Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

Phosphorus (P) is a crucial macronutrient, and recently a venture agent of pollutant, in aquatic systems worldwide. Most of P circulates with sediment through rivers, and the relationship between P and sediment is the basis for understanding the biogeochemical processes in rivers. Although studies of fluvial P have been carried out at specific sites and for particular problems, the general regime by which sediment affects P recirculation still warrants attention. In this study, a series of water samples were collected from six different rivers in China whose sediment concentration and size distribution vary widely and their P properties were analyzed in the laboratory. From this analysis of field samples, a highly consistent comet shaped pattern of sediment effects on P is revealed, i.e., generally the range of the concentration of total P has a diverge-converge trend as the sediment concentration increases. It is further supported by examining the strictly composed samples from P adsorption experiments. Furthermore, case analyses were performed on the basis of the above relationships to illustrate the impact of sediment on P cycling in rivers. The results can infer the following: 1) There is a strong positive correlation between total P and sediment concentration, indicating that sediment is a crucial agent in the movement and fate of P. 2) The negative correlation between dissolved P and sediment concentration indicates a buffering effect of sediment, especially fine sediment, on dissolved P, interpreting the intricate phenomena of increased dissolved P concentration caused by sediment reduction. Hence, natural sediment has the prevailing advantage in moderating the water quality of rivers, which is directly relevant to mitigating the present pollution and eutrophication of waters. 3) River damming causes a P blockage tendency, altering the fluvial nourishment to contamination in the river.

Fine sediment transport is currently attracting increasing attentions owing to its importance in the dynamics of sediment-contaminant interaction in the fluvial environment downstream of dams, which calls for more detailed and accurate flume experiments. However, because of inaccurate loading and undesired recirculation of fine sediment in the usual short laboratory flumes, such experiments are often unrepeatable and unreliable. In this technical note, we propose a new sediment feeder, to load dry sediment sample at the inlet, and a pressurized sediment filter, to screen the sediment out at the outlet, to implement a clear-water supply system for the flumes. It can improve fine sediment experiments not only by accurate loading but also through preventing undesired sediment recirculation, which can interfere and even modify the designated upstream input conditions. These devices have been constructed and tested, shown to be practical, simple and effective. Using them together can also provide a way to reclaim all the samples of experimental sediment that are of crucial importance for repeat and multiple tests for different contamination with a given sediment without losing the prescribed composition and other properties. This implementation is especially suitable for simulating fine particle affinity contaminant transport in fluvial turbulent flows in low sediment concentrations.

The upper Xin’an River basin serves as a critical ecological barrier and water-conservation area for the Yangtze River Delta. However, with rapid economic development, nitrogen pollution in the surface waters of this region has become increasingly pronounced. This study analyzed river water samples collected on four occasions from the upper Xin’an River basin for ammonium (NH₄⁺–N), nitrate-nitrogen (NO₃⁻–N), total nitrogen (TN), and nitrate isotopic (δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻). The sources of nitrate (NO₃⁻) were apportioned using the MixSIAR stable-isotope mixing model, and the spatial distribution of these sources across the basin was characterized. Across the four sampling rounds, the mean TN concentration exceeded 1.3 mg/L, with NO₃⁻–N accounting for over 45% of TN, indicating that nitrate was the dominant inorganic nitrogen species. The δ¹⁵N–NO₃⁻ values ranged from 2.17‰ to 13.0‰, with mean values following the order summer > winter > autumn > spring. The δ¹⁸O–NO₃⁻ values varied from −5.20‰ to −3.48‰, and the average value showed a completely opposite seasonal variation pattern to that of δ¹⁵N–NO₃⁻. Process-based analysis of nitrogen transformations revealed that nitrification predominates during nitrate transport and transformation, whereas denitrification is comparatively weak. MixSIAR-based estimates indicate marked seasonal differences in the source composition of nitrate pollution in the upper Xin’an River basin; NO₃⁻ derives primarily from soil nitrogen (SN) and livestock/sewage manure nitrogen (LSN). LSN was the dominant contributor in spring and summer (49.2% and 59.9%, respectively). SN dominated in autumn (49.2%) and winter (54.1%). Fertilizer nitrogen (FN) contributed more during summer and autumn, when fertilization is concentrated and rainfall is higher. Atmospheric deposition (AN) contributed approximately 1% across all seasons and was thus considered negligible. These findings provide a scientific basis for source control of nitrogen pollution and water-quality management in the upper Xin’an River.