کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11017754 | 1721146 | 2018 | 40 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Estimating PM1 concentrations from MODIS over Yangtze River Delta of China during 2014-2017
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
علم هواشناسی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Compared to the space-borne estimation of PM2.5 (particulate matter with aerodynamic diameter â¤2.5â¯Î¼m), the investigation of PM1 (â¤1â¯Î¼m) remains less intensive and thus unclear. Here we estimated four years (2014-2017) of ground-level PM1 concentrations from MODIS aerosol optical depth (AOD) in attempt to gain a better understanding of much finer particles. The Yangtze River Delta (YRD) region, with a relatively dense ground-based PM1 station network, was selected as the study area. The geographically and temporally weighted regression (GTWR) model simultaneously accounting for spatial and temporal variability existing within various predictors was constructed. Validation of satellite-estimated PM1 against ground-measured PM1 yields a high consistence, significant improvement over previous work (R2â¯=â¯0.74 VS 0.59, RMSEâ¯=â¯13.02â¯Î¼g/m3 VS 22.5â¯Î¼g/m3). This suggests the PM1 estimates from GTWR model are reliable and robust enough to obtain large-scale fine particle contents. The population exposure of air pollution in the YRD region, therefore, has been analyzed by calculating population-weighted mean PM1 concentrations, which reaches as high as 37.22â¯Î¼g/m3. Further analysis indicates that near half the people live in locations with high-level PM1 concentration (>35â¯Î¼g/m3), which has profounding implication for improving our understanding of human exposure to fine aerosol particles.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Atmospheric Environment - Volume 195, December 2018, Pages 149-158
Journal: Atmospheric Environment - Volume 195, December 2018, Pages 149-158
نویسندگان
Kai Qin, Jiaheng Zou, Jianping Guo, Meng Lu, Muhammad Bilal, Kefei Zhang, Fangfang Ma, Yishu Zhang,