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Influence of sampling intake position on suspended solid measurements in sewers: two probability/time-series-based approaches

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Abstract

Total suspended solid (TSS) measurements in urban drainage systems are required for several reasons. Aiming to assess uncertainties in the mean TSS concentration due to the influence of sampling intake vertical position and vertical concentration gradients in a sewer pipe, two methods are proposed: a simplified method based on a theoretical vertical concentration profile (SM) and a time series grouping method (TSM). SM is based on flow rate and water depth time series. TSM requires additional TSS time series as input data. All time series are from the Chassieu urban catchment in Lyon, France (time series from 2007 with 2-min time step, 89 rainfall events). The probability of measuring a TSS value lower than the mean TSS along the vertical cross section (TSS underestimation) is about 0.88 with SM and about 0.64 with TSM. TSM shows more realistic TSS underestimation values (about 39 %) than SM (about 269 %). Interquartile ranges (IQR) over the probability values indicate that SM is more uncertain (IQR = 0.08) than TSM (IQR = 0.02). Differences between the two methods are mainly due to simplifications in SM (absence of TSS measurements). SM assumes a significant asymmetry of the TSS concentration profile along the vertical axis in the cross section. This is compatible with the distribution of TSS measurements found in the TSM approach. The methods provide insights towards an indicator of the measurement performance and representativeness for a TSS sampling protocol.

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Acknowledgments

The data used in this work have been collected and made available by the OTHU project in Lyon, France (see www.othu.org). Santiago Sandoval is grateful to COLCIENCIAS (Colombian Institute for the Development of Science and Technology) for funding his PhD studies in France.

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Sandoval, S., Bertrand-Krajewski, JL. Influence of sampling intake position on suspended solid measurements in sewers: two probability/time-series-based approaches. Environ Monit Assess 188, 347 (2016). https://doi.org/10.1007/s10661-016-5335-y

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