ACS Environmental Au最新文献

Global wetlands have declined by 21-35% since the 18th century, losing approximately 1.3 million square miles. Infrastructure development, specifically, river channelization via levee construction, is a driver of this decline. In response, large-scale river diversion projects have been proposed to enhance sediment deposition and stabilize coastal wetlands. However, the role of aquatic chemistry in controlling the fluvial sediment deposition remains elusive. Here, we demonstrate that land formation by fluvial sediment deposition is intrinsically linked to wetland water salinity, which influences the sediment aggregation and settling kinetics. In laboratory experiments, Mississippi River sediments were exposed to a range of salinities that mimic the conditions in Louisiana wetlands. Our results show that higher ionic strength accelerates sediment aggregation and settling due to electrical double-layer compression while also reducing the packing density of deposited sediments, potentially impacting land stability. These findings point to the importance of incorporating salinity effects to optimize sediment diversion strategies.

Sulfur (S)-containing compounds can be unambiguously identified by their distinctive isotope patterns in mass spectrometry (MS) when the instrument has a mass resolution exceeding 500,000. However, many environmental research laboratories that perform nontargeted analysis rely on high-resolution mass spectrometry (HRMS) instruments, such as quadrupole time-of-flight mass spectrometry (QTOF MS). These HRMS instruments typically operate at a mass resolution of less than 50,000. At such limited resolution, confidently recognizing sulfur isotope patterns is challenging. This work develops a machine learning (ML) strategy for recognizing and predicting the number of S present using HRMS at a mass resolution as low as 25,000. We benchmarked our ML strategy on experimental data, where 200 S-containing standard compounds were mixed into complex environmental samples. In positive electrospray ionization (ESI) mode, our ML strategy achieved accuracies ranging from 87.4 to 95.0% for S recognition and accuracies ranging from 86.3 to 96.6% for S number prediction. Notably, the ML method performed similarly well in negative ESI mode. Our ML strategy was further evaluated on an external experimental water dataset where it correctly recognized the presence of S for all 24 previously reported 2-mercaptobenzothiazole disinfection byproducts (DBPs). The developed ML strategy was implemented into SulfurFinder, an R program, to facilitate automated data cleaning, S recognition, and S number prediction in HRMS data. SulfurFinder combined with HPLC-HRMS analysis of a wastewater sample tentatively identified 169 potential S-containing features. Of these, three were confirmed as S-containing pharmaceuticals. An additional S-containing drug was also putatively annotated using molecular networking. The development of SulfurFinder significantly boosts the capability of conventional HRMS to address the challenge of S recognition in the era of exposomics, supporting a wide range of environmental applications.

Environmental sciences, including environmental chemistry and toxicology, are highly interdisciplinary fields that integrate researchers with various backgrounds and expertise. This interdisciplinary aspect is critical to addressing issues of chemical pollution, environmental sustainability, and health. However, a standardized method for reporting chemical data is needed to address these issues effectively. This becomes increasingly important as both the number of chemical structures and our reliance on and use of computational analysis and cheminformatics tools grow. This paper provides background, examples, and recommendations on how to report chemical data in a findable, accessible, interoperable, and reproducible (FAIR) manner within environmental science disciplines. Ultimately, the goal is to broaden the scope and applicability of environmental research to help the entire community tackle the issues of chemical pollution and sustainability in a comprehensive manner.

Decision-making during the early stages of research and development (R&D) should be informed by both economic and ecological perspectives. While early stage cost assessments are well established, life cycle assessment (LCA) is still largely descriptive but should expand to a more prospective tool for early assessing the ecological effects of future processes. Chemical processes should be first assessed as early as when only the reaction equation is known. Our previous comparison of estimation methods based on the reaction equation identified three requirements to foster early stage LCA: (1) estimate inventories rather than final impacts to ensure flexibility, (2) distinguish between processes, as single values cannot reflect the variety of chemical processes, (3) provide a measure of uncertainty. In this publication, we propose regression trees to estimate key inputs for industry-scale life-cycle inventories of chemical processes directly from the underlying reaction equation. In detail, the regression trees yield the raw materials' impact, the direct greenhouse gas (GHG) emissions in CO₂eq, and the demands for electricity, steam, natural gas, cooling water, and process water. The regression trees outperform the current best available proxy values and provide inventory information that is as accurate as cost estimates. Thus, our work enables decision-makers to consider environmental aspects with the same level of accuracy as costs projections.

Environmental stressors, such as contaminants and physical factors, rarely act in isolation, and studying their joint effects provides a more accurate reflection of real-world scenarios. To capture these interactions and disentangle the direct and indirect influences on algal responses, we applied partial least squares structural equation modeling (PLS-SEM), allowing us to reveal the hierarchical relationships among stressors and their cumulative impact on algal physiology. We examined combined effects of microplastics (MP; presence/absence), polycyclic aromatic hydrocarbons (PAHs; a mixture of acenaphthene, fluorene, phenanthrene, and fluoranthene at a total chemical activity in the sediment of 0 or 0.14), and sediment resuspension (turbidity: 0.8–3.9 NTU) on Ceramium tenuicorne, a coastal macroalga that is likely to encounter all these stressors in its natural habitats. Mechanical mixing at two intensities (low and high) was applied as an experimental treatment to induce resuspension. The analysis separated the effects of mechanical mixing and turbidity, given their nonlinear relationship, as stronger mechanical mixing did not consistently result in proportional turbidity increases. The algal physiological responses were evaluated using changes in pigment composition (Chl a, Chl c, and carotenoids), photosystem II (PSII) performance, total antioxidant capacity, and algal stoichiometry measured as elemental (%C, %N, %H, and C/N) ratios. We found that PAH exposure was the main suppressor of pigment concentrations and PSII performance, underscoring the mechanisms of its adverse effects on the photosynthetic machinery and nutrient assimilation. Moreover, stronger turbulence further decreased pigment concentrations, while sediment resuspension increased antioxidant capacity in algae, possibly due to physical damage from abrasion and scouring. We also found that MP addition significantly increased turbidity, thus aggravating the effects of the sediment resuspension. In conclusion, we provide a mechanistic explanation of how the combined exposure to MPs, PAHs, and sediment resuspension can impact pigment composition, photosynthesis, and stoichiometry of the algae, leading to decreased productivity.

Municipal wastewater is a known point source of organic contaminants, including pharmaceuticals and neonicotinoid insecticides. Emergent aquatic insects can provide a direct aquatic-to-terrestrial contaminant transfer route to the food web, with implications for terrestrial food web dispersal of wastewater-derived organic contaminants. We quantified 17 target pharmaceuticals and insecticides (log K_ow: −1.43 to 4.75) in surface water, fish, aquatic insects, and web-building riparian spiders at a wastewater effluent-dominated stream in eastern Iowa, USA. Two neonicotinoids, imidacloprid and clothianidin, had spider tissue concentrations of 8.9–84 ng/g and 1.2–11 ng/g, respectively. The imidacloprid/clothianidin ratios in spider tissues were reflective of the concentration ratios in the effluent-dominated streamwater and opposite of nearby agriculturally dominated waters. In contrast, no pharmaceuticals were detectable in the riparian spiders; however, only pharmaceuticals were present in both fish and aquatic insects (1.1–11 ng/g and 5.9–51 ng/g, respectively). Neonicotinoids are not predicted to enter aquatic food webs based on their log K_ow and bioconcentration factor values; therefore, an implication of this study is to warrant caution when using traditional bioaccumulation models for polar hydrophilic contaminants. This work provides further evidence that neonicotinoids undergo trophic transfer and represents the initial measurements, implicating such a transfer from effluent-dominated streams into terrestrial food webs. While this study emphasizes field-relevant observations, it is limited by environmental variability, including uncertainties in the biomass of emergent insects that likely contribute to spider diets. Future research could investigate contaminant metabolites within individual organisms or use complementary techniques to better understand the underlying mechanisms.