Will PM control undermine China's efforts to reduce soil acidification?

China’s strategies to control acidifying pollutants and particulate matter (PM) may be in conflict for soil acidification abatement. Acidifying pollutant emissions are estimated for 2005 and 2020 with anticipated control policies. PM emissions including base cations (BCs) are evaluated with two scenarios, a base case applying existing policy to 2020, and a control case including anticipated tightened measures. Depositions of sulfur (S), nitrogen (N) and BCs are simulated and their acidification risks are evaluated with critical load (CL). In 2005, the area exceeding CL covered 15.6% of mainland China, with total exceedance of 2.2 Mt S. These values decrease in the base scenario 2020, implying partial recovery from acidification. Under more realistic PM control, the respective estimates are 17.9% and 2.4 Mt S, indicating increased acidification risks due to abatement of acid-neutralizing BCs. China’s anthropogenic PM abatement will have potentially stronger chemical implications for acidification than developed countries.     

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。     

广州市空气可吸入性颗粒物的污染水平

报告了中美合作“广州市大气污染对儿童肺功能影响研究”课题可吸入性颗粒物的两年监测结果。用安德森双道采样器、Ｔｅｆｌｏｎ膜采集细颗粒物（＜２５μｍ,ＰＭ２５）和粗颗粒物（２５≤＜１０μｍ,ＰＭ２５１０）,两者之和为ＰＭ１０,每季度采１５天,每天采一个２４小时样品。结果表明,广州市城区大气可吸入性颗粒物的污染相当严重,尤其是细颗粒物（ＰＭ２５）的污染,应引起公众和政府有关部门的重视。     

浅析可吸入颗粒物（PM10）对西宁市民呼吸系统疾病的影响

可吸人颗粒物(PM10)是目前我国各城市大气环境质量的首要污染物,对大气质量起到了决定性的影响作用,与医院心肺疾病、诸多呼吸道疾病就诊率之间关系密切.针对西宁市近几年大气污染的现状,根据呼吸系统疾病发病门诊量,分析了西宁市民呼吸系统疾病发病人数与PM10的相关性.     

隧道实验测定南京市机动车PM_（10）排放因子

选取南京城市隧道进行机动车PM10平均排放因子的测试研究。采用质量平衡模型和多元线性回归方法计算了4种车型PM10的综合排放因子。结果表明：隧道内机动车PM10平均排放因子为0.347±0.100 g/（km·辆）;大型车的PM10排放因子远高于其他车型的排放因子,其次是中型车和摩托车,小型车最小,其综合排放因子分别为1.440 g/（km·辆）、0.850 g/（km·辆）、0.790 g/（km·辆）和0.320 g/（km.辆）;在车速相似的情况下,本隧道实验所测机动车的PM10排放因子与国内隧道实验结果相仿,却远大于国外隧道实验结果。     

南昌市大气PM2.5中多环芳烃的来源解析

在南昌市布设5个采样点,分别代表工业区、居住区、交通干线区、商业区以及郊区,于2007年7～8月进行大气PM2.5的采样.根据5个采样点测得的数据,通过因子分析法判断南昌市大气PM2.5中多环芳烃的主要来源,再利用多元线性回归法确定各主要来源对多环芳烃的贡献率.结果表明,南昌市多环芳烃的主要来源为车辆排放源、高温加热源、燃煤污染源,对多环芳烃的贡献率分别为37.9%、28.2%、22.0%.     

Estimating the contribution of industrial facilities to annual PM10 concentrations at industrially influenced sites

If measures to reduce the industrial discharge of PM10 shall be planned with high accuracy, a first step must be to estimate the contribution of single industrial facilities to the overall PM10 burden as accurately as possible. In northern Duisburg as an example, an area where iron and steel producing industry is concentrated, PM10 was measured at 4 sampling sites very close to an industrial complex of blast furnaces, a sinter plant, oxygen steel works and a coke oven plant for 9 months in 2006. At two sites metals in PM10 were determined. The results, together with analytical data of urban background sites in the region and data of wind direction and wind speed were used for an estimation of the contribution of single plants to the PM10 burden. A careful analysis of the data showed, that the data of PM10, calcium, iron and zinc measured at two sites close to the industrial area and information about the urban background aerosol were sufficient to calculate the PM10 contribution of the main single plants. The data could be compared with those of modelling.     

平顶山市大气颗粒物污染水平研究

为了初步调查平顶山市大气中颗粒物PM10和PM2.5的污染水平,于2006年9月-2007年8月春、夏、秋、冬4季在平顶山市分别采集了80个样品,并对其进行分析.分析结果表明,平顶山市PM10和PM2.5的质量浓度分别为0.045-0.872 mg/m3,0.023-0.044 4 mg/m3,年均值分别为0.162 mg/m3,0.093 mg/m3,PM10超国家标准0.62倍,PM2.5超美国EPA标准5.20倍.PM10和PM2.5的季节变化趋势足冬季最高,春、秋季次之,夏季最低,PM10中PM2.5约占64%.     

利用糖蜜废水生产微生物絮凝剂及条件优化和效果实验研究

用糖蜜废水取代葡萄糖作为发酵培养基中的碳源和能源培养微生物絮凝剂产生菌Pseudomonas alcaligenesPS-25。通过单因素试验和正交试验得到该菌株产絮凝剂的最佳培养条件:糖蜜废水COD浓度5 000 mg/L、培养基初始pH值6.5、接种量5%(体积比)、温度30℃、培养时间为72 h、摇床转速160 r/min,在此条件下,PS-25所产絮凝剂对高岭土悬浊液絮凝率达96.75%,并且对多种废水都有较好的净化效果,对废水中浊度和色度的去除率分别在90%和80%以上,COD去除率在73.60%～91.10%。研究表明,用糖蜜废水培养PS-25生产微生物絮凝剂处理废水是完全可行的,从而实现废物的资源化利用。     

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。