ACS ES&T water最新文献

Following wildfires, riverine water quality in forested watersheds is prone to degradation, impacting drinking water treatment and potentially causing increased carbon emissions because of additional electricity consumption during treatment. We explore the potential for climate-based financing to support wildfire mitigation and watershed restoration by reducing potential water treatment energy demand following a fire within the Provo River watershed, Utah, USA. We model pre- and post-wildfire erosion and water quality in the Provo River using GeoWEPP. We use energy data from a water treatment plant in the watershed and literature data to estimate the increase in energy use for treating degraded water. We find that most watershed areas are not subject to large treatment demand changes, but a few hotspots are prone to increased sediment loads. In the Provo River watershed, on average, a fire in a single 12-digit hydrologic unit code (HUC) subwatershed corresponds to an additional 350 metric tonnes of carbon-dioxide-equivalent (CO₂e) emissions for one year following a wildfire event due to increased energy required by the water treatment plant. If wildfire risk is reduced, the avoided emissions can generate a potential of $88,500 annually in carbon credit revenue (at $10/CO₂e credit) for the contributing HUC8 sub-basin.

This study demonstrates a method for modeling pre- and post-fire erosion and connects the impacts to energy use and emissions associated with a downstream drinking water treatment plant.

Water polluted with excess phosphates and metals poses significant risks to human health and the environment. These elements, however, also hold value as nonrenewable resources essential for agriculture and renewable energy. Nanostructured sorbents, with their high surface area/volume ratio, offer a solution by enhancing sorption capacity and selectivity. Given this, we developed a sponge nanocomposite (SNC) consisting of a cellulose sponge coated with iron oxide nanoparticles. The SNC features a robust hierarchical porosity and structure more suitable for scaled deployment, while also minimizing byproducts and providing reusability. Tested in a flow-through column setup, it demonstrated the effective removal of phosphate, copper, and zinc. Selective recovery was then achieved by using a pH-assisted selective extraction approach, where phosphorus was recovered at a mildly basic pH, while metals were recovered at a mildly acidic pH. This process regenerates the adsorption sites on the SNC for subsequent reuse. The methodology exhibited in this report shows the potential for sustainable advancements in the circular economy, resource reclamation, and water treatment.

The Malwa region of Punjab, India, is witnessing an increase in cancer patients, but the origin of high uranium concentrations in groundwater remains unclear. In this study, 91 groundwater samples from the Malwa region were analyzed using ion chromatography for cations and anions and inductively coupled plasma–mass spectrometry for heavy element concentrations. Uranium concentrations ranged from 1.13 to 299.40 μg/L (mean: 54.03 μg/L), with 73% of samples exceeding the permissible limit of 30 μg/L for uranium in drinking water prescribed by the Bureau of Indian Standards and the World Health Organization. Elevated arsenic and selenium levels were observed in 3 and 10% of the samples. The groundwater primarily was of Mg-HCO₃ type and alkaline due to silicate and carbonate rock weathering. Cluster analysis grouped uranium with nitrate, sodium, and potassium, indicating interconnected behavior. Spearman correlation analysis showed correlations of uranium with electrical conductivity, total dissolved solids, alkalinity, nitrate, sulfate, sodium, and potassium, suggesting salt-induced ion competition as the primary cause of uranium mobilization. Hydrogeochemical correlations showed that geogenic factors like rock water interactions, carbonic water type, and mineral saturation influence uranium mobilization. This study demonstrates that hydrogeochemical analysis can provide insights into drivers and the potential origin of uranium.

Uranium concentrations in groundwater of the Malwa region in the Punjab state, one of India’s most important agricultural production areas, are critically elevated. Hydrogeochemical analysis reveals mechanisms of uranium mobilization and potential mitigation options.

The photodemethylation of monomethylmercury (CH₃Hg) is one of the most important natural degradation processes of this toxic compound and is therefore key to understanding Hg exposure. The isotopic composition of CH₃Hg contains information about its sources and transformation pathways. The stable isotopes of Hg during photodemethylation of CH₃Hg bound to dissolved organic matter (DOM) have been shown to undergo unique mass-independent fractionation (MIF), as well as mass-dependent fractionation (MDF). Here we present different photodegradation experiments (DOM, Cl^– ligands) where, in addition to Hg isotopes and degradation kinetics, the δ¹³C of CH₃Hg was analyzed by compound-specific isotope analysis (CSIA): purge and trap–gas chromatography–combustion–isotope ratio mass spectrometry (PT-GC-C-IRMS). Our results show covariation of odd Hg MIF and light C isotope enrichment in CH₃Hg photodemethylation products, with δ¹³C fractionation factors from −5 to −16‰, depending on the presence of Cl^– and DOM. We also find a linear relationship between C MDF and Hg MIF, indicating that both isotope effects occur during C–Hg bond breaking. We suggest that biota Hg MIF can potentially be used to correct for photochemical C MDF, bringing us one step closer to exploring the origin of the methyl group contributing to CH₃Hg formation.

Measuring dissolved gas concentrations such as methane (CH₄), carbon dioxide (CO₂), and hydrogen (H₂) (e.g., groundwater or surface water samples) is important for ensuring safe and effective subsurface energy development and storage. A common method is to collect water samples in fixed-volume sealed vessels and then use static headspace equilibrium techniques to quantify the dissolved gas concentrations by gas chromatography. Previously, the presence of multiple gas components was not considered during the analysis of water samples but is necessary. A mass balance approach considering multicomponent mass transfer was developed and validated, and the impact on dissolved gas measurements was quantified. It was found that mass transfer occurring in fixed-volume vessels leads to pressurization of sample headspace during analysis, causing error. Higher solubility gases (e.g., CO₂) exhibit higher headspace pressurization and larger errors than lower solubility gases (e.g., CH₄). In addition, it was found that the volume of the headspace induced and the co-occurrence of multiple dissolved gas species in a sample can exacerbate headspace pressurization and error. Overall, caution must be taken when using static headspace equilibrium techniques; if multicomponent mass transfer is not considered, error and potential under reporting of dissolved concentrations is possible.

Small diameter gravity sewers (SDGSs) have a wide range of applications in rural wastewater collection due to their low construction costs, fast implementation, and simple operation and maintenance. However, the mechanism of sediment accumulation urgently needs to be solved. This study investigated the sedimentation mechanisms in different components of SDGS through pilot-scale experiments and computational fluid dynamics (CFD) simulations. The results indicate that the sedimentation rate of SDGSs decreased as the flow velocity increased, with sediment primarily accumulating at the end section of upstream pipes and within manholes, accounting for 90.37 ± 5.15% of the total accumulation. Areas with relatively high sedimentation rates exhibited lower turbulent kinetic energy (TKE), and this trend became more pronounced as the flow velocity decreased. TKE and flow velocity were identified as the key factors influencing the sedimentation process in the SDGSs. This study provides important theoretical foundations and technical support for the design and maintenance of SDGSs.

The availability of detailed river flow data across large geographic areas is needed for several scientific applications, and the focus of this work was to develop a spatially referenced global river flow data set for use in environmental risk assessments for substances entering rivers. This paper provides a publicly available spatially resolved global spatial data set, which can be readily used in aquatic exposure models. This paper explores applying the well-established curve number (CN) method to estimate surface water runoff, which was used as the basis for estimating river flows. Input needed to implement the CN method was from freely and publicly available global data sets on hydrologic soil groups, land cover, and precipitation. The runoff data were then spatially combined with publicly available global hydrological data sets of catchments and rivers to estimate daily mean annual flows across the globe on a level-12 catchment scale. Estimated daily mean annual flows were compared with measured gauge flows at rivers in several countries, which showed good correlation (R² of 0.71–0.99) on a river catchment level. Additionally, flows were compared on a sub-basin level, which also correlated well with measured gauge flows, with an R² of 0.9 (log transformed) across basins in several countries.

The next generation of photocatalysts should exhibit visible-light absorption, high pollutant adsorption capacity, and stability. The activated carbons (AC) obtained from sustainable procedures, such as the pyrolysis of agroindustrial residues, show high adsorption capacities and an interesting ability to photoinduce reactive oxygen species (ROS). For this reason, herein, the synthesis of a hybrid TiO₂-TPA@AC material consisting of a UV–visible-light active photocatalyst as the TiO₂-TPA and an AC obtained from the pyrolysis of H₃PO₄-activated sunflower seed shells (SSS) is reported. TiO₂-TPA@AC photocatalytic activity was evaluated under simulated sunlight irradiation to oxidize ibuprofen (IB) and diclofenac (DI) in water, finding that the hybrid material exhibited the highest removal of both pollutants (98%) after 1 h through a dark adsorption step and, by a photoinduced process, where both the TiO₂-TPA and the AC generated ROS able to oxidize 95% of the adsorbed pollutants. The adsorption–photooxidation capacity of the TiO₂-TPA@AC material was stable after four reusing cycles, leaching Ti and W in solution at concentrations of 0.48 and 0.31 mg L^–1, respectively.

The integration of artificial intelligence (AI) and the Internet-of-Things (IoT) heralds the advent of sixth-generation sensing technologies, offering transformative potential for water quality assessment and the empowerment of sustainable ecosystems. This approach offers efficient monitoring through predictive analysis, risk assessment, and timely decision-making. However, this approach requires diverse expertise and requires connecting multiple dots. This paper explores the convergence of AI and IoT in developing advanced sensor networks capable of real-time monitoring and data analysis, providing comprehensive insights into water quality. AI algorithms can predict pollution events, optimize resource management, and enhance decision-making processes. IoT-enabled sensors provide extensive coverage and connectivity, facilitating continuous monitoring and immediate reporting of water conditions. This synergy ensures accurate detection of contaminants and supports proactive environmental management, aligning with global sustainability goals. Implementing AI and IoT in water quality assessment is crucial for maintaining healthy aquatic ecosystems, fostering biodiversity, and ensuring safe water resources for communities. The paper highlights the effectiveness and scalability of AI and IoT-supported sensing technologies, underscoring their critical role in a sustainable future.

Legionella pneumophila is a major cause of waterborne disease in the United States, but little is known about its prevalence in rural homes supplied by domestic well water. With a citizen science campaign involving 57 such homes in Illinois, conclusive results from 39 homes showed intact L. pneumophila in 31% and 28% of warm and hot water samples, respectively. We conducted a quantitative microbial risk assessment (QMRA) for L. pneumophila infection risk using L. pneumophila data from three studies (ours, another on rural homes in North Carolina, and another on a public water supply). The risk of illness was non-negligible in all cases and sometimes exceeded the 10^–4 target annual risk. The L. pneumophila concentration and exposure time had the greatest impact on the risk of illness due to a one-time exposure. Aerosol concentration had the greatest impact on the annual risk of illness. A maximum L. pneumophila concentration of 6 × 10^–3 copies/mL was needed to achieve the 10^–4 target. This study showed that L. pneumophila risk could be present in homes supplied by private wells, like those studied here, but can be mitigated by reducing L. pneumophila concentration, reducing exposure time, and careful consideration of fixtures that produce high concentrations of respirable aerosols.