PM2.5 exposure of various microenvironments in a community: Characteristics and applications

Hsu, Wen-Ling; Chen, Jyh-Larng; Lung, Shih-Chun Candice; Chen, Yu‐Cheng

doi:10.1016/j.envpol.2020.114522

Cited by 19 publications

(7 citation statements)

References 60 publications

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“…The majority of data points were classified as home for the highlighted participants (mean: 65%, median: 69%) (Table S2). This is slightly higher, but consistent with previous personal exposure studies (Bekö et al, 2015;Hsu et al, 2020;Quinn et al, 2018). Data points were classified in these microenvironments at an average of 31% (median: 16%) of the time, therefore non-home exposures may be significant in the long-term (Table S2).…”

Section: Discussionsupporting

confidence: 85%

A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

Velasquez

et al. 2021

Journal of Aerosol Science

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Section: Discussionsupporting

confidence: 85%

A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

Velasquez

et al. 2021

Journal of Aerosol Science

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“…also reported that the within-subject variability (81.3% of σ Total 2 ) in personal exposure concentrations of PM2.5 was higher than the between-subject variability in concentrations (18.7% of σTotal 2 ) for university students (Hsu et al, 2020). In contrast, long-term (annual) concentrations of PM2.5 were fairly attributable to the between-site or between-group variability (47.8%).…”

Section: Accepted Manuscriptmentioning

confidence: 93%

Evaluation of Variability in the Ambient PM2.5 Concentrations from FEM and FRM-like Measurements for Exposure Estimates

Mou

Hsu

Chen

et al. 2021

Aerosol Air Qual. Res.

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This study aims to evaluate the variability in ambient fine particulate matter (PM2.5) concentrations obtained from the federal equivalent method (FEM) and federal reference method (FRM)-like measurements at the national air quality monitoring stations (AQMSs) for exposure estimates and to examine the effect of environmental factors and sampling site characteristics affecting the spatial and temporal variations in PM2.5 concentrations. A mixed-effects model was used to evaluate the temporal and spatial variability in daily and annual PM2.5 concentrations during 20142017 at 16 AQMSs in the Big Taipei City, Taiwan. The mean FEM PM2.5 concentrations were ~30% higher than the FRM-like PM2.5 concentrations. The FRM-like PM2.5 concentrations obtained by applying the calibration procedures presented a negligible betweensite variability. The daily FEM PM2.5 concentrations were dominated by the within-site variability (~90%), whereas the annual concentrations were reasonably attributable to the between-site variability (47.8%). Ambient PM2.5 was mainly affected by the gaseous pollutants (such as NO2, O3, and SO2), accounting for 45.8% and 26.8% of the within-site and between-site variability in concentrations, respectively. The FEM measurements rather than the FRM-like measurements at the AQMSs could provide a higher between-site variability for exposure estimates of PM2.5 in the epidemiological studies.

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“…Cooking emissions from commercial restaurants are also outdoor sources for urban‐community‐level PM 2.5 (Lung et al., 2020 ; H. Xu et al., 2020 ). Of these emissions, barbecues and Chinese restaurants are the main sources of PM 2.5 pollution (Hsu et al., 2020 ). Thus, based on the points data of barbecues and Chinese restaurants, the impact of cooking emissions was calculated by using the buffering analysis, separately.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, based on the points data of barbecues and Chinese restaurants, the impact of cooking emissions was calculated by using the buffering analysis, separately. In addition, more than 2 million people in Hong Kong, follow the Buddhist/Taoist religion, and during the periods of prayer incense and joss paper burnt in temples both during festival periods and as a common part of daily life (C. Chen et al., 2021 ; Hsu et al., 2020 ). Based on the points data of temples, the impact of incense/joss paper burning was calculated using buffering analysis.…”

Section: Methodsmentioning

confidence: 99%

Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Tong

Shi

et al. 2022

GeoHealth

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How to reduce the health risks for commuters, caused by air pollution such as PM 2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on‐road PM 2.5 throughout the whole city. First, the micro‐scale PM 2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index‐based built‐up index, are incorporated within the TOA‐LUR modeling. On‐road PM 2.5 distributions are then mapped in high‐spatial‐resolution. The maps obtained can be used to find healthy travel routes with less PM 2.5 . The proposed framework was applied in high‐density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models is at a high‐level with an R 2 of 0.70–0.90. The newly introduced GVI index played an important role in these estimations. The PM 2.5 distribution maps at high‐spatial‐resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high‐density cities.

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PM2.5 exposure of various microenvironments in a community: Characteristics and applications

Cited by 19 publications

References 60 publications

A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

Evaluation of Variability in the Ambient PM2.5 Concentrations from FEM and FRM-like Measurements for Exposure Estimates

Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

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PM2.5 exposure of various microenvironments in a community: Characteristics and applications

Cited by 19 publications

References 60 publications

A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

Evaluation of Variability in the Ambient PM2.5 Concentrations from FEM and FRM-like Measurements for Exposure Estimates

Estimation of On‐Road PM2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

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Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning