江苏省环境监测站建设标准(续二)

第二十九条县级环境监测站人员编制标准1、人员编制测算公式:P=K(M+a.A+b.B+c.c)其中:P-测算所得编制数,单位:人; A-全县人口数,单位:万人; K-考虑区域特点等综合因素b-“国内生产总值”系数,1人/(1.2亿元); 后取的调节系数; B-全县国内生产总值,单位:亿元; M-县站人员基数,12人; c-“面积”系数,1人/(760 Km2); a-“人口”系数,1人(13万人);C-全县面积,单位:Km2。2、调节系数(K):江宁、海安、如东、赣榆、灌云、沭阳、泗洪、盐城郊区、射阳、建湖、大丰、     

废弃电器电子产品产生量测算方法探究

选取我国废弃电器电子产品中回收处理数量最多、获得政策基金补贴最多的电视机产品,通过对其销量、最长使用年限、报废高峰期、报废系数的分析,采用市场供给A模型测算其报废产生量,并进一步对废弃电视机产生量测算结果与实际处理量做对比分析,提出废电视机产生量的修正方法。最终测算得到废电视机产生量为4 213万台,与同年电视机的实际处理量持平。     

淮河支流污染物综合降解系数动态测算

确定河流污染物综合降解系数动态变化规律对提高水环境容量测算精度和水环境管理具有重要意义。通过现场模拟法,采用一维稳态模型测算了淮河支流洪河五沟营-塔桥乡河段COD、氨氮和总磷在枯水期、平水期和丰水期的综合降解系数,COD、氨氮和总磷降解系数在各水期的关系为枯水期平水期丰水期,提出了建议值并利用实测浓度对计算结果进行了检验,结果表明,不同水期综合降解系数吻合情况较好。     

上海市机动车污染实时排放预警系统设计与应用

研究上海市机动车污染的动态排放测算和网格化动态排放清单构建,在实时的交通数据和交通环境监测数据的基础上,结合交通模型、机动车排放清单模型等业务模型和算法,依托大数据存储、可视化和GIS等技术,开发了上海市机动车污染物实时排放预警系统,实现了上海市全市道路的机动车动态排放测算、交通环境政策实施情景模拟和网格化排放清单,更新频率为每30 min一次,包含PM、NOx、CO、SO2、VOCs等污染物和9种车型。系统建成后直接服务于首届中国国际进口博览会,为大气污染排放实时总量跟踪评估、污染源管控措施分析及监测成因分析等提供了有力的实时数据和技术支撑。     

长江南通段水环境容量核定及总量控制研究

近年来南通市废水排放量逐年增大，一旦污染物排放量超过水环境容量，将影响到南通市供水水源的安全性，因此，对长江南通段进行水环境容量核定及总量控制研究十分必要。探讨了适用于长江水体的水环境容量计算方法和模型参数，对长江南通段水环境容量、污染物入江量及剩余环境容量进行了测算，并提出控制污染物入江量、合理利用水环境容量的措施建议。     

二次灰色关联分析模型在环境质量评价中的应用研究——以山东省淄博市为例

简单分析了指数评价法、模糊综合评价法和灰色关联分析法的优点和不足之处．在此基础上，本文提出了二次灰色关联分析模型，该模型即能对某环境监测点的环境质量进行综合评价，又能对所有环境监测点的某一环境污染物的污染状况进行综合评价，使评价结果更加完善．同时，该模型也能避免指数评价法、模糊综合评价法和灰色关联分析法的不足之处。     

铁岭市大气环境容量测算中几种运用方法的比较

主要针对铁岭市大气容量应用研究中用到的三种目前世界上普遍应用的测算方法，对铁岭市的环境容量进行测算，并通过对测算结果的分析及验证，对三种方法的利弊加以分析，探索一条适合中小城市的大气容量测算方法．     

我国环境监测的回顾与展望

回顾了我国环境监测事业２５年的发展历程,指出在新形势下,环境监测面临着极好的发展机遇;国家重视,民众支持、投入增大,任务繁重。发展方向是加强环境监测能力的现代化建设,是以提高质量和水平为中心的发展方式,而不是扩大机构和人员编制。     

松花江哈尔滨段主干流常规污染物动态衰减速度系数的测算研究

采用2011—2013年松花江哈尔滨段各断面水质监测数据和老头湾水文数据,运用实测资料反推法和河流一维水质模型对该段干流的COD、BOD5、NH3-N的衰减速度系数K进行测算,结果表明:1—12月朱顺屯—大顶子山的KCOD、KBOD5、KNH3-N分别在0.035 d-1~3.372 d-1、0.368 d-1~5.459 d-1和0.245 d-1~5.600 d-1之间;冰封期各污染物K值小于明水期,夏季(6—8月)K值大于其他季节。以2013年阿什河口下—呼兰河口下河段上、下游各断面的监测数据对测算的各污染物动态K值进行率定,实际监测值与预测浓度年内变化趋势基本一致,相对误差在25%以内,水质验证结果较满意。     

基于决策树技术及在线监测的水质预测

利用北方某城市水源的水质在线监测系统,建立了基于决策树技术,具有较强可视性和实际应用,以及能预测次日源水中叶绿素水平的决策树模型.该模型将某城市水源在线监测的溶解氧和太阳辐射照度数据转换计算为每日平均标准偏差及均值,并与每日定时取样测定的叶绿素含量一起作为预测因子,通过将115组数据的前100组数据作为训练集建立预测次日叶绿素水平决策树模型,并采用后15组数据进行模型的仿真预测检验,结果只有3 d的预测出错,预测准确率达80%.并讨论了模型建立对数据的要求及解读预测规则等问题.     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价，应用了人工神经网络Ｂ—Ｐ算法，构造了水环境质量评价模型，该模型应用于实例评价结果表明，人工神经网络用于环境质量评价具有客观性，通用性和实用性。     

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

中国地下水环境监测网的建设和管理

为实现《全国地下水污染防治规划(2011—2020年)》关于"2015年全面建立地下水环境监管体系"的目标,首先要建立地下水环境监测网络。在充分分析中国地下水环境监测现状的基础上,总结中国地下水环境监测的不足,并从法律法规的完善、监测网的建设和运行、先进技术的引进和人才培养以及信息共享系统的构建等4个方面,提出中国地下水环境监测网建设及管理的建议。     

考虑道路扬尘特性的环境空气质量在线监测方法

为判断环境空气污染的程度,提出一种考虑道路扬尘特性的环境空气质量在线监测方法。通过前端数据监测模块采集大气环境数据,基于光全散射法监测道路扬尘的质量浓度和密度;通过监测通信模块将监测结果传送至云服务器;云服务器利用基于极限学习机神经网络的预测模型,采用自适应粒子群优化算法,获取最佳的环境空气质量在线监测结果。结果表明,该方法学习速率的取值为0.5时,能够完成颗粒物浓度和密度的准确检测,且解释方差<2%,同时能够监测扬尘颗粒的扩散时间,确定适合活动的区域。     

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.     

Transport spatial model for the definition of green routes for city logistics centers

This paper presents a transport spatial decision support model (TSDSM) for carrying out the optimization of green routes for city logistics centers. The TSDSM model is based on the integration of the multi-criteria method of Weighted Linear Combination (WLC) and the modified Dijkstra algorithm within a geographic information system (GIS). The GIS is used for processing spatial data. The proposed model makes it possible to plan routes for green vehicles and maximize the positive effects on the environment, which can be seen in the reduction of harmful gas emissions and an increase in the air quality in highly populated areas. The scheduling of delivery vehicles is given as a problem of optimization in terms of the parameters of: the environment, health, use of space and logistics operating costs. Each of these input parameters was thoroughly examined and broken down in the GIS into criteria which further describe them. The model presented here takes into account the fact that logistics operators have a limited number of environmentally friendly (green) vehicles available. The TSDSM was tested on a network of roads with 127 links for the delivery of goods from the city logistics center to the user. The model supports any number of available environmentally friendly or environmentally unfriendly vehicles consistent with the size of the network and the transportation requirements.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

基于BP神经网络的齐齐哈尔地区地下水水质评价

阐述了人工神经网络基本原理,介绍了BP网络的地下水水质评价模型.在传统的评价方法基础上,根据各评价因子对环境和人类影响程度不同,给水质监测指标分组,从实用角度对水质进行评价.与传统评价方法相比,该评价模型在某评价因子数值极大的情况下,也能准确反映地下水的污染情况,并且通过GIS技术利用评价结果得到地下水水质分布图,从空间反映地下水水质变化规律.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

长三角区域环境空气质量预测预警体系建设的思考

世博会期间长三角区域空气质量自动监测网络和数据共享平台的成功搭建和有效运行,为探索长三角区域空气质量预测预警长期合作模式提供了宝贵的经验和启示。需要建立全方位区域空气质量监测网络和数据资源共享系统、源排放清单、开发区域多模式集合预报系统,构建区域多层面运作机制和会商制度,建设一批专业技术和复合型人才队伍。区域不同层面有效的管理体制机制的保障是区域环境空气质量预测预警体系的基础支撑。