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In situ estimation of temporal variability with different active and passive sampling strategies

Abstract : In the context of the water framework directive, in order to evaluate the aquatic environment quality for organic and mineral micropollutants, Water Agencies proceed to regular monitoring by collecting spot water sampling. There are some questions about the spatial and temporal representativeness of this “active” sampling technique carried out 6 to 12 times a year. As highlighted by Ort et al. (2010), the uncertainty associated to the sampling step increases when the discrete water sampling frequency decreases. Furthermore, the total uncertainty of spot sampling generally take into account only the contribution of the analytical step, and not the contribution of the sampling step. Thereby, this approach is often insufficient for a reliable contamination level assessment, particularly in the case of fleeting events (floods, punctual discharges…). Alternatively, passive sampling techniques consist to the immersion of a device in the aquatic environment over a period (from several days to a few weeks), providing time-weighted average pollutants concentrations. In this case, the measurements obtained with this device take into account the uncertainty associated with the preparation and analysis step, and the sampling step unlike the classical approach (Allan et al. 2006). Thus, the aim of this work is to determine and compare, in situ, the temporal uncertainty between active and passive sampling techniques. Three different kinds of POCIS (HLB, MAX and MIP) were exposed in triplicate during 6 periods of 14 days spread over 2016 in the Jalle de Blanquefort river (Gironde, France). In parallel, two different types of “active” sampling were carried out: weekly grab and high frequency composite samplings. These different techniques were used to detect and quantify a panel of 38 neutral pesticides (POCIS-HLB), 19 anionic pesticides (POCIS-MAX), glyphosate and AMPA (POCIS-MIP). In a first time, this study showed an important temporal variability for grab water sample collected during each 14-d period. Furthermore, the comparison between the data acquired with the three different techniques highlighted the contribution of passive sampling for improving quantification rates. In a second time, an estimation of the temporal variability related to the sampling step was carried out by the normalization of spot water sampling and POCIS data by a reference value (composite samples). The total variability associated to the spot and passive samplings were in the same range, and mainly explained by the uncertainty due to the sampling step. For example, the sampling step uncertainty, for annual concentration estimate of imidacloprid, were 48% and 45%, for POCIS-HLB and weekly spot sampling, respectively. For POCIS, this uncertainty could be attributed to a typical range of factor 2 of the sampling rates, depending on environmental conditions, like the flow velocity (Harman et al. 2012, Morin et al. 2012). For spot water sampling this variability could be rather explained by both temporal heterogeneity and transport/conservation steps before analysis. Allan, I. J., et al. (2006). "Strategic monitoring for the European water framework directive." TrAC Trends in Analytical Chemistry 25(7): 704-715. Harman, C., et al. (2012). "Calibration and use of the polar organic chemical integrative sampler—a critical review." Environmental Toxicology and Chemistry 31(12): 2724-2738. Morin, N., et al. (2012). "Chemical calibration, performance, validation and applications of the polar organic chemical integrative sampler (POCIS) in aquatic environments." TrAC Trends in Analytical Chemistry 36: 144-175. Ort, C., et al. (2010). "Sampling for PPCPs in wastewater systems: Comparison of different sampling modes and optimization strategies." Environmental Science and Technology 44(16): 6289-6296.
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https://hal.inrae.fr/hal-02606838
Contributor : Migration Irstea Publications <>
Submitted on : Saturday, May 16, 2020 - 1:09:19 PM
Last modification on : Monday, August 31, 2020 - 4:50:06 PM

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  • HAL Id : hal-02606838, version 1
  • IRSTEA : PUB00056099

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M. Bernard, Anne Togola, C. Berho, Nicolas Mazzella. In situ estimation of temporal variability with different active and passive sampling strategies. 9th International Passive Sampling Workshop and Symposium, Jun 2017, Toronto, Canada. pp.38. ⟨hal-02606838⟩

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