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Source apportionment of groundwater pollutants in Apulian agricultural sites using multivariate statistical analyses: case study of Foggia province

机译:基于多元统计分析的普利亚农业区地下水污染物源头分配-以福贾省为例

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摘要

BackgroundGround waters are an important resource of water supply for human health and activities. Groundwater uses and applications are often related to its composition, which is increasingly influenced by human activities.In fact the water quality of groundwater is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area. During the years 2004-2007 the Agricultural and Food Authority of Apulia Region has implemented the project “Expansion of regional agro-meteorological network” in order to assess, monitor and manage of regional groundwater quality. The total wells monitored during this activity amounted to 473, and the water samples analyzed were 1021. This resulted in a huge and complex data matrix comprised of a large number of physical-chemical parameters, which are often difficult to interpret and draw meaningful conclusions. The application of different multivariate statistical techniques such as Cluster Analysis (CA), Principal Component Analysis (PCA), Absolute Principal Component Scores (APCS) for interpretation of the complex databases offers a better understanding of water quality in the study region.
机译:背景技术地下水是人类健康和活动的重要水源。地下水的使用和应用通常与其组成有关,而其组成越来越受到人类活动的影响。事实上,地下水的水质受到许多因素的影响,包括降水,地表径流,地下水流量和集水区的特征。在2004年至2007年期间,普利亚地区农业和食品管理局实施了“扩展区域农业气象网络”项目,以评估,监测和管理区域地下水质量。在此活动期间监控的总井数为473,分析的水样为1021。这导致了庞大而复杂的数据矩阵,其中包含大量的物理化学参数,这些参数通常难以解释和得出有意义的结论。运用不同的多元统计技术,例如聚类分析(CA),主成分分析(PCA),绝对主成分评分(APCS)来解释复杂的数据库,可以更好地了解研究区域的水质。

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