1
|
Shi W, Liu C, Annesi-Maesano I, Norback D, Deng Q, Huang C, Qian H, Zhang X, Sun Y, Wang T, van Donkelaar A, Martin RV, Zhang Y, Li B, Kan H, Zhao Z. Ambient PM 2.5 and its chemical constituents on lifetime-ever pneumonia in Chinese children: A multi-center study. ENVIRONMENT INTERNATIONAL 2021; 146:106176. [PMID: 33220537 DOI: 10.1016/j.envint.2020.106176] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/28/2020] [Accepted: 09/30/2020] [Indexed: 05/23/2023]
Abstract
The long-term effects of ambient PM2.5 and chemical constituents on childhood pneumonia were still unknown. A cross-sectional study was conducted in 30,315 children in the China Children, Homes, Health (CCHH) project, involving 205 preschools in six cities in China, to investigate the long-term effects of PM2.5 constituents on lifetime-ever diagnosed pneumonia. Information on the lifetime-ever pneumonia and demographics were collected by validated questionnaires. The lifetime annual average ambient PM2.5, ozone and five main PM2.5 constituents, including SO42-, NO3-, NH4+, organic matter (OM) and black carbon (BC), were estimated according to preschool addresses by a combination of satellite remote sensing, chemical transport modeling and ground-based monitors. The prevalence of lifetime-ever diagnosed pneumonia was 34.5% across six cities and differed significantly among cities (p = 0.004). The two-level logistic regression models showed that the adjusted odds ratio for PM2.5 (per 10 µg/m3) and its constituents (per 1 µg/m3)-SO42-, NO3-, NH4+, and OM were 1.12 (95% CI:1.07-1.18), 1.02 (1.00-1.04), 1.06 (1.04-1.09), 1.05 (1.03-1.07) and 1.09 (1.06-1.12), respectively. Children in urban area, aged < 5 years and breastfeeding time < 6 months enhanced the risks of pneumonia. Our study provided robust results that long-term levels of ambient PM2.5 and its constituents increased the risk of childhood pneumonia, especially NH4+, NO3- and OM.
Collapse
Affiliation(s)
- Wenming Shi
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China
| | - Cong Liu
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China
| | - Isabella Annesi-Maesano
- Epidemiology of Allergic and Respiratory Diseases Department, IPLESP, Sorbonne Université and INSERM, Medical School Saint-Antoine, F75012 Paris, France
| | - Dan Norback
- Department of Medical Sciences, Uppsala University, Uppsala SE-751, Sweden
| | - Qihong Deng
- School of Energy Science and Engineering, Central South University, Changsha 410083, China
| | - Chen Huang
- School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Hua Qian
- School of Energy & Environment, Southeast University, Nanjing 210096, China
| | - Xin Zhang
- Research Center for Environmental Science and Engineering, Shanxi University, Taiyuan 030006, China
| | - Yuexia Sun
- Tianjin Key Lab of Indoor Air Environmental Quality Control, Tianjin University, Tianjin 300072, China
| | - Tingting Wang
- School of Nursing & Health Management, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
| | - Aaron van Donkelaar
- Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada; Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall V Martin
- Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada; Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, USA
| | - Yinping Zhang
- Department of Building Science, Tsinghua University, Beijing 100084, China
| | - Baizhan Li
- Key Laboratory of Three Gorges Reservoir Region's Eco-Environment, Chongqing University, Chongqing 400030, China
| | - Haidong Kan
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety of the Ministry of Education, NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China.
| | - Zhuohui Zhao
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety of the Ministry of Education, NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China.
| |
Collapse
|
2
|
Lin MY, Guo YX, Chen YC, Chen WT, Young LH, Lee KJ, Wu ZY, Tsai PJ. An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 636:1139-1148. [PMID: 29913576 DOI: 10.1016/j.scitotenv.2018.04.248] [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: 01/25/2018] [Revised: 04/12/2018] [Accepted: 04/18/2018] [Indexed: 06/08/2023]
Abstract
People living near roadways are exposed to high concentrations of ultrafine particles (UFP, diameter < 100 nm). This can result in adverse health effects such as respiratory illness and cardiovascular diseases. However, accurately characterizing the UFP number concentration requires expensive sets of instruments. The development of an UFP surrogate with cheap and convenient measures is needed. In this study, we used a mobile measurement platform with a Fast Mobility Particle Sizer (FMPS) and sound level meter to investigate the spatiotemporal relations of noise and UFP and identify the hotspots of UFP. UFP concentration levels were significantly influenced by temporal and spatial variations (p < 0.001). We proposed a Generalized Additive Models to predict UFP number concentration in the study area. The model uses noise and meteorological covariates to predict the UFP number concentrations at an industrial site in Taichung, Taiwan. During the one year sampling campaign from fall 2013 to summer 2014, mobile measurements were performed at least one week for each season, both on weekdays and weekends. The proposed model can explain 80% of deviance and has coefficient of determination (R2) of 0.77. Moreover, the developed UFP model was able to adequately predict UFP concentrations, and can provide people with a convenient way to determine UFP levels. Finally, the results from this study could help facilitate the future development of noise mobile measurement.
Collapse
Affiliation(s)
- Ming-Yeng Lin
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yi-Xin Guo
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, 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
| | - Wei-Ting Chen
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Li-Hao Young
- Department of Occupational Safety and Health, China Medical University, 91 Hsueh-Shih Road, Taichung 40402, Taiwan
| | - Kuo-Jung Lee
- Department of Statistics, College of Management, National Cheng Kung University, Tainan, Taiwan
| | - Zhu-You Wu
- Department of Statistics, College of Management, National Cheng Kung University, Tainan, Taiwan
| | - Perng-Jy Tsai
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| |
Collapse
|
3
|
Particulate Matter Exposure in a Police Station Located near a Highway. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2015; 12:14541-56. [PMID: 26580641 PMCID: PMC4661666 DOI: 10.3390/ijerph121114541] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 11/03/2015] [Accepted: 11/06/2015] [Indexed: 11/17/2022]
Abstract
People living or working near roadways have experienced an increase in cardiovascular or respiratory diseases due to vehicle emissions. Very few studies have focused on the PM exposure of highway police officers, particularly for the number concentration and size distribution of ultrafine particles (UFP). This study evaluated exposure concentrations of particulate matter (PM) in the Sinying police station near a highway located in Tainan, Taiwan, under different traffic volumes, traffic types, and shift times. We focused on periods when the wind blew from the highway toward the police station and when the wind speed was greater than or equal to 0.5 m/s. PM2.5, UFP, and PM-PAHs concentrations in the police station and an upwind reference station were measured. Results indicate that PM2.5, UFP, and PM-PAHs concentrations in the police station can be on average 1.13, 2.17, and 5.81 times more than the upwind reference station concentrations, respectively. The highest exposure level for PM2.5 and UFP was observed during the 12:00 PM–4:00 PM shift while the highest PAHs concentration was found in the 4:00 AM–8:00 AM shift. Thus, special attention needs to be given to protect police officers from exposure to high PM concentration.
Collapse
|
4
|
Nury C, Bregant S, Czarny B, Berthon F, Cassar-Lajeunesse E, Dive V. Detection of endogenous matrix metalloprotease-12 active form with a novel broad spectrum activity-based probe. J Biol Chem 2012; 288:5636-44. [PMID: 23271741 DOI: 10.1074/jbc.m112.419499] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Matrix metalloproteases (MMPs) have attracted considerable attention as critical mediators of pathological tissue remodeling processes. However it remains an unresolved challenge to detect their active forms in biological samples. To prove the efficacy of a recently developed MMP activity-based probe, we examined the content in MMP active forms of bronchoalveolar lavage fluids (BALf) from male C57BL/6 mice exposed to ultrafine carbon black nanoparticles, a model of chronic obstructive pulmonary disease. This probe was shown to label proteins, mostly expressed in BALf of mice exposed to nanoparticles. Using competition assays with a selective MMP-12 inhibitor as well as MMP-12 knock-out mice, one of these proteins was identified as the active form of the catalytic domain of MMP-12. This new probe can detect the active form of MMP-12 down to a threshold of 1 fmol. Radioactive counting showed the concentration of the active form of MMP-12 to be around 1 fmol/μl in BALf from nanoparticle-treated mice. A less sensitive probe would therefore not have detected MMP-12. As the probe can detect other MMPs in the femtomolar range, it is a potentially powerful tool for monitoring the levels of MMP active forms in various diseases.
Collapse
Affiliation(s)
- Catherine Nury
- CEA (Commissariat à l'Energie Atomique), iBiTec-S, Service d'Ingénierie Moléculaire de Protéines (SIMOPRO), CE-Saclay, 91191 Gif /Yvette, Cedex, France
| | | | | | | | | | | |
Collapse
|