کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8855271 | 1619014 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
شیمی زیست محیطی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China](/preview/png/8855271.png)
چکیده انگلیسی
Concentrations of particulate matter with aerodynamic diameter <2.5â¯Î¼m (PM2.5) are relatively high in China. Estimation of PM2.5 exposure is complex because PM2.5 exhibits complex spatiotemporal patterns. To improve the validity of exposure predictions, several methods have been developed and applied worldwide. A hybrid approach combining a land use regression (LUR) model and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals were developed to estimate the PM2.5 concentrations on a national scale in China. This hybrid model could potentially provide more valid predictions than a commonly-used LUR model. The LUR/BME model had good performance characteristics, with R2â¯=â¯0.82 and root mean square error (RMSE) of 4.6â¯Î¼g/m3. Prediction errors of the LUR/BME model were reduced by incorporating soft data accounting for data uncertainty, with the R2 increasing by 6%. The performance of LUR/BME is better than OK/BME. The LUR/BME model is the most accurate fine spatial scale PM2.5 model developed to date for China.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environment International - Volume 116, July 2018, Pages 300-307
Journal: Environment International - Volume 116, July 2018, Pages 300-307
نویسندگان
Li Chen, Shuang Gao, Hui Zhang, Yanling Sun, Zhenxing Ma, Sverre Vedal, Jian Mao, Zhipeng Bai,