that are expensive and time consuming.
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Affiliation(s)
- Nkosana Jafta
- Discipline of Occupational and Environmental Health, School of Nursing and Public Health, University of KwaZulu-Natal, 321 George Campbell Building, Howard College Campus, Durban 4041, South Africa.
| | - Lars Barregard
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital and Sahlgrenska Academy at Gothenburg University, Box 414, S-405 30 Gothenburg, Sweden
| | - Prakash M Jeena
- Discipline of Pediatrics and Child Health, School of Clinical Medicine, University of KwaZulu-Natal, Private Bag X1, Congella, Durban 4013, South Africa
| | - Rajen N Naidoo
- Discipline of Occupational and Environmental Health, School of Nursing and Public Health, University of KwaZulu-Natal, 321 George Campbell Building, Howard College Campus, Durban 4041, South Africa
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Han B, Hu LW, Bai Z. Human Exposure Assessment for Air Pollution. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1017:27-57. [PMID: 29177958 DOI: 10.1007/978-981-10-5657-4_3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Assessment of human exposure to air pollution is a fundamental part of the more general process of health risk assessment. The measurement methods for exposure assessment now include personal exposure monitoring, indoor-outdoor sampling, mobile monitoring, and exposure assessment modeling (such as proximity models, interpolation model, air dispersion models, and land-use regression (LUR) models). Among these methods, personal exposure measurement is considered to be the most accurate method of pollutant exposure assessment until now, since it can better quantify observed differences and better reflect exposure among smaller groups of people at ground level. And since the great differences of geographical environment, source distribution, pollution characteristics, economic conditions, and living habits, there is a wide range of differences between indoor, outdoor, and individual air pollution exposure in different regions of China. In general, the indoor particles in most Chinese families comprise infiltrated outdoor particles, particles generated indoors, and a few secondary organic aerosol particles, and in most cases, outdoor particle pollution concentrations are a major contributor to indoor concentrations in China. Furthermore, since the time, energy, and expense are limited, it is difficult to measure the concentration of pollutants for each individual. In recent years, obtaining the concentration of air pollutants by using a variety of exposure assessment models is becoming a main method which could solve the problem of the increasing number of individuals in epidemiology studies.
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Affiliation(s)
- Bin Han
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.,Atmospheric Chemistry & Aerosol Division, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Li-Wen Hu
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District,, Guangzhou, 510080, Guangdong, China
| | - Zhipeng Bai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China. .,Atmospheric Chemistry & Aerosol Division, Chinese Research Academy of Environmental Sciences, Beijing, China.
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