The removal of high-concentration C.I. acid orange 7 (AO7) in aqueous solution by the prepared iron/copper (Fe/Cu) bimetallic particles and zerovalent iron (ZVI) was investigated thoroughly. Fe/Cu bimetallic particles were prepared by planting Cu on the surface of Fe. Experimental results confirmed the superiority of Fe/Cu bimetallic particles for the degradation of AO7 in aqueous solution. Under the optimal conditions ([Fe/Cu] 0 = 40 g•L −1 , [AO7] 0 = 1000 mmol•L −1 , initial pH = 6.5, TML Cu = 0.62 g of Cu/g of Fe), the AO7 concentration and chemical oxygen demand (COD) and total organic carbon (TOC) removal efficiencies could reach 94.3%, 61.8%, and 60.8%, respectively, after only 10 min of treatment by Fe/Cu bimetallic particles. Under the same conditions, however, the AO7 concentration and COD and TOC removal efficiencies by ZVI only reached 35.5%, 25.1%, and 21.2%, respectively. Thus, the planting of Cu could improve the reactivity of Fe. The degradation of AO7 was analyzed by UV−vis and Fourier transform infrared spectra, and the results show that the chromophore part (i.e., −NN−) of AO7 could be destructed by Fe/Cu bimetallic particles. Additionally, its degradation products might be sulfanilamide and 1-amino-2-naphtol, which would be removed by further mineralization of Fe/Cu bimetallic particles or sedimentation of Fe ions. Therefore, the Fe/Cu bimetallic system is a promising process for the toxic and refractory wastewater from the printing and dyeing industry.

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

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