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Shi Y, Du Z, Zhang J, Han F, Chen F, Wang D, Liu M, Zhang H, Dong C, Sui S. Construction and evaluation of hourly average indoor PM 2.5 concentration prediction models based on multiple types of places. Front Public Health 2023; 11:1213453. [PMID: 37637795 PMCID: PMC10447970 DOI: 10.3389/fpubh.2023.1213453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/28/2023] [Indexed: 08/29/2023] Open
Abstract
Background People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations. Methods In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models. Results The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables. Conclusion In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.
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Affiliation(s)
- Yewen Shi
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Zhiyuan Du
- Department of Environmental Health, Key Laboratory of the Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Jianghua Zhang
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Fengchan Han
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Feier Chen
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Duo Wang
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Mengshuang Liu
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Hao Zhang
- Department of Environmental Health, Key Laboratory of the Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Chunyang Dong
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Shaofeng Sui
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
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Indoor Exposure to Selected Air Pollutants in the Home Environment: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17238972. [PMID: 33276576 PMCID: PMC7729884 DOI: 10.3390/ijerph17238972] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/22/2020] [Accepted: 11/27/2020] [Indexed: 11/17/2022]
Abstract
(1) Background: There is increasing awareness that the quality of the indoor environment affects our health and well-being. Indoor air quality (IAQ) in particular has an impact on multiple health outcomes, including respiratory and cardiovascular illness, allergic symptoms, cancers, and premature mortality. (2) Methods: We carried out a global systematic literature review on indoor exposure to selected air pollutants associated with adverse health effects, and related household characteristics, seasonal influences and occupancy patterns. We screened records from six bibliographic databases: ABI/INFORM, Environment Abstracts, Pollution Abstracts, PubMed, ProQuest Biological and Health Professional, and Scopus. (3) Results: Information on indoor exposure levels and determinants, emission sources, and associated health effects was extracted from 141 studies from 29 countries. The most-studied pollutants were particulate matter (PM2.5 and PM10); nitrogen dioxide (NO2); volatile organic compounds (VOCs) including benzene, toluene, xylenes and formaldehyde; and polycyclic aromatic hydrocarbons (PAHs) including naphthalene. Identified indoor PM2.5 sources include smoking, cooking, heating, use of incense, candles, and insecticides, while cleaning, housework, presence of pets and movement of people were the main sources of coarse particles. Outdoor air is a major PM2.5 source in rooms with natural ventilation in roadside households. Major sources of NO2 indoors are unvented gas heaters and cookers. Predictors of indoor NO2 are ventilation, season, and outdoor NO2 levels. VOCs are emitted from a wide range of indoor and outdoor sources, including smoking, solvent use, renovations, and household products. Formaldehyde levels are higher in newer houses and in the presence of new furniture, while PAH levels are higher in smoking households. High indoor particulate matter, NO2 and VOC levels were typically associated with respiratory symptoms, particularly asthma symptoms in children. (4) Conclusions: Household characteristics and occupant activities play a large role in indoor exposure, particularly cigarette smoking for PM2.5, gas appliances for NO2, and household products for VOCs and PAHs. Home location near high-traffic-density roads, redecoration, and small house size contribute to high indoor air pollution. In most studies, air exchange rates are negatively associated with indoor air pollution. These findings can inform interventions aiming to improve IAQ in residential properties in a variety of settings.
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Vissing NH, Chawes BL, Rasmussen MA, Bisgaard H. Epidemiology and Risk Factors of Infection in Early Childhood. Pediatrics 2018; 141:peds.2017-0933. [PMID: 29794229 DOI: 10.1542/peds.2017-0933] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/28/2018] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND There is a large, unexplained variation in the frequency of childhood infections. We described incidence and risk factors of infections in early childhood. METHODS Simple infections were captured during the first 3 years of life in the Copenhagen Prospective Studies on Asthma in Childhood 2000 birth cohort. Environmental exposures were analyzed by quasi-Poisson regression and sparse principal component analysis. RESULTS The 334 children experienced a median of 14 (range 2-43) infectious episodes at ages 0 to 3 years. The overall rate of infections was associated with the number of children in the day care (adjusted incidence rate ratio [aIRR] 1.09 [1.2-1.16]) and the m2 per child in the day care (aIRR 0.96 [0.92-0.99]). Upper respiratory infections were also associated with the number of children in the day care (aIRR 1.11 [1.03-1.20]) and the m2 per child in the day care (aIRR 0.95 [0.91-0.99]), whereas lower respiratory infections were associated with caesarean section (aIRR 1.49 [1.12-1.99]), maternal smoking (aIRR 1.66 [1.18-2.33]), older siblings (aIRR 1.54 [1.19-2.01]), and the age at entry to day care (aIRR 0.77 [0.65-0.91]). The sparse principal component analysis revealed a risk factor profile driven by tobacco exposure, social circumstances, and domestic pets, but could only be used to explain 8.4% of the infection burden. CONCLUSIONS Children experienced around 14 infections during the first 3 years of life, but incidences varied greatly. Environmental exposures only explained a small fraction of the variation, suggesting host factors as major determinants of infectious burden.
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Affiliation(s)
- Nadja Hawwa Vissing
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Bo Lund Chawes
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Morten Arendt Rasmussen
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Hans Bisgaard
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
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Chen J, Li C, Ristovski Z, Milic A, Gu Y, Islam MS, Wang S, Hao J, Zhang H, He C, Guo H, Fu H, Miljevic B, Morawska L, Thai P, Lam YF, Pereira G, Ding A, Huang X, Dumka UC. A review of biomass burning: Emissions and impacts on air quality, health and climate in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 579:1000-1034. [PMID: 27908624 DOI: 10.1016/j.scitotenv.2016.11.025] [Citation(s) in RCA: 342] [Impact Index Per Article: 48.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 11/04/2016] [Accepted: 11/04/2016] [Indexed: 05/17/2023]
Abstract
Biomass burning (BB) is a significant air pollution source, with global, regional and local impacts on air quality, public health and climate. Worldwide an extensive range of studies has been conducted on almost all the aspects of BB, including its specific types, on quantification of emissions and on assessing its various impacts. China is one of the countries where the significance of BB has been recognized, and a lot of research efforts devoted to investigate it, however, so far no systematic reviews were conducted to synthesize the information which has been emerging. Therefore the aim of this work was to comprehensively review most of the studies published on this topic in China, including literature concerning field measurements, laboratory studies and the impacts of BB indoors and outdoors in China. In addition, this review provides insights into the role of wildfire and anthropogenic BB on air quality and health globally. Further, we attempted to provide a basis for formulation of policies and regulations by policy makers in China.
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Affiliation(s)
- Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China; Collaborative Innovation Center of Climate Change, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
| | - Chunlin Li
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China
| | - Zoran Ristovski
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Andelija Milic
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Yuantong Gu
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Mohammad S Islam
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
| | - Hefeng Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Congrong He
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Hai Guo
- Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
| | - Hongbo Fu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China
| | - Branka Miljevic
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia.
| | - Phong Thai
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Yun Fat Lam
- School of Energy and Environment, City University of Hong Kong, Hong Kong, China
| | - Gavin Pereira
- School of Public Health, Curtin University, Perth, WA, 6000, Australia
| | - Aijun Ding
- Collaborative Innovation Center of Climate Change, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Xin Huang
- Collaborative Innovation Center of Climate Change, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Umesh C Dumka
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China; Aryabhatta Research Institute of Observational Sciences, Manora Peak, Nainital 263001, India
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[Indoor air pollution by fine particulate matter in the homes of newborns]. ACTA ACUST UNITED AC 2016; 87:343-350. [PMID: 27242100 DOI: 10.1016/j.rchipe.2016.04.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Revised: 04/17/2016] [Accepted: 04/22/2016] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Air pollution by particulate matter (PM) is a major public health problem. In Chile, the study has focused on outdoor air and PM10, rather than indoor air and PM2.5. Because newborns and infants spend most of their time at home, it is necessary to evaluate the exposure to indoor air pollution in this susceptible population. OBJECTIVE To determine concentration of PM2.5 in the homes of newborns and identify the emission sources of the pollutants. PATIENTS AND METHOD The PM2.5 concentration ([PM2.5]) was collected over a 24hour period in 207 households. Baseline sociodemographic information and environmental factors (heating, ventilation, smoking and house cleaning), were collected. RESULTS The median [PM2.5] was 107.5μg/m3. Family history of asthma was associated with lower [PM2.5] (P=.0495). Homes without heating showed a lower median [PM2.5], 58.6μg/m3, while those using firewood, kerosene, and electricity ranged between 112.5 and 114.9, and coal users' homes reached 162.9μg/m3. Wood using homes had significant differences (P=.0164) in median [PM2.5] whether the stove had complete combustion (98.2μg/m3) vs. incomplete (112.6μg/m3), or a salamander stove (140.6μg/m3). Cigarette smoking was reported in 8.7% of the households, but was not associated with the [PM2.5]. Ventilation was associated with a higher median [PM2.5] (120.6 vs. 99.1μg/m3, P=.0039). CONCLUSION We found homes with high [PM2.5]. Residential wood consumption was almost universal, and it is associated with the [PM2.5]. Natural ventilation increased MP2.5, probably due to infiltration from outside.
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Brown T, Dassonville C, Derbez M, Ramalho O, Kirchner S, Crump D, Mandin C. Relationships between socioeconomic and lifestyle factors and indoor air quality in French dwellings. ENVIRONMENTAL RESEARCH 2015; 140:385-396. [PMID: 25935319 DOI: 10.1016/j.envres.2015.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 04/20/2015] [Accepted: 04/21/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND To date, few studies have analyzed the relationships between socioeconomic status (SES) and indoor air quality (IAQ). OBJECTIVE The aim of this study was to examine the relationships between socioeconomic and other factors and indoor air pollutant levels in French homes. METHODS The indoor air concentrations of thirty chemical, biological and physical parameters were measured over one week in a sample of 567 dwellings representative of the French housing stock between September 2003 and December 2005. Information on SES (household structure, educational attainment, income, and occupation), building characteristics, and occupants' habits and activities (smoking, cooking, cleaning, etc.) were collected through administered questionnaires. Separate stepwise linear regression models were fitted to log-transformed concentrations on SES and other factors. Logistic regression was performed on fungal contamination data. RESULTS Households with lower income were more likely to have higher indoor concentrations of formaldehyde, but lower perchloroethylene indoor concentrations. Formaldehyde indoor concentrations were also associated with newly built buildings. Smoking was associated with increasing acetaldehyde and PM2.5 levels and the risk of a positive fungal contamination index. BTEX levels were also associated with occupant density and having an attached garage. The major predictors for fungal contamination were dampness and absolute humidity. CONCLUSION These results, obtained from a large sample of dwellings, show for the first time in France the relationships between SES factors and indoor air pollutants, and believe they should be considered alongside occupant activities and building characteristics when study IAQ in homes.
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Affiliation(s)
- Terry Brown
- Institute of Environment, Health, Risks and Futures, School of Environment, Energy & Agrifoods, Cranfield University, Cranfield, Milton Keynes, Bedfordshire MK43 0AL, UK.
| | - Claire Dassonville
- Paris-East University/Scientific and Technical Centre for Building (CSTB), Observatory on Indoor Air Quality, 84 Avenue Jean Jaurès, Champs-sur-Marne, F-77447 Marne-la-Vallée Cedex 2, France
| | - Mickael Derbez
- Paris-East University/Scientific and Technical Centre for Building (CSTB), Observatory on Indoor Air Quality, 84 Avenue Jean Jaurès, Champs-sur-Marne, F-77447 Marne-la-Vallée Cedex 2, France
| | - Olivier Ramalho
- Paris-East University/Scientific and Technical Centre for Building (CSTB), Observatory on Indoor Air Quality, 84 Avenue Jean Jaurès, Champs-sur-Marne, F-77447 Marne-la-Vallée Cedex 2, France
| | - Severine Kirchner
- Paris-East University/Scientific and Technical Centre for Building (CSTB), Observatory on Indoor Air Quality, 84 Avenue Jean Jaurès, Champs-sur-Marne, F-77447 Marne-la-Vallée Cedex 2, France
| | - Derrick Crump
- Institute of Environment, Health, Risks and Futures, School of Environment, Energy & Agrifoods, Cranfield University, Cranfield, Milton Keynes, Bedfordshire MK43 0AL, UK
| | - Corinne Mandin
- Paris-East University/Scientific and Technical Centre for Building (CSTB), Observatory on Indoor Air Quality, 84 Avenue Jean Jaurès, Champs-sur-Marne, F-77447 Marne-la-Vallée Cedex 2, France
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Nasir ZA, Colbeck I. Particulate pollution in different housing types in a UK suburban location. THE SCIENCE OF THE TOTAL ENVIRONMENT 2013; 445-446:165-176. [PMID: 23333512 DOI: 10.1016/j.scitotenv.2012.12.042] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2012] [Revised: 12/14/2012] [Accepted: 12/16/2012] [Indexed: 06/01/2023]
Abstract
To investigate the levels of particulate pollution in residential built environments measurements of PM(10), PM(2.5), and PM(1) and concentrations were made between 2004 and 2008 in various residencies in a UK suburban location. Measurements were carried out in three different residential settings (Types I, II and III). In type I non-smoking living rooms, the highest 24-hour mean concentrations were found in summer. When smoking took place in type I residences, the concentrations of PM(10), PM(2.5) and PM(1), during the winter were almost double those in summer. In type II houses the concentrations were higher in the houses with open plan kitchens than in those with separate kitchens. In type III houses, mean concentrations were significantly higher in wood heated living rooms than those using central heating. In kitchens, cooking resulted in substantially higher concentrations of particulate matter with levels above those in smoking living rooms in winter. The hourly maximum values of number concentration were considerably higher in smoking rooms than non-smoking ones. Cooking resulted in increased number concentrations, with the average hourly maximum concentration of 179,110 #/cm(3). Particle mass and number emission rates were determined for a number of activities. In kitchens grilling had the highest average number emission rate, followed by boiling and frying. The results clearly highlight the impact of different forms of dwelling and their use and management by occupants on the levels of particulate matter in naturally ventilated residential built environments.
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Affiliation(s)
- Zaheer Ahmad Nasir
- School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, UK
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Pickett AR, Bell ML. Assessment of indoor air pollution in homes with infants. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2011; 8:4502-20. [PMID: 22408586 PMCID: PMC3290986 DOI: 10.3390/ijerph8124502] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2011] [Revised: 11/16/2011] [Accepted: 11/28/2011] [Indexed: 01/23/2023]
Abstract
Infants spend most of their indoor time at home; however, residential air quality is poorly understood. We investigated the air quality of infants’ homes in the New England area of the U.S. Participants (N = 53) were parents of infants (0–6 months) who completed telephone surveys to identify potential pollutant sources in their residence. Carbon monoxide (CO), carbon dioxide (CO2), particulate matter with aerodynamic diameter ≤0.5 µm (PM0.5), and total volatile organic compounds (TVOCs) were measured in 10 homes over 4–7 days, and levels were compared with health-based guidelines. Pollutant levels varied substantially across homes and within homes with overall levels for some homes up to 20 times higher than for other homes. Average levels were 0.85 ppm, 663.2 ppm, 18.7 µg/m3, and 1626 µg/m3 for CO, CO2, PM0.5, and TVOCs, respectively. CO2, TVOCs, and PM0.5 levels exceeded health-based indoor air quality guidelines. Survey results suggest that nursery renovations and related potential pollutant sources may be associated with differences in urbanicity, income, and presence of older children with respiratory ailments, which could potentially confound health studies. While there are no standards for indoor residential air quality, our findings suggest that additional research is needed to assess indoor pollution exposure for infants, which may be a vulnerable population.
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Affiliation(s)
- Anna Ruth Pickett
- School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA.
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