Science of the Total Environment最新文献

Solid waste is one of the primary contributors to environmental pollution currently, it is crucial to enhance the prevention and control of solid waste pollution in environmental management. The effectiveness of the second stage of purification in the industrial zinc hydrometallurgy is determined by the concentration of cobalt ion. Manual testing and monitoring of cobalt ion concentration are time consuming and costly, and prone to delays, which can result in discharge of cobalt ion concentration that does not meet the standards, leading to water pollution. Additionally, over-addition of zinc powder leads to a waste of resources, increasing the production cost of the company. Here, this work proposes a hybrid prediction model that combines the advantages of data decomposition and machine learning algorithms to predict the metal cobalt ion concentration in the effluent solution of a section of zinc hydrometallurgy refining purification in factory A. According to the different types of experiments, ablation experiments and contrast experiments are designed in this work under the same training and test data were used in the modeling process. Analytic and experimental results show that the proposed hybrid prediction model has the smallest error and the best fit between the actual and predicted values of cobalt ion concentration, and the appropriate graphs were finally selected for quantitative metrics analysis. The root mean square error was reduced by 4.2 %-73.9 %, the mean absolute error by 7.1 %-93.4 %, the mean percentage error by 7.7 %-86.7 % and the coefficient of determination by 1.3 %-134.6 %. The hybrid prediction model not only avoided the pollution of water resources by the cobalt ion concentration discharged in the purification, which is also of practical significance for the technicians to control the input quantity of zinc powder according to the prediction data in time and reduce the waste of resources.

The International Convention for the Prevention of Pollution from Ships (MARPOL) has prohibited ships using of HFO in ports. For this reason, during in port operations, different strategies must be adopted, based on the use of cleaner fuels or on the transition towards marine electrical technologies. In this context, the purpose of the present research is to analyze and compare, from an environmental and economic points of view, different technical solutions for in port operations. Four alternative configurations have been proposed: Solution 1, Battery & Internal Combustion Engine (ICE); Solution 2, Battery & Fuel Cell (FC) with yellow hydrogen; Solution 3, Battery & Fuel Cell (FC) with green hydrogen, and Solution 4, Battery & Cold Ironing (CI). From the environmental perspective, the Well-to-Waves (WTW) analysis has been carried out; from the economic point of view, the investment costs and the operating costs have been calculated and compared. Moreover, an economic performance indicator, the Economic-Environmental Correlation specific Index (ECI), has been introduced and evaluated. Results highlighted that from environmental point of view, the best solution is achieved implementing the Battery & Fuel Cell (FC) solution. On the other hand, the Battery & Cold Ironing (CI) solution represents the best solution from the economic point of view, allowing to obtain the lowest ECI.

Wastewater-based surveillance (WBS) can monitor for the presence of human health pathogens in the population. During COVID-19, WBS was widely used to determine wastewater SARS-CoV-2 RNA concentration (concentrations) providing information on community COVID-19 cases (cases). However, studies examining the relationship between concentrations and cases tend to be localised or focussed on small-scale institutional settings. Few have examined this relationship in multiple settings, over long periods, with large sample numbers, nor attempted to quantify the relationship between concentrations and cases or detail how catchment characteristics affected these. This 18-month study (07/20-12/21) explored the correlation and quantitative relationship between concentrations and cases using censored regression. Our analysis used >94,000 wastewater samples collected from 452 diverse sampling sites (259 Sewage Treatment Works (STW) and 193 Sewer Network Sites (SNS)) covering ~65 % of the English population. Wastewater concentrations were linked to ~6 million diagnostically confirmed COVID-19 cases. High correlation coefficients were found between concentrations and cases (STW: median r = 0.66, IQR: 0.57-0.74; SNS: median r = 0.65, IQR: 0.54-0.74). The quantitative relationship (regression coefficient) between concentrations and cases was variable between catchments. Catchment and sampling characteristics (e.g. size of population and grab vs automated sampling) had significant but small effects on correlation and regression coefficients. During the last six months of the study correlation coefficients reduced and regression coefficients became highly variable between catchments. This coincided with a shift towards younger cases, a highly vaccinated population and rapid emergence of the variant Omicron. The English WBS programme was rapidly introduced at scale during COVID-19. Laboratory methods evolved and study catchments were highly diverse in size and characteristics. Despite this diversity, findings indicate that WBS provides an effective proxy for establishing COVID-19 dynamics across a wide variety of communities. While there is potential for predicting COVID-19 cases from wastewater concentration, this may be more effective at smaller scales.

Mangrove sediments in southern China are a large reservoir for microplastics (MPs). In particular, polyethylene microplastics (PE-MPs) are environmentally toxic and have accumulated in large quantities in these sediments, posing a potential threat to the overall mangrove and the organisms that inhabit it. We screened sediments from 5 mangrove sites and identified a potential source of PE-MP degrading bacteria. We purified the bacterial strains Acinetobacter venetianus E1-1, Serratia marcescens E1-2, Chryseobacterium cucumeris E1-3 and Bacillus albus E1-4 from P1 that were able to reduce the mass of the 75 μm PE-MPs substrate by 3.67 to 6.59 %, respectively and use it as a sole carbon source. The degradation was accompanied by surface deformation of the MPs and introduction of polar oxygen-containing carbonyl and carboxylic acid functional groups thereby decreasing the hydrophobicity of the substrate. Whole-genome sequencing of S. marcescens E1-2, the most effective degrader, revealed it possesses a variety of enzymes and metabolic pathways related to PE degradation. Our results indicated that the PE-MP degrading bacteria isolated from screened mangrove sediments represent an effective strategy for in situ MP pollution remediation and uncovering mechanisms associated with PE degradation.

This study aimed to quantify the environmental impact of microplastic (MP) emissions from wastewater treatment plants (WWTPs) using life cycle assessment (LCA). The investigation comprehensively evaluated the contribution of MPs to overall WWTP midpoint and endpoint impacts, with a detailed analysis of the influence of particle size, shape, polymer type, and the environmental costs and benefits of individual wastewater treatment processes on MP removal. The LCA model was developed using SimaPro software, with impact assessments conducted via the USEtox framework and the IMPACT World+ methodology. Results showed that at the midpoint level, MPs accounted for 1.24E+05 CTUe (94 % of the total plant impact), representing the potential harm to aquatic species per cubic meter of discharged wastewater-surpassing the impacts of other contaminants (e.g., heavy metals, nutrients) by at least two orders of magnitude. At the endpoint level, the damage of 8.39E-02 PDF·m²·yr (1.7 % of the total) indicated the potential loss of species diversity, comparable to other pollutant contributions. Polyethylene, polystyrene, and polypropylene were identified as the most impactful polymer types. In terms of environmental costs and benefits, secondary, tertiary, and primary treatments demonstrated decreasing environmental benefits, directly correlated with their respective MP removal efficiencies. These findings underscore the critical role of MP emissions in WWTP life cycle inventories and highlight the urgent need for targeted environmental policies and advanced treatment technologies to address MP contamination in both natural and engineered aquatic systems.

Understanding the dynamics of fecal bacterial communities is crucial for managing public health risks and protecting drinking water resources. While extensive research exists on how abiotic factors influence the survival of fecal microbial communities in water, less attention has been paid to the impact of predation by higher organisms, such as the widely distributed grazer Daphnia. Nevertheless, Daphnia plays a significant role in regulating bacterial communities in natural aquatic ecosystems, and recent studies highlighted its potential as a biofilter in alternative tertiary wastewater treatment systems. In this study, we investigated the influence of three different Daphnia species on a wastewater bacterial community, including fecal indicator bacterium E. coli. Using a microcosm setup to simulate the discharge of untreated sewage into surface water, we conducted in-depth analysis of bacterial community dynamics through sequencing the 16S rRNA gene. Our results revealed significant changes in microbial diversity and composition following exposure to Daphnia grazing, with variations observed among the three Daphnia species. D. pulicaria exerted the most pronounced impact on microbial diversity, followed by D. middendorffiana and D. mendotae. A total of 90 taxa exhibited significantly reduced relative abundance in the presence of Daphnia, with Firmicutes phylum being the most affected. At genus level, bacteria typically associated with wastewater (e.g., Zoogloea and Arcobacter) and gut microbiome constituents (e.g., Prevotella and Akkermansia) were notably affected by Daphnia exposure. The influence of Daphnia on bacterial community composition was most pronounced for D. pulicaria, while D. middendorffiana and D. mendotae primarily impacted community structure. Furthermore, we demonstrated that the microbial response to Daphnia exposure is phylogenetically conserved, potentially reflecting a grazing resistance or grazer feeding trait. Our findings shed new light on the role of Daphnia in controlling bacterial communities in polluted water bodies and underscore its potential as biofilter in wastewater treatment and reuse contexts.

We present the results of a 1-year study that quantified salt levels in stormwater, soils, and plant tissues from 14 stormwater detention basins across Northern VA in an above-average snow year. We characterize (1) the level of salt stress plants experience, (2) the extent to which current plant communities feature salt tolerant species, and (3) the capacity of these species to phytoremediate soils and reduce the impacts of deicer and anti-icer use. Our results suggest that detention basin vegetation experience a range of salt stress levels that depend on drainage area type (roads: moderate to high > parking lots: low to moderate > pervious areas: none). Established thresholds for salt sensitive vegetation (Na⁺, Cl⁺, electrical conductivity, sodium adsorption ratio, exchangeable sodium percentage) were exceeded at least twice in stormwater or soils from all systems draining roads and half of systems draining parking lots. Winter exceedances were most common, but saline conditions did persist into the growing season, particularly at sites draining roads. Two hundred fifty-five plant species were identified across all detention basins, including 48 natives capable of tolerating elevated salt levels (electrical conductivity ≥2 dS/m). Within-tissue concentrations of sodium and chloride ions were highest in Typha (latifolia and angustifolia) (11.1 mg Na⁺/g; 30 mg Cl^-/g), making it our top phytoremediation candidate. Scaling these concentrations up, we estimate that a standard-size highway detention basin (2000-3000 m²) with 100 % cattail cover can phytoremediate up to 100 kg of Na⁺ and 200 kg of Cl^- per year. Uptake at this level is not sufficient to offset winter salt application, constituting only 5-6 % of basin inputs. This suggests that phytoremediation should not be considered a standalone solution to basin salinization, although it could be one approach of many in a broader salt management strategy.

This pilot-scale study investigated nitrifying moving bed biofilm reactors (MBBRs) in a post-lagoon treatment setup over two years to evaluate the impact of seasonal ammonia fluctuations on winter nitrification. In Year 2, reactors without fall ammonia starvation achieved significantly higher winter ammonia removal (97.2 ± 1.5 %) and surface area ammonia removal rates (SARR) (0.69 ± 0.06 g N/m²·d) compared to Year 1 (63.7 ± 2.5 % ammonia removal, SARR of 0.35 ± 0.04 g N/m²·d), demonstrating the critical role of fall ammonia availability for winter nitrification. Biofilms in Year 2 were thinner and denser, with higher biomass concentrations, potentially supporting more active biomass and improved substrate uptake. Seasonal shifts and diversity loss were observed within the biofilm microbial community, and nitrifiers were identified as Nitrosomonadaceae and Nitrospiraceae. Moreover, linear relationships were explored between winter ammonia removals and two ratios: (1) days with influent ammonia levels ≤ 5 mg N/L to days with temperatures above 5 °C, and (2) average ammonia concentration during fall to peak winter ammonia concentration. The modeling results indicated that winter ammonia removal performance could be enhanced by minimizing low-ammonia periods in the fall and maximizing pre-winter ammonia concentration. Overall, this study not only provided a deeper understanding of the year-round nitrifying MBBR process but also highlighted the importance of maintaining adequate substrate levels during fall to ensure sufficient biomass accumulation and activity for robust winter nitrification performance. These findings are essential for enhancing wastewater treatment performance in cold climates and offer practical guidance for optimizing biofilm-based nitrification systems.

The widespread occurrence of new and emerging and persistent organic pollutants (NEPs and POPs) in surface water poses a risk to drinking water supply and consequently human health. The aim of this work was to investigate the occurrence and potential transport of 42 target NEPs and POPs (including per-and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides and bisphenols) along the rural and urban environments of three rivers in England. The type and concentrations of pollutants varied between the sampling days and points. Two pharmaceuticals (diclofenac and ibuprofen), two pesticides (diethyl-meta-toluamide (DEET) and prosulfocarb) and a range of PFAS were detected above the method detection limit. The observed PFAS include restricted perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS) and a newer generation substitute 6:2 fluorotelomer sulfonate (6:2 FTS). The levels of PFOS and diclofenac observed in all studied rivers exceeded the European environmental quality standard (EQS). PFOS and diclofenac high detection frequency in the river Ouse suggests their persistence and potential to contaminate connecting tributaries. An assessment of the ecological risk of prosulfocarb levels in the samples from river Ouse, using the risk quotient method, showed a potential risk to algae, planktonic crustaceans, and fish. Our results suggest that the presence of 12 NEPs and POPs, could potentially be influenced by anthropogenic activities across urban and rural environments of the studied rivers. The study highlights the need for continuous monitoring of restricted and new-generation chemicals in the surface waters to understand their impact on the ecosystem and public health.