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The present paper is based on discharges and suspended particulate matter concentrations from a 9-years high-resolution database for the Garonne River (large plain river) covering contrasted hydrologic years, and a 12-months high frequency sampling for the Nivelle River (small mountainous river). Annual SPM fluxes in the Garonne River range from 0.6×106 t year-1 (1997) to 3.9×106 t year-1 (1996). In contrast, the Nivelle River transported 11×103 t year-1 from December 1995 to December 1996. From the long-term observation of the Garonne River an empirical relation between SPM* (discharge-weighted mean annual SPM concentrations) and annual discharge was established. This relation allows estimating annual SPM fluxes for the Garonne River with less than 30% deviation from reference values for the whole range of mean annual discharge observed during the past decade. Specific (=area-normalized) annual SPM fluxes (YSPM) range from 11 to 74 t km-2 year-1 for the Garonne River. Comparison of these results with YSPM of the Nivelle River (69 t km-2 year-1 in 1996) suggests that interannual hydrological variations may have a greater impact on fluvial SPM transport than basin-specific parameters. By extracting individual SPM concentrations and corresponding discharge values from the database, different sampling frequencies were simulated and resulting SPM fluxes were then compared to reference fluxes derived from the complete database. If a deviation of simulated flux estimates from reference fluxes lower than ±20% is accepted, the Garonne River (large plain river) must be sampled at least every 3 days (10 samples per month) and the Nivelle River every 7 h (approx. 100 samples per month). For the Garonne River this minimum sampling frequency is valid for all contrasted hydrologic years of the observation period. Below these minimum sampling frequencies, annual SPM flux estimates may greatly differ from reference fluxes (up to 200%) and there is high probability of systematic underestimation. Consequently, annual SPM flux estimates for the Garonne River derived from the empirical relation (SPM*-annual discharge) are likely to be more satisfactory (errors <30%) than estimates based on sampling frequencies lower than the minimum frequency. These findings underline the need of adapted sampling strategies for erosion assessment, reliable chemical (e.g. nutrients and pollutants) mass balances and characterisation of fluvial transport mechanisms in the world''s contrasted watersheds. [Copyright &y& Elsevier]

We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and smaller one-month antecedent rainfall. It is noted that these three selected factors are easy-to-collect. We demonstrate that the proposed methodology is a useful and effective methodology, which can be adapted to other similar settings to reliably model water quality based on parameters of interest and/or study areas of interest for universal usage. The proposed methodology gives a quick and reliable way to estimate arsenic concentration, which makes good contribution to water environment management. [ABSTRACT FROM AUTHOR]

Since 1945, a large amount of heterogeneous data has been acquired to survey river sediment quality, especially concerning regulatory metals such as Cd, Cr, Cu, Hg, Ni, Pb, and Zn. Large-scale syntheses are critical to assess the effectiveness of public regulations and the resiliency of the river systems. Accordingly, this data synthesis proposes a first attempt to decipher spatio-temporal trends of metal contamination along seven major continental rivers in Western Europe (France, Belgium, Germany, and the Netherlands). A large dataset (>12,000 samples) from various sediment matrices (bed and flood deposits - BFD, suspended particulate matter - SPM, dated sediment cores - DSC) was set up based on monitoring and scientific research from the 1950s to the 2010s. This work investigates the impact of analytical protocols (matrix sampling, fractionation, extraction), location and time factors (related to geology and anthropogenic activities) on metal concentration trends. Statistical analyses highlight crossed-interactions in space and time, as well as between sediment matrices (metal concentrations in SPM ≃ DSC > BFD) and extraction procedures (also related to river lithology). Major spatio-temporal trends are found along several rivers such as (i) an increase of metal concentrations downstream of the main urban industrial areas (e.g. Paris-Rouen corridor on the Seine River, Bonn-Duisburg corridor on the Rhine River), (ii) a long-term influence of former mining areas located in crystalline zones, releasing heavily contaminated sediments for decades (Upper Loire River, Middle Meuse section), (iii) a decrease of metal concentrations since the 1970s (except for Cr and Ni, rather low and stable over time). The improvement of sediment quality in the most recent years in Europe reflects a decisive role of environment policies, such as more efficient wastewater treatments, local applications of the Water Framework Directive and urban industrial changes in the river valleys., Competing Interests: Declaration of competing interest We, the authors, declare that we have no conflict of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)