  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, etc.
Conditionally to the temporal segment and the spatial cluster, one may
eventually analyse 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.
