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| Titel |
Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements |
| VerfasserIn |
Yixuan Zheng, Qiang Zhang, Yang Liu, Guannan Geng, Kebin He |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250125645
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| Publikation (Nr.) |
 EGU/EGU2016-5246.pdf |
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| Zusammenfassung |
| Numerous previous studies have revealed that statistical models which combine
satellite-derived aerosol optical depth (AOD) and PM2.5 measurements acquired at scattered
monitoring sites provide an effective method for deriving continuous spatial distributions of
ground-level PM2.5 concentrations. Using the national monitoring networks that have
recently been established by central and local governments in China, we developed
linear mixed-effects (LMEs) models that integrate Moderate Resolution Imaging
Spectroradiometer (MODIS) AOD measurements, meteorological parameters, and
satellite-derived tropospheric NO2 column density measurements as predictors to
estimate PM2.5 concentrations over three major industrialized regions in China,
namely, the Beijing-Tianjin-Hebei region (BTH), the Yangtze River Delta region
(YRD), and the Pearl River Delta region (PRD). The models developed for these three
regions exploited different predictors to account for their varying topographies
and meteorological conditions. Considering the importance of unbiased PM2.5
predictions for epidemiological studies, the correction factors calculated from the surface
PM2.5 measurements were applied to correct biases in the predicted annual average
PM2.5 concentrations introduced by non-stochastic missing AOD measurements.
Leave-one-out cross-validation (LOOCV) was used to quantify the accuracy of our models.
Cross-validation of the daily predictions yielded R2 values of 0.77, 0.8 and 0.8
and normalized mean error (NME) values of 22.4%, 17.8% and 15.2% for BTH,
YRD and PRD, respectively. For the annual average PM2.5 concentrations, the
LOOCV R2 values were 0.85, 0.76 and 0.71 for the three regions, respectively,
whereas the LOOCV NME values were 8.0%, 6.9% and 8.4%, respectively. We
found that the incorporation of satellite-based NO2 column density into the LMEs
model contribute to considerable improvements in annual prediction accuracy for
both BTH and YRD. The satisfactory performance of our models indicates that
constructing LMEs models using various combinations of predictors for different
regions would be helpful for predicting PM2.5 concentrations with high accuracy. |
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