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Chen J, Mölter A, Gómez-Barrón JP, O'Connor D, Pilla F. Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:56114-56129. [PMID: 39254809 PMCID: PMC11420298 DOI: 10.1007/s11356-024-34903-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 08/30/2024] [Indexed: 09/11/2024]
Abstract
Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM2.5), nitrogen monoxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and carbon dioxide (CO2) concentrations at hyperlocal levels. The average daytime median concentrations of NO2 (28.4 ± 15.7 µg/m3) and PM2.5 (7.6 ± 4.7 µg/m3) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4% and 9% of the 1-year sampling time, respectively. For the diurnal variation of measured pollutants, the morning (8:00) and early evening (18:00) showed higher concentrations for NO2 and PM2.5, mostly happening in the winter season, while the afternoon is the least polluted time except for O3. The low-percentile approach along with 1-h and daytime minima method allowed for decomposing pollutant time series into the background and local contributions. Background contributions for NO2 and PM2.5 changed along with the seasonal variation. Local contributions for PM2.5 changed slightly; however, NO2 showed significant diurnal and seasonal variability related to traffic emissions. Short-lived event enhancement (1 min to 1 h) accounts for 36.0-40.6% and 20.8-42.2% of the total concentration for NO2 and PM2.5. The highly polluted days account for 56.3% of total NO2, highlighting local traffic is the dominant contributor to short-term NO2 concentrations. The longer-lived events (> 8 h) enhancement accounts for 25% of the monitored concentrations. Additionally, conducting optimal hotspot analysis enables mapping the spatial distribution of "hot" spots for PM2.5 and NO2 on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM2.5 and NO2 pollution in urban areas and emphasize the urgent need for mitigating NO2 from traffic pollution in Dublin.
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Affiliation(s)
- Jiayao Chen
- School of Architecture, Planning and Environmental Policy, University College Dublin, Dublin, Ireland.
| | - Anna Mölter
- School of Architecture, Planning and Environmental Policy, University College Dublin, Dublin, Ireland
- School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland
| | - José Pablo Gómez-Barrón
- School of Architecture, Planning and Environmental Policy, University College Dublin, Dublin, Ireland
| | - David O'Connor
- School of Chemical Sciences, Dublin City University, Dublin, Ireland
| | - Francesco Pilla
- School of Architecture, Planning and Environmental Policy, University College Dublin, Dublin, Ireland
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Jiang YX, Zhou LX, Yang LL, Huang QS, Xiao H, Li DW, Zhou YM, Hu YG, Tang EJ, Li YF, Ji AL, Luo P, Cai TJ. The association between short-term exposure to ambient carbon monoxide and hospitalization costs for bronchitis patients: A hospital-based study. ENVIRONMENTAL RESEARCH 2022; 210:112945. [PMID: 35202627 DOI: 10.1016/j.envres.2022.112945] [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: 09/30/2021] [Revised: 12/28/2021] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Ambient carbon monoxide (CO) is associated with bronchitis morbidity, but there is no evidence concerning its correlation with hospitalization costs for bronchitis patients. This study aimed to investigate the relationship between short-term ambient CO exposure and hospitalization costs for bronchitis patients in Chongqing, China. Baseline data for 3162 hospitalized bronchitis patients from November 2013 to December 2019 were collected. Multiple linear regression analysis was used to determine the association, delayed and cumulative, between short-term CO exposure and hospitalization costs. Additionally, subgroup analyses were performed by gender, age, season, and comorbidity. Positive association between CO and hospitalization costs for bronchitis patients was observed. The strongest association was observed at lag 015 days, with per 1 mg/m3 increase of CO concentrations corresponded to 5834.40 Chinese Yuan (CNY) (95% CI: 2318.71, 9350.08; P < 0.001) (845.97 US dollars) increment in hospitalization costs. Stratified analysis results showed that the association was more obvious among those males, elderly, with comorbidities, and in warm seasons. More importantly, there was strongest correlation between CO and bronchitis patients with coronary heart disease. In summary, short-term exposure to ambient CO, even lower than Chinese and WHO standards, can be associated with increased hospitalization costs for bronchitis. Controlling CO exposure can be helpful to reduce medical burden associated with bronchitis patients. The results also suggest that when setting air quality standards and formulating preventive measures, susceptible subpopulations ought to be considered.
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Affiliation(s)
- Yue-Xu Jiang
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China; Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Lai-Xin Zhou
- Medical Department, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China
| | - Li-Li Yang
- Department of Information, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China
| | - Qing-Song Huang
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China; Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Hua Xiao
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Da-Wei Li
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yu-Meng Zhou
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yue-Gu Hu
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - En-Jie Tang
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Ya-Fei Li
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Ai-Ling Ji
- Department of Preventive Medicine, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, China.
| | - Peng Luo
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China.
| | - Tong-Jian Cai
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
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Lin H, Long Y, Su Y, Song K, Li C, Ding N. Air pollution and hospital admissions for critical illness in emergency department: a tertiary-center research in Changsha, China, 2016-2020. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:21440-21450. [PMID: 34761317 DOI: 10.1007/s11356-021-17295-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/27/2021] [Indexed: 06/13/2023]
Abstract
We aimed to comprehensively investigate the associations of air pollutants with hospital admissions for critical illness in ED. Patients with critical illness including level 1 and level 2 of the Emergency Severity Index admitted in ED of Changsha Central Hospital from January 2016 to December 2020 were enrolled. Meteorological and air pollutants data source were collected from the National Meteorological Science Data Center. A Poisson generalized linear regression combined with a polynomial distributed lag model (PDLM) was utilized to explore the effect of air pollution on hospital admissions for critical illness in ED. Benchmarks as references (25th) were conducted for comparisons with high levels of pollutant concentrations (75th). At first, lagged effects of all different air pollutants were analyzed. Then, based on the most significant factor, analyses in subgroups were performed by gender (male and female), age (< 45, 45-65, and > 65), disorders (cardiovascular, neurological, respiratory), and seasons (spring, summer, autumn, and winter). A total of 47,290 patients with critical illness admitted in ED were included. The effects of air pollutants (PM2.5, PM10, SO2, NO2, O3 and CO) on critical illness ED visits were statistically significant. Strong collinearity between PM2.5 and PM10 (r = 0.862) was found. Both single-day lag and cumulative-day lag day models showed that PM2.5 had the strongest effects (lag 0, RR = 1.025, 95% CI 1.008-1.043, and lag 0-14, RR = 1.067, 95% CI 1.017-1.120, respectively). In both PM2.5 and PM10, the risks of critical illness in male, > 65 ages, respiratory diseases, and winter increased the most significant. Air pollutants, especially PM2.5 and PM10 exposure, could increase the risk of critical illness admission.
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Affiliation(s)
- Hang Lin
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, Hunan, 410004, China
| | - Yong Long
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, Hunan, 410004, China
| | - Yingjie Su
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, Hunan, 410004, China
| | - Kun Song
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, Hunan, 410004, China
| | - Changluo Li
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, Hunan, 410004, China
| | - Ning Ding
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, Hunan, 410004, China.
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