The aim of this study is to investigate the complex response of optical turbidity sensors (side-and backscattering sensors) to Suspended Particle Matter (SPM) characteristics and the consequences when investigating SPM dynamics from long-term high frequency monitoring networks. Our investigation is based on the analysis of a unique dataset of monthly 12 h cycle measurements of SPM characteristics such as turbidity, concentration, floc size distribution, floc density and organic matter content in the macrotidal Seine Estuary (France) between February 2015 and June 2016. Results reveal that despite calibration to a Formazin standard, turbidity sensor response to SPM concentrations (in the range of 7-7000 mg L−1) are strongly variable, from the tidal scale to the annual scale and in different compartments of the Seine Estuary. The variability in the calibration relationships is related to changes in the sensor sensitivity according to (i) the sensor intern technology (mainly due to optical geometry) and (ii) the variability in inherent optical properties (IOP) of SPM. Highlights ► Optical sensors responses are evaluated from a unique dataset of SPM characteristics. ► Turbidity/concentration relationships evaluated from the tidal to the annual scale. ► Flocculation affects the turbidity response of optical sensors. ► The floc size distribution and floc dry density modify the scattering efficiency. ► Discussion about long term monitoring network calibration and related uncertainties

Measurement of suspended particulate matter concentration (SPMC) spanning large time and geographical scales have become a matter of growing importance in recent decades. At many places worldwide, complex observation platforms have been installed to capture temporal and spatial variability over scales ranging from cm (turbulent regimes) to whole basins. Long-term in situ measurements of SPMC involve one or more optical and acoustical sensors and, as the ground truth reference, gravimetric measurements of filtered water samples. The estimation of SPMC from optical and acoustical proxies Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site. generally results from the combination of a number of independent calibration measurements, as well as regression or inverse models. Direct or indirect measurements of SPMC are inherently associated with a number of uncertainties along the whole operation chain, the autonomous field deployment, to the analyses necessary for converting the observed proxy values of optical and acoustical signals to SPMC. Controlling uncertainties will become an important issue when the observational input comprises systems of sensors spanning large spatial and temporal scales. This will be especially relevant for detecting trends in the data with unambiguous statistical significance, separating anthropogenic impact from natural variations, or evaluating numerical models over a broad ensemble of different conditions using validated field data. The aim of the study is to present and discuss the benefits and limitations of using optical and acoustical backscatter sensors to acquire long-term observations of SPMC. Additionally, this study will formulate recommendations on how to best acquire quality-assured SPMC data sets, based on the challenges and uncertainties associated with those long-term observations. The main sources of error as well as the means to quantify and reduce the uncertainties associated with SPMC measurements are also illustrated. Highlights ► Errors associated with optical and acoustical sensors for SPMC are quantified. ► A strict protocol limits the uncertainties. ► Systematic errors may reach up to ±20% and errors due to biofouling to 100% or more. ► Changes of the inherent particle properties may result in uncertainties up to 200%. ► A model based on the R 2 quantifies the uncertainty of the sensor derived SPMC. ► Hach-turbidities could be a cheap alternative for sample SPMC.