| 基于空气质量监测数据的钢铁行业污染源识别方法 |
| 摘要点击 2474 全文点击 633 投稿时间：2021-09-29 修订日期：2021-11-29 |
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| 中文关键词 空气质量监测 京津冀及周边地区 污染特征 来源解析 钢铁行业 |
| 英文关键词 air quality monitoring data Beijing-Tianjin-Hebei and the surrounding area pollution characteristics source apportionment iron and steel industry |
| 作者 | 单位 | E-mail | | 石耀鹏 | 中国环境科学研究院, 北京 100012 | shiyaopeng188@163.com | | 胡京南 | 中国环境科学研究院, 北京 100012
国家大气污染防治攻关联合中心, 北京 100012 | hujn@craes.org.cn | | 褚旸晰 | 中国环境科学研究院, 北京 100012
国家大气污染防治攻关联合中心, 北京 100012 | | | 段菁春 | 中国环境科学研究院, 北京 100012
国家大气污染防治攻关联合中心, 北京 100012 | | | 胡丙鑫 | 中国环境科学研究院, 北京 100012 | | | 殷丽娜 | 中国环境科学研究院, 北京 100012 | | | 吕玲玲 | 中国环境科学研究院, 北京 100012 | |
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| 中文摘要 |
| 京津冀及周边地区等重点区域已经建立具有高时空分辨率特点的空气质量监测网，但监测数据主要用于环境空气质量评价，在大气污染来源识别中应用较少.采用特征雷达图中的双重归一化算法对区域上2018~2019年秋冬季空气质量监测数据中的SO2、NO2、CO、PM2.5和粗颗粒物（PM10与PM2.5的浓度差值）这5种因子进行分析，识别出偏SO2、偏NO2、偏CO、偏PM2.5、偏粗颗粒物、偏SO2-CO、偏NO2-CO和偏PM2.5-CO这8种典型污染特征.以偏SO2-CO特征为例，结合污染特征时空分布、主要污染源排放特征和PM2.5源解析，判断该特征下对空气质量影响最突出的污染源，并将该方法用于一次典型污染过程的分析.结果表明，研究时段内偏SO2-CO特征的平均占比为7.6%.①偏SO2-CO特征在非采暖期占比较采暖期高出11.5个百分点，空间上主要分布在唐山、安阳和长治等钢铁企业集中的城市；②和区域内电厂、机动车、民用燃煤、建材行业等排放源相比，钢铁行业SO2和CO排放量同高，是各行业平均水平的1.3倍和4.0倍；③唐山、安阳和长治的PM2.5源解析表明，偏SO2-CO特征时段内钢铁源对PM2.5的贡献分别为48.6%、36.9%和40.2%，高于燃煤源和二次源等其他污染源的贡献，其它时段下PM2.5主要源自燃煤或扬尘排放；④在由唐山市东部钢铁企业排放所导致的污染过程中，市区的偏SO2-CO特征和钢铁排放示踪元素同步变化.综上，偏SO2-CO特征可用于识别钢铁行业污染源排放对空气质量的影响.典型污染过程分析表明，特征雷达图中的双重归一化算法拓展了空气质量监测数据的应用，为快速识别钢铁行业排放对空气质量的影响提供了一种新的方法. |
| 英文摘要 |
| An air quality monitoring network with high temporal and spatial resolution has been established in Beijing-Tianjin-Hebei (BTH) and the surrounding area. However, those data are mainly used for ambient air quality assessment and are rarely applied for identifying air pollution sources. In this study, a data analysis method referring to the characteristic radar chart (CRC) was used to analyze air pollution characteristics in BTH and the surrounding area based on air monitoring data, including SO2, NO2, CO, PM2.5, and coarse particulate matter (PM10 minus PM2.5) mass concentration. Eight pollution characteristics were identified, which included characteristics dominated by SO2, NO2, CO, PM2.5, or coarse particulate matter and characteristics co-dominated by SO2-CO, NO2-CO, or PM2.5-CO. As an example, to illustrate the major cause of the pollution characteristic co-dominated by SO2-CO, we combined the analysis of the spatio-temporal distribution pattern of this pollution characteristic, emission intensity of major pollution sources, and PM2.5 source apportionment. The results showed that the percentage of days with the pollution characteristic co-dominated by SO2-CO in the study region and period was 7.6%. ① The occurrence frequency of the pollution characteristic co-dominated by SO2-CO in the non-heating season was 11.5% higher than that in the heating season. This pattern mainly existed in cities where the iron and steel industry were densely located, e.g., in Tangshan, Anyang, and Changzhi. ② Furthermore, the iron and steel industry had higher SO2 and CO emission intensity, which were 1.3 times and 4.0 times the average intensity of major sectors, including coal-fired power plants, vehicular exhaust, residential coal combustion, etc. in BTH and the surrounding area. ③ According to PM2.5 source apportionment, the contributions of the iron and steel industry to PM2.5 when the pollution characteristic was co-dominated by SO2-CO were 48.6%, 36.9%, and 40.2% in Tangshan, Anyang, and Changzhi, respectively. In the other period, PM2.5 was mainly from coal-burning or fugitive dust emissions. ④ The pollution characteristic co-dominated by SO2-CO and the tracer elements of the iron and steel industry changed simultaneously during the pollution episode in east Tangshan. In summary, the pollution characteristic co-dominated by SO2-CO can indicate the impact of iron and steel industry emissions on air quality. This method expands the application of air quality monitoring data and provides a new tool for the instant identification of iron and steel industry pollution. |
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