上海市中心城区空气中可吸入颗粒物污染水平和变化状况

2005年对上海市闸北区空气中可吸入颗粒物(PM10--空气动力学当量直径小于10μm的颗粒物的总称)的污染状况进行了一年的连续监测.结果表明:全年各月PM10污染均以2级为主,占总样本数的67.6%;其次是1级占20.0%,3级占11.0%,4级占1.4%.全年空气质量状况最好的为9月份,最差为11月份.PM10浓度与CO和NO2浓度呈现出显著的相关性,说明机动车尾气是该区域PM10的主要来源;PM10污染程度还明显地受到降水、气温、气压、风向、风速、相对湿度等气象条件以及北方沙尘暴的影响.     

应用稳定碳同位素组成特征研究环境空气颗粒物中多环芳烃的来源

采集了乌鲁木齐市与郑州市非采暖季的环境空气颗粒物 ,用二氯甲烷做溶剂提取、硅胶柱层分离出多环芳烃样品 .用气相色谱 /燃烧系统 /同位素质谱测定了多环芳烃单化合物的稳定碳同位素组成 .结果表明 :这 2个城市的TSP与PM10 中多环芳烃单化合物稳定碳同位素组成相比较没有明显的区别 ;两城市的颗粒物样品中 ,分子量较小菲、蒽、荧蒽、芘和苯并 (e)芘的稳定碳同位素组成没有明显的区别 ,平均值范围为 - 2 3 4‰～ - 2 4 8‰ ,分子量较大的多环芳烃的δ13 C出现了明显差异 ,乌鲁木齐市环境空气颗粒物中多环芳烃单化合物的δ13 C随着其分子量的增大比郑州市更贫13 C ,乌鲁木齐市的环境空气颗粒物中的苯并 (a)芘、茚并 (1,2 ,3 cd)芘、苯并 (ghi)的δ13 C值分别为 - 2 8 3‰、- 31 8‰和 - 30 2‰ ,郑州市为 - 2 4 4‰、- 2 9 4‰和 - 2 6 3‰ .结合对两城市燃煤量和机动车拥有量的对比分析 ,本研究认为 :在非采暖季 ,这两个城市环境空气颗粒物中多环芳烃的污染主要是以煤的炭化、气化、燃烧以及机动车尾气为主的复合型污染 ,而机动车尾气...     

厦门市不同功能区冬季PM10中多环芳烃的污染特征

2004年冬季在厦门市4个不同功能区连续10d采集并分析了PM10中16种优控多环芳烃(PAHs).研究发现,各个功能区大气PM10中多环芳烃总浓度(∑PAHs)存在明显差别:工业区(湖里)10.87～27.54ng·m-3、旅游区(鼓浪屿)7.79～21.14ng·m-3、居民区(洪文)6.52～13 39ng·m-3、森林区(小坪)5.20～11.43ng·m-3;但各个功能区PM10中各种PAH化合物的相对组成趋于一致,所占比例最高的前4种化合物为菲、芘、(艹屈)和芴,表明冬季不同功能区PM10中PAHs的主要污染来源在很大程度上相似或相同.根据典型污染来源中特征化合物比值如苯并(a)蒽/(艹屈)、荧蒽/芘和芘/苯并(a)芘及其有机碳/元素碳的值,推断厦门市PM10中的PAHs主要来源于汽车尾气的排放.     

南京市大气气溶胶中颗粒物和正构烷烃特征及来源分析

于2002年夏季（7月）和冬季（12月）采集南京市5个功能区的大气气溶胶（PM2．5和PM10）样品，对两个季节不同功能区颗粒物及其颗粒物中正构烷烃的分布特征和污染来源进行了分析。结果表明，南京市大气颗粒物含量冬季高于夏季，细颗粒高于粗颗粒。正构烷烃的变化规律同颗粒物一致，且主要分布在细颗粒物上。根据各个功能区正构烷烃（C15-C32）的CPI（CPI1、CPI2和CPI3）结果，可知南京市大气气溶胶中正构烷烃由生物源和人为源共同排放产生。％waxCn的结果表明生物源对气溶胶中正构烷烃的贡献率为20％～43％，对南京市大气颗粒物的贡献率为1．66％～4．76％。     

Social determinants of soil and water conservation in rural Kenya

Soil erosion is a major environmental problem and threat to rural development in Kenya. Numerous attempts to address the problem have apparently had little success. There are however some districts that have been very successful, notably Machakos. In this study we search for the factors that determine successful development in soil conservation such as social capital, human capital and market integration. One of our main results is that social capital measures are significant determinants of investment in soil conservation. A better understanding of the relevant mechanisms is essential for developing policies targeting improvement in natural resource management.

Particulate air pollution in Lanzhou China

Concentrations of total suspended particles (TSP) and PM(10) in Lanzhou China have been kept high for the past two decades. Data collected during the intensive observational period from October 1999 to April 2001 show high TSP and PM(10) concentrations. Starting from November, the PM(10) pollution intensifies, and reaches mid to high alert level of air pollution, continues until April next year, and is at low alert level in the summer. In the winter and spring, the TSP concentration is 2-10 times higher than the third-level criterion of air quality (severe pollution). Effects of intrinsic factors (sources of pollution) and remote preconditions (propagation of dust storms) for severe PM(10) and TSP pollution in Lanzhou are analyzed.     

Using geoaccumulation index to study source profiles of soil dust in China

Source apportionment studies of TSP(atmospheric particulate matter with aerodynamic diameters≤100μm)and PM10(atmospheric particulate matter with aerodynamic diameters≤10μm)have revealed that soil dust is all important source of these particulates in China In this study, the contamination of soil dust was assessed through the use of a geoaccumulation index(Igeo). The mass concentration profiles ofl7 elements(Na, Mg, AI, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Br, Ba, andPb)were establishedforurban soil dusts. Geochemical cOmpositions of soils from 15 cities were used to represent background urban soil compositions. The results of this Studv indicated that a number of cities are severely polluted by particulates containing Ca, Cr, Ni, and Cu in both size fractions(TSP and PM10). Contamination with Zn, Pb, Co, and Br was moderate to severe(Igeo>2). The AI and Fe concentrations were not high enough for them to be considered contaminants.     

基于B—P神经网络优化算法的城市环境空气中PM10浓度预测模型

将B—P神经网络应用于西安市环境空气中P10浓度预测,对网络结构和算法进行了优化,建立了PM10浓度预测模型。经验证模型精确度比较高,PM10日平均浓度绝对误差0．015～0．020mg／m^3,相对误差在-20％～20％范围内。     

氨基黑10B的EM脱色研究

研究采用有效微生物(EM)对偶氮染料氨基黑10B进行脱色试验研究.试验结果表明.通过对 EM 茵种的培养务件的筛选试验.得到了活性强的、种类多的复合微生物茵群,其中酵母茵和乳酸茵占70%左右.其它茵种为光合细茵、放线茵以及霉茵等.而且该茵种在敞开静止培养方式、红糖质量浓度为lg/L、氨基黑10B的质量浓度为24mg/L条件下有利于生长.     

城市雾天单个矿物颗粒物理和化学特征

应用场发射扫描电镜(FESEM)和带能谱的扫描电镜(SEM-EDX)对北京冬季雾天和正常天气下收集的4个样品进行研究.雾天中矿物颗粒的数量-粒度分布的峰值出现在0.1～0.3 μm和1～2.5 μm之间,并且发现雾天出现的二次矿物颗粒的数量百分含量(4.67%)高于正常天气条件(0.12%).单个矿物颗粒的EDX能谱显示,雾天和正常天气中单个矿物颗粒的主要化学成分有一定的差别,尤其是S元素.矿物颗粒分为9种不同类型:"富Si"、"富Ca"、"富S"、"富Fe""富Mg"、"富Al"、"富Ti"、"富K"和"富Cl",其中雾天"富Ca"中55%的颗粒含有Ca(50%±1.2%)和S(37%±1.6%),"富S"中72%的颗粒含有S(44%±1.5%)和Ca(33%±2%),说明了雾天中绝大部富集S的颗粒物中同时富集Ca,这在一定程度上说明了北京市大气中这些含Ca的碱性矿物对雾水酸性有一定的缓冲作用.雾天样品中S/Ca的平均比值为6.11,如果以冬季正常天气条件下S/Ca的均值0.73为背景值,雾天中S/Ca的比值是冬季正常天气的8倍,可见雾天中颗粒物表面的硫化现象相当严重,同时也显示出雾天SO2向硫酸盐的转化率比较高.