上海市青浦区PM_(10)污染与气象要素的关系

利用2006年-2010年上海市青浦区PM10和同期地面气象要素的监测资料,定量分析PM10的季节变化规律以及PM10与降雨量、大气湿度和风速之间的关系。分析结果表明:PM10浓度在夏季处于低值,冬季处于高值;5 mm/d以上的降雨对PM10有显著的清除作用,且春夏季降雨的清除作用大于秋冬季节。PM10浓度与大气湿度基本呈负相关关系。风速在一定范围内有利于PM10的扩散但不至造成扬尘,春季的适宜风速是1.5～3.5m/s,冬季的适宜风速是1.5～2.5 m/s。     

手工法与自动监测法测定PM2.5质量浓度比对研究

采用小流量颗粒物采样器手工采样重量法和基于微振荡天平原理自动监测法监测PM2.5质量浓度。通过2010年9月至2011年8月期间两种方法的监测数据比对显示出季节差异,冬、春两季手工法获得质量浓度高于自动法,夏、秋两季手工法低于自动法,可能与两种采样方法下膜置于不同的温度有关。当受沙尘影响、持续污染或者高温污染时,两种方法结果偏差较大,可能与仪器的粒径切割效率改变有关。     

巴彦浩特镇PM（10）年度变化特征分析

本文对巴彦浩特镇2009年至2011年三年间的PM10现状及变化趋势进行了分析,探讨PM10变化原因,并对改善环境质量状况提出了对策建议。     

Evaluation of levels, sources and distribution of toxic elements in PM10 in a suburban industrial region, Rio de Janeiro, Brazil

The Oswaldo Cruz Foundation Campus (FIOCRUZ), in a suburban region of the city of Rio de Janeiro, was selected as a case study to assess the pollution released from vehicle and industrial facilities in Basin III, the most polluted area of the city. Concentrations of particulate matter (PM10) and trace metals in airborne particles were determined in an intensive field campaign. The samplings were performed every six days for 24 h periods, using a PM10 high volume sampler, from September 2004 to August 2005. PM10 mass concentrations were determined gravimetrically and the metals by ICP-OES. For PM10, the arithmetic mean for the period is 169 ± 42 μg m⁻³ which is 3.4 times the national recommended standard of 50 μg m⁻³. Additionally, 51% of the samplings exceeded the recommended 24 h limit of 150 μg m⁻³. Ca, Mg, Fe, Zn and Al were the metals that presented the higher concentrations. The correlation matrix gave two main clusters and three significant principal components (PC). Both PC1 and PC2 are associated to crustal, vehicular and industrial emissions while PC3 is mainly associated to geological material. Enrichment factors for Zn, Cu, Cd and Pb indicate that for these elements, anthropic sources prevail over natural inputs. PM10 levels showed a good correlation with hospital admissions for respiratory diseases in children and elderly people.     

PM2.5污染危害分析及防控措施研究

本文论述了PM2.5对环境的危害,在剖析PM2.5组成与来源的基础上,提出防控PM2.5的政策与技术方面的建议,以期为今后PM2.5的防控工作提供理论基础。     

天津市1月份大气PM10中酞酸酯的污染

酞酸酯是环境中广泛存在的一种塑料增塑剂,大气中的酞酸酯会随着呼吸进入人体,对人体产生危害.采用超声提取,气相色谱-质谱联用的方法分析了天津市2010年1月15-19日7个采样点大气PM10中邻苯二甲酸二甲酯(DMP)、邻苯二甲酸二乙酯(DEP)、邻苯二甲酸二丁酯(DBP)、邻苯二甲酸苄基丁基酯(BBP)、邻苯二甲酸双(2-乙基己基)酯(DEHP)、邻苯二甲酸二辛基酯(DOP)6种酞酸酯的污染.结果表明,除了BBP的回收率为77.62%之外,其他5种物质的回收率均在80%以上,基本满足分析要求.采样期间天津市大气PM10中酞酸酯以DBP和DEHP为主.从空间变化来看,DBP和DEHP点位均值分别为12.6(红桥,交通稠密区)～39.68 ng·m-3(北辰,工业区)和80.2(红桥,交通稠密区)～283.13 ng·m-3(昆程园,居住区); 从时间变化来看,DBP和DEHP日均值分别为24.65～40.58 ng·m-3和155.55～283.48 ng·m-3,其日均最大值均出现在有雾的采样日. 与国内其他城市相比,DBP的质量浓度较低,而DEHP的质量浓度则相对较高; DMP、DEP、BBP及DOP 4种物质均有检出,但质量浓度较低,质量浓度变化范围分别为nd～7.90 ng·m-3、nd～12.79 ng·m-3、nd～0.92 ng·m-3和nd～12.20 ng·m-3.     

杭州市大气PM2.5和PM10污染特征及来源解析

2006年在杭州市两个环境受体点位采集不同季节大气中PM2.5和PM10样品,同时采集了多种颗粒物源类样品,分析了其质量浓度和多种化学成分,包括21种无机元素、5种无机水溶性离子以及有机碳和元素碳等,并据此构建了杭州市PM2.5和PM10的源与受体化学成分谱;用化学质量平衡(CMB)受体模型解析其来源。结果表明,杭州市PM2.5和PM10污染较严重,其年均浓度分别为77.5μg/m3和111.0μg/m3;各主要源类对PM2.5的贡献率依次为机动车尾气尘21.6%、硫酸盐18.8%、煤烟尘16.7%、燃油尘10.2%、硝酸盐9.9%、土壤尘8.2%、建筑水泥尘4.0%、海盐粒子1.5%。各主要源类对PM10贡献率依次为土壤尘17.0%、机动车尾气尘16.9%、硫酸盐14.3%、煤烟尘13.9%、硝酸盐粒8.2%、建筑水泥尘8.0%、燃油尘5.5%、海盐粒子3.4%、冶金尘3.2%。     

Volatile organic compounds at swine facilities: A critical review

Volatile organic compounds (VOCs) are regulated aerial pollutants that have environmental and health concerns. Swine operations produce and emit a complex mixture of VOCs with a wide range of molecular weights and a variety of physicochemical properties. Significant progress has been made in this area since the first experiment on VOCs at a swine facility in the early 1960s. A total of 47 research institutions in 15 North American, European, and Asian countries contributed to an increasing number of scientific publications. Nearly half of the research papers were published by U.S. institutions.Investigated major VOC sources included air inside swine barns, in headspaces of manure storages and composts, in open atmosphere above swine wastewater, and surrounding swine farms. They also included liquid swine manure and wastewater, and dusts inside and outside swine barns. Most of the sample analyses have been focusing on identification of VOC compounds and their relationship with odors. More than 500 VOCs have been identified. About 60% and 10% of the studies contributed to the quantification of VOC concentrations and emissions, respectively. The largest numbers of VOC compounds with reported concentrations in a single experimental study were 82 in air, 36 in manure, and 34 in dust samples.The relatively abundant VOC compounds that were quantified in at least two independent studies included acetic acid, butanoic acid (butyric acid), dimethyl disulfide, dimethyl sulfide, iso-valeric, p-cresol, propionic acid, skatole, trimethyl amine, and valeric acid in air. They included acetic acid, p-cresol, iso-butyric acid, butyric acid, indole, phenol, propionic acid, iso-valeric acid, and skatole in manure. In dust samples, they were acetic acid, propionic acid, butyric acid, valeric acid, p-cresol, hexanal, and decanal. Swine facility VOCs were preferentially bound to smaller-size dusts.Identification and quantification of VOCs were restricted by using instruments based on gas Chromatography (GC) and liquid chromatography (LC) with different detectors most of which require time-consuming procedures to obtain results. Various methodologies and technologies in sampling, sample preparation, and sample analysis have been used. Only four publications reported using GC based analyzers and PTR-MS (proton-transfer-reaction mass spectrometry) that allowed continuous VOC measurement. Because of this, the majority of experimental studies were only performed on limited numbers of air, manure, or dust samples. Many aerial VOCs had concentrations that were too low to be identified by the GC peaks.Although VOCs emitted from swine facilities have environmental concerns, only a few studies investigated VOC emission rates, which ranged from 3.0 to 176.5 mg d⁻¹ kg⁻¹ pig at swine finishing barns and from 2.3 to 45.2 g d⁻¹ m⁻² at manure storages. Similar to the other pollutants, spatial and temporal variations of aerial VOC concentrations and emissions existed and were significantly affected by manure management systems, barn structural designs, and ventilation rates.Scientific research in this area has been mainly driven by odor nuisance, instead of environment or health concerns. Compared with other aerial pollutants in animal agriculture, the current scientific knowledge about VOCs at swine facilities is still very limited and far from sufficient to develop reliable emission factors.     

Application of positive matrix factorization in characterization of PM(10) and PM(2.5) emission sources at urban roadside

The 24-h average coarse (PM₁₀) and fine (PM_2.5) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM₁₀ and PM_2.5 concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM₁₀ concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM_2.5 concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM₁₀ and PM_2.5 masses showed a high concentration in winter followed by monsoon and summer seasons.The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM₁₀ and PM_2.5 concentrations at the study area. Results indicated that marine aerosol (40.4% in PM₁₀ and 21.5% in PM_2.5) and secondary PM (22.9% in PM₁₀ and 42.1% in PM_2.5) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM₁₀ and 6% in PM_2.5), biomass burning (0.7% in PM₁₀ and 14% in PM_2.5), tire and brake wear (4.1% in PM₁₀ and 5.4% in PM_2.5), soil (3.4% in PM₁₀ and 4.3% in PM_2.5) and other sources (12.7% in PM₁₀ and 6.8% in PM_2.5).