太湖入湖河流水环境综合治理

简述了太湖入湖河流水污染控制的基本思路、关键环节和主要方法。分析了太湖流域15条主要入湖河流规划综合治理区污染源现状,提出污染控制对策建议和重点整治工程,并预测削减入湖河流的污染物总量。通过整治工程的实施,截至2009年5月,15条主要入湖河流中劣Ⅴ类水质的河流已从2007年的9条下降为3条,湖体也由中度富营养转为轻度富营养,综合治理初见成效。     

太湖流域河流入河污染负荷通量与入湖水质响应关系分析——以殷村港为例

以2020年1—12月太湖主要入湖河流殷村港水质自动监测站的监测数据及2020年太湖水位资料为依据,构建了一维水量水质耦合数学模型,建立了入河污染负荷通量与入湖控制断面水质响应关系,以入太湖控制断面殷村港站达Ⅲ类水质水为目标,模拟计算了殷村港站主要污染物入湖水质变化过程。结果表明,殷村港站高锰酸盐指数、氨氮、总磷等水质指标浓度最大值均明显的降低,其中氨氮浓度降低幅度相对较大,主要集中于3—6月;高锰酸盐指数和总磷日均入河污染负荷通量变化相对较小,氨氮日均入河污染负荷通量降低幅度相对较大;殷村港站高锰酸盐指数、氨氮、总磷等水质指标年入河污染负荷削减量分别为24.17,41.43,3.87 t。提出,基于核算出的削减量需进一步结合污染负荷通量过程和污染源溯源分析,确定不同水质指标下入河污染负荷控制方向,为科学合理规划殷村港主要污染物的入河污染负荷总量控制提供科学依据。     

简讯

江苏下达太湖水污染减排任务江苏省太湖水污染防治委员会近日分别向苏州、无锡、常州、镇江等沿太湖地区的市政府下达了太湖流域为期5年的水污染物总量削减任务。要求沿湖各地通过控制增量、结构调整、集中控污、提高标准、严格执法和工程建设6项硬措施,重点抓好太湖上、下游主要河流以及行政交界河流入河排污口的排污总量控制。江苏省确立了为期5年的太湖污染减排总体目标:到2010年,COD排放量控制在25.4万t,削减率为15.1%;氨氮排放量控制在1.9万t,削减率17.4%;太湖湖体、太湖上游主要河流、主要出湖河流、行政交界河流、城镇集中式饮用水…     

太湖主要入湖河流排污控制量研究

利用2006—2008年的监测数据对太湖主要入湖河流的水环境状况进行了分析,通过对研究区工业污染源、农业污染源和城镇生活污水排污的分布以及入河情况的调查,对各种污染源的入河量进行了计算,根据确定的水质目标,分别计算出主要入湖河流以及区域水系的水环境容量和排污控制量。结果表明:15条主要入湖河流超标现象显著,近3a来污染程度有所波动,N、P污染最为严重。研究区内污染物入河量较大,未接管的生活源污染物入河量所占比重最大,各类污染物均在50%～60%之间;张家港市的污染物入河量最大,各类污染物所占比重达总入河量的18%～20%。研究区内河网密布,水环境容量分布不均匀,望虞河、直湖港、武进港等7条河流水环境容量较大,张家港市区域水环境容量较大。为保证水质达标,研究区内近期共须削减CODCr66554.38t/a、NH4-N8105.71t/a、TP1324.42t/a;远期共须削减CODCr96719.08t/a、NH4-N11541.45t/a、TP1788.71t/a。     

滇池东南岸农业和富磷区入湖河流地表径流及污染特征

应用聚类分析与因子分析方法,通过8次常规监测,对滇池东南岸10条以农业面源和受磷矿开采区影响的入湖河流的地表径流及其水质污染特征进行了分析,并探讨了其空间差异性。在南岸选取降雨过程相同的3条河流,开展暴雨径流监测,探讨污染物在降雨过程中的流失特征。结果表明,新宝象河的平均流量为2.6 m3/s,占总入湖流量的26.5%;总氮、总磷、化学需氧量、悬浮物是滇池的主要污染指标,许多河流均已严重超标。河流水质在空间上可分为3类,具有明显的空间差异性。总氮、总磷、溶解磷、硝态氮对水质污染的贡献率达到了53.636%,氮、磷含量是河流水质污染的主要贡献因子。降雨条件下化学需氧量、悬浮物浓度增长迅速,流量、悬浮物与大多数水质指标均有相关性,磷矿开采对河流水质的影响在降雨条件下更加明显,其悬浮物浓度在降雨条件下比只受农业面源影响的河流最高高出1.9倍。     

2009年太湖治理目标

“确保饮用水安全,确保太湖水质有所改善,实现主要人湖河流劣V类水体数量下降,污染物人湖总量下降和湖体富营养化指数下降。到2009年底,53个国家考核断面水质达标率达到国家考核要求;入湖河流水质全年月均值劣V类的数量下降到20％以下;主要污染物COD、氨氮、总氮、总磷入湖总量比上年下降5％。”近日召开的江苏省环保局长会议为2009年太湖治理描绘了新的目标。     

江苏省太湖流域国家考核断面污染来源调查与评价

从水系关联和地域分布特征出发,将江苏省太湖流域53个国控考核断面归类划分为京杭大运河片区、入湖河流片区等6大片区,全面调查和系统分析水质状况和断面的污染来源,并提出对策建议,为环境管理提供决策支持。     

洪泽湖水质富营养化评价

对洪泽湖及其入湖河流水质现状进行了评价,得知洪泽湖及其入湖河流总体水质为劣Ⅴ类,影响两者水质的主要污染物为总磷和总氮。对洪泽湖浮游植物的时空分布及相关因子进行分析,得知洪泽湖目前为轻富营养水平。最后对洪泽湖第一次发生蓝藻聚集现象的原因进行了分析。     

基于3S技术对太湖底质及水质总磷的研究

应用3S技术研究了太湖底质与水质总磷(TP)的分布情况,并结合水华频次分析了其相关性。结果表明:2016—2018年,太湖底质TP年均值在433~537 mg/kg波动,水质TP年均值从0.064 mg/L上升至0.087 mg/L。从空间分布来看,底质TP、水质TP和水华频次均呈现“西高东低”的规律,太湖西部区尤其是竺山湖区是需要开展治理的重点区域。3年间,太湖西部区水质TP上升,而底质TP与入湖河流TP下降,说明内源磷污染是太湖西部区水质TP升高的主要原因,须加强科学清淤。     

太湖入湖河流流量简易测量法--中泓一点法

通过利用经典测试法与多普勒测流仪对太湖入湖河流流量进行的比对分析,找出了太湖入湖河流流量的简易测试方法——中泓一点法。在河流中泓0．6倍水深处测流．可得到入湖河流流量70％的精度;若要保证得到入湖河流流量为80％精度,则需在中泓0．8倍水深处施测。     

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.     

Support Vector Machine Modeling Using Particle Swarm Optimization Approach for the Retrieval of Atmospheric Ammonia Concentrations

This study was performed in order to improve the estimation accuracy of atmospheric ammonia (NH₃) concentration levels in the Greater Houston area during extended sampling periods. The approach is based on selecting the appropriate penalty coefficient C and kernel parameter σ ². These parameters directly influence the regression accuracy of the support vector machine (SVM) model. In this paper, two artificial intelligence techniques, particle swarm optimization (PSO) and a genetic algorithm (GA), were used to optimize the SVM model parameters. Data regarding meteorological variables (e.g., ambient temperature and wind direction) and the NH₃ concentration levels were employed to develop our two models. The simulation results indicate that both PSO-SVM and GA-SVM methods are effective tools to model the NH₃ concentration levels and can yield good prediction performance based on statistical evaluation criteria. PSO-SVM provides higher retrieval accuracy and faster running speed than GA-SVM. In addition, we used the PSO-SVM technique to estimate 17 drop-off NH₃ concentration values. We obtained forecasting results with good fitting characteristics to a measured curve. This proved that PSO-SVM is an effective method for estimating unavailable NH₃ concentration data at 3, 4, 5, and 6 parts per billion (ppb), respectively. A 4-ppb NH₃ concentration had the optimum prediction performance of the simulation results. These results showed that the selection of the set-point values is a significant factor in compensating for the atmospheric NH₃ dropout data with the PSO-SVM method. This modeling approach will be useful in the continuous assessment of NH₃ sensor discrete data sources.     

Water Quality Prediction of Water Sources Based on Meteorological Factors using the CA-NARX Approach

With the increasingly serious problem of surface water environmental safety, it is of great significance to study the changing trend of reservoir water quality, and it is necessary to establish a water quality prediction and early warning system for the management and maintenance of water resources. Aiming at the problem of water quality prediction in reservoirs, a CA-NARX algorithm is designed, which combines the improved dynamic clustering algorithm with the idea of machine learning and the forward dynamic regression neural network. The improved dynamic clustering algorithm is used to classify the eutrophication degree of waterbodies according to the total phosphorus and total nitrogen content. Considering four meteorological factors, air temperature, water temperature, water surface evaporation, and rainfall, synthetically for each water quality condition, the total phosphorus and total nitrogen in the waterbody are forecasted by an improved forward NARX dynamic regression neural network. Based on this, the CA-NARX prediction algorithm can realize short period water quality prediction. Compared with the traditional support vector regression machine model, improved GA-BP neural network, and exponential smoothing method, the CA-NARX model has the least prediction error.

     

Soil water content forecasting by ANN and SVM hybrid architecture

Soil water content prediction is essential to the development of advanced agriculture information systems. Because soil water content series are inherently noise and non-stationary, it is difficult to get an accurate forecasting result. Considering the problems, in this paper, a novel hybrid learning architecture is proposed according to divide-and-conquer principle, the forecasting accuracy is improved. This novel hierarchical architecture is composed of ANN (Kohonen neural network) and SVM (support vector machine). The Kohonen network is used as a classifier, which partitions the whole input space into several distinct feature regions. Then, the best SVM predictor combined with an appropriate kernel function can be achieved for correspondence regions. The experimental results based on the hybrid model exhibit good agreement with actual soil water content measurements and outperform ANN and SVM single-stage models.     

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir,Beijing, China

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R², RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.     

OPAQ系统两种模式对O 3预报准确率的探讨

以江苏省宿迁环境监测中心OPAQ系统为例,基于人工神经网络算法的OPAQ空气质量预报系统的2种模式对O 3预报准确率的进行了分析,结果表明,趋势最优模式(RMSE模式)对预报当天及未来3 d的预报值与监测值的相关性系数均>0.78,相对误差在25%以下,在预测当天及未来24、48及72 h优-良天的预测准确率较高,分别为88.8%、87.2%、86.3%及84.7%,在预测轻度污染-重度污染的准确率较低;极值最优模式(SI模式)对预报当天及未来3 d的预报值与监测值的相关性系数(R)均>0.76,相对误差<32%,预测未来24和48 h的轻度污染-中度污染的级别准确率>60%。OPAQ系统的极值最优模式(SI模式)更适合作为夏季ρ(O 3)较高时的预测工具。     

2019年江苏省PM2.5和O3多模式集合预报算法效果评估

基于江苏省重污染天气监测预报预警系统多模式预报结果,分析了不同数值模式对江苏省13个城市细颗粒物(PM_2.5)和臭氧(O₃)的预报偏差特征,发展了多模式集合预报算法,并对其进行了评估。结果表明,相较于单一数值模式,集合预报算法显著改善了PM_2.5和O₃预报的准确率,其对江苏省PM_2.5和O₃空气质量分指数等级的预报准确率超过了80%。就江苏省整体而言,PM_2.5集合预报的准确率相比最优单一数值模式提升了6%。O₃浓度较低时,集合预报能有效改善各模式存在的高估现象。但受限于目前的校正策略,出现高浓度O₃污染时,集合预报对预报效果的提升相对有限。     

钱塘江南源流域异常水质预测及影响因素分析

基于数据驱动的水质预测模型存在局限性,对突发水质异常事件的预测效果不佳。该研究选取钱塘江南源流域马戍口监测断面为研究对象,综合采用相关性分析方法对水质异常事件的主要影响因素进行分析,明确流域内、外因及土壤条件对水质异常指标的影响程度,探究造成模型局限性的深层原因。结果表明:异常水质的影响因素及其耦合机制复杂多变,异常浊度受降雨量、径流量的直接影响更大,异常高锰酸盐指数与温度、降雨量相关关系更明显,而总磷、总氮的异常变化与相对湿度、降雨量、径流量具有相关关系。研究结果对于提高气候变化背景下的水质预报能力具有重要的参考价值,可为自然流域水污染防治提供科学参考。     

基于集合经验模态分解和支持向量机的溶解氧预测

应用集合经验模态分解(EEMD)和支持向量机(SVM)相结合的方法,建立一种天然水体溶解氧浓度预测模型。首先,利用EEMD方法将溶解氧时序分解成不同频段的分量,以降低序列的非平稳性;然后,根据各序列分量的自身特征建立合适的SVM预测模型,此过程通过相关分析确定各分量输入量;最后,将各子分量预测值合成得到最终的预测结果。使用该模型对嘉陵江北温泉段的溶解氧浓度进行预测,结果表明,与传统单一的SVM和BP神经网络模型相比,该模型能有效提高预测精密度,具有良好的应用前景。     

Optimizing the Monitoring Strategy of Wastewater Treatment Plants by Multiobjective Neural Networks Approach

This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN’s structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable (μ) that represents the level of aspiration achieved in one objective and the conjugate of μ, (1 − μ), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm (GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant (WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate (Q), CN⁻, SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying μ (aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN⁻ and SS in equalization basin; aeration tank hydraulic retention time (HRT) and percentage of returned sludge (R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.