ACS Environmental Au最新文献

In May 2021, the M/V X-Press Pearl container ship burned for 2 weeks, leading to the largest maritime spill of resin pellets (nurdles). The disaster was exacerbated by the leakage of other cargo and the ship’s underway fuel. This disaster affords the unique opportunity to study a time-stamped, geolocated release of plastic under real-world conditions. Field samples collected from beaches in Sri Lanka nearest to the ship comprised nurdles exposed to heat and combustion, burnt plastic pieces (pyroplastic), and oil-plastic agglomerates (petroplastic). An unresolved question is whether the 1600+ tons of spilled and recovered plastic should be considered hazardous waste. Due to the known formation and toxicity of combustion-derived polycyclic aromatic hydrocarbons (PAHs), we measured 20 parent and 21 alkylated PAHs associated with several types of spilled plastic. The maximum PAH content of the sampled pyroplastic had the greatest amount of PAHs recorded for marine plastic debris (199,000 ng/g). In contrast, the sampled unburnt white nurdles had two orders of magnitude less PAH content. The PAH composition varied between the types of spilled plastic and presented features typical of and conflicting with petrogenic and pyrogenic sources. Nevertheless, specific markers and compositional changes for burning plastics were identified, revealing that the fire was the main source of PAHs. Eight months after the spill, the PAH contents of sampled stray nurdles and pyroplastic were reduced by more than 50%. Due to their PAH content exceeding levels allowable for plastic consumer goods, classifying burnt plastic as hazardous waste may be warranted. Following a largely successful cleanup, we recommend that the Sri Lankans re-evaluate the identification, handling, and disposal of the plastic debris collected from beaches and the potential exposure of responders and the public to PAHs from handling it. The maritime disaster underscores pyroplastic as a type of plastic pollution that has yet to be fully explored, despite the pervasiveness of intentional and unintentional burning of plastic globally.

Developing advanced onsite wastewater treatment systems (OWTS) requires accurate and consistent water quality monitoring to evaluate treatment efficiency and ensure regulatory compliance. However, off-line parameters such as chemical oxygen demand (COD), total suspended solids (TSS), and Escherichia coli (E. coli) require sample collection and time-consuming laboratory analyses that do not provide real-time information of system performance or component failure. While real-time COD analyzers have emerged in recent years, they are not economically viable for onsite systems due to cost and chemical consumables. This study aimed to design and implement a real-time remote monitoring system for OWTS by developing several multi-input and single-output soft sensors. The soft sensor integrates data that can be obtained from well-established in-line sensors to accurately predict key water quality parameters, including COD, TSS, and E. coli concentrations. The temporal and spatial water quality data of an existing field-tested OWTS operated for almost two years (n = 56 data points) were used to evaluate the prediction performance of four machine learning algorithms. These algorithms, namely, partial least square regression (PLS), support vector regression (SVR), cubist regression (CUB), and quantile regression neural network (QRNN), were chosen as candidate algorithms for their prior application and effectiveness in wastewater treatment predictions. Water quality parameters that can be measured in-line, including turbidity, color, pH, NH₄⁺, NO₃^–, and electrical conductivity, were selected as model inputs for predicting COD, TSS, and E. coli. The results revealed that the trained SVR model provided a statistically significant prediction for COD with a mean absolute percentage error (MAPE) of 14.5% and R² of 0.96. The CUB model provided the optimal predictive performance for TSS, with a MAPE of 24.8% and R² of 0.99. None of the models were able to achieve optimal prediction results for E. coli; however, the CUB model performed the best with a MAPE of 71.4% and R² of 0.22. Given the large fluctuation in the concentrations of COD, TSS, and E. coli within the OWTS wastewater dataset, the proposed soft sensor models adequately predicted COD and TSS, while E. coli prediction was comparatively less accurate and requires further improvement. These results indicate that although water quality datasets for the OWTS are relatively small, machine learning-based soft sensors can provide useful predictive estimates of off-line parameters and provide real-time monitoring capabilities that can be used to make adjustments to OWTS operations.

Rising CO₂ emissions are responsible for increasing global temperatures causing climate change. Significant efforts are underway to develop amine-based sorbents to directly capture CO₂ from air (called direct air capture (DAC)) to combat the effects of climate change. However, the sorbents’ performances have usually been evaluated at ambient temperatures (25 °C) or higher, most often under dry conditions. A significant portion of the natural environment where DAC plants can be deployed experiences temperatures below 25 °C, and ambient air always contains some humidity. In this study, we assess the CO₂ adsorption behavior of amine (poly(ethyleneimine) (PEI) and tetraethylenepentamine (TEPA)) impregnated into porous alumina at ambient (25 °C) and cold temperatures (−20 °C) under dry and humid conditions. CO₂ adsorption capacities at 25 °C and 400 ppm CO₂ are highest for 40 wt% TEPA-incorporated γ-Al₂O₃ samples (1.8 mmol CO₂/g sorbent), while 40 wt % PEI-impregnated γ-Al₂O₃ samples exhibit moderate uptakes (0.9 mmol g^–1). CO₂ capacities for both PEI- and TEPA-incorporated γ-Al₂O₃ samples decrease with decreasing amine content and temperatures. The 40 and 20 wt % TEPA sorbents show the best performance at −20 °C under dry conditions (1.6 and 1.1 mmol g^–1, respectively). Both the TEPA samples also exhibit stable and high working capacities (0.9 and 1.2 mmol g^–1) across 10 cycles of adsorption–desorption (adsorption at −20 °C and desorption conducted at 60 °C). Introducing moisture (70% RH at −20 and 25 °C) improves the CO₂ capacity of the amine-impregnated sorbents at both temperatures. The 40 wt% PEI, 40 wt % TEPA, and 20 wt% TEPA samples show good CO₂ uptakes at both temperatures. The results presented here indicate that γ-Al₂O₃ impregnated with PEI and TEPA are potential materials for DAC at ambient and cold conditions, with further opportunities to optimize these materials for the scalable deployment of DAC plants at different environmental conditions.

With 28–34 times the greenhouse effect of CO₂ over a 100-year period, methane is regarded as the second largest contributor to global warming. Reducing methane emissions is a necessary measure to limit global warming to below 1.5 °C. Photocatalytic conversion of methane is a promising approach to alleviate the atmospheric methane concentrations due to its low energy consumption and environmentally friendly characteristics. Meanwhile, this conversion process can produce valuable chemicals and liquid fuels such as CH₃OH, CH₃CH₂OH, C₂H₆, and C₂H₄, cutting down the dependence of chemical production on crude oil. However, the development of photocatalysts with a high methane conversion efficiency and product selectivity remains challenging. In this review, we overview recent advances in semiconductor-based photocatalysts for methane conversion and present catalyst design strategies, including morphology control, heteroatom doping, facet engineering, and cocatalysts modification. To gain a comprehensive understanding of photocatalytic methane conversion, the conversion pathways and mechanisms in these systems are analyzed in detail. Moreover, the role of electron scavengers in methane conversion performance is briefly discussed. Subsequently, we summarize the anthropogenic methane emission scenarios on earth and discuss the application potential of photocatalytic methane conversion. Finally, challenges and future directions for photocatalytic methane conversion are presented.

Products and starting materials containing volatile organic compounds (VOCs) can easily be found in a variety of businesses, making them a common source of occupational exposure. To prevent negative impacts on employee health, field industrial hygienists must conduct regular sampling to ensure exposures remain below the regulatory limits set by governmental and professional associations. As such, the need for sensitive and reliable exposure assessment techniques becomes evident. Over the preceding decade, the industrial hygiene research group at the University of Alabama at Birmingham (UAB) has been working on the development of an emerging, preanalytical technique known as photothermal desorption (PTD) to improve upon the analytical sensitivity of currently employed methods. PTD’s novel design uses pulses of high-energy light to desorb analytes from thermally conductive, carbonaceous sorbents, to be delivered to downstream analytical detectors. Since PTD’s conception, the theoretical framework and advances in sorbent fabrication have been investigated; however, further work is needed to produce a field-ready sampling device for use with PTD. As such, objectives of the present work were to design a PTD-compatible diffusive sampler prototype and characterize the prototype’s sampling efficiencies for toluene, n-hexane, trichloroethylene, and isopropyl alcohol. In pursuit of these objectives, the study empirically quantified the sampled masses of toluene, n-hexane, trichloroethylene, and isopropyl alcohol, at occupationally relevant air concentrations, to be 12.17 ± 0.06, 8.2 ± 0.1, 3.97 ± 0.06, and 8.0 ± 0.1 mg, respectively. Moreover, the analyte sampling efficiencies were found to be 2.2 ± 0.1, 1.7 ± 0.1, 1.2 ± 0.1, and 0.51 ± 0.05 (unitless) when comparing empirically (i.e., laboratory observed) sample mass values to theoretically predicted values. The sampling efficiencies and collected sample masses reported herein demonstrate the promising design of PTD-compatible diffusive samplers. When used in conjunction with the PTD method, the prototype samplers present strong evidence for improving analytical sensitivity in exposure assessments of VOCs in the workplace.

Although in vitro simulation and in vivo feeding experiments are commonly used to evaluate the carrier role of microplastics in the bioaccumulation of toxic chemicals, there is no direct method for quantitatively determining their vector effect. In this study, we propose a dual-labeled method based on spiking unlabeled hydrophobic organic contaminants (HOCs) into soils and spiking their respective isotope-labeled reference compounds into microplastic particles. The bioaccumulation of the unlabeled and isotope-labeled HOCs in Eisenia fetida earthworms was compared. Earthworms can assimilate both unlabeled and isotope-labeled HOCs via three routes: dermal uptake, soil ingestion, and microplastic ingestion. After 28 days of exposure, the relative fractions of bioaccumulated isotope-labeled HOCs in the soil treated with 1% microplastics ranged from 15.5 to 55.8%, which were 2.9–47.6 times higher than those in the soils treated with 0.1% microplastics. Polyethylene microplastics were observed to have higher relative fractions of bioaccumulated isotope-labeled HOCs, potentially because of their surface hydrophobicity and amorphous rubbery state. The general linear models suggested that the vector effects were mainly due to the microplastic concentration, followed by polymer properties and HOC hydrophobicity. This proposed method and the derived empirical formula contribute to a more comprehensive understanding of the vector effects of microplastics for HOC bioaccumulation.

Catalytic complete oxidation is an efficient approach to reducing methane emissions, a significant contributor to global warming. This approach requires active catalysts that are highly resistant to sintering and water vapor. In this work, we demonstrate that Pd nanoparticles confined within silicalite-1 zeolites (Pd@S-1), fabricated using a facile in situ encapsulation strategy, are highly active and stable in catalyzing methane oxidation and are superior to those supported on the S-1 surface due to a confinement effect. The activity of the confined Pd catalysts was further improved by co-confining a suitable amount of Ce within the S-1 zeolite (PdCe_0.4@S-1), which is attributed to confinement-reinforced Pd–Ce interactions that promote the formation of oxygen vacancies and highly reactive oxygen species. Furthermore, the introduction of Ce improves the hydrophobicity of the S-1 zeolite and, by forming Pd–Ce mixed oxides, inhibits the transformation of the active PdO phase to inactive Pd(OH)₂ species. Overall, the bimetallic PdCe_0.4@S-1 catalyst delivers exceptional outstanding activity and durability in complete methane oxidation, even in the presence of water vapor. This study may provide new prospects for the rational design of high-performance and durable Pd catalysts for complete methane oxidation.

Achieving safely managed sanitation and resource recovery in areas that are rural, geographically challenged, or experiencing rapidly increasing population density may not be feasible with centralized facilities due to space requirements, site-specific concerns, and high costs of sewer installation. Nonsewered sanitation (NSS) systems have the potential to provide safely managed sanitation and achieve strict wastewater treatment standards. One such NSS treatment technology is the NEWgenerator, which includes an anaerobic membrane bioreactor (AnMBR), nutrient recovery via ion exchange, and electrochlorination. The system has been shown to achieve robust treatment of real waste for over 100 users, but the technology’s relative life cycle sustainability remains unclear. This study characterizes the financial viability and life cycle environmental impacts of the NEWgenerator and prioritizes opportunities to advance system sustainability through targeted improvements and deployment. The costs and greenhouse gas (GHG) emissions of the NEWgenerator (general case) leveraging grid electricity were 0.139 [0.113–0.168] USD cap^–1 day^–1 and 79.7 [55.0–112.3] kg CO₂-equiv cap^–1 year^–1, respectively. A transition to photovoltaic-generated electricity would increase costs to 0.145 [0.118–0.181] USD cap^–1 day^–1 but decrease GHG emissions to 56.1 [33.8–86.2] kg CO₂-equiv cap^–1 year^–1. The deployment location analysis demonstrated reduced median costs for deployment in China (−38%), India (−53%), Senegal (−31%), South Africa (−31%), and Uganda (−35%), but at comparable or increased GHG emissions (−2 to +16%). Targeted improvements revealed the relative change in median cost and GHG emissions to be −21 and −3% if loading is doubled (i.e., doubled users per unit), −30 and −12% with additional sludge drying, and +9 and −25% with the addition of a membrane contactor, respectively, with limited benefits (0–5% reductions) from an alternative photovoltaic battery, low-cost housing, or improved frontend operation. This research demonstrates that the NEWgenerator is a low-cost, low-emission NSS treatment technology with the potential for resource recovery to increase access to safe sanitation.