کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6317131 | 1619190 | 2013 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improved retrieval of PM2.5 from satellite data products using non-linear methods
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
شیمی زیست محیطی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Satellite observations may improve the areal coverage of particulate matter (PM) air quality data that nowadays is based on surface measurements. Three statistical methods for retrieving daily PM2.5 concentrations from satellite products (MODIS-AOD, OMI-AAI) over the San Joaquin Valley (CA) are compared - Linear Regression (LR), Generalized Additive Models (GAM), and Multivariate Adaptive Regression Splines (MARS). Simple LRs show poor correlations in the western USA (R2Â â
 0.2). Both GAM and MARS were found to perform better than the simple LRs, with a slight advantage to the MARS over the GAM (R2 = 0.71 and R2 = 0.61, respectively). Since MARS is also characterized by a better computational efficiency than GAM, it can be used for improving PM2.5 retrievals from satellite aerosol products. Reliable PM2.5 retrievals can fill in missing surface measurements in areas with sparse ground monitoring coverage and be used for evaluating air quality models and as exposure metrics in epidemiological studies.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Pollution - Volume 182, November 2013, Pages 417-423
Journal: Environmental Pollution - Volume 182, November 2013, Pages 417-423
نویسندگان
M. Sorek-Hamer, A.W. Strawa, R.B. Chatfield, R. Esswein, A. Cohen, D.M. Broday,