| 关中地区细颗粒物碳组分特征及来源解析 |
| 摘要点击 4214 全文点击 1190 投稿时间：2018-10-25 修订日期：2019-01-16 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 关中地区 细颗粒物 有机碳 元素碳 正矩阵因子分解(PMF) |
| 英文关键词 Guanzhong area fine particulate matter organic carbon element carbon positive matrix factorization(PMF) |
|
| 中文摘要 |
| 为研究关中地区细颗粒物中碳组分的污染特征及其来源，于2017年9月4日至2018年1月19日在关中地区5个主要典型城市（西安、渭南、铜川、宝鸡和咸阳）设置采样点（XA、WN、TCH、BJ和XY）进行细颗粒物（PM2.5）的手工采样观测，碳组分环境样品采用热光透射法（TOT）分析.结果表明，细颗粒物中OC和EC平均浓度分别为（14.48±7.86）μg·m-3和（2.27±0.95）μg·m-3，占比分别为18.04%和2.99%，与其他城市相比污染较为严重.碳组分占比的空间分布为XY > WN > XA > BJ > TCH，且季节差异明显，冬季占比高于秋季.OC与EC的相关性显著（R2=0.79），有着较为相同的污染来源.OC1在碳组分中比例最高为23.44%，碳组分的浓度顺序为OC1 > EC2 > EC3 > OC4 > EC1 > OC2 > OC3 > EC4 > EC6 > EC5.正矩阵因子分解模型的源解析结果表明，该地区碳组分的4类主要贡献源为生物质燃烧与燃煤源、汽油车尾气、柴油车尾气和道路扬尘污染源，贡献率分别为48.63%、23.07%、18.82%和9.47%，各点位的污染贡献结构有着明显的差异. |
| 英文摘要 |
| In order to study the pollution characteristics and sources of fine particulate matter in the Guanzhong area of China, PM2.5 samples were collected and observed by hand from September 4, 2017 to January 19, 2018 at five sites (XA, WN, TCH, BJ and XY). The carbonaceous component of these samples was analyzed by thermal-optical transmission, which showed that the average concentrations of OC and EC in the fine particulate matter were (14.48±7.86) μg·m-3 and (2.27±0.95) μg·m-3, respectively, Percentages of OC and EC were 18.04% and 2.99%, respectively. Compared with other cities, the measured levels of pollution in the Guanzhong areas were more severe. The spatial distribution of percentage of carbon component in PM2.5 was XY > WN > XA > BJ > TCH, and the concentrations in winter were higher than in autumn. The correlation between OC and EC was significant (R2=0.79), which indicates a common source. The highest proportion of OC1 was 23.44%. The concentration of the carbonaceous component from high to low was OC1 > EC2 > EC3 > OC4 > EC1 > OC2 > OC3 > EC4 > EC6 > EC5. The results of PMF modeling show that the four main contributing sources of carbon components in pollution in this area are biomass combustion and coal-burning, gasoline vehicle exhaust emissions, diesel vehicle exhaust emissions, and road dust, contribution 48.63%, 23.07%, 18.82%, and 9.47%, respectively. Furthermore, there were clear differences in the pollution structure at each study site. |
|
|
|
