The excessive presence of geogenic ammonium (NH4+) in groundwater poses a global environmental concern, commonly linked to the degradation of nitrogen-containing dissolved organic matter (DOM). However, there is a gap in systematic studies on the combination of soluble organic matter (SOM) in sediments and DOM in groundwater, with few indoor incubation experiments to validate their degradation pathways. This study utilized ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry to analyze the molecular characteristics of DOM and SOM in aquifer systems affected by geogenic NH4+. Subsequently, indoor incubation experiments spanning up to 140 d were conducted to verify the degradation pathways. The experimental results revealed a two-phase degradation process for both the DOM and SOM. The initial stage was characterized by the degradation of aliphatic compounds (ALC) with the production of polyphenols (PPE) and highly unsaturated compounds (HUC). The second stage was dominated by the degradation of PPE and HUC, accompanied by the re-consumption of some ALC, while more recalcitrant HUC persisted. Notably, the first stage of SOM degradation exceeded that of DOM degradation, indicating that SOM exhibited greater resistance to aging. This phenomenon may be attributed to a wider range of active enzymes in sediments, the rapid replenishment of SOM by organic matter in sediments, or the accelerated degradation of DOM. The experimental results aligned with the molecular characterization of DOM and SOM in actual aquifer systems. It is hypothesized that NH4+ produced through the direct mineralization of SOM may contribute more to the enrichment of NH4+ in groundwater than that produced through the mineralization of DOM. This study is the first to analyze DOM and SOM together in aquifer systems and validate their degradation pathways through incubation experiments, thereby providing novel insights into the enrichment of geogenic NH4+ in groundwater.

Understanding the interaction mechanisms between complex heavy metals and soil components is a prerequisite for effectively forecasting the mobility and availability of contaminants in soils. Soil organic matter (SOM), with its diverse functional groups, has long been a focal point of research interest. In this study, four soils with manipulated levels of SOM, cadmium (Cd) and lead (Pb) were subjected to a 90-day incubation experiment. The competitive interactions between Cd and Pb in soils were investigated using Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and X-ray adsorption near-edge structure (XANES) analysis. Our results indicate that Pb competed with Cd for adsorption sites on the surface of SOM, particularly on carboxyl and hydroxyl functional groups. Approximately 22.6 % of Cd adsorption sites on humus were occupied by Pb. The use of sequentially extracted exchangeable heavy metals as indicators for environment risk assessments, considering variations in soil physico-chemical properties and synergistic or antagonistic effects between contaminants, provides a better estimation of metal bioavailability and its potential impacts. Integrating comprehensive contamination characterization of heavy metal interactions with the soil organic phase is an important advancement to assess the environmental risks of heavy metal dynamics in soil compared to individual contamination assessments.

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of \"unknown common\" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

Polychlorinated biphenyls (PCB) both continue to spread into the environment and to bioaccumulate from primary urban and industrial sources as well as from secondary sources such as soils and the oceans. Fractions of congeners in PCB mixtures, i.e. PCB profiles, can be used as fingerprints to trace contamination pathways from sources to sinks because PCB mixtures fractionate during transport due to congener specific phase changes and degradation. Using a statistical analysis of a total of 8584 PCB profiles with seven congeners (CB28, CB52, CB101, CB118, CB138, CB153, CB180) for contaminated fish from two international datasets as well as a modelling of profiles, two major fractionation processes related to distinct contamination pathways were identified: (1) A relative enrichment of lighter congeners (CB28, CB52, CB101) in seawater fish due to a predominantly atmospheric transport, whereas freshwater and some coastal fish had higher fractions of heavier congeners (CB138, CB153) because those were mainly contaminated by particle-sorbed PCB from surface runoff. (2) A temperature driven fractionation tended to affect congeners with a medium molecular weight (CB118) as well as the heaviest congeners (CB180), a fractionation process which was conceptually associated with transport of PCB from secondary sources. Specifically, medium chlorinated PCB is sufficiently volatile and persistent for a preferred transport into cooler waters. In warmer climates, only the highest chlorinated congeners are persistent enough to ultimately accumulate in fish. Our analysis and modelling provide a starting point for the development of systems to trace - better than before - sources of PCB contaminations observed in fish.

Invasive macrofauna influence the biophysical state and function of soil, helping to drive ecological changes over time. Many soil-dwelling invertebrates affect soil stability by facilitating or hindering the soil aggregation process, changing the availability of plant and soil organic matter (SOM) for aggregate incorporation, and shifting the predominant mechanisms by which carbon is incorporated into soil aggregates. Using mass fractionation and stable carbon isotope techniques, this 17-month experimental study examined silt-clay-loam mesocosms either infested or not infested with soil-dwelling larvae of the invasive Japanese beetle, Popillia japonica Newman (JB). We hypothesized that larval root-herbivory would promote a pathway of large aggregate formation that features the mixing of digested root tissue with mineral soil and subsequent fecal deposition. These newly deposited, large soil aggregates will then grow by agglomeration of particles, thereby occluding a larger pool of fresh organic carbon, or be broken apart, exposing fresh organic inputs to microbial activity and mineralization processes, depending on soil conditions. Findings show a proportional increase of larger soil size fractions (2- 8 mm) in the rhizosphere of infested soil after 1½ life cycles of the beetle, but a decrease in the smaller soil size fractions (0.053-2 mm). In infested bulk surface soil (0-2.5 cm) carbon increased, primarily due to greater carbon content in the largest size fractions. Carbon also increased in all size fractions, although the proportion of total carbon in fractions was greater only in the largest fractions due to their greater relative abundance. There may also be an increase of microbially derived carbon in the largest size fractions, possibly indicating significant priming effects associated with JB larval herbivory. The implications of these findings for relative stabilization of the bulk surface soil carbon pool in JB-infested soil likely depends on the residence time of, and stable microaggregate formation within these large size fractions.

The Arctic Ocean, as a significant carbon sink, is attracting increased attention within the scientific community. This study focused on the main marginal sea ice zone, which has been the most sensitive to environmental changes in recent decades. Using data from reanalysis, models, and on-site observations, the changes in air-sea CO2 flux (FCO2) were analyzed during the influence of Arctic cyclones (ACs) in 2021-2022. Results indicated that the passage of ACs tended to increase the average carbon sink in the main marginal ice zone, with a more pronounced effect during the cold season. During ACs, the average FCO2 could reach -6.95 mmolC m-2 d-1. This was mainly associated with the stronger and more concentrated distribution of ACs where there was lower pCO2 (air-sea gradient of CO2 partial pressure) in the cold season. Additionally, the change in FCO2 during ACs was primarily affected by the sea surface wind and sea-ice concentration in the cold season, while it was influenced by a variety of environmental factors in the warm season, including the sea surface wind, sea-ice concentration, and ecological factors.

Previous air pollution control strategies didn\'t pay enough attention to regional collaboration and the spatial response sensitivities, resulting in limited control effects in China. This study proposed an effective PM2.5 and O3 control strategy scheme with the integration of Self-Organizing Map (SOM), Genetic Algorithm (GA) and WRF-CAMx, emphasizing regional collaborative control and the strengthening of control in sensitive areas. This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management, with SOM identifying the collaborative subregions, GA providing the optimized subregion-level priority of precursor emission reductions, and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions. With Beijing-Tianjin-Hebei and the surrounding area (BTHSA, \"2 + 26\" cities) as the case study area, the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM2.5-dominant seasons, and strengthen VOCs reductions but moderate NOx reductions in O3-dominant season. The spatiotemporally differentiated control strategy, without additional emission reduction burdens than the 14th Five-Year Plan proposed, reduced the average annual PM2.5 and MDA8 O3 concentrations in 28 cities by 3.2%-8.2% and 3.9%-9.7% respectively in comparison with non-differential control strategies, with the most prominent optimization effects occurring in the heavily polluted seasons (6.9%-18.0% for PM2.5 and 3.3%-14.2% for MDA8 O3, respectively). This study proposed an effective scheme for the collaborative control of PM2.5 and O3 in BTHSA, and shows important methodological implications for other regions suffering from similar air quality problems.

Soils constitute the major reservoir of mercury (Hg) in terrestrial ecosystems, whose stability may be threatened by wildfires. This research attempts to look at the effect of prescribed fire on the presence of Hg in a shrubland ecosystem from NE Portugal, delving into its relationship with soil aggregate size and the molecular composition of soil organic matter (SOM). During the prescribed fire, on average 347 mg Hg ha-1 were lost from the burnt aboveground biomass of shrubs and 263 mg Hg ha-1 from the combustion of the soil organic horizon. Overall, Hg concentration and pools in the mineral soil did not show significant changes due to burning, which highlights their role as long-term Hg reservoirs. The higher Hg concentrations found in smaller aggregates (<0.2 mm) compared to coarser ones (0.5-2 mm) are favored by the higher degree of organic matter decomposition (low C/N ratio), rather than by greater total organic C contents. The Hg-enriched finest fraction of soil (<0.2 mm) could be more prone to be mobilized by erosion, whose potential arrival to water bodies increases the environmental concern for the Hg present in fire-affected soils. The SOM quality (molecular composition) and the main organic families, analyzed by Fourier-transform infrared spectroscopy in combination with multivariate statistical analysis, significantly conditioned the retention/emission of Hg in the uppermost soil layers. Thus, before the fire, Hg was strongly linked to lipid and protein fractions, while Hg appeared to be linked to aromatic-like compounds in fire-affected SOM.