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DEVELOPMENT OF WATER POLLUTION EARLY WARNING SYSTEM FOR OYSTER HARVESTING AREAS

机译:牡蛎捕捞区水污染预警系统的研制

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The objective of this study is to develop a Pollution Early Warning System (PEWS) for efficient management of water quality in oyster harvesting areas. To that end, this paper presents a web-enabled, user-friendly PEWS for managing water quality in oyster harvesting areas along Louisiana Gulf Coast, USA. The PEWS consists of (1) an Integrated Space-Ground Sensing System (ISGSS) gathering data for environmental factors influencing water quality, (2) an Artificial Neural Network (ANN) model for predicting the level of fecal coliform bacteria, and (3) a web-enabled, user-friendly Geographic Information System (GIS) platform for issuing water pollution advisories and managing oyster harvesting waters. The ISGSS (data acquisition system) collects near real-time environmental data from various sources, including NASA MODIS Terra and Aqua satellites and in-situ sensing stations managed by the USGS and the NOAA. The ANN model is developed using the ANN program in MATLAB Toolbox. The ANN model involves a total of 6 independent environmental variables, including rainfall, tide, wind, salinity, temperature, and weather type along with 8 different combinations of the independent variables. The ANN model is constructed and tested using environmental and bacteriological data collected monthly from 2001 - 2011 by Louisiana Molluscan Shellfish Program at seven oyster harvesting areas in Louisiana Coast, USA. The ANN model is capable of explaining about 77% of variation in fecal coliform levels for model training data and 44% for independent data. The web-based GIS platform is developed using ArcView GIS and ArcIMS. The web-based GIS system can be employed for mapping fecal coliform levels, predicted by the ANN model, and potential risks of norovirus outbreaks in oyster harvesting waters. The PEWS is able to inform decision-makers of potential risks of fecal pollution and virus outbreak on a daily basis, greatly reducing the risk of contaminated oysters to human health.
机译:这项研究的目的是开发一种污染预警系统(PEWS),以有效管理牡蛎收获地区的水质。为此,本文提出了一种基于Web的,用户友好的PEWS,用于管理美国路易斯安那州墨西哥湾沿岸牡蛎收获地区的水质。 PEWS由(1)集成的地面空间传感系统(ISGSS)收集有关影响水质的环境因素的数据,(2)人工神经网络(ANN)模型来预测粪大肠菌的水平以及(3)一个基于Web的,易于使用的地理信息系统(GIS)平台,用于发布水污染咨询和管理牡蛎收获水域。 ISGSS(数据采集系统)从各种来源收集近乎实时的环境数据,包括NASA MODIS Terra和Aqua卫星以及由USGS和NOAA管理的现场感测站。 ANN模型是使用MATLAB Toolbox中的ANN程序开发的。 ANN模型涉及总共6个独立的环境变量,包括降雨,潮汐,风,盐度,温度和天气类型,以及8个不同的独立变量组合。 ANN模型是使用路易斯安那州软体动物贝类计划在2001年至2011年每月从美国路易斯安那州海岸的七个牡蛎收获区收集的环境和细菌学数据构建和测试的。 ANN模型能够为模型训练数据解释粪便大肠菌水平约77%的变化,对于独立数据能够解释44%。基于Web的GIS平台是使用ArcView GIS和ArcIMS开发的。基于网络的GIS系统可用于绘制由ANN模型预测的粪便大肠菌群水平以及牡蛎收获水中诺如病毒暴发的潜在风险。 PEWS能够每天将粪便污染和病毒爆发的潜在风险告知决策者,从而大大降低了受污染的牡蛎对人类健康的风险。

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