A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Biosensors for pathogen surveillance

Biologists, chemists, and physicists are collaborating to develop highly sensitive and specific biosensors for pathogen detection in the food, healthcare, and environmental sectors. Those novel biosensors allow quick detection and are thus expected to solve the issues of the emergence of highly virulent or antibiotic-resistant pathogens. This article reviews different types of biosensors used for pathogen detection, classified based on the type of transducer used. Optical biosensors integrate labeled means, e.g., fluorophores, quantum dots, and carbon dots to overcome photobleaching. Surface plasmon resonance is also used for enhanced sensitivity. Mechanical biosensors with piezoelectric crystals and cantilevers are adapted for the detection of food pathogens without sample preparation or labels. Conventional methods using electrodes for the measurement of electrochemical changes with differential pulse voltammetry or impedance spectroscopy are fast and highly sensitive. Immunosensors are developed for pathogen detection at trace levels using sample enrichment, signal amplification, and new visual detection techniques.     

我国环境监测体制改革探讨

本文分析了目前我国环境监测工作中普遍存在的体制不顺、职责不明、行政干预、重复监测、资源浪费、信息混乱等问题,提出监测机构垂直管理,调整监测职能,打破条块分割,整合社会监测资源,引入第三方检测的具体构想。     

Occurrence of pesticides and polychlorinated biphenyls in water of the Nile river at the estuaries of Rosetta and Damiatta branches,north of delta,Egypt

Abstract

A study was conducted from summer 1995 to summer 1997 to assess the seasonal occurrence of pesticide residues and other organic contaminants, polychlorinated biphenyls (PCBs), in water at the estuaries of Rosetta and Damiatta branches of the Nile river. The results indicated that organochlorine compounds (OCs) including HCB, lindane, p,p‘‐DDE, p,p‘DDD, p,p‘‐DDT, aroclor 1254 and aroclor 1260 were present in all the water samples at concentration levels ranging between 0.195–0.240, 0.286–0.352, 0.035–0.067, 0.019–0.033, 0.024–0.031, 0.390–0.70 and 0.166–0.330 μg/l, respectively. The levels of these compounds were higher in water of Damiatta branch than those found in water of Rosetta branch. Aldrin, dieldrin and endrin were not detected in all water samples. Only 4 compounds from 36 organophosphorus insecticides, fungicides and s‐triazine herbicides tested were detected in water samples collected during summer and autumn seasons from Rosetta branch. The concentration levels of these detected compounds, dimethoate, malathion, captan, and ametryne, ranged from 0.011 to 0.340 μg/l, respectively. Similar compounds during the same seasons as found in water of Rosetta branch were also detected in water of Damiatta branch except ametryne. The levels of the detected compounds (dimethoate, malathion and captan) ranged between 0.030 and 0.330 μg/l. The levels of detected organophosphorus insecticides, fungicides and s‐triazine herbicides were in the order: dimethoate > malathion > captan > ametryne.