辽河流域畜禽养殖污染源仿真研究

随着国家对流域水质改善要求的逐渐提高,流域污染源管理变得日益重要。提出了一种基于CPNTOOLS的辽河流域畜禽养殖行业污染源仿真技术,并采用CPNTOOLS软件对辽河流域内一家典型畜禽养殖基地进行了仿真研究,经过多次仿真与数据统计分析,分别得出了该畜禽养殖基地的CODCr,BOD5,NH3-N,TP,TN日排放量的正态分布均值在90%,95%,99%置信水平下的置信区间,并结合排水量分别计算出各污染物指标排放浓度的区间。结合BAT处理技术对污染物浓度削减进行了计算仿真,并得出了处理后CODCr,BOD5,NH3-N,TP,TN排放浓度的正态分布均值在90%,95%,99%置信水平下的置信区间以及排放浓度的极大值和极小值。     

Occurrence and distribution of pharmaceuticals in surface water, suspended solids and sediments of the Ebro river basin, Spain

The occurrence of 43 pharmaceuticals belonging to predominant therapeutic classes and their distribution in surface water, suspended solids and sediments has been investigated in the Ebro river basin in the Northeast of Spain. WWTP effluents were found to be a main source of contamination and the spatial distribution was affected by the river flow at the sampling point and corresponding dilution factor, resulting in higher concentrations and higher loads in small tributary rivers than in the Ebro river. The study showed that some compounds are preferentially found bound to suspended solids and not detected in river water. Generally, compounds with basic characteristics (pKa > 7) showed higher tendency to bind to suspended solids. The sediment samples generally presented lower concentrations than suspended solids.     

伊犁河伊宁市段水质污染特征分析

在对伊犁河伊宁市段历年水质状况进行综合评价的基础上,探讨了城市河段水质的变化规律、污染特征和成因。     

B—P网络逐步聚类法在环境测点优选中的应用

将Ｂ－Ｐ网络原理与逐步聚类分析思想相结合,用于环境测点聚类优选。该方法用于水清河几个监测断面的优选结果是符合客观实际的。     

大气监测网络通信系统设计与实现

根据监测网络通信的特点,设计了大气监测网络通信体系,并在系统中引入了学习算法和缓存机制,有效地提高了大气监测网络通信系统的灵活性和效率。     

Optimal Design of a Multi-Pollutant Air Quality Monitoring Network in a Metropolitan Region Using Kaohsiung, Taiwan as an Example

An air quality monitoring network (AQMN) usually performs the basic function of assessment of regional air quality and demonstration of compliance with ambient air quality standards in an urban area. Different pollutants, however, may present different characteristic variabilities due to their specific emission patterns, rates of diffusion, and transport and transformation behaviors. But the costs of siting in a pollutant-specific monitoring network would be higher than that for a common network with respect to several pollutants monitored simultaneously. This paper presents a survey of multi-pollutant design principles and optimal searches for siting patterns of an AQMN using both simulation and optimization models as a combined tool. While conservative, quasi-stable, and reactive pollutants are considered in the design principles, cost, coverage effectiveness, and spatial correlation characteristics are included in the multi-criteria decision making process. For illustrative purpose, a series of technical settings and two types of objectives were examined in the case study for the city of Kaohsiung in Taiwan.     

Determination of organophosphorus pesticides and related compounds in water samples by membrane extraction and gas chromatography

A membrane extraction-gas chromatography method was developed fordetermination of organophosphorus pesticides and related compounds including methamidophos, DDVP, dimethoate, methyl parathion, parathion, thiophosphoric acid trimethyl ester, and thiophosphoramidic acid dimethyl ester in water samples. In thismethod, surface-modified acetic cellulose membranes were used to extract the target analytes in water samples, the extracted analytes were back-extracted into a small amount of methanol, andgas chromatography with pulsed flame photometric detector (GC-PFPD) was used to determine the concentrations of targetanalytes in the extracts. The recoveries obtained for thetarget analytes spiked into the water samples ranged from 66to 94%. The method detection limit for each target analyte was 0.05 g L^-1. The method developed in this study had shown the advantages of being cheap, simple, fast, and reliable. It had been used successfully to determine the concentrations of target analytes in river water samples.     

Modelling the Effects of Inflow Parameters on Lake Water Quality

A one-dimensional lake water quality model which includes water temperature, phytoplankton, phosphorus as phosphate, nitrogen as ammonia, nitrogen as nitrate and dissolved oxygen concentrations, previously calibrated for Lake Calhoun (USA) is applied to Uokiri Lake (Japan) for the year 1994. The model simulated phytoplankton and nutrient concentrations in the lake from July to November. Most of the water quality parameters are found to be the same as for Lake Calhoun. To predict probable lake water quality deterioration from algal blooming due to increased nutrient influx from river inflow, the model was run for several inflow water conditions. Effects of inflow nutrient concentration, inflow volume, inflow water temperatures are presented separately. The effect of each factor is considered in isolation although in reality more than one factor can change simultaneously. From the results it is clear that inflow nutrient concentration, inflow volume and inflow water temperature show very regular and reasonable impacts on lake water quality.     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.