Ambient PM2.5 in the residential area near industrial complexes: Spatiotemporal variation, source apportionment, and health impact

Hsu, Cheng-Ying; Chiang, Hung Che; Chen, Mu Jean; Chuang, Chun Yu; Tsen, Chao Ming; Fang, Guor Cheng; Tsai, Ying I.; Chen, Nai Tzu; Lin, Tzu Yu; Lin, Sheng Lun; Chen, Yu Cheng

doi:10.1016/j.scitotenv.2017.02.212

Cited by 99 publications

(35 citation statements)

References 67 publications

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“…The other method is to mine the data from longterm or frequently monitored databases, termed positive matrix factorization (PMF), and is effective in analyzing the contributions of sources by multiregression without the demand for source chemical profiles. However, the amount of data should be large enough (suggested to be more than 100) to ensure the accuracy of results (Hsu et al 2017;Jaeckels, Bae, and Schauer 2007;Okuda et al 2010;Pandolfi et al 2011;Sofowote et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Vehicular emissions, industrial exhaust gases, power plants, open burning, and other potential sources are generally indicated as contributors to the PM in the ambient air (Hsu et al 2017;Liu et al 2015;Pipalatkar et al 2014b;Tseng et al 2016;Wang et al 2015). Numerous epidemiological studies suggest that exposure sites that have heavy traffic emission-related air pollutants may play a big role in these increased health risks among all sources and that PM is a major contributor in the ambient air.…”

Section: Introductionmentioning

confidence: 99%

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Reduction of atmospheric fine particle level by restricting the idling vehicles around a sensitive area

Lee

Lin

Yuan

et al. 2018

Journal of the Air & Waste Management Association

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The emission of idling vehicles strongly affects the levels of particles and relative pollutants in near-ground air around a school area. The PM mass concentration at a campus site increased from the background site by 15%, whereas NO and anthropogenic metals also significantly increased. Meanwhile, the PM contribution from mobile source in the campus increased 6.6% from the upwind site. An idling prohibition took place and showed impressive results. Reductions of PM, ionic component, and non-natural metal contents were found after the idling prohibition. The mobile monitoring also pointed out a significant improvement with the spatial analysis of PM, PM, PAH, and black carbon concentrations. These findings are very useful to effectively improve the local air quality of a densely city during the rush hour.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduction of atmospheric fine particle level by restricting the idling vehicles around a sensitive area

Lee

Lin

Yuan

et al. 2018

Journal of the Air & Waste Management Association

Self Cite

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“…Unlike NO 2 and SO 2 which are mostly emitted from motor vehicles and combustion of coal in manufacturing factories, respectively, PM 2.5 , on the other hand, has been released from multiple sources, including fossil-fuel combustion by motor vehicles and stationary sources such as power plants. Apart from the petrochemical airborne effluents, a recent Taiwanese study estimated that coal combustion, iron ore and steel industry, and non-ferrous metallurgy accounted for some 70% of the primary PM 2.5 in Taiwan [30]; and that these particles have the capacity to deposit in the lungs. Compared to the first quartile exposure to PM 2.5 (8.5-10.8 µg/m 3 ), Fleisch et al [7] found women with the highest quartile exposure (12.8-15.9 µg/m 3 ) during the 2nd trimester had a 2.63 (95% CI 1.15-6.01) times higher risk of having IGT.…”

Section: Discussionmentioning

confidence: 99%

Maternal exposure to air pollutants and risk of gestational diabetes mellitus in Taiwan

Shen

Hua

Chiu

et al. 2018

EJEA

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Abstract:Mounting evidence has shown an increased risk of gestational diabetes mellitus (GDM) in association with elevated exposure to air pollution. However, limited evidence is available concerning the effect of specific air pollutant(s) on GDM incidence. We conducted this case-control study on 6717 mothers with GDM diagnosed in 2006-2013 and 6717 age-and year of delivery-matched controls to further address the risk of GDM in relation to specific air pollutant. Both cases and controls were selected from a cohort of 1-million beneficiaries of Taiwan's National Health Insurance program registered in 2005. Maternal exposures to mean daily air pollutant concentration, derived from 76 fixed air quality monitoring stations within the 12-week period prior to pregnancy and during the 1st and 2nd trimesters, were assessed by the spatial analyst method (i.e., ordinary kriging) with the ArcGIS software. After controlling for potential confounders and other air pollutants, an increase in pre-pregnancy exposure of 1 inter-quartile range (IQR) for PM 2.5 and SO 2 was found to associate with a significantly elevated odds ratio (OR) of GDM at 1.10 (95% confidence interval (CI) 1.03-1.18 and 1.37 (95% CI 1.30-1.45), respectively. Exposures to PM 2.5 and SO 2 during the 1st and 2nd trimesters were also associated with significantly increased ORs, which were 1.09 (95% CI 1.02-1.17) and 1.07 (95% CI 1.01-1.14) for PM 2.5 , and 1.37 (95% CI 1.30-1.45) and 1.38 (95% CI 1.31-1.46) for SO 2 . It was concluded that higher pre-and post-pregnancy exposures to PM 2.5 and SO 2 for mothers were associated with a significantly but modestly elevated risk of GDM.

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“…Examples of environmental studies utilizing PM 2.5 data include studies on the associations between various health outcomes and long‐term (Chan et al, ; Kaufman et al, ; Künzli et al, ; Miller et al, ; Pope III et al, ) or short‐term (Achilleos et al, ; Gold et al, ; Hsu et al, ; Pascal et al, ; Tian et al, ; Tolbert, Klein, Peel, Sarnat, & Sarnat ) exposures to PM 2.5 . For instance, Chan et al () found significant associations between long‐term exposure to PM 2.5 and higher systolic blood pressure, pulse pressure, and mean arterial pressure in the Sister Study.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations

Larson

Szpiro

2019

Environmetrics

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Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM 2.5 ), in which data are usually not measured at all study locations. PM 2.5 is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lowerdimensional representative scores of such multipollutant data. Spatial prediction can then be used to estimate these scores at new locations. Recently developed predictive PCA modifies the traditional PCA algorithm to obtain scores with spatial structures that can be well predicted at unmeasured locations. However, these approaches require complete data, whereas multipollutant data tend to have complex missing patterns in practice. We propose probabilistic versions of predictive PCA, which allow for flexible model-based imputation that can account for spatial information and subsequently improve the overall predictive performance.

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Ambient PM2.5 in the residential area near industrial complexes: Spatiotemporal variation, source apportionment, and health impact

Cited by 99 publications

References 67 publications

Reduction of atmospheric fine particle level by restricting the idling vehicles around a sensitive area

Reduction of atmospheric fine particle level by restricting the idling vehicles around a sensitive area

Maternal exposure to air pollutants and risk of gestational diabetes mellitus in Taiwan

Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations

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