| 基于多种新型受体模型的PM2.5来源解析对比 |
| 摘要点击 4513 全文点击 1239 投稿时间：2021-06-24 修订日期：2021-07-21 |
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| 中文关键词 PM2.5 来源解析 正定矩阵分解/多元线性引擎2-物种比值模型(PMF/ME2-SR) 偏目标转换-正定矩阵分解模型(PTT-PMF) 新型受体模型 |
| 英文关键词 PM2.5 source apportionment positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR) partial target transformation-positive matrix factorization (PTT-PMF) new receptor models |
| 作者 | 单位 | E-mail | | 王振宇 | 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | wangzhenyu818@mail.nankai.edu.cn | | 李永斌 | 晋中市生态环境局, 晋中 030600 | | | 郭凌 | 晋中市生态环境局, 晋中 030600 | | | 宋志强 | 晋中市生态环境局, 晋中 030600 | | | 许艳玲 | 生态环境部环境规划院区域空气质量模拟与管控研究中心, 北京 100012 | | | 王丰 | 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | | | 梁维青 | 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | | | 史国良 | 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | nksgl@nankai.edu.cn | | 冯银厂 | 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | |
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| 中文摘要 |
| 为了解多种新型受体模型的适用性，利用正定矩阵分解/多元线性引擎2-物种比值（PMF/ME2-SR）、偏目标转换-正定矩阵分解（PTT-PMF）、正定矩阵分解（PMF）和化学质量平衡（CMB）这4种受体模型对我国北方典型城市细颗粒物（PM2.5）数据进行同步解析并互相验证.结果发现，燃煤源（25%~26%）、扬尘源（19%~21%）、二次硝酸盐（17%~19%）、二次硫酸盐（16%）、机动车源（13%~15%）、生物质燃烧源（4%~7%）和钢铁源（1%~2%）这7种主要污染源对研究地区PM2.5有贡献.通过比较不同模型获得的源成分谱和源贡献以及计算各源的差异系数（CD）和平均绝对误差（AAE），发现4种模型的解析结果具有较高的一致性（平均CD值在0.6~0.7之间），但不同模型对各污染源中组分的识别存在差异.相比于传统PMF模型，PMF/ME2-SR模型由于纳入一次源类的特征比值，能够更好地区分源谱特征较为相似的源类，如扬尘源的CD和AAE分别比PMF模型低15%和54%；PTT-PMF模型以实测一次源谱和虚拟二次源谱为约束目标，计算的二次硫酸盐的CD和AAE分别为0.25和17%，比PMF低55%和23%，获得了更"纯净"的二次源类并识别了其他模型未识别的钢铁源，对源类的精细化解析更具优势. |
| 英文摘要 |
| In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources. |
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