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Hsu WT, Ku CH, Chen MJ, Wu CD, Lung SCC, Chen YC. Model development and validation of personal exposure to PM 2.5 among urban elders. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 316:120538. [PMID: 36330878 DOI: 10.1016/j.envpol.2022.120538] [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: 07/27/2022] [Revised: 09/13/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM2.5 exposure based on the coefficient of determination, accuracy, and mean bias error. Ambient and home outdoor measures as exposure surrogates are likely to under- and overestimate personal exposure to PM2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM2.5 exposure by 46-70%. The model incorporating home indoor PM2.5 can achieve the highest agreement (R2 = 0.790) with personal exposure and the lowest measurement error. The ME model with the GEE analysis combining home outdoor PM2.5 determined by LUR model with a machine learning technique can improve the prediction (R2 = 0.592) of personal PM2.5 exposure, compared with the prediction of the traditional LUR model (R2 = 0.385).
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Affiliation(s)
- Wei-Ting Hsu
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Chun-Hung Ku
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Mu-Jean Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Chih-Da Wu
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
| | | | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Department of Occupational Safety and Health, China Medical University, Taichung, Taiwan; Department of Safety, Health and Environmental Engineering, National United University, Miaoli, Taiwan.
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2
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Zhang X, Fung JCH, Lau AKH, Hossain MS, Louie PKK, Huang W. Air quality and synergistic health effects of ozone and nitrogen oxides in response to China's integrated air quality control policies during 2015-2019. CHEMOSPHERE 2021; 268:129385. [PMID: 33383278 DOI: 10.1016/j.chemosphere.2020.129385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 11/23/2020] [Accepted: 12/18/2020] [Indexed: 06/12/2023]
Abstract
O3 pollution had been worsening in mainland China in the past decade, posing significant human health challenges. The NOx control would trigger increasing O3 concentrations in response to a series of released China emission reduction policies. This study used sensitivity analysis methodology to explore the effectiveness of integrated sectoral emission control policies that have been expanded throughout China. Air quality and synergistic health effects of O3 and NO2 were investigated to obtain an in-depth understanding of the O3 control, especially under a VOC-limited regime. The findings demonstrated that although the NOx-titration effect triggered an increase in O3, the combined health effects of two pollutants tended to improve in most regions of China under a VOC-limited regime. The region-based annual average NO2 concentrations exhibited a larger reduction in Hong Kong (HK) than in the Pearl River Delta Economic Zone (PRD EZ). The short-term measures led to substantial health benefits for Shenzhen and HK. The sectoral emission controls demonstrated a considerable health improvement for the major PRD EZ cities. Joint national control efforts confined the domain-wide health risks below the safety line in China. National cooperative efforts in China could avoid more than 1.5-2% of the emergency hospital admissions for cardiovascular and respiratory diseases attributed to NO2 and O3 exposure. The observed O3 increases due to the NOx-titration effect for calculating the integral health effects of emission control on concentration reduction called for simultaneously strengthened controls on both NOx and VOC in areas subject to a VOC-limited regime.
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Affiliation(s)
- Xuguo Zhang
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, China; Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Jimmy C H Fung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Md Shakhaoat Hossain
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Peter K K Louie
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Wei Huang
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, China; College of Business, Southern University of Science and Technology, Shenzhen, 518055, China.
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3
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Che W, Li ATY, Frey HC, Tang KTJ, Sun L, Wei P, Hossain MS, Hohenberger TL, Leung KW, Lau AKH. Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools. INDOOR AIR 2021; 31:170-187. [PMID: 32731301 DOI: 10.1111/ina.12725] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 07/13/2020] [Accepted: 07/13/2020] [Indexed: 06/11/2023]
Abstract
School-age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children's exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real-time PM10 , PM2.5 , NO2, and O3 concentrations were measured in 76 classrooms and 23 non-classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM2.5 , PM10 , O3, and NO2 microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM2.5 , PM10 , O3, and NO2 microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations.
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Affiliation(s)
- Wenwei Che
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Alison T Y Li
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Henry Christopher Frey
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
- Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | - Kimberly Tasha Jiayi Tang
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Li Sun
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Peng Wei
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Md Shakhaoat Hossain
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Tilman Leo Hohenberger
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - King Wai Leung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
- Institute for the Environment, The Hong Kong University of Science & Technology, Hong Kong, China
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4
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Liu M, Guo W, Cai Y, Yang H, Li W, Yang L, Lai X, Fang Q, Ma L, Zhu R, Zhang X. Personal exposure to fine particulate matter and renal function in children: A panel study. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 266:115129. [PMID: 32688076 DOI: 10.1016/j.envpol.2020.115129] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/31/2020] [Accepted: 06/26/2020] [Indexed: 06/11/2023]
Abstract
There is a lack of evidence regarding the association of short-term exposure to fine particulate matter (PM2.5) with renal function in children and its underlying mechanism. We included 105 children aged 4-13 years from a panel study conducted in Wuhan, China with up to 3 repeated visits across 3 seasons from October 9, 2017 to June 1, 2018. We measured personal real-time PM2.5 exposure concentration continuously for 72 h preceding each round of health examinations that included serum creatinine and cytokines. Linear mixed-effects models were performed to estimate the effects of PM2.5 on estimated glomerular filtration rate (eGFR) over various lag times, and a mediation analysis was applied for the role of cytokines in association between PM2.5 and eGFR. Results showed that personal exposure to PM2.5 was dose-responsive related to decreased eGFR within lag 2 days. The effect was the strongest at lag 0 day with estimation of -1.69% [95% confidence interval (CI): -2.27%, -1.10%] in eGFR by a 10-μg/m3 increase in PM2.5, and reached peak at lag 3 h, then declined over time. Such inverse relationships were more evident among children aged 4-6 years, or boys or those who lived proximity to major roadways <300 m. Notably, eGFR still held on to decrease even when PM2.5 was below Class II Chinese ambient air quality standard at lag 0 day. Moreover, the effect of PM2.5 on eGFR was significantly reduced in children with high and medium levels of serum chemokine ligand 27 (CCL27), but not in those with low CCL27. Furthermore, CCL27 was positively relevant to PM2.5, and mediated proportion of CCL27 ranged from 3.75% to 6.61% in relations between PM2.5 and decreased eGFR over various lag times. In summary, short-term PM2.5 exposure might be dose-responsive associated with reduced eGFR whereby a mechanism partly involving CCL27 among healthy children.
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Affiliation(s)
- Miao Liu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenting Guo
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunyao Cai
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huihua Yang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenze Li
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Liangle Yang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xuefeng Lai
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qin Fang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lin Ma
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Rui Zhu
- Department of Traditional Chinese Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, School of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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5
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Hsu WT, Chen JL, Candice Lung SC, Chen YC. PM 2.5 exposure of various microenvironments in a community: Characteristics and applications. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 263:114522. [PMID: 32298940 DOI: 10.1016/j.envpol.2020.114522] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 03/31/2020] [Accepted: 03/31/2020] [Indexed: 06/11/2023]
Abstract
While the measurement of particulate matter (PM) with a diameter of less than 2.5 μm (PM2.5) has been conducted for personal exposure assessment, it remains unclear how models that integrate microenvironmental levels with resolved activity and location information predict personal exposure to PM. We comprehensively investigated PM2.5 concentrations in various microenvironments and estimated personal exposure stratified by the microenvironment. A variety of microenvironments (>200 places and locations, divided into 23 components according to indoor, outdoor, and transit modes) in a community were selected to characterize PM2.5 concentrations. Infiltration factors calculated from microenvironmental/central-site station (M/S) monitoring campaigns with time-activity patterns were used to estimate time-weighted exposure to PM2.5 for university students. We evaluated exposures using a four-stage modeling approach and quantified the performance of each component. It was found that the SidePak monitor overestimated the concentration by 3.5 times as compared with the filter-based measurements. Higher mean concentrations of PM2.5 were observed in the Taoist temple and night market microenvironments; in contrast, lower concentrations were observed in air-conditioned offices and car microenvironments. While the exposure model incorporating detailed time-location information and infiltration factors achieved the highest prediction (R2 = 0.49) of personal exposure to PM2.5, the use of indoor, outdoor, and transit components for modeling also generated a consistent result (R2 = 0.44).
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Affiliation(s)
- Wei-Ting Hsu
- National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli, 35053, Taiwan
| | - Jyh-Larng Chen
- Department of Environmental Engineering and Health, Yuanpei University of Medical Technology, Hsinchu, 30015, Taiwan
| | | | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli, 35053, Taiwan; Department of Occupational Safety and Health, China Medical University, 91 Hsueh-Shih Road, Taichung, 40402, Taiwan.
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6
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Cao G, Bi J, Ma Z, Shao Z, Wang J. Seasonal Characteristics of the Chemical Composition of Fine Particles in Residences of Nanjing, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E1066. [PMID: 30934562 PMCID: PMC6466138 DOI: 10.3390/ijerph16061066] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 03/05/2019] [Accepted: 03/21/2019] [Indexed: 11/16/2022]
Abstract
Indoor fine particulate matter (PM2.5) and its chemical composition is important for human exposure as people spend most of their time indoors. However, few studies have investigated the multiseasonal characteristics of indoor PM2.5 and its chemical composition in China. In this study, the chemical composition of PM2.5 samples in residences was analyzed over four seasons in Nanjing, China. Indoor water-soluble ions exhibited similar seasonal variations (winter > autumn > summer > spring) to those from outdoors (winter > autumn > spring > summer) except in summer. Whereas, indoor metallic elements exhibited a different seasonal pattern from that of outdoors. The highest concentrations of indoor metallic elements were observed in summer when the outdoor concentrations were low. The different seasonal variations of the chemical composition between indoor and outdoor PM2.5 indicated that people should consider both indoor and outdoor sources to reduce their exposure to air pollutants in different seasons. The carcinogenic risks for metallic elements were within the acceptable levels, while manganese (Mn) was found to have potential noncarcinogenic risk to humans. More attention should be paid to the pollution of Mn in the study area in the future. Moreover, the cumulative effect of noncarcinogenic PM2.5-bound elements should not be ignored.
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Affiliation(s)
- Guozhi Cao
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing 100012, China.
| | - Jun Bi
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Zongwei Ma
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Zhijuan Shao
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
| | - Jinnan Wang
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing 100012, China.
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7
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Che W, Frey HC, Li Z, Lao X, Lau AKH. Indoor Exposure to Ambient Particles and Its Estimation Using Fixed Site Monitors. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:808-819. [PMID: 30398338 DOI: 10.1021/acs.est.8b04474] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Ambient PM2.5 concentrations measured at fixed site monitors (FSM) are often biased with respect to exposure concentrations because of spatial variability and infiltration. Based on comparison of ambient concentrations from 14 FSMs and of exposure concentrations measured indoors and outdoors at two schools in Hong Kong for winter and summer seasons, the magnitude and sources of exposure error based on using FSMs as a surrogate for exposure are quantified. An approach for bias correcting surrogate exposure estimates from FSMs is demonstrated. The approach is based on a proximity factor (PF) that accounts for differences in spatial locations, proximity to emissions and deviation from dominant wind direction, and an infiltration factor (IF) that varies by season. The combination of the PF and IF reduce bias in mean school exposure estimates from ±90% to ±20%. Bias in exposure estimates from using FSMs as surrogates tend to be smaller for which the exposure site and FSM are aligned with wind direction, have similar sampling height, and are in close proximity. The methodology demonstrated to assess concordance between FSMs and exposure measurement sites can be applied more broadly to help reduce exposure error, which may help to interpret seasonal variations in health estimates.
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Affiliation(s)
- Wenwei Che
- Department of Civil and Environmental Engineering , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China
- HKUST Jockey Club Institute for Advanced Study , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China
- Institute for Environment and Climate Research , Jinan University , Guangzhou , China
| | - H Christopher Frey
- Division of Environment and Sustainability , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China
- Department of Civil, Construction and Environmental Engineering , North Carolina State University , Campus Box 7908, Raleigh , North Carolina 27695-7908 , United States
| | - Zhiyuan Li
- Division of Environment and Sustainability , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China
| | - Xiangqian Lao
- JC School of Public Health and Primary Care , The Chinese University of Hong Kong , Hong Kong SAR , China
| | - Alexis K H Lau
- Department of Civil and Environmental Engineering , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China
- Division of Environment and Sustainability , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China
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Shao Z, Yin X, Bi J, Ma Z, Wang J. Spatiotemporal Variations of Indoor PM 2.5 Concentrations in Nanjing, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E144. [PMID: 30621102 PMCID: PMC6339030 DOI: 10.3390/ijerph16010144] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 12/19/2018] [Accepted: 12/27/2018] [Indexed: 11/20/2022]
Abstract
Indoor fine particulate matter (PM2.5) is important since people spend most of their time indoors. However, knowledge of the spatiotemporal variations of indoor PM2.5 concentrations within a city is limited. In this study, the spatiotemporal distributions of indoor PM2.5 levels in Nanjing, China were modeled by the multizone airflow and contaminant transport program (CONTAM), based on the geographically distributed residences, human activities, and outdoor PM2.5 concentrations. The accuracy of the CONTAM model was verified, with a good agreement between the model simulations and measurements (r = 0.940, N = 110). Two different scenarios were considered to examine the building performance and influence of occupant behaviors. Higher PM2.5 concentrations were observed under the scenario when indoor activities were considered. Seasonal variability was observed in indoor PM2.5 levels, with the highest concentrations occurring in the winter and the lowest occurring in the summer. Building characteristics have a significant effect on the spatial distribution of indoor PM2.5 concentrations, with multistory residences being more vulnerable to outdoor PM2.5 infiltration than high-rise residences. The overall population exposure to PM2.5 in Nanjing was estimated. It would be overestimated by 16.67% if indoor exposure was not taken into account, which would lead to a bias in the health impacts assessment.
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Affiliation(s)
- Zhijuan Shao
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
| | - Xiangjun Yin
- Nanjing Urban Planning & Research Center, Nanjing 210029, China.
| | - Jun Bi
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Zongwei Ma
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Jinnan Wang
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing 100012, China.
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Niu X, Ho SSH, Ho KF, Huang Y, Cao J, Shen Z, Sun J, Wang X, Wang Y, Lee S, Huang R. Indoor secondary organic aerosols formation from ozonolysis of monoterpene: An example of d-limonene with ammonia and potential impacts on pulmonary inflammations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 579:212-220. [PMID: 27842959 DOI: 10.1016/j.scitotenv.2016.11.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 11/02/2016] [Accepted: 11/03/2016] [Indexed: 06/06/2023]
Abstract
Monoterpene is one class of biogenic volatile organic compounds (BVOCs) which widely presents in household cleaning products and air fresheners. It plays reactive role in secondary organic aerosols (SOAs) formation with ozone (O3) in indoor environments. Such ozonolysis can be influenced by the presence of gaseous pollutants such as ammonia (NH3). This study focuses on investigations of ozone-initiated formation of indoor SOAs with d-limonene, one of the most abundant indoor monoterpenes, in a large environmental chamber. The maximum total particle number concentration from the ozonolysis in the presence of NH3 was 60% higher than that in the absence of NH3. Both of the nuclei coagulation and condensation involve in the SOAs growth. The potential risks of pulmonary injury for the exposure to the secondary particles formed were presented with the indexes of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interleukin-10 (IL-10) expression levels in bronchoalveolar lavage fluid (BALF) upon intratracheal instillation in mice lung for 6 and 12h. The results indicated that there was 22-39% stronger pulmonary inflammatory effect on the particles generated with NH3. This is a pilot study which demonstrates the toxicities of the indoor SOAs formed from the ozonolysis of a monoterpene.
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Affiliation(s)
- Xinyi Niu
- School of Human Settlements and Civil Engineering, , Xi'an Jiaotong University, Xi'an, China; Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
| | - Steven Sai Hang Ho
- Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China; Division of Atmosphere Sciences, Desert Research Institute, Reno, NV89512, United States
| | - Kin Fai Ho
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Yu Huang
- Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China; State Key Lab of Loess and Quaternary Geology (SKLLQG), Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China.
| | - Junji Cao
- Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China; State Key Lab of Loess and Quaternary Geology (SKLLQG), Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China; Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, China.
| | - Zhenxing Shen
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jian Sun
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xiumei Wang
- School of Pharmacy, Xi'an Jiaotong University, Xi'an, China
| | - Yu Wang
- School of Pharmacy, Xi'an Jiaotong University, Xi'an, China
| | - Shuncheng Lee
- Department of Civil and Structural Engineering, Research Center for Environmental Technology and Management, The Hong Kong Polytechnic University, Hong Kong, China
| | - Rujin Huang
- Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China; State Key Lab of Loess and Quaternary Geology (SKLLQG), Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
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10
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Submicron Particle-Bound Mercury in University Teaching Rooms: A Summer Study from Two Polish Cities. ATMOSPHERE 2016. [DOI: 10.3390/atmos7090117] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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