کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6963667 | 1452289 | 2014 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen
ترجمه فارسی عنوان
پیش بینی غلظت ذرات نیتروژن در محیط های شهری با استفاده از رگرسیون گاوسی بر اساس اندازه گیری اکسید نیتروژن
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
Gaussian process regression is used to predict ultrafine particle (UFP) number concentrations. We infer their number concentrations based on the concentrations of NO, NO2, CO and O3 at half hour and 5Â min resolution. Because UFP number concentrations follow from a dynamic process, we have used a non-stationary kernel based on the addition of a linear and a rational quadratic kernel. Simultaneous measurements of UFP and gaseous pollutants were carried out during one month at three sampling locations situated within a 1Â km2 area in a Belgian city, Antwerp. The method proposed provides accurate predictions when using NO and NO2 as covariates and less accurate predictions when using CO and O3. We have also evaluated the models for different training periods and we have found that a training period of at least seven days is suitable to let the models learn the UFP number concentration dynamics in different typologies of traffic.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 61, November 2014, Pages 135-150
Journal: Environmental Modelling & Software - Volume 61, November 2014, Pages 135-150
نویسندگان
Matteo Reggente, Jan Peters, Jan Theunis, Martine Van Poppel, Michael Rademaker, Prashant Kumar, Bernard De Baets,