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Because of the diversification of industries in developing cities, the phenomenon of the simultaneous contamination of various kinds of pollutants is becoming common, and the environmental process of pollutants in multi-contaminated environmental mediums has attracted attention in recent years. In this study, p-arsanilic acid (ASA), a kind of organic arsenic feed additive that contains the arsenic group in a chemical structure, is used as a typical contaminant to investigate its adsorption on iron oxides and its implication for contaminated soils. The adsorption kinetics on all solids can be fitted to the pseudo-second-order kinetic model well. At the same mass dosage conditions, the adsorption amount per unit surface area on iron oxides follows the order α-FeOOH > γ-Fe2O3 > α-Fe2O3, which is significantly higher than that for actual soil, because of the lower content of iron oxides in actual soil. Lower pH conditions favor ASA adsorption, while higher pH conditions inhibit its adsorption as a result of the electrostatic repulsion and weakened hydrophobic interaction. The presence of phosphate also inhibits ASA adsorption because of the competitive effect. Correlations between the amount of ASA adsorption in actual soil and the Fe2O3 content, total phosphorus content, arsenic content, and organic matter content of actual soil are also investigated in this work, and a moderate positive correlation (R2 = 0.630), strong negative correlation (R2 = 0.734), insignificant positive correlation (R2 = 0.099), and no correlation (R2 = 0.006) are found, respectively. These findings would help evaluate the potential hazard of the usage of organic arsenic feed additives, as well as further the understanding of the geochemical processes of contaminants in complicated mediums.

The cost-benefit is a key factor when selecting an appropriate sponge city construction scheme. The research of applying intelligent technology to find cost-benefit efficient planning and construction of sponge city is urgently required. This paper established a multi-objective simulation optimization framework of sponge city construction which considered minimization of runoff control rate, pollutant control rate and life-cycle cost Non-dominated sorting genetic algorithm (NSGA-II) was successfully coupled to Storm water management model to complete the simulation-optimization process. A case study in Xining, China, was conducted to demonstrate the proposed framework. The results of this research suggested that 1) different sponge city construction schemes lead to different runoff control rates and pollutant control rates although under the same investment; 2) the runoff control rate and pollutant control rate total suspended solids decreased with the increase of the rainfall return period, while the cost of sponge city construction increased with the increase of rainfall return period. Furthermore, for T = 2-year, the sponge facility exhibited the most stable control effect on runoff and pollutants among the three different return periods (T = 2-year, 5-year, 10-year); 3) sponge city construction exhibited a “cost-benefit” efficient interval. For T = 2-year, the cost-benefit high efficiency interval of sponge city construction is calculated between 1.2 billion and 1.8 billion; for T = 5-year, the interval is between 1.2 billion and 1.8 billion, while for T = 10-year, the interval is between 1.3 billion and 2.1 billion. The above observations provide reference for reasonable and effective sponge city construction in Xining, China.

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Interactions among society, water resources, and environment systems have become increasingly prominent with the progressively far-reaching impact of human activities. Therefore, this paper aims to construct a co-evolution model to establish the mutual feedback relationship among society, water resources, and environment from the perspective of socio-hydrology. Firstly, social factors such as environmental sensitivity, environmental protection awareness, and technological level are introduced to this model to describe the coevolutionary trajectory of society, water resources and environment subsystems. Then, this model is implemented in 11 provincial administrative regions in the Yangtze River Economic Belt, and the degree of coordination of their coupling is evaluated. Results show that the water-use efficiency of each provincial administrative region in the Yangtze River Economic Belt gradually increases during the forecast period. The coupling-coordinated degree of each provincial administrative region of the Yangtze River Economic Belt has greatly improved during the 14th Five-Year Plan period, reflecting that policy support has played a significant role in the coordinated development of the Yangtze River Economic Belt. The dynamic fluctuation process of environmental sensitivity effectively depicts the co-evolution process of the coupling system, which provides a reference for the subsequent exploration and cognition of the human-water coevolutionary mechanism.

Main drawbacks of fuel cell systems, namely, high cost, poor reliability, and short lifespan, limit the large-scale commercial application of fuel cell systems. The health status detection of fuel cell systems for locomotives is of great significance to the safe and stable operation of locomotives. To identify the failure modes of the fuel cell system accurately and quickly, this study proposed an intelligent health status detection method for locomotive fuel cells based on data-driven techniques. In this study, the actual test data of a 150-kW fuel cell system for locomotives was analyzed. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was combined with the general regression neural network (GRNN) to intelligently detect the health status of the fuel cell system for locomotives. Specifically, t-SNE was used to process the high-dimensionality and strong coupling raw data of health status, enabling the dimensional reduction of the raw data to reflect essential features. Then, GRNN was used to identify the feature data to achieve the fast and accurate detection of the health status of the fuel cell system. Results show that the proposed method can effectively detect four health conditions, namely, normal state, high inlet coolant temperature, low air pressure, and low spray pump pressure, with a diagnostic accuracy of 98.75%. This study is applicable to the analysis of the actual measurement data of high-power level fuel cell systems and provides a reference for the health status detection of fuel cell systems for locomotives. Keywords: fuel cell system for locomotive; data-driven; general regression neural network; t-distributed stochastic neighbor embedding; health status detection

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Urbanization has notably changed the characteristics and functions of watershed ecosystems worldwide, influencing the characteristics of chromophoric dissolved organic matter (CDOM) and dissolved organic matter (DOM) of sediments in urban streams. In this study, the biogeochemical characteristics of 42 water samples and the optical absorption and excitation–emission matrix spectra (EEMs) of 14 sediment samples collected from 14 urban streams in Wuhan were systematically examined. In addition, five water samples and one sediment sample were collected in Mulan Lake as a reference for non-urban areas. The a254 values of sediments in urban streams ranged widely (25.7–197.6 m−1), and the mean (116.32 ± 60.5 m−1) was significantly higher than the reference (51.52 m−1), indicating clear individual differences and a higher concentration of CDOM. Two humus-like components and one tryptophan-like component were effectively identified by parallel factor analysis (PARAFAC). The fluorescence index (FI)/biological index (BIX) of DOM of sediments in urban streams was mostly within 1.4–1.7/0.8–1.0, indicating a compound of both allochthonous and autochthonous sources. Compared with the reference, lower FI and BIX and higher humification index (HIX) revealed a higher allochthonous input and humification degree of DOM of sediments in urban streams. Spearman’s correlation analysis and redundancy analysis demonstrated that heavy metals and other water quality parameters had a considerable impact on CDOM concentrations and DOM components. This study could support the use of DOM as an effective tool to monitor the water environment and provide insights into future water pollution management strategies.