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| 1 | 基于PMF模型的PM2.5中金属元素污染及来源的区域特征分析显示文摘金属元素是大气PM2.5的重要组成成分,对人群危害性极强且兼具源特异性,分析不同经济模式地区大气细颗粒物中金属污染状况及来源差异,可以为科学规划城市产业布局和保护大气环境提供参考.通过霾/非霾期大气PM2.5采样,使用电感耦合等离子体发射光谱仪(ICP-OES)测定成都市及仁寿县样品中18种金属元素质量浓度,分析其污染水平,并基于正定矩阵因子分解模型(PMF)解析两地大气PM2.5中金属元素的来源.结果表明,成都市扬尘源、移动源和燃煤源特征元素占元素总和的比值大于仁寿县,而仁寿县生物质燃烧源、工业源以及燃油源特征元素占比则较高.两地Cr、Cd和As元素浓度均超标,表明PM2.5中重金属污染严重.随着霾污染加剧,两地PM2.5中金属元素总量上升,但增幅远低于PM2.5浓度增长.此外,不同元素在霾期和非霾期浓度比值存在差异,成都市变化范围为0.7(Al)~2.8(Ba),仁寿县介于0.8(Al)~3.1(Mn)之间,但总的来说两地大致呈现出燃煤和工业活动排放元素增幅较大,机动车污染源次之,扬尘源增幅较缓的状况.受地区产业布局、经济规模和发展模式的影响,大气PM2.5中金属元素污染水平及来源呈现出不同的区域特征.在重点发展第三产业的大型城市,如成都,更易遭受交通运输和城市建设等带来的大气污染,而在仁寿等第二产业占比不断增加的郊县,其污染主要受化石燃料燃烧和工业过程排放的影响. | 邓林俐 张凯山 殷子渊 李欣悦 武文琪 向锌鹏 | 2020 | 环境科学2020，41，12： | 21 |
| 2 | Association of emergency room visits for respiratory diseases with sources of ambient PM2.5显示文摘Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter(PM2.5)and increased mortality or hospitalizations for respiratory diseases.Few studies,however,have focused on the short-term effects of source-specific PM2.5 on emergency room visits(ERVs)of respiratory diseases.Source apportionment for PM2.5 was performed with Positive Matrix Factorization(PMF)and generalized additive model was applied to estimate associations between source-specific PM2.5 and respiratory disease ERVs.The association of PM2.5 and total respiratory ERVs was found on lag4(RR=1.011,95%CI:1.002,1.020)per interquartile range(76μg/m3)increase.We found PM2.5 to be significantly associated with asthma,bronchitis and chronic obstructive pulmonary disease(COPD)ERVs,with the strongest effects on lag5(RR=1.072,95%CI:1.024,1.119),lag4(RR=1.104,95%CI:1.032,1.176)and lag3(RR=1.091,95%CI:1.047,1.135),respectively.The estimated effects of PM2.5 changed little after adjusting for different air pollutants.Six primary PM2.5 sources were identified using PMF analysis,including dust/soil(6.7%),industry emission(4.5%),secondary aerosols(30.3%),metal processing(3.2%),coal combustion(37.5%)and traffic-related source(17.8%).Some of the sources were identified to have effects on ERVs of total respiratory diseases(dust/soil,secondary aerosols,metal processing,coal combustion and traffic-related source),bronchitis ERVs(dust/soil)and COPD ERVs(traffic-related source,industry emission and secondary aerosols).Different sources of PM2.5 contribute to increased risk of respiratory ERVs to different extents,which may provide potential implications for the decision making of air quality related policies,rational emission control and public health welfare. | Rui Chi Hongyu Li Qian Wang Qiangrong Zhai Daidai Wang Meng Wu Qichen Liu Shaowei Wu Qingbian Ma Furong Deng Xinbiao Guo | 2019 | Journal of Environmental Sciences2019，31，12： | 8 |
| 3 | Size distribution,directional source contributions and pollution status of PM from Chengdu,China during a long-term sampling campaign显示文摘Long-term and synchronous monitoring of PM_(10) and PM_(2.5)was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models(PMF2, ME2-2way and ME2-3way). Consistent results were found: the primary source categories contributed 63.4%(PMF2), 64.8%(ME2-2way) and 66.8%(ME2-3way) to PM_(10), and contributed 60.9%(PMF2), 65.5%(ME2-2way) and 61.0%(ME2-3way) to PM_(2.5). Secondary sources contributed 31.8%(PMF2), 32.9%(ME2-2way) and 31.7%(ME2-3way) to PM_(10), and35.0%(PMF2), 33.8%(ME2-2way) and 36.0%(ME2-3way) to PM_(2.5). The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment(SDA) was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east(ENE) contributed the highest to both PM_(10)(12.7%) and PM_(2.5)(9.7%) in Chengdu, followed by the crustal dust from south-east(SE) for PM_(10)(9.8%) and secondary nitrate & secondary organic carbon from ENE for PM_(2.5)(9.6%). Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and to develop effective pollution control strategies. | Guo-Liang Shi Ying-Ze Tian Tong Ma Dan-Lin Song Lai-Dong Zhou Bo Han Yin-Chang Feng Armistead G.Russell | 2017 | Journal of Environmental Sciences2017，29，6： | 1 |
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