Academic Journal
Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0
| العنوان: | Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0 |
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| المؤلفون: | Dinkelacker, Brian T., Garcia Rivera, Pablo, Kioutsioukis, Ioannis, Adams, Peter J., Pandis, Spyros N. |
| بيانات النشر: | Copernicus Publications |
| سنة النشر: | 2022 |
| المجموعة: | Niedersächsisches Online-Archiv NOA (Gottfried Wilhelm Leibniz Bibliothek Hannover) |
| مصطلحات موضوعية: | article, Verlagsveröffentlichung |
| الوصف: | Accurately predicting urban PM2.5 concentrations and composition has proved challenging in the past, partially due to the resolution limitations of computationally intensive chemical transport models (CTMs). Increasing the resolution of PM2.5 predictions is desired to support emissions control policy development and address issues related to environmental justice. A nested grid approach using the CTM PMCAMx-v2.0 was used to predict PM2.5 at increasing resolutions of 36 km × 36 km, 12 km × 12 km, 4 km × 4 km, and 1 km × 1 km for a domain largely consisting of Allegheny County and the city of Pittsburgh in southwestern Pennsylvania, US, during February and July 2017. Performance of the model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Novel surrogates were developed to allocate emissions from cooking and on-road traffic sources to the 1 km × 1 km resolution grid. Total PM2.5 mass is reproduced well by the model during the winter period with low fractional error (0.3) and fractional bias (+0.05) when compared to regulatory measurements. Comparison with speciated measurements during this period identified small underpredictions of PM2.5 sulfate, elemental carbon (EC), and organic aerosol (OA) offset by a larger overprediction of PM2.5 nitrate. In the summer period, total PM2.5 mass is underpredicted due to a large underprediction of OA (bias = −1.9 µg m−3, fractional bias = −0.41). In the winter period, the model performs well in reproducing the variability between urban measurements and rural measurements of local pollutants such as EC and OA. This effect is less consistent in the summer period due to a larger fraction of long-range-transported OA. Comparison with total PM2.5 concentration measurements from low-cost sensors showed improvements in performance with increasing resolution. Inconsistencies in PM2.5 nitrate predictions in both periods are believed to be due to errors in ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | electronic |
| اللغة: | English |
| Relation: | Geoscientific Model Development -- http://www.bibliothek.uni-regensburg.de/ezeit/?2456725 -- http://www.geosci-model-dev.net/ -- 1991-9603; https://doi.org/10.5194/gmd-15-8899-2022; https://noa.gwlb.de/receive/cop_mods_00063945; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062862/gmd-15-8899-2022.pdf; https://gmd.copernicus.org/articles/15/8899/2022/gmd-15-8899-2022.pdf |
| DOI: | 10.5194/gmd-15-8899-2022 |
| الاتاحة: | https://doi.org/10.5194/gmd-15-8899-2022 https://noa.gwlb.de/receive/cop_mods_00063945 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062862/gmd-15-8899-2022.pdf https://gmd.copernicus.org/articles/15/8899/2022/gmd-15-8899-2022.pdf |
| Rights: | https://creativecommons.org/licenses/by/4.0/ ; uneingeschränkt ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.A9B368E3 |
| قاعدة البيانات: | BASE |
| DOI: | 10.5194/gmd-15-8899-2022 |
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