Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015

Jung, Chau-Ren; Hwang, Bing‐Fang; Chen, Wei‐Ting

doi:10.1016/j.envpol.2017.11.016

Cited by 66 publications

(36 citation statements)

References 40 publications

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“…MODIS AOD has been widely used for PM2.5 concentration retrieval (Guo et al, 2017b;Jung et al, 2017;Ma et al, 2014;Xie et al, 2015), but as our study shows, PM2.5 is accounting for a less and less part in AOD and their correlation has been decreasing since 2014, so AOD's predicting ability for PM2.5 concentration needs to be further validated. We retrieve the PM2.5 concentration with AOD and find the retrieval R 2 are decreasing from 2013 to 2017, indicating the performance of GWR model are getting worse.…”

Section: Interannual Variations Of Gwr Accuracymentioning

confidence: 87%

The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations

Yang

Yuan

Yue

et al. 2019

Environmental Pollution

127

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Section: Interannual Variations Of Gwr Accuracymentioning

confidence: 87%

The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations

Yang

Yuan

Yue

et al. 2019

Environmental Pollution

127

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“…Second, we used a novel satellite-based hybrid model to integrate satellite AOD with meteorological variables and landuse data with an excellent temporal resolution to estimate ground PM 2.5 in Taiwan. 22 This model allowed us to explore vulnerable time windows on a weekly basis through conception to the infancy periods.…”

Section: Discussionmentioning

confidence: 99%

“…We previously reported the 10-km resolution hybrid model to be a reliable and validated method for estimating ground-level PM 2.5 concentrations in Taiwan. 22 We further adapted this hybrid model for environmental epidemiology. The details of the 10-km hybrid model are described in the Methods section in this article's Online Repository.…”

Section: Exposure Assessmentsmentioning

confidence: 99%

Fine particulate matter exposure during pregnancy and infancy and incident asthma

Jung

Chen

Tang

et al. 2019

Journal of Allergy and Clinical Immunology

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Background: Lung development is a multistage process from conception to the postnatal period, disruption of which by air pollutants can trigger later respiratory morbidity. Objective: We sought to evaluate the effects of weekly average fine particulate matter (particulate matter with an aerodynamic diameter less than 2.5 mm [PM 2.5 ]) exposure during pregnancy and infancy on asthma and identify vulnerable times to help elucidate possible mechanisms of the effects of PM 2.5 on asthma symptoms. Methods: A birth cohort study including 184,604 children born during 2004-2011 in Taichung City was retrieved from the Taiwan Maternal and Child Health Database and followed until 2014. A daily satellite-based hybrid model was applied to estimate PM 2.5 exposure for each subject. A Cox proportional hazard model combined with a distributed lag nonlinear model was used to evaluate the associations of asthma with PM 2.5 exposure during pregnancy and infancy. Results: The birth cohort contained 34,336 asthmatic patients, and the mean age of children given a diagnosis of asthma was 3.39 6 1.78 years. Increased exposure to PM 2.5 during gestational weeks 6 to 22 and 9 to 46 weeks after birth were significantly associated with an increased incidence of asthma. The exposure-response relationship indicated that the hazard ratio (HR) of asthma increased steeply at PM 2.5 exposure of greater than 93 mg/m 3 during pregnancy. Additionally, the HRs remained significant with postnatal exposure to PM 2.5 between 26 and 72 mg/m 3 (range, 1.01-1.07 mg/m 3), followed by a sharp increase in HRs at PM 2.5 exposure of greater than 73 mg/m 3. Conclusion: Both prenatal and postnatal exposures to PM 2.5 were associated with later development of asthma. The vulnerable time windows might be within early gestation and midgestation and infancy.

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“…EPA uses an automatic continuous monitoring system to immediately identify and respond to uncommom emerging signals. This system collects air quality data every hour 39,40 . EPA provided more than 260,000 samples from 77 air stations in 2017.…”

Section: Methodsmentioning

confidence: 99%

Forecasting Air Quality in Taiwan by Using Machine Learning

Lee¹,

Lin

Chen

et al. 2020

Sci Rep

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fang 1,2* this study proposes a gradient-boosting-based machine learning approach for predicting the pM 2.5 concentration in Taiwan. The proposed mechanism is evaluated on a large-scale database built by the Environmental Protection Administration, and Central Weather Bureau, Taiwan, which includes data from 77 air monitoring stations and 580 weather stations performing hourly measurements over 1 year. By learning from past records of PM 2.5 and neighboring weather stations' climatic information, the forecasting model works well for 24-h prediction at most air stations. This study also investigates the geographical and meteorological divergence for the forecasting results of seven regional monitoring areas. We also compare the prediction performance between Taiwan, Taipei, and London; analyze the impact of industrial pollution; and propose an enhanced version of the prediction model to improve the prediction accuracy. The results indicate that Taipei and London have similar prediction results because these two cities have similar topography (basin) and are financial centers without domestic pollution sources. The results also suggest that after considering industrial impacts by incorporating additional features from the Taichung and Thong-Siau power plants, the proposed method achieves significant improvement in the coefficient of determination (R 2) from 0.58 to 0.71. Moreover, for Taichung City the root-mean-square error decreases from 8.56 for the conventional approach to 7.06 for the proposed method.

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Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015

Cited by 66 publications

References 40 publications

The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations

The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations

Fine particulate matter exposure during pregnancy and infancy and incident asthma

Forecasting Air Quality in Taiwan by Using Machine Learning

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