Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning

Environmentally friendly molecularly imprinted polymers as an insert for SPE type columns in the gentamicin monitoring process

The quantity and variety of micro-pollutants infiltrating water resources have increased rapidly in recent times. The appearance of many harmful substances in the waters has resulted in so-called chemical cocktails which significantly contribute to the deterioration of water quality. Additionally, the variety of these compounds, often similar to each other in terms of molecular weights, makes their separation and identification very difficult. In this paper we present the possibility of using self-regenerating mechanism of molecularly imprinted polymers to measure the concentration of micropollutants in the aquatic environment. Molecularly imprinted polymers toward gentamicin were prepared by monomer polymerization in aqueous solution at ambient temperature. Results from computer-based molecular modelling demonstrated potential binding sites between gentamicin and functional monomers in water. Various compositions of polymerization mixtures were tested. The ratio of monomers to each other was 1.1:1.4:0.0015 and 1:1:1 for N-isopropylacrylamine:acrylamide:acrylic acid, respectively. For each composition, various amounts of the standard were tested: 0, 3, 5, 7, 10,15 mol% in relation to monomers. The best results were obtained for 5 % gentamicin with an excess of acrylamide in relation to the other monomers. Sorption for this system was 0.783 mg/g at ambient temperature and desorption 0.593 at 4 °C. The synthesized materials, thanks to the incorporation of thermosensitive poly(N-isopropylacrylamide) into their structures, were able to release 89 % of adsorbed gentamicin. This made it possible to use the designed SPE columns repeatably with similar efficiency. The prepared materials were selective in the presence of other antibiotics like amoxicillin and norfloxacin.

Surveillance and environmental risk of very mobile pollutants in urban stormwater and rainwater in a water-stressed city

Urban stormwater and rainwater in water-stressed cities serve as critical vectors for the transport and dispersion of pollutants, including very mobile compounds These pollutants, which can be influenced by factors such as land use, rainfall intensity, and urban infrastructure, pose significant risks to both human and environmental health. Although several priority pollutants have traditionally been detected in urban stormwater, little is known about the presence of very mobile compounds that may threaten urban drinking water supplies and pose environmental risks to aquatic species. In this study, 131 urban rain and stormwater samples were collected from three districts of Barcelona (Spain) and analysed for 26 very mobile pollutants that are often overlooked in conventional monitoring efforts. The findings reveal that stormwater and rainwater are major contributors to the spread of pollutants in water-stressed cities, with particular emphasis on substances like lifestyle products, pharmaceuticals, and industrial chemicals. Among the 23 compounds detected, 12 were reported for the first time to occur in urban stormwater with concentrations as high as 271 µg L-1. Measurements of dissolved organic carbon, electrical conductivity, and the presence of wastewater-borne pollutants within the correlation analysis, suggested the contribution of sanitary sewer overflows (SSO) to urban stormwater. Finally, an environmental risk assessment (worst-case scenario) was performed, showing a moderate risk of target analytes such as acesulfame and 1-naphthalenesulfonic acid (ERQ > 0.1). The results highlight the need for improved surveillance systems, more sustainable stormwater management practices, and strategies for mitigating the environmental risk posed by very mobile pollutants in regions facing water scarcity.

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

Nationwide profiling and source identification of organophosphate esters in Korean surface waters using target, suspect, and non-target HRMS analysis

Organophosphate esters (OPEs) are emerging contaminants that serve as alternatives to regulated substances in aquatic environments. A nationwide large-scale assessment for OPEs, including point sources, remains insufficient. To address this issue, we aimed to investigate OPEs occurrence and novel OPEs via comprehensive target, suspect and non-target analysis. Among the 11 target OPEs, 10 were detected at sampling sites distributed evenly nationwide. The highest mean concentrations were measured for tris-(2-butoxyethyl) phosphate (TBOEP) and tris(2-chloroisopropyl) phosphate (TCIPP). The multivariate statistical analysis revealed that TBOEP and TCIPP are essential components for assessing total OPEs pollution. The systematic risk assessment results evaluated the overall risk contribution of TBOEP and the significant risk impact of 2-ethylhexyl diphenyl phosphate. Promising suspect and non-target analysis enabled frequent detection and identification of 6 antioxidant transformation products (TPs), as well as the tentative identification of 14 OPEs and TPs, including 3 di-OPEs. Based on sampling site classification, we confirmed that major OPEs are significantly discharged near point sources. We believe that this is the first attempt to assess the nationwide risk and potential sources of OPEs in Korean surface waters, providing insights that could support further prioritization and regulation efforts.

Navigating the difference of riverine microplastic movement footprint into the sea: Particle properties influence

As a critical source of marine microplastics (MPs), estuarine MPs community varied in movement due to particle diversity, while tide and runoff further complicated their transport. In this study, a particle mass gradient that represents MPs in the surface layer of the Yangtze River estuary was established. This was done by calculating the masses of 16 particle types using the particle size probability density function (PDF), with typical shapes and polymers as classifiers. Further, Aschenbrenner shape factor and polymer density were embedded into drag coefficients to categorically trace MP movement footprints. Results revealed that the MPs in North Branch moved northward and the MPs in South Branch moved southeastward in a spiral oscillation until they left the model boundary under Changjiang Diluted Water front and the northward coastal currents. Low-density fibrous MPs are more likely to move into the open ocean and oscillate more than films, with a single PE fiber trajectory that reached a maximum oscillatory width of 16.7 km. Over 95 % of the PVC fiber particles settled in nearshore waters west of 122.5°E. Elucidating the aggregation and retention of different MPs types can provide more accurate environmental baseline reference for more precise MP exposure levels and risk dose of ingestion for marine organisms.

Review of trace organic chemicals in urban stormwater: Concentrations, distributions, risks, and drivers

Urban stormwater, increasingly seen as a potential water resource for cities and towns, contains various trace organic chemicals (TrOCs). This study, conducted through a comprehensive literature review of 116 publications, provides a detailed report on the occurrence, concentration distribution, health, and ecological risks of TrOCs, as well as the impact of land use and rainfall characteristics on their concentrations. The review uncovers a total of 629 TrOCs detected at least once in urban stormwater, including 228 pesticides, 132 pharmaceutical and personal care products (PPCPs), 29 polycyclic aromatic hydrocarbons (PAHs), 30 per- and polyfluorinated substances (PFAS), 28 flame retardants, 24 plasticizers, 22 polychlorinated biphenyls (PCBs), nine corrosion inhibitors, and 127 other industrial chemicals/intermediates/solvents. Concentration distributions were explored, with the best fit being log-normal distribution. Risk assessment highlighted 82 TrOCs with high ecological risk quotients (ERQ > 1.0) and three with potential health risk quotients (HQ > 1.0). Notably, 14 TrOCs (including six PAHs, five pesticides, three flame-retardants, and one plasticizer) out of 68 analyzed were significantly influenced by land-use type. Relatively weak relationships were observed between rainfall characteristics and pollutant concentrations, warranting further investigation. This study provides essential information about the occurrence and risks of TrOCs in urban stormwater, offering valuable insights for managing these emerging chemicals of concern.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Distribution and release of PFAS from AFFF-impacted asphalt: How does it compare to concrete?

Aqueous film forming foam (AFFF)-impacted asphalt and concrete may serve as potential secondary sources of per- and polyfluoroalkyl substances (PFAS) to the environment through surficial leaching. We aimed to understand the vertical distribution and surficial release of PFAS from AFFF-impacted asphalt and concrete cores collected from various locations (∼10-70 m distance between samples). Among the PFAS analyzed, 6:2 FTS was observed as having the highest concentration in the surface layer (0 - 0.5 cm) of concrete (225 µg kg-1) and in the runoff from the concrete (2600 ng L-1). PFOS was detected at the highest concentration in the surface layer (0 - 0.5 cm) of asphalt (47 µg kg-1) and associated runoff (780 ng L-1). The total mass of PFAS released during three rainfall simulations accounts for a fraction of the total mass in the surface layer (0 - 0.5 cm), ranging from 0.10 - 9.8% and 0.078 - 2.4% for asphalt and concrete cores, respectively. Asphalt exhibited a higher release rate than concrete, demonstrated by the higher total release coefficient of PFAS (4 - 16 m-2) compared to that of concrete cores (1 - 5 m-2). These results suggested that, similar to concrete, AFFF-impacted asphalt may be a secondary source of PFAS to the environment.

Estimating the temporal and spatial distribution and threats of bisphenol A in temperate lakes using machine learning models

Bisphenol A (BPA) is easily enriched in many human-disturbed watersheds, particularly lakes with poor water mobility, which is posing a threat to aquatic biota. While previous studies have focused on the concentration of BPA in water and its toxicity to aquatic organisms, a small amount of measured data is not enough to reveal the temporal and spatial distribution and threats of BPA, and estimate the ecological risk in watersheds. Therefore, we collected 164 measured BPA data points from Taihu Lake to develop machine learning models using random forest (RF), support vector machine (SVM) and least square regression (LSR) and created month-by-month watershed prediction maps in temperate lakes to estimate the spatiotemporal distribution and threats of BPA. Due to RF's superior robustness to noisy data, the RF model exhibits the best performance among the three algorithms. The RF model showed acceptable predictive performance on the modeling dataset (coefficients of determination and root-mean-square error for the training set were 0.927 and 17.499, respectively, and 0.607, 39.645 for the validation set, respectively). The maps indicated that areas susceptible to anthropogenic activities were more severely polluted by BPA, and rainy climate may favor the migration of BPA to aquatic ecosystems. The model was also applied to predict 42 data points of BPA collected from Dianchi Lake, and the results showed that most predicted data were within a factor of 10 of the measured data, but the prediction accuracy of the model has declined. The ecological risks in the two lakes were evaluated and attention should be paid to the regions with higher risks. Our study provided a novel idea for comprehensive monitoring of an unconventional trace pollutant with endocrine disrupting effects in aquatic ecosystems and analyzing their spatiotemporal distribution, which will contribute to the scientific assessment of the ecological risk of BPA.