重庆大气可吸入颗粒物中多环芳烃的分析测定

本文报告了重庆大气可吸入颗粒物(IP)中多环芳烃(PAH)的分析测定方法和结果.用GC-MS定性和定量分析了重庆IP中的PAH;研究了IP中PAH的种类和分布情况;确认重庆IP中存在甲基多环芳烃(MePAH)、约占33%;颗粒大小分布表明,约有98%PAH存在于小于3μm的颗粒中.     

物理化学法治理印染废水的研究

本文提出了两个治理印染废水的综合物化流程:(1)SE—J型絮凝剂絮凝→气浮→次氯酸钙氧化→炉渣吸附;(2)SE—型絮凝剂絮凝→气浮→活性炭吸附,并对每个处理过程进行了条件实验。实验结果表明:废水经流程(1)处理后,COD去除率达82—87%,色度去除率达88—92%,废水经流程(2)处理后,COD去除率达89—92%,色度去除率达97—98%。经两个流程处理后的出水水质分别接近或达到国家排放标准。     

水厂排泥水的处理技术：分离水回用 泥饼综合利用及铝盐回收

介绍了水厂排泥水处理后,分离水的水质和回用、泥饼制砖和污泥用作水泥掺合料的方法和试验结果,以及铝盐回收的工艺。     

鄱阳湖水质状况分析与评价

鄱阳湖是我国最大的淡水湖,对其水质的监测和分析有着重要意义。本文通过1980～1988年水质监测资料的搜集整理,分析了鄱阳湖水质的时空分布规律,并作出了比较全面的评价。评价结果表明,鄱阳湖已受到轻度污染,但其水质总体状况尚属良好,是全国淡水湖泊中最好的湖泊之一。     

铬在土壤-地下水系统中的污染研究

根据铬在土壤-地下水环境中的化学特性,通过实验,研究了两种价态的铬[Cr(Ⅲ),Cr(Ⅵ)]在水、土中的污染过程,并着重分析了Cr(Ⅵ)在水土体系中所产生的物理化学作用及其在土中的稳定性。结合铬污染实例,阐明了铬在水-土体系中污染的条件和迁移过程。     

The responses of the hepatosomatic index (HSI), 7-ethoxyresorufin-O-deethylase (EROD) activity and glutathione-S-transferase (GST) activity in sea bass (Dicentrarchus labrax, Linnaeus 1758) caged at a polluted site: implications for their use in environmental risk assessment

The present study investigates the response of three hepatic biomarkers in adult sea bass (Dicentrarchus labrax, Linnaeus 1758) caged at a wastewater outlet of an oil refinery with fish caged at a pristine site used as controls. The biomarkers that were investigated were the hepatosomatic index (HSI), 7-ethoxyresorufin-O-deethylase (EROD) activity and glutathione-S-transferase (GST) activity. In addition, we have measured the levels of polycyclic aromatic hydrocarbons (PAHs) and selected heavy metals (lead, cadmium, mercury, copper and zinc) in sediment samples at the polluted site. Although the polluted site had high environmental levels of PAHs and heavy metals, there was no difference in hepatic EROD activity and HSI between fish caged at the polluted site and controls. On the other hand, GST activity was significantly lower in fish caged at the polluted site compared to controls. Our results point out that the studied biomarkers have limited use in environmental risk assessment studies, at least when caged adult sea bass is used as the sentinel species and complex toxicant mixtures are involved.     

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.     

Development of a multi-criteria index ranking system for urban runoff best management practices (BMPs) selection

Low impact development best management practices (LID-BMPs) are considered to be cost-effective measures for mitigating the water quantity and quality impact of urban runoff. Currently, there are many types of LID-BMPs, and each type has its own intrinsic technical and/or economical characteristics and limitations for implementation. The selection of the most appropriate BMP type(s) for a specific installation site is therefore a very important planning step. In the present study, a multi-criteria selection index system (MCIS) for LID-BMP planning was developed. The selection indexes include 12 first-level indices and 26 second-level indices which reflect the specific installation site characteristics pertaining to site suitability, runoff control performance, and economics of implementation. A mechanism for ranking the BMPs was devised. First, each individual second-level index was assigned a numeric value that was based on site characteristics and information on LID-BMPs. The quantified indices were normalized and then integrated to obtain the score for each of the first-level index. The final evaluation scores of each LID-BMP were then calculated based on the scores for the first-level indices. Finally, the appropriate BMP types for a specific installation site were determined according to the rank of the final evaluation scores. In order to facilitate the application of the MCIS BMP ranking system, the computational process has been coded into a software program, BMPSELEC. A case study demonstrating the MCIS methodology, using an LID-BMP implementation planning at a college campus in Foshan, Guangdong Province, is presented.     

Determination of total petroleum hydrocarbons (TPH) in agricultural soils near a petrochemical complex in Guangzhou,China

The total petroleum hydrocarbons (TPH) pollution in regional agricultural soils was investigated. Seventy soil samples collected from surface layers (0–20 cm) and horizons of five selected pedons in the vicinity of a petrochemical complex in Guangzhou, China were analyzed, and the vertical variation and spatial variability of TPH were evaluated. The TPH concentration in top soils around the petrochemical complex ranged from 1,179.3 to 6,354.9 mg kg^− 1, with the average of 2,676.6 mg kg^− 1. Furthermore, significant differences between land-use types showed that the TPH concentration in top soils was strongly influenced by accidental spills. Both the TPH trends in pedons and the identified hot-spot areas also showed that the accidental explosions or burning accidents were mainly responsible for the pollution. The results reported here suggest that the regular monitoring and inspection shall be conducted for safety and to avoid or minimize the accidents, and the effective measures should be taken to remediate the contaminated areas and to assure that the important industrialization of Guangzhou area would not mean human health risks near the petrochemical complex.     

Characteristic, composition, and sources of TSP investigated by HRTEM/EDS and ESEM/EDS

Total suspended particle (TSP) collected at the fifth floor of House Dust in Hunan University, China, was analyzed in terms of microscopic morphology and chemical composition. The fine particles (50?nm-2?μm) in the TSP were analyzed by a high-resolution transmission electron microscope equipped with an energy-dispersive X-ray analyzer (HRTEM/EDS). Results showed that the particles were in shapes of plate, irregular and agglomerate. Based on EDS results, these fine particulate matter was primarily composed of Fe-rich (35.82-61.29%), Ca-rich (30.18-36.77%) and Si-rich (18.95-32.28%) particles. Other elements mainly including Mg (0.47-4.97%), Al (0.45-14.57%), S (0.45-4.73%), K (1.13-2.13%) and Zn (0.67-3.85%) were also observed. The sources analysis indicated that the HRTEM particles mainly originated from coal combustion, traffic emission, vehicles exhaust emission and fugitive soil or cement particulate matter. The coarse particles (4-50?μm) were detected by environmental scanning electron microscopy coupled with energy-dispersive X-ray detector (ESEM/EDS). Based on a simple algorithm, ESEM particles were categorized into five groups: C-bearing (46.15%, 67% and 86.98%), Si + Ca-bearing (21.48?+?11.80%, 16.51?+?10.81% and 16.32?+?10.62%), Si + Al-bearing (20.06?+?12.40%, 20.16?+?11.22% and 15.31?+?11.25%), Si-bearing (34.40%, 26.92% and 27.15%) particles and aggregates, most of which exhibit obvious crystalline structure, and these ESEM particles mainly derived from vehicles exhaust emission, coal combustion, soil, and biomass burning, while the aggregates are indicative of atmospheric reaction progress. HRTEM/EDS and ESEM/EDS are mutual complementary in analyzing the characteristic and determining the sources of TSP.