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Mutual interference issues between heavy metal ions tremendously affect the detection reliability and accuracy in water quality analysis, especially the serious interference of Cu(II) on the detection of As(III) is greatly hard to overcome, which needs to be solved urgently. Herein, iron single-atom catalysts with different coordination structures of FeN 2 C 2 and FeN 3 P are constructed to selectively catalyze the detection of As(III) in the coexistence of Cu(II). FeN 3 P achieves a high sensitivity of 3.90 µA ppb−1 toward As(III) in NH 4 Cl/NH 3 ·H 2 O electrolyte (pH 8.0), completely avoiding Cu(II)-interference. Moreover, the turnover frequency (TOF) of FeN 3 P is an order of magnitude higher than that of FeN 2 C 2. X-ray absorption fine structure (XAFS) spectroscopy and density functional theory (DFT) calculations demonstrate that an As-O bond of H 3 AsO 3 is broken by the strong affinities between both P and O atoms and Fe and As atoms, and H 3 AsO 3 are preferentially reduced by FeN 3 P during adsorptive process. Meanwhile, the low reaction energy barrier of the rate-determined step for As(III) reduction over FeN 3 P also accelerates the deposition of As(III) and enhances its response signals. The free-Cu(II) are difficult to adsorb on FeN 3 P and do not compete with As(III) for Fe active sites, which contributes to the excellent anti-Cu(II) interference capability. [Display omitted] • Catalytic behavior of Fe single-atoms for As(III) were regulated by N, C, and P atoms. • FeN 3 P showed a high sensitivity of 3.90 µA ppb−1, which was not affected by Cu(II). • Strong affinities of P and Fe atoms with O and As atoms broke an As-O bond of H 3 AsO 3. • Low energy barriers and fast reaction rate on FeN 3 P enhanced As(III) current signals. • Cu(II) did not compete for active sites of FeN 3 P or change their interaction way. [ABSTRACT FROM AUTHOR]

Approximately 1.4 billion m3 of fluid tailings produced from oil sands mining operations are currently being held in Alberta, Canada and pose a significant risk to the environment if not properly treated and managed. The ability to quantify levels of toxic compounds, such as naphthenic acids (NAs) and phenol, accurately and rapidly in the produced oil sands process-affected water (OSPW) is required to ensure the protection of the surrounding aquatic environment. In this paper, fluorescence techniques are investigated to rapidly quantify NAs and phenol concentrations in natural surface waters. Machine learning approaches were applied to identify relevant spectral features to improve detection accuracy in the presence of background interference from organic matter in natural waters. NAs were relatively easy to detect by all methods, however deep convolutional neural networks (CNN) resulted in optimized performance for phenol with mean absolute errors of 1.78 – 1.81 mg/L and 4.68–5.41 µg/L, respectively. Visualization of spectral areas of importance revealed that deep CNNs utilized logical areas of the fluorescence spectra associated with NAs and phenol signals. Results suggest machine learning approaches to interpreting fluorescence data can accurately predict individual toxic components of OSPW in natural waters at environmentally relevant concentrations. [Display omitted] • Rapid quantification of toxic compounds from oil sands tailings was investigated. • Convolutional neural networks improved fluorescence-based quantification. • Increasing network depth improved CNN performance, despite a small sample size. • An occlusion method helped identify the spectral regions used by the networks. • The method could provide rapid assessment of oil sands impacts on natural waters. [ABSTRACT FROM AUTHOR]