The strongest predictor of the observed sublethal toxicity to amphipod and copepod reproduction was a combination of total PAHs and metals (primarily Cu, Pb and Zn). Total PAHs was the strongest predicting variable for toxicity to organism survival. While high total PAH concentrations were attributed to the former gas works, high background concentrations of metals existed throughout much of this region of the estuary. Thus, without remediation at the estuary-scale, resuspension of the surrounding sediments by tidal currents and boat movements is predicted to re-contaminate remediated areas with sediments that may continue to cause chronic toxicity due to metals. The assessment indicated that remedial actions that remove or isolate sediments that caused toxicity to benthic organism survival would lead to significant improvements in ecosystem health, but toxicity to organism reproduction may remain at similar levels that exist throughout much of this region of the estuary due to high metal concentrations.Since the establishment of the world-class Three Gorges Dam (TGD) across the Yangtze River, China, the downstream reach has experienced a long-term adjustment with regard to the river morphology and hydrodynamics, imposing a profound impact on the environmental conditions of human living and aquatic ecosystem. This study presents an investigation on the river channel morphological characteristics and hydrodynamic environment of a large bifurcation-confluence complex downstream of the TGD through detailed field survey and numerical modeling. Results show that the main stem, before being bifurcated into two sub-channels (the North Channel and the South Channel), experiences a meander, leading to the severe bed scouring near the outer bank (pools) resulted from a high flow mass flux and bed shear stress. Because of being bifurcated, the river width with largely growing may result in the reduction of flow velocity and sediment deposition (riffles), and thereby two plugbars are formed near the entrance of two sub-sub-channel.Accurate instantaneous vehicle emissions models are vital for evaluating the impacts of road transport on air pollution at high temporal and spatial resolution. In this study, we apply machine learning techniques to a dataset of 70 diesel vehicles tested in real-world driving conditions to (i) cluster vehicles with similar emissions performance, and (ii) model instantaneous emissions. https://www.selleckchem.com/products/ml355.html The application of dynamic time warping and clustering analysis by NOx emissions resulted in 17 clusters capturing 88% of trips in the dataset. We show that clustering effectively groups vehicles with similar emissions profiles, however no significant correlation between emissions and vehicle characteristics (i.e. engine size, vehicle weight) were found. For each cluster, we evaluate three instantaneous emissions models a look-up table (LT) approach, a non-linear regression (NLR) model and a neural network multi-layer perceptron (MLP) model. The NLR model provides accurate instantaneous NOx predictions, on par with the MLP relative errors in prediction of emission factors are below 20% for both models, average fractional biases are -0.01 (s.d. 0.02) and -0.0003 (s.d. 0.04), and average normalised mean squared errors are 0.25 (s.d. 0.14) and 0.29 (s.d. 0.16), for the NLR and MLP models respectively. However, neural networks are better able to deal with vehicles not belonging to a specific cluster. The new models that we present rely on simple inputs of vehicle speed and acceleration, which could be extracted from existing sources including traffic cameras and vehicle tracking devices, and can therefore be deployed immediately to enable fast and accurate prediction of vehicle NOx emissions. The speed and the ease of use of these new models make them an ideal operational tool for policy makers aiming to build emission inventories or evaluate emissions mitigation strategies.The focus of this paper is to briefly discuss the major advances in scientific thinking regarding a) processes governing the fate and transport of mercury in the environment; b) advances in measurement methods; and c) how these advances in knowledge fit in within the context of the Minamata Convention on Mercury. Details regarding the information summarized here can be found in the papers associated with this Virtual Special Issue of STOTEN.Disinfection, which aims to eliminate pathogenic microorganisms, is an essential step of water treatment. Hydrodynamic cavitation (HC) has emerged as a promising technology for large-scale disinfection without introducing new chemicals. HC, which can effectively induce sonochemistry by mechanical means, creates extraordinary conditions of pressures of ~1000 bar, local hotspots with ~5000 K, and high oxidation (hydroxyl radicals) in room environment. These conditions can produce highly destructive effects on microorganisms in water. In addition, the enhancements of chemical reactions and mass transfers by HC produce the synergism between HC and disinfectants or other physical treatment methods. HC is generated by hydrodynamic cavitation reactors (HCRs), therefore, their performance basically determines the effectiveness, economical efficiency, and applicability of HC disinfection. Therefore, developing high-performance HCRs and revealing the corresponding disinfection mechanisms are the most crucial issues today. In this review, we summarize the fundamental principles of HC and HCRs and recent development in HC disinfection. The energy release from cavitation phenomenon and corresponding mechanisms are elaborated. The performance (effectiveness, treatment ratio, and cost) of various HCRs, effects of treatment conditions on performance, and applicability of HC disinfection are evaluated and discussed. Finally, recommendations are provided for the future progress based on the analysis of previous studies.Taking into account meteorological data in urban planning increases in relevance in the context of changing climate and enhanced urbanisation. The present article focusses on the nocturnal urban heat island intensity (UHII) simulated with a physically based atmospheric model for >200,000 Reference Spatial Units (RSU), which correspond to building patches delimited by roads or water bodies in 42 French urban agglomerations. First are investigated the statistical relationships between the UHII and six predictors Local Climate Zone, distance to the agglomeration centre, population, distance to the coast, climatic region, and elevation differences. It is found that the maximum UHII of an agglomeration increases proportional to the logarithm of its population, decreases for cities closer than 10 km to the coast, and is shaped by the regional climate. Secondly, a Random Forest model and a regression-based model are developed to predict the UHII based on the predictors. The advantage of the regression-based model is that it is easier to understand than the black box Random Forest model.