Environmental Science & Technology Letters Environ.最新文献

Nickel (Ni) is an essential cofactor in many proteins. Ni stable isotopes have been shown to undergo isotope fractionation in microorganisms and plants. However, the mechanisms driving this fractionation are poorly understood. Here, we present experimental data on Ni isotope fractionation during binding by common Ni-binding amino acids: glutamate (carboxylate side chain), histidine (imidazole side chain), and cysteine (sulfhydryl side chain). We used an equilibrium Donnan dialysis approach to separate free versus bound Ni and measured the isotopic composition of both pools via multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS). Our results reveal that the glutamate and cysteine favor heavy ⁶⁰Ni (Δ^60/58Ni_glutamate = +0.80 ± 0.08; Δ^60/58Ni_cysteine = +1.27 ± 0.08‰), while histidine causes little isotope shift (−0.12 ± 0.16‰). We then conducted experiments using a short peptide that is a structural analogue for acetyl-CoA synthetase and Ni-iron hydrogenase metal-binding sites. The peptide preferentially bound the heavy ⁶⁰Ni with a Δ^60/58Ni_peptide value of +0.74 ± 0.04‰. The Ni isotope effect associated with peptide binding corresponded directly to the fractionation expected based on the coordinating ligands. This work represents an important first step in understanding the mechanistic controls on Ni isotope fractionation and the drivers of Ni isotope fractionation in biological and environmental systems.

Nickel (Ni) is an essential cofactor in many proteins. Ni stable isotopes have been shown to undergo isotope fractionation in microorganisms and plants. However, the mechanisms driving this fractionation are poorly understood. Here, we present experimental data on Ni isotope fractionation during binding by common Ni-binding amino acids: glutamate (carboxylate side chain), histidine (imidazole side chain), and cysteine (sulfhydryl side chain). We used an equilibrium Donnan dialysis approach to separate free versus bound Ni and measured the isotopic composition of both pools via multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS). Our results reveal that the glutamate and cysteine favor heavy ⁶⁰Ni (Δ^60/58Ni_glutamate = +0.80 ± 0.08; Δ^60/58Ni_cysteine = +1.27 ± 0.08‰), while histidine causes little isotope shift (-0.12 ± 0.16‰). We then conducted experiments using a short peptide that is a structural analogue for acetyl-CoA synthetase and Ni-iron hydrogenase metal-binding sites. The peptide preferentially bound the heavy ⁶⁰Ni with a Δ^60/58Ni_peptide value of +0.74 ± 0.04‰. The Ni isotope effect associated with peptide binding corresponded directly to the fractionation expected based on the coordinating ligands. This work represents an important first step in understanding the mechanistic controls on Ni isotope fractionation and the drivers of Ni isotope fractionation in biological and environmental systems.

The combined process of treating alkaline solid wastes and obtaining CO₂ sequestration has recently garnered significant attention. However, studies focusing on low chemical consumption alongside high-purity CaCO₃ production are still limited. Herein, a leaching/regeneration-mineralization process for CO₂ mineralization to concurrently produce CaCO₃ was proposed. First, selective leaching of Ca from municipal solid waste incineration fly ash (MSWI FA) was conducted with the assistance of protonated tripropylamine (TPA) at pH = 10.5, with a concentration of 23,500 mg/L. Concurrently, the protonated TPA was regenerated for subsequent mineralization, completing the mineralization kinetics within 30 min. Temperature had no significant effect on mineralization efficiency or product purity. As the temperature increased, the crystal form transitioned from vaterite to pure calcite. The amount of TPA added significantly influenced mineralization performance, exceeding the stoichiometric ratio allowed for nearly 100% of mineralization efficiency and promoted the transformation of the product’s crystal form toward pure vaterite. Based on experimental results and product characterization, a potential proton transfer-based leaching/regeneration-mineralization mechanism was proposed. Under optimal conditions, the CO₂ sequestration reached 77.5 g/kgFA, yielding 176 g/kgFA of pure CaCO₃. This work offers a promising option for waste disposal, CO₂ sequestration, and CaCO₃ production in a reagent-saving manner.

Although large language models (LLMs) have demonstrated significant value in numerous fields, there remains limited research on evaluating their performance or enhancing their capabilities within water science and technology. This study initially assessed the performance of eight foundational models (i.e., GPT-4, GPT-3.5, Gemini, GLM-4, ERNIE, QWEN, Llama3-8B, and Llama3-70B) on a wide range of water knowledge tasks in engineering and research by developing an evaluation suite called WaterER (i.e., 1043 tasks). GPT-4 was demonstrated to excel in diverse water knowledge tasks in engineering and research. Llama3-70B was best for Chinese engineering queries, while Chinese-oriented models outperformed GPT-3.5 in English engineering tasks. Gemini demonstrated specialized academic capabilities in wastewater treatment, environmental restoration, drinking water treatment, sanitation, anaerobic digestion, and contaminants. To further advance LLMs, we employed prompt engineering (i.e., five-shot learning) and fine-tuned open-sourced Llama3-8B into a specialized model, namely, WaterGPT. WaterGPT exhibited enhanced reasoning capabilities, outperforming Llama3-8B by over 135.4% on English engineering tasks and 18.8% on research tasks. Additionally, fine-tuning proved to be more reliable and effective than prompt engineering. Collectively, this study established various LLMs’ baseline performance in water sectors while highlighting the robust evaluation frameworks and augmentation techniques to ensure the effective and reliable use of LLMs.

The voluntary carbon market can become a productive mechanism for channeling performance-based financing toward both water security and climate adaptation and mitigation challenges. Typically, carbon credit methodologies support projects that directly avoid or remove emissions, including forest preservation and renewable energy generation. As such, emission avoidance or carbon sequestration monitoring can be conducted directly and in the same location as the projects. However, our analysis across five carbon credit-generating water subsectors identifies four novel monitoring parameters that, while critical for evaluating project impact and calculating carbon credits, are not direct measures of emission reductions, avoidance, or sequestration. We identify these novel parameters and explore how digital technologies can enhance monitoring, reporting, and verification of these parameters. We identify that microbial water quality and drinking water access can be monitored directly and reported remotely using sensor-based technologies, while irrigation management can be tracked using soil moisture sensors and satellite-based evapotranspiration data, while instream water quality can monitored using in situ sensors and land management models. Advancing these technology capabilities and improving data security, reliability, and accessibility can strengthen the credibility of water-sector carbon credit methodologies, ultimately promoting financially, programmatically, and climatically sustainable projects.

Quantitative analyses of per- and polyfluoroalkyl substances (PFAS) commonly rely on low-resolution targeted tandem mass spectrometry (MS/MS) due to its high sensitivity and relatively low operational threshold. Perfluoro-3,5,7,9-butaoxadecanoic acid (PFO4DA), an important constituent of the perfluoropolyether carboxylic acid (PFECA) family, has been widely detected in biotic and abiotic matrices. Nevertheless, marked interference has been observed in the MS/MS transition for PFO4DA (377 > 85) in biological samples, particularly in aquatic organisms, resulting in substantial overestimation of concentrations, reaching up to 66 ng/g. In this study, nontargeted molecular networking strategies, combined with the spectral simulation tools SIRIUS and QCxMS, were applied to identify the source of interference, which was confirmed as an oxygenated metabolite of docosahexaenoic acid (DHA) based on fragmentation patterns matching reference standards. The study further clarified that fatty acids generated the fragment ion [C₄H₅O₂]⁻ (85.0296 Da), which overlapped with the [CF₃O]⁻ (84.9907 Da) ion of PFECA compounds under low-resolution conditions, causing signal interference. Analysis of river water and fish samples revealed that this interference led to a 24-fold overestimation of PFO4DA bioaccumulation in aquatic organisms. These findings are essential for improving the selectivity of environmental exposure assessments for PFECA compounds.

Carbon capture and storage (CCS) is crucial for mitigating atmospheric carbon dioxide (CO₂) levels in the clean energy transition. Depleted hydrocarbon reservoirs, with their proven containment integrity, are promising candidates for CO₂ storage. However, maximizing pore space utilization to enhance storage capacity remains underexplored, particularly in depleted reservoirs where CO₂ transitions from gas to supercritical state during injection and storage. We experimentally investigated the impact of surfactants on CO₂ storage dynamics at the microscale using synchrotron-based high-resolution 3D microcomputed tomography. Experiments were conducted at pressures ranging from 6 to 16 MPa, and a constant temperature of 80 °C, covering both gas and supercritical phases of CO₂. Surfactants significantly reduced CO₂-brine interfacial tension (IFT) and created new flow paths through small pores, increasing CO₂ saturation by 30% at 6 MPa. Although surfactant effectiveness decreased at higher pressures, it still enhanced storage efficiency by 12%, 14%, and 17% at 8, 12, and 16 MPa, respectively. These findings highlight the potential of surfactant-assisted CO₂ storage to optimize injection strategies, thereby contributing to more efficient utilization of depleted reservoirs. By improving storage efficiency, this approach supports global efforts to achieve substantial reductions in CO₂ emissions and combat climate change.

Ozone (O₃) significantly impacts air quality. Despite reductions in PM_2.5 since the 2013 Clean Air Act, the level of the O₃ concentration has continued to rise in China, underscoring the need for targeted pollution control measures. This study examined the seasonal and spatial variations of pollutants and meteorological variables in a major industrial city in Eastern China. Three widely used approaches, including the ozone formation potential (OFP) calculation, an observation-based model (OBM), and a random forest algorithm, were employed to investigate O₃ formation in urban environments. Results show that oxygenated volatile organic compounds were the most significant contributors to summer urban O₃, whereas their impact was significantly reduced during the winter. Each O₃ formation evaluation model provided unique insights, with OFP offering rapid estimates, the OBM revealing detailed chemistry, and random forest capturing nonlinear interactions. However, the study also identified limitations in these models. OFP failed to account for seasonal variations in the level of O₃ formation, and the random forest model struggled to distinguish causal relationships from correlations. These findings highlight the need for caution when relying on a single model and underscore the importance of integrating multiple methods to gain an accurate understanding of urban O₃ formation dynamics, which is crucial to developing effective control strategies.

Many chemicals found in the environment and commerce have been characterized for potential hazards by using in vitro screens. Translating concentrations that cause bioactivity into real-world exposures, in other words, in vitro–in vivo extrapolation (IVIVE), requires chemical-specific parameters and mathematical models. An administered (for example, oral) equivalent dose rate (milligrams per kilogram per day) that causes steady-state human plasma concentrations (micromolar) equivalent to bioactive in vitro concentrations can be derived using a simple IVIVE equation. Herein, we explain how this IVIVE equation approximates a physiologically based toxicokinetic (PBTK) model. Through the derivation of the steady-state solution of the PBTK model, we show how the simple IVIVE equation approximates relevant flows and tissues. We then extend the simple IVIVE equation by modifying the oral exposure PBTK model to include gas inhalation and exhalation. Gas exhalation increases clearance, potentially allowing more accurate prediction of the oral equivalent dose for semivolatile organic chemicals. The revised IVIVE equations also allow prediction of inhalation equivalent doses, in other words, the parts per million concentration that would cause steady-state human plasma concentrations equivalent to bioactive in vitro concentrations. Through comparison to an inhalation PBTK model, new simple IVIVE equations for plasma concentration have been developed, describing exhaled oral and inhalation doses.