Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations
Abstract Sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) are the key components of PM2.5, but predicting their concentrations remains a challenge because of high uncertainties in the modeling. Employing the Nested Air Quality Prediction Modeling System (NAQPMS) developed by the...
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| Format: | Article |
| Language: | English |
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Springer
2020-07-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.2020.02.0075 |
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| author | Qian Wu Xiao Tang Lei Kong Zirui Liu Duohong Chen Miaomiao Lu Huangjian Wu Jin Shen Lin Wu Xiaole Pan Jie Li Jiang Zhu Zifa Wang |
| author_facet | Qian Wu Xiao Tang Lei Kong Zirui Liu Duohong Chen Miaomiao Lu Huangjian Wu Jin Shen Lin Wu Xiaole Pan Jie Li Jiang Zhu Zifa Wang |
| author_sort | Qian Wu |
| collection | DOAJ |
| description | Abstract Sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) are the key components of PM2.5, but predicting their concentrations remains a challenge because of high uncertainties in the modeling. Employing the Nested Air Quality Prediction Modeling System (NAQPMS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, this study investigated the uncertainties in Monte Carlo simulations of these aerosols in the Pearl River Delta (PRD) region during 2015. 50 ensemble simulations with a 15 km horizontal resolution were derived by perturbing the emission data for sulfate, nitrate, ammonium, OC and BC from an emission inventory, which is one of the largest sources of uncertainty. Then, surface observations of these species collected from 10 sites across the region for 1 year were used to evaluate the performance of the ensemble simulations. The high correlation coefficients (> 0.74) and low mean biases (< 2 µg m−3) between the mean values of the ensemble and the observation data suggested that the model fairly accurately reproduced spatial and temporal variations in the nitrate, ammonium, OC and BC. However, the predicted sulfate concentrations, which displayed a correlation coefficient of 0.26, were far less reliable, particularly owing to the significant underestimation during winter. Further analysis revealed that uncertainties in the emission data explained most of the discrepancies for the OC and BC, but the mean biases for the sulfate and ammonium, especially during winter, probably stemmed from uncertainties in the heterogeneous reaction modeling. |
| format | Article |
| id | doaj-art-03713f5d2a8f4e7aaf23984555691b29 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2020-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-03713f5d2a8f4e7aaf23984555691b292025-02-09T12:20:45ZengSpringerAerosol and Air Quality Research1680-85842071-14092020-07-0121111710.4209/aaqr.2020.02.0075Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo SimulationsQian Wu0Xiao Tang1Lei Kong2Zirui Liu3Duohong Chen4Miaomiao Lu5Huangjian Wu6Jin Shen7Lin Wu8Xiaole Pan9Jie Li10Jiang Zhu11Zifa Wang12LAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesState Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Monitoring CenterState Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai UniversityLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesState Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Monitoring CenterLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesLAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of SciencesAbstract Sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) are the key components of PM2.5, but predicting their concentrations remains a challenge because of high uncertainties in the modeling. Employing the Nested Air Quality Prediction Modeling System (NAQPMS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, this study investigated the uncertainties in Monte Carlo simulations of these aerosols in the Pearl River Delta (PRD) region during 2015. 50 ensemble simulations with a 15 km horizontal resolution were derived by perturbing the emission data for sulfate, nitrate, ammonium, OC and BC from an emission inventory, which is one of the largest sources of uncertainty. Then, surface observations of these species collected from 10 sites across the region for 1 year were used to evaluate the performance of the ensemble simulations. The high correlation coefficients (> 0.74) and low mean biases (< 2 µg m−3) between the mean values of the ensemble and the observation data suggested that the model fairly accurately reproduced spatial and temporal variations in the nitrate, ammonium, OC and BC. However, the predicted sulfate concentrations, which displayed a correlation coefficient of 0.26, were far less reliable, particularly owing to the significant underestimation during winter. Further analysis revealed that uncertainties in the emission data explained most of the discrepancies for the OC and BC, but the mean biases for the sulfate and ammonium, especially during winter, probably stemmed from uncertainties in the heterogeneous reaction modeling.https://doi.org/10.4209/aaqr.2020.02.0075PM2.5 componentsPRD regionMonte Carlo simulationsUncertainty analysis |
| spellingShingle | Qian Wu Xiao Tang Lei Kong Zirui Liu Duohong Chen Miaomiao Lu Huangjian Wu Jin Shen Lin Wu Xiaole Pan Jie Li Jiang Zhu Zifa Wang Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations Aerosol and Air Quality Research PM2.5 components PRD region Monte Carlo simulations Uncertainty analysis |
| title | Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations |
| title_full | Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations |
| title_fullStr | Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations |
| title_full_unstemmed | Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations |
| title_short | Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations |
| title_sort | model evaluation and uncertainty analysis of pm2 5 components over pearl river delta region using monte carlo simulations |
| topic | PM2.5 components PRD region Monte Carlo simulations Uncertainty analysis |
| url | https://doi.org/10.4209/aaqr.2020.02.0075 |
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