Monitoring pesticide concentration is very important for public authorities given the major concerns for environmental safety and the likelihood for increased public health risks. An important aspect of this process consists in locating abnormal signals, from a large amount of collected data. This kind of data is usually complex since it suffers from limits of quantification leading to left censored observations, and from the sampling procedure which is irregular in time and space across measuring stations. The present manuscript tackles precisely the issue of detecting spatio-temporal collective anomalies in pesticide concentration levels, and introduces a novel methodology for dealing with spatio-temporal heterogeneity. The latter combines a change-point detection procedure applied to the series of maximum daily values across all stations, and a clustering step aimed at a spatial segmentation of the stations. Limits of quantification are handled in the change-point procedure, by supposing an underlying left-censored parametric model, piece-wise stationary. Spatial segmentation takes into account the geographical conditions, and may be based on river network, wind directions and so forth. Conditionally to the temporal segment and the spatial cluster, one may eventually analyze the data and identify contextual anomalies. The proposed procedure is illustrated in detail on a data set containing the prosulfocarb concentration levels in surface waters in Centre-Val de Loire region.

中文翻译：

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农药浓度监测：研究左删失数据中的时空模式

鉴于对环境安全的主要关注和增加公共卫生风险的可能性，监测农药浓度对公共当局非常重要。这个过程的一个重要方面在于从大量收集的数据中定位异常信号。这种数据通常很复杂，因为它受到导致左截尾观测值的量化限制，以及跨测量站在时间和空间上不规则的采样程序。本手稿恰好解决了检测农药浓度水平时空集体异常的问题，并介绍了一种处理时空异质性的新方法。后者结合了一个变化点检测程序，该程序适用于所有站点的每日最大值系列，以及旨在对站点进行空间分割的聚类步骤。量化的限制在变点程序中处理，通过假设一个基本的左截尾参数模型，分段平稳。空间分割考虑了地理条件，可以基于河网、风向等。根据时间段和空间集群的条件，最终可能会分析数据并识别上下文异常。拟议的程序在一个数据集上进行了详细说明，该数据集包含中心-卢瓦尔河谷地区地表水中的百草威浓度水平。分段平稳。空间分割考虑了地理条件，可以基于河网、风向等。根据时间段和空间集群的条件，最终可能会分析数据并识别上下文异常。拟议的程序在一个数据集上进行了详细说明，该数据集包含中心-卢瓦尔河谷地区地表水中的百草威浓度水平。分段平稳。空间分割考虑了地理条件，可以基于河网、风向等。根据时间段和空间集群的条件，最终可能会分析数据并识别上下文异常。拟议的程序在一个数据集上进行了详细说明，该数据集包含中心-卢瓦尔河谷地区地表水中的百草威浓度水平。