Seasonal variation and size distribution of biogenic secondary organic aerosols at urban and continental background sites of China

Size-resolved biogenic secondary organic aerosols(BSOA) derived from isoprene and monoterpene photooxidation in Qinghai Lake, Tibetan Plateau(a continental background site) and five cities of China were measured using gas chromatography/mass spectrometry(GC/MS). Concentrations of the determined BSOA are higher in the cities than in the background and are also higher in summer than in winter. Moreover, strong positive correlations(R2= 0.44–0.90) between BSOA and sulfate were found at the six sites,suggesting that anthropogenic pollution(i.e., sulfate) could enhance SOA formation,because sulfate provides a surface favorable for acid-catalyzed formation of BSOA. Size distribution measurements showed that most of the determined SOA tracers are enriched in the fine mode( 3.3 μm) except for cis-pinic and cis-pinonic acids, both presented a comparable mass in the fine and coarse( 3.3 μm) modes, respectively. Mass ratio of oxidation products derived from isoprene to those from monoterpene in the five urban regions during summer are much less than those in Qinghai Lake region. In addition, in the five urban regions relative abundances of monoterpene oxidation products to SOA are much higher than those of isoprene. Such phenomena suggest that BSOA derived from monoterpenes are more abundant than those from isoprene in Chinese urban areas.     

基于SOM-SVM组合分类器的涂层防护性能研究

目的为避免EIS,EN技术可能出现的问题,建立一个准确、高效的评价模型,以探究现役军用有机涂层防护性能。方法利用电化学阻抗谱(EIS)、电化学噪声(EN)技术分析了两种军车有机涂层在循环暴露试验中的腐蚀行为,提取低频阻抗模值|Z|_(0.1 Hz)与涂层噪声电阻R_n两种电化学评价参数作为自组织神经网络(SOM)的输入训练样本,同时结合支持向量机(SVM)方法建立涂层防护性能组合分类器。结果将涂层失效过程自适应地分为涂层防护性能良好、防护性能下降、基本失效三个阶段。结论所建立的SOM-SVM组合分类器对于辅助分析涂层防护性能具有可行性。     

Concentration and Distribution of Persistent Organic Pollutants (POPs) in Harbour Dredging Sediments: A Pilot Study Using a Statistical Approach for a More Efficient Characterisation and Management

The Italian guidelines concerning the dumping of dredgingmaterials implies chemical, physical and microbiologicalcharacterisation of dumping sediments collected through aspecific sampling strategy.POP's such as PCB's and organochlorine pesticides, areconsidered as priority pollutants in all the InternationalConventions for the protection of the marine environment, inconsequence of their persistence, toxicity and bioaccumulation capacity.In the present article the concentrations and distribution ofPCB's and organochlorine pesticides in dredging sediments ofPiombino harbour (northern Thyrrenian sea) were investigates.The results showed high POP's concentrations in all the studiedareas. The maximum values were: 3.94 mg kg^-1 dry weight for PCB's(IUPAC Nos. 28; 52; 101, 118; 137; 153; 180; 209); 2.04 mg kg^-1 d.w.for HCH's (-, -, - and -hexachlorocyclohexane isomers); 0.28 mg kg^-1 d.w. for DD's (DDT + DDE + DDD).Subsequently, the data related to each pollutant were treatedwith statistical tests, in order to verify how well the samplingstrategy is able to represent the distribution of contaminantsin the dredging area. Finally, management strategies werederived for the studied dredging sediments.     

环境样品中 PCBs 的测定

通过碱解、酸洗、萃取、净化、浓缩、脱硫等步骤处理实际样品,利用毛细管柱GC/ECD进行PCBs的定性和定量.方法定性准确,定量良好,具有较高的回收率.各同系物的方法检出限,水样为0.01～0.08ng/L;土壤样为0.003～0.03μg/kg,可以应用于土壤、沉积物、水样、油样等环境样品中PCBs的测定     

小清河水体和沿岸地下水中有机污染物的监测及其毒性分析

本文对小清河水体和沿岸地下水中的有机污染物进行了色－质联用分析，共检出有机污染物１３类９３种。并对其毒性效应进行了分析和评价。     

在线自动监测仪与实验室国标方法测定地表水中总氮的比对分析

随着监测技术的发展,地表水中越来越多的污染物可采用自动监测仪器实现实时监测。采用自动监测与实验室国标方法测定地表水中总氮的结果是否可比以及造成差异的原因,文章根据实际样品比对实验对此进行了探讨。     

土壤石油类(红外法)标样制作方法的研究

对我国环境监测用于土壤矿物油污染监测所必备的标准样品制作方法作了探索,尤其对该类标样的液-固态吸附饱和及其浓度平衡关系作了探讨,从理论上保证标样的均匀性和稳定性,为标样最后的定值分析提供依据.     

水中氮类污染物的联系与区别

叙述了水体中含氮类污染物的来源，种类，相互转化方式和条件，表征意义等，以便对水中氮类污染物有一个全面，系统的认识。     

氨氮测定过程中有关问题的探讨

针对纳氏比色法测定氨氮过程中存在的问题，提出了相应的解决办法。消除了混浊和泡沫干扰问题，降低了空白吸光度，提高了测定的准确度。     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.