The influence of effluent organic matter (EfOM) on phosphate polishing removal by adsorption plays an important role in determining the application potential of adsorbents. Molecular understanding of EfOM regarding its impact on adsorption is insufficient due to a lack of appropriate EfOM fractionation/characterization protocols, corresponding to a particular structure-function property of adsorbents. In this work, a combinative method of coupling DEAE/XAD fractionation with molecular characterization was proposed, targeting the versatile structure-function characters of nanocomposite, for investigating EfOM and its impact on phosphate removal by nanocomposite during long-term adsorption/regeneration runs. Zirconium-based polystyrene anion exchanger (HZO-201) was selected as a representative nanocomposite, featuring with porous networking matrix, positively charged surface and multiple adsorptive sites. The EfOM samples from three biologically treated sewage effluent sources were separated into fractions of negatively charged organic acid (OA) and hydrophobic-, transphilic-, hydrophilic-neutral/base (HPO-n/b, TPI-n/b and HPI-n/b). The combinative method effectively differentiated the charge, aromaticity, molecular weight and functionalities between the fractions, which corresponded to the multiple structural/surface characteristics of HZO-201 and favored the evaluation on the impact of the EfOM fractions. The extent of interference of the EfOM fractions on phosphate removal followed an order of OA > HPO-n/b > TPI-n/b > HPI-n/b. The OA fraction, characterized by negatively charged, aromatic functionalities and broad molecular weight distribution (1–5 kDa and 14 kDa), was recognized as the key interfering fraction, presumably due to its multiple adsorption pathways (i.e., ion exchange, π–π interactions and pore filling). Particularly, the low-molecular-weight OA moieties (1–4 kDa) progressively accumulated onto the nanocomposite via irreversible adsorption, causing a continuous phosphate-capacity loss by 32.70% over multiple cycles. We believe the combinative fractionation/characterization method may widely apply to complex water systems for identifying key influential organic matters in polishing treatment of various pollutants by adsorption.

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Functional dependencies are important for the definition of constraints and relationships that have to be satisfied by every database instance. Relaxed functional dependencies (RFDs) can be used for data exploration and profiling in datasets with lower data quality. In this work, we present an approach for RFD discovery in heterogeneous data lakes. More specifically, the goal of this work is to find RFDs from structured, semi-structured, and graph data. Our solution brings novelty to this problem in the following aspects: (1) We introduce a generic metamodel to the problem of RFD discovery, which allows us to define and detect RFDs for data stored in heterogeneous sources in an integrated manner. (2) We apply clustering techniques during RFD discovery for partitioning and pruning. (3) We performed an intensive evaluation with nine datasets, which shows that our approach is effective for discovering meaningful RFDs, reducing redundancy, and detecting inconsistent data.@en