中国特大城市水资源消费及其与经济和气候的关系——以北京、上海和广州为例

选取了北京、上海、广州三个不同地理纬度、经济较为发达的代表性城市作为研究对象,应用多项式模型把气候耗水量从水资源消费总量中分离出来,用统计分析的方法探讨三个城市水资源消费变化及其与经济和气候的关系。研究表明:城市万元GDP用水量与经济水平有明显的对数相关关系,一般都随经济水平的提高而不断下降,三个城市中,上海的水资源利用率最高,广州居中,北京最低。温度是影响城市气候耗水量的主要气候因子,在1986—1991年,三个城市的气候耗水量与温度的相关性不明显,自1992年以来,气候耗水量与温度距平值的波动趋势愈来愈密切,这说明随着经济的发展和人民生活水平的提高,城市水资源消费与温度的关系越来越密切。     

城市绿地空气颗粒物及其与空气微生物的关系

通过对北京市元大都遗址公园内不同林地类型及其旁边道路空气颗粒物及微生物浓度的研究,得出:空气颗粒物中直径大于10μm的粗颗粒所占比例较小,而人可以通过呼吸吸入的PM10所占比例相对较高,对人体最为有害的PM2.5所占比例也很小;对照点(道路)的PM10、TSP浓度都显著高于其临近绿地的浓度,可见绿地具有显著的滞尘作用;元大都公园各林地中PM10浓度都超过了国家环境质量标准中的Ⅱ级标准,是标准的1.36~2.54倍,对人体最为有害的PM2.5浓度只有个别绿地超标;空气微生物浓度与空气TSP、PM10具有极显著相关性,与PM2.5、PM1相关关系不显著。     

Air Pollution Concentrations of PM2.5, PM10 and NO2 at Ambient and Kerbsite and Their Correlation in Metro City – Mumbai

Ambient concentrations of PM_2.5 and PM₁₀ are of concern with respect to effects on human health and environment. Increased levels of mortality and morbidity have been associated with respirable particulate air pollution. In India, it is not yet mandatory to monitor PM_2.5 levels therefore very limited information is available on PM_2.5 levels. To understand the fine particle pollution and also correlate with PM₁₀ which are monitored regularly in compliance with ambient air quality standards. This study was carried out to monitor PM_2.5, PM₁₀, and NO₂ for about one year in a residential cum commercial area of Mumbai city with a view to understand their correlation. The average PM_2.5 concentration at ambient and Kerbsite was 43 and 69 μg/m³. The correlation coefficients between PM_2.5 and PM₁₀ at ambient and Kerbsite were 0.83 and 0.85 respectively thus indicating that most of the PM_2.5 and PM₁₀ are from similar sources. TSP, PM₁₀ levels exceeded Central Pollution Control Board(CPCB) standard during winter season. PM_2.5 levels also exceeded 24 hourly average USEPA standard during winter season indicating unhealthy air quality.     

Analysis of Polycyclic Aromatic Hydrocarbons in Street Soil Dust in Kumasi Metropolis of Ghana

Concentrations of polycyclic aromatic hydrocarbons (PAHs) in street soil dust from streets in Kumasi Metropolis in the Ashanti Region of the Republic of Ghana have been measured in this study. The concentrations of the various types of PAHs identified in this study are as follows: Naphthalene (m/e 128) – 41,700 μg/kg, Acenaphthylene (m/e 152) – 99,300 μg/kg, Acenaphthene (m/e 154) – 111,200 μg/kg, Fluorene (m/e 166) – 8,900 μg/kg, Carbazole (m/e 167) – 3,500 μg/kg, phenathrene (m/e 178) – 12,900 μg/kg, Anthracene (m/e 178) – 5,400 μg/kg, Fluoranthene (m/e 202) – 16,200 μg/kg, Pyrene (m/e 202) – 15,000 μg/kg, Benzo[a]anthracene (m/e 228) – 13,800 μg/kg, Chrysene (m/e 228) – 33,600 μg/kg, Benzo[k]fluoranthene (m/e 252) – 45,700 μg/kg, Benzo[a]pyrene (m/e 252) – 27,900 μg/kg, Perylene (m/e 252) – 57,200 μg/kg and Benzo[g, h, i]perylene (m/e 276) – 47,000 μg/kg. The results of the study shows that road users, like resident living in buildings within these areas, those engaged in commercial activities like hawking, and the general public are at risk of exposure to the toxic effects of the various types of PAHs from the exhaust of vehicles into the environment. According to these results, there is the potential for exposure to high levels of PAHs for road users and those living in urban environments or along highways.     

Distribution of PM2.5 and PM10-2.5 in PM10 Fraction in Ambient Air Due to Vehicular Pollution in Kolkata Megacity

This research paper aims at establishing baseline PM₁₀ and PM_2.5 concentration levels, which could be effectively used to develop and upgrade the standards in air pollution in developing countries. The relative contribution of fine fractions (PM_2.5) and coarser fractions (PM_10-2.5) to PM₁₀ fractions were investigates in a megacity which is overcrowded and congested due to lack of road network and deteriorated air quality because of vehicular pollution. The present study was carried out during the winter of 2002. The average 24h PM₁₀ concentration was 304 μg/m³, which is 3 times more than the Indian National Ambient Air Quality Standards (NAAQS) and higher PM₁₀ concentration was due to fine fraction (PM_2.5) released by vehicular exhaust. The 24h average PM_2.5 concentration was found 179 μg/m³, which is exceeded USEPA and EU standards of 65 and 50 μg/m³ respectively for the winter. India does not have any PM_2.5 standards. The 24 h average PM_10-2.5 concentrations were found 126 μg/m³. The PM_2.5 constituted more than 59% of PM₁₀ and whereas PM₁₀-PM_2.5 fractions constituted 41% of PM₁₀. The correlation between PM₁₀ and PM_2.5 was found higher as PM_2.5 comprised major proportion of PM₁₀ fractions contributed by vehicular emissions.     

Multivariate Statistical Analysis of Meteorological and Air Pollution Data in the ‘Campo De Gibraltar’ Region, Spain

A complete statistical analysis of meteorological and air pollution data was carried out in the ‘Campo de Gibraltar’ region (in the South of Spain) from 1999 to 2002. This is a heavy industrialized area where, up to date, very few air pollution studies have been made. The main objectives of the study presented here have been the characterization of the meteorological and (gaseous and particulate) air pollution conditions in the area, and the relations between them. Multivariate statistical techniques, such as Principal Component Analysis (PCA), have been applied to the data. The results show that air quality in the area is highly dependent on meteorological conditions such as wind persistence and direction, dispersion capability of the atmosphere, and humidity content. On average, sulphur dioxide and nitrogen oxide air pollution, mainly caused by fuel-oil combustion and traffic, respectively, is not very high. However, an important number of exceedences of the limits established by the EU Directive 1999 for PM10 (particulate matter with a diameter less than 10 μm) have been observed in some points of the area. A significant percentage of these exceedences (about 22% on average) are likely caused by African dust intrusions, which usually take place from May to August. From gaseous and particulate air correlations, it seems that anthropogenic activities contribute with about 19% on average.     

北京市大气PM10源解析研究

于2004年在北京市定陵、车公庄、古城、亦庄、房山和奥体中心6个采样点采集大气PM10环境样品,针对北京市颗粒物主要排放源采集土壤尘、建筑水泥尘、燃煤等污染源PM10样品,分别对其中的无机元素、离子、有机碳(OC)和元素碳(EC)进行测定。采用代表北京市颗粒物主要排放源PM10组分特征的成分谱,利用CMB受体模型对PM10来源进行解析。结果表明,PM10的最大来源为土壤尘,其它贡献源类依次为燃煤排放、机动车/燃油排放、二次粒子(SO42-、NO3-和NH4 )、建筑水泥尘。污染源贡献具有明显的季节变化,并存在一定的地域变化。     

可吸入颗粒上多环芳烃来源的识别和解析

根据化学质量平衡(CMB)受体模型对安阳市大气颗粒物中多环芳烃进行源解析。测定安阳市非采暖季和采暖季可吸入颗粒物中多环芳烃的浓度,对其污染水平进行比较分析。根据污染源调查结果,确定市区多环芳烃的主要排放源类,并建立相应的源成分谱。应用CMB受体模型解析安阳市可吸入颗粒物中多环芳烃主要来源的分担率。     

空气中PM_(10)浓度的BP神经网络预报研究

建立了某市PM10浓度预报的分段BP神经网络模型,经验证,所建立的BP预报模型,预测精度比较高,PM10日平均浓度误差大多在-0．010～0．010mg／m^3范围内,相对误差在-20％～20％,表明BP神经网络对PM10的浓度预报是一种有效的工具。     

TEOM RP1400a PM_(10)测尘仪的日常维护

基于TEOM RP1400a PM10型测尘仪的工作原理,文中提出了仪器的日常维护的注意点;同时,对出现负值、流量不正常、显示屏监测数据上下非连续性明显波动、屏幕显示不正常等情况进行了分析,并且提出了相应的解决方案。