Powdered activated carbon (PAC) adsorption is widely applied for the removal of organic micropollutants in drinking water treatment. However, conventional single-dose PAC application requires high dosages to overcome competitive adsorption from natural organic matter (NOM). This study evaluates a multi-stage PAC dosing strategy to mitigate NOM competition and enhance the removal efficiency of representative odorous micropollutants. Experiments with NOM-rich surface water showed that multi-stage dosing increased adsorption capacity and achieved up to 48% PAC savings at an 80% micropollutant removal benchmark, compared to single-stage application. In contrast, no improvement was observed in NOM-free water. Two-stage PAC dosing considerably improved the removal of weakly adsorbing compounds such as 2-methylisoborneol (MIB) and 2-ethyl-5,5-dimethyl-1,3-dioxane (EDD), while incremental gains were observed for strongly adsorbing micropollutants (e.g., 2-butyl-5,5-dimethyl-1,3-dioxane) with three- or four-stage configurations. Mechanistic analysis indicated that early-stage PAC doses preferentially adsorbed competitive NOM components, preserving high-affinity sites for micropollutants in later stages. PACs enriched in narrow mesopores outperformed microporous PACs in the staged dosing configuration. The proposed strategy, as compared to existing designs summarized from the literature, requires minimal infrastructural modifications and offers a cost-effective, scalable approach for improving micropollutant removal under NOM-rich conditions.

Activated carbon (AC) is widely used for organic micro-pollutants (OMPs) removal, yet adsorbability evaluation remains challenging due to molecular diversity and adsorbent heterogeneity, especially given the limitations of traditional assessment metrics such as hydrophobicity (logD). This study proposed a machine learning (ML)-driven assessment strategy by aligning the adsorbability of various AC adsorbents with a hypothetical “Standard AC” to evaluate the adsorbabilities across 56 OMPs. XGBoost, RF, and ET models achieved high prediction accuracy on the test set (R² = 0.88–0.98, RMSE = 0.17–0.38, MAE = 0.13–0.27), and were further validated against a published experimental dataset. Interpretable ML analysis identified a logD threshold of ≈ 2, at which the dominant adsorption mechanisms transitioned from hydrophobic interactions for OMPs with higher hydrophobicity to π-π interactions, hydrogen bonding, and pore-filling for those with lower hydrophobicity. Adsorbability increased with molecular weight, as flexible molecules (rotatable bond ratio > 0.012) overcame steric hindrance in micropores, enhancing pore-filling efficiency through improved accessibility. By introducing a standardized, data-driven adsorbability reference and elucidating the intrinsic interplay between molecular properties and adsorption mechanisms, this study offers a robust framework for knowledge-informed treatability evaluation and a practical benchmark to guide adsorption process design.

Activated carbon is employed for the adsorption of organic micropollutants (OMPs) from water, typically present in concentrations ranging from ng L⁻¹ to μg L⁻¹. However, the efficacy of OMP removal is considerably deteriorated due to competitive adsorption from background dissolved organic matter (DOM), present at substantially higher concentrations in mg L⁻¹. Interpreting the characteristics of competitive DOM is crucial in predicting OMP adsorption efficiencies across diverse natural waters. Molecular weight (MW), aromaticity, and polarity influence DOM competitiveness. Although the aromaticity-related metrics, such as UV₂₅₄, of low MW DOM were proposed to correlate with DOM competitiveness, the method suffers from limitations in understanding the interplay of polarity and aromaticity in determining DOM competitiveness. Here, we elucidate the intricate influence of aromaticity and polarity in low MW DOM competition, spanning from a fraction level to a compound level, by employing direct sample injection liquid chromatography coupled with ultrahigh-resolution Fourier-transform ion cyclotron resonance mass spectrometry. Anion exchange resin pre-treatment eliminated 93% of UV₂₅₄-active DOM, predominantly aromatic and polar DOM, and only minimally alleviated DOM competition. Molecular characterization revealed that nonpolar molecular formulas (constituting 26% PAC-adsorbable DOM) with medium aromaticity contributed more to the DOM competitiveness. Isomer-level analysis indicated that the competitiveness of highly aromatic LMW DOM compounds was strongly counterbalanced by increased polarity. Strong aromaticity-derived π-π interaction cannot facilitate the competitive adsorption of hydrophilic DOM compounds. Our results underscore the constraints of depending solely on aromaticity-based approaches as the exclusive interpretive measure for DOM competitiveness. In a broader context, this study demonstrates an effect-oriented DOM analysis, elucidating counterbalancing interactions of DOM molecular properties from fraction to compound level.

It was widely acknowledged that dissolved organic matter (DOM) in natural water has ubiquitous competitiveness against organic micropollutants (OMPs) during adsorption onto activated carbon. However, some (model) low molecular weight organics have been reported to adsorb onto activated carbon, but were not competitive against co-adsorbates. The objective of this study is to identify which adsorbable DOM fractions in natural water contribute to the DOM competitiveness, and what is the impact of the OMP adsorbability and initial OMP concentration on this competitiveness. We, therefore, disassociated the adsorption of DOM fractions and OMPs (carbamazepine, caffeine and sulfamethoxazole) using a two-stage adsorption procedure, removing various adsorbable DOM fractions with powdered activated carbon pretreatment and then unraveling the competitiveness against OMPs of the remaining DOM. Our results demonstrated that DOM competition was not ubiquitous for all adsorbable fractions in natural water, and ∼ 25% of the adsorbable DOM was not competitive. The poorly adsorbable DOM was shown to be a non-competitive co-adsorbate, and its complexation even elevated the adsorption capacity of one of the OMPs (carbamazepine). The amount of DOM competitors increased for weaker adsorbable OMPs, and at higher initial OMP concentrations. The variability in DOM competition, differentiated by DOM adsorbability, has advanced the understanding of DOM competition, from ubiquitous competition to variable roles (varying competitiveness/complementary adsorption) of differently adsorbable DOM fractions during OMP adsorption.

Low molecular weight (LMW) dissolved organic matter (DOM) is the predominant competitor for adsorption sites against organic micropollutants (OMPs) in activated carbon adsorption. However, top-down approaches using highly complex mixtures of real water DOM do not allow to concisely examine the impacts of specific LMW DOM molecular properties on competitive adsorption. Therefore, we followed a bottom-up approach using fifteen model compounds (mDOM) to elucidate how important DOM characteristics, including hydrophobicity and unsaturated structures (ring, double/triple bond), impact competitiveness. Large concentration asymmetry (~500 μg DOC/μg OMP) made mDOM compounds, which were overall less preferentially adsorbed than OMPs, become competitive against OMPs and inhibit OMP adsorption kinetics by pre-occupation of adsorption sites. Our results revealed that both hydrophobicity interactions and π-interactions increased mDOM competitiveness, while π-interactions outweighed hydrophobic interactions. However, π-interactions could not be satisfactorily evaluated with a parameter such as specific ultraviolet absorbance (SUVA) due to interferences of carboxyl groups in aromatic mDOMs. Instead, mDOM adsorbability, described by mDOM adsorption capacity, proved to be a comprehensive indicator for mDOM competitiveness. To our knowledge, this is the first study that systematically clarifies the impacts of intricately interacting molecular properties on DOM adsorption and the related competition against OMP adsorption. DOM adsorbability may inspire a new fractionation, and assist the further isolation, identification and detailed characterization of LMW DOM competitors in real DOM-containing waters.

Though the ozone-activated carbon process has been widely applied for drinking water purification, little is known about how ozone-modified natural organic matter (NOM) competes with micropollutants in activated carbon adsorption. In this study, three natural waters and one synthetic water (standard humics solution) with highly heterogeneous NOM compositions were employed to investigate the interference of ozonated NOM with the adsorption of 2-methylisoborneol (MIB). Analysis using liquid chromatography with online carbon and UV₂₅₄ detection (LC-OCD-UVD) revealed that ozonation led to various disintegration patterns of macromolecules in NOM, and UV absorbance was reduced markedly for nearly all NOM fractions. Powdered activated carbon (PAC) adsorption experiments showed that increasing ozone consumption coincided with reducing NOM competition against MIB in the three natural waters, as expressed by the fitted initial concentrations of the equivalent background compound (c_0,EBC). In the synthetic water, in contrast, competition increased under low/moderate specific ozone consumptions and then decreased with further elevation of ozone consumptions. Regarding the significance on affecting ozonated NOM interference, aromaticity reduction outweighed formation of low molecular weight (LMW) organics in most cases, enhancing MIB adsorption capacity. However, disintegration of the humics fraction with larger molecular weight (1,103 g/mol, as compared to 546–697 g/mol in three natural waters) into smaller, more competitive fractions caused the observed initial deteriorated MIB adsorption in synthetic water. A superior correlation between c_0,EBC and the UV absorbance of LMW organics (R² = 0.93) over concentrations of LMW organics underlined the importance of the aromatic properties in competitive adsorption projection for ozone pretreated natural waters. Furthermore, the change of relative concentration of UV absorbing compounds during ozonation could help estimate the decrease of c_0,EBC, which could be a promising tool for waterworks to adjust PAC doses for MIB removal in ozonated waters.

Integrating updates from timely sources such as volunteered geographic information (VGI) into the spatial data maintained at official agencies is becoming a more demanding requirement but presents many challenges. This paper proposes an approach to addressing the technical challenge of propagating updates from timely sources (e.g. OpenStreetMap) to spatial data maintained at separate map scales. The main idea is to establish a multiple representation database (MRDB) for datasets at different scales and time to facilitate incremental update, where linkages between corresponding objects at different datasets are made explicit. First, two ways in which the timely sources can be integrated into official data for incremental update are discussed. To derive the linkages between different datasets, a data matching procedure based on computer vision is presented and fine-tuned to match data in different scale ranges. Furthermore, the generalization history used to produce smaller scale data from the larger ones in official data is inferred based on the linkages, and is then used to guide the update propagation. Finally, a framework for incremental generalization in MRDBs is proposed, where crucial issues like strategies for update propagation, cartographic generalization, and the so-called ‘chain reaction’ are addressed. The framework is implemented as a fully automated process where operators like simplification, enlargement, compression, displacement and typification are incorporated into the incremental update process. By testing the framework against real world data sets (i.e. OpenStreetMap and official data at 1:10k, 1:50k and 1:100k), we show that the updates are integrated consistently into existing data in terms of spatial relations and cartographic quality. Our work suggests that making use of timely sources by official mapping agencies and companies in a continuous or event-driven data update is technically feasible, with further improvement and extensions discussed.

The representation of vector data at variable scales has been widely applied in geographic information systems and map-based services. When the scale changes across a wide range, a complex generalization that involves multiple operations is required to transform the data. To present such complex generalization, we proposed a matrix model to combine different generalization operations into an integration. This study was carried on a set of river network data, where two operations, i.e., network pruning accompanied with river simplification, were hierarchically constructed as the rows and columns of a matrix. The correspondence between generalization operations and scale, and the scale linkage of multiple operations were also explicitly defined. In addition, we developed a vario-scale data structure to store the generalized river network data based on the proposed matrix. The matrix model was validated and assessed by a comparison with traditional methods that conduct generalization operations in sequence. It was shown that the matrix model enabled complex generalization with good generalization quality. Taking advantage of the corresponding vario-scale data structure, the river network data could be obtained at any arbitrary scale, and the vario-scale representation was achieved across a wide scale range.