Iron corrosion in drinking water distribution systems (DWDSs) is the root cause of the deterioration of drinking water quality. Humic acid (HA) is a critical component of dissolved organic matter in drinking water. However, the influences of HA on iron pipe corrosion in DWDSs have not been fully understood, especially the combined effects of corrosive microorganisms and HA with different molecular weights (MWs). This study used bench-scale reactors to explore the impacts of iron-oxidizing bacteria (IOB) (Microbacterium oxydans ZT-1, a common iron-oxidizing bacterium) and HA with different MWs on iron pipe corrosion. Before 6 d, loose and porous goethite (α-FeOOH) was the most prevalent compound in the corrosion products. The addition of ZT-1 and HA promoted iron corrosion and release. Under the condition of ZT-1 + > 100-kDa HA, the maximum values of corrosion rate and total iron concentrations were 0.23 mm/a and 9.94 mg/L, respectively. As corrosion proceeded, magnetite (Fe3O4) formed from FeOOH, and Fe-HA complexes accumulated, resulting in deceleration of iron corrosion. After 54 d, the corrosion rate and total iron concentration had decreased by ZT-1, and HA with different MWs.
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global emergency that exposed the urgent need for surveillance approaches to monitor the dynamics of viral transmission. Several epidemiological tools that may help anticipate outbreaks have been developed. Wastewater-based epidemiology is a non-invasive and population-wide methodology for tracking the epidemiological evolution of the virus. However, thorough evaluation and understanding of the limitations, robustness, and intricacies of wastewater-based epidemiology are still pending to effectively use this strategy. The aim of this study was to train highly accurate predictive models using SARS-CoV-2 virus concentrations in wastewater in a region consisting of several municipalities. The chosen region was Catalonia (Spain) given the availability of wastewater SARS-CoV-2 quantification from the Catalan surveillance network and healthcare data (clinical cases) from the regional government. By using various feature engineering and machine learning methods, we developed a model that can accurately predict and successfully generalize across the municipalities that make up Catalonia. Explainable Machine Learning frameworks were also used, which allowed us to understand the factors that influence decision-making. Our findings support wastewater-based epidemiology as a potential surveillance tool to assist public health authorities in anticipating and monitoring outbreaks.
Moving toward a circular economy requires improvement of the economic and environmental performance of municipalities in their provision of municipal solid waste (MSW) services. Understanding performance changes over years is fundamental to support decision-making. This study employs the Luenberger-Hicks-Moorsteen productivity indicator to evaluate eco-productivity change and its drivers in the MSW sector in Chile over the years 2015–2019. The further use of decision tree and linear regression analysis allows exploration of the interaction between operating characteristics and eco-productivity estimations. The results of the eco-productivity assessment show that, although the Chilean MSW sector was still facing a transitional period, from 2015 to 2019, eco-productivity increased 1.28% per year. Gains in eco-productivity were due to technical progress and small gains in efficiency, whereas scale effect had an adverse impact. Other factors such as waste spending per inhabitant and the amount of waste collected and recycled per inhabitant had a significant impact on the eco-productivity of Chilean municipalities.
As water quality is a combination of multiple optically active parameters, there is a growing interest in probabilistic models to predict water quality. This study aims to add to the water quality prediction studies by introducing ensemble learning with deep learning-based mixture density networks with multiple probabilistic Gaussian distributions. We named the approach as Ensembled Gaussian Mixture Density Network (GMDN). Many existing water quality algorithms rely on localized data sets, which limits their applicability. This research addresses this by developing and evaluating the proposed model using the global in situ water quality data set GLORIA (Global Reflectance community data set for Imaging and optical sensing of Aquatic environments). We focused on estimating two key biogeochemical components (BPs): Total Suspended Solids (TSS) and Chlorophyll-a(Chla), along with one inherent optical property (IoP), the absorption coefficient of colored dissolved organic matter (αCDOM). The proposed approach performs quite reliably when evaluated on the data samples of individual countries. The GMDN algorithm has been fine-tuned on the satellite-matchup for the river Ganga near Varanasi city. The fine-tuning was implemented using the remote sensing reflectance (Rrs) of the spaceborne hyperspectral data set PRISMA (PRecursore IperSpettrale della Missione Applicativa). The contribution of the riverbed floor to the Rrs of PRISMA has been computed using physics-based simulations in the Water Color Simulator (WASI). Overall, the simultaneous use of multiple probabilistic distributions and ensembled architectures improves the predictive accuracy of WQ parameters compared to the existing operational algorithms.
Exploring the nonlinear relationship between air pollution and precursor emissions in Qingdao, eastern China is crucial for improving air quality. We simulated 32 emission reduction scenarios based on different volatile organic compound (VOC) and nitrogen oxide (NOx) emission reduction ratios using the Weather Research and Forecasting-Comprehensive Air Quality Model Extensions model. The emission reduction of VOCs was beneficial for reducing fine particulate matter (PM2.5) concentration in January and ozone (O3) concentration in June. However, NOx must be reduced by at least 48% and 70% to decrease PM2.5 and O3 concentrations, respectively, when VOCs are not reduced. The responses of PM2.5 and O3 concentrations to emission reductions from different sources were also evaluated. The reduction in VOC emissions from different sources decreased the PM2.5 concentration in January, and O3 concertation in June, while NOx reduction resulted in an increase. Controlling VOC emissions from industry has a positive effect on improving local PM2.5 and O3, while the emission reductions of NOx from transportation and industry are not conducive to reducing PM2.5 and O3 concentrations. The synergistic emission reduction pathways for NOx and VOCs during PM2.5 and O3 combined pollution were also analyzed. The VOC and NOx emission reductions were beneficial for reducing the comprehensive Air Quality Index (sAQI) values. When the NOx emission reduction was large, the sAQI improvement gradually exceeded that of VOCs. A collaborative optimization path should be adopted that focuses on controlling VOCs first, and further control of combined pollution should depend on the deep reduction of NOx.
The use of chemical disinfectants inactivates pathogens, but it also leads to the formation of disinfection byproducts (DBPs). Brominated disinfection byproducts (Br-DBPs) exhibit a high level of toxicity, so a comprehensive understanding of their generation, toxicity and control strategies is needed. This study examines the research papers covering bromide concentrations in surface water, groundwater, or wastewater, involving 380 sampling sites. Additionally, the cytotoxicity, genotoxicity and developmental toxicity of Br-DBPs are summarized. The formation mechanisms of Br-DBPs in ozonation, chlorine-based, and persulfate-based disinfection processes are summarized, and an evaluation of control strategies for Br-DBPs and their associated toxicity is provided. The concentrations of bromide in surface water, groundwater, and wastewater in coastal areas are generally higher than those in inland areas, which are also affected by climate, topography, and the source of water. The toxicity of different types of Br-DBPs is different. The elevation of bromide concentration enhances the water toxicity, particularly in relation to ozonation. The introduction of 1000 μg/L bromide results in a 3.06-fold increase in cytotoxicity and a 4.72-fold increase in genotoxicity. Hydrogen peroxide (H2O2) and ammonia (NH3–N) exhibit effective bromate control, but H2O2 demonstrates limits efficacy in controlling Br-DBPs, while NH3–N poses the risk of increased toxicity, up to a 2.86-fold increase in genotoxicity. Ultraviolet/ozone (UV/O3) and Ultraviolet/persulfate (UV/PS) can effectively control Br-DBPs and toxicity but may promote bromate generation. This review will deepen the understanding of Br-DBPs and their toxicity generation behavior, thereby contributing to the further optimization and development of processes for Br-DBPs control.
The photochemical interactions between nitrate (NO3–) and natural organic matter (NOM) are vital for aquatic chemistry. However, the effects of guest iron minerals, which may enter the aquatic environments due to both human and natural activities, on those interactions are widely ignored. This work evaluated the effects of hematite (α-Fe2O3) on the photochemical conversion products and pathways of NO3–, fulvic acid (FA) under 12 h of ultraviolet irradiation. The addition of 0.4 g/L of guest α-Fe2O3 accelerated the reduction of NO3– by 24.3%, with NH4+ as the primary reduction product, and hampered the mineralization of FA. These effects were dependent on the dosage amount of α-Fe2O3 and FA concentrations. The studies on the molecule-level changes of FA revealed that the complete oxidation to CO2 and the partial oxidation pathways that alter the molecular composition of FA were suppressed, and the mineralization rate decreased by 27.8%. Particularly, the conversion rates of CHON and CHONS were reduced by 21.0% and 20.3%, respectively, increasing the unsaturated products. The scavenging experiments and quantitative measurements of hydroxyl radicals (•OH) proposed that the photogenerated electrons and holes from α-Fe2O3 were the key for the altered transformation of NO3– and FA. This work revealed the guest effects of iron mineral particles on the photochemical interactions between NO3– and NOM in the natural surface waters.
The escalating use of plastic materials in viticulture causes release of microplastics (MPs) into vineyard soils. This study examines the impact on soil health of polypropylene (PP) raffia and polyvinyl chloride (PVC) tube strings, commonly mulched into the topsoil after use. A 120-d incubation experiment was conducted with soils exposed to high doses (10 g/kg) of microplastics (MPs) from standard, new and used strings. The study investigated alterations in the microbial community, bioavailability of macronutrients (NH4+ and NO3–, P, K, Ca, Mg), and bioavailability of micronutrients (Cu, Zn, Fe, Mg). The presence of MPs significantly stressed the soil microbial community, reducing microbial biomass by 30% after 30 d, with the exception of PVC in acid soil, which caused an unexpected increase of about 60%. The metabolic quotient (qCO2) doubled in MP-polluted soils, with PVC exerting a more pronounced effect than PP. Basal respiration increased by 25% relative to the acid control soil. PVC MPs raised soil pH from 6.2 to 7.2 and firmly reduced the bioavailability of micronutrients, particularly in acidic soils, and led to a 98% reduction in nitrate (NO3–). The availability of NH4+, P, K, Mg decreased by 10% and Cu, Fe, Mn, Zn by 30%. However, Ca availability increased by 30%, despite shifting from the acid-soluble fraction to soil organic matter and crystalline minerals. Calcareous soil was generally more resilient to changes than the acid soil. These findings underscore the urgent need to investigate the long-term effects of MPs from viticulture on soil properties and health.
The enrichment of phosphate is necessary for high-efficiency nutrient recovery from wastewater through struvite precipitation. However, the majority of current nutrient enrichment processes focus on membrane-based technologies driven by external energy input. Here, the phosphate enrichment by negatively charged Poly(sodium acrylate) hydrogel beads as the self-driven dewatering agent under different conditions was investigated. The phosphate rejection decreased as its concentration increased but retained 56.9% even in 10 mmol/L PO43− solution, which is well beyond the phosphate concentration in typical wastewater concentrates. Phosphate was concentrated 3.6 folds with a recovery of 70% using ~1 wt% of hydrogel beads in 0.5 mmol/L phosphate solution. The effects of the pH, ionic strength of the nutrient stream, and the swelling ratio of hydrogels on the rejection of phosphate were investigated. In addition, the hydrogel beads removed 100% of nickel ions during the dewatering of the phosphate solution (0.5 mmol/L Ni2+ and 0.5 mmol/L PO43−), presenting an opportunity for simultaneous phosphate enrichment and purification during the pretreatment for nutrient recovery from wastewater. This study demonstrated that the spontaneous dewatering process utilizing ion-selective hydrogels is promising for nutrient enrichment to promote recovery efficiency.
● Under thermodynamic, urban ecosystem fits scaling law due to self-organization.
● Urban ecosystem has similar scaling to social economic system.
● The scaling law transitions are reflected in the multistable coexistence.
Prior research has consistently demonstrated that urban economic and social systems adhere to the empirical scaling law. Furthermore, a plethora of evidence, including the scale-free networks of energy metabolism, the allometric growth patterns of species and populations, and the scaling law relationship between exergy and transformity in biosphere systems across various levels, indicates that urban ecosystems exhibit multi-level scaling law characteristics in energy metabolism under self-organization, alongside significant human activity imprints. This study synthesizes these findings to hypothesize that urban ecological components are also aligned with system-level scaling theory within the urban metabolism framework. This encompasses: 1) the existence of multistable coexistence and mutual transformation phenomena, mirroring the dynamic nature of scaling laws; and 2) a nuanced balance between the ecosystem and the socio-economic system, particularly in the realms of spatial competition and output efficiency. The ecosystem scaling theory hypotheses of urban metabolic processes offer a theoretical foundation for identifying ecological security tipping points, which are pivotal in the strategic decision-making for ecological planning and management in the future.
Mineral scaling represents a major constraint that limits the efficiency of membrane desalination, which is becoming increasingly important for achieving sustainable water supplies in the context of a changing climate. Different mineral scales can be formed via distinct mechanisms that lead to a significant variation of scaling behaviors and mitigation strategies. In this article, we present a comprehensive review that thoroughly compares gypsum scaling and silica scaling, which are two common scaling types formed via crystallization and polymerization respectively, in membrane desalination. We show that the differences between scale formation mechanisms greatly affect the thermodynamics, kinetics, and mineral morphology of gypsum scaling and silica scaling. Then we review the literatures on the distinct behaviors of gypsum scaling and silica scaling during various membrane desalination processes, examining their varied damaging effects on desalination efficiency. We further scrutinize the different interactions of gypsum and silica with organic foulants, which result in contrasting consequences of combined scaling and fouling. In addition, the distinctive mitigation strategies tailored to controlling gypsum scaling and silica scaling, including scaling-resistant membrane materials, antiscalants, and pretreatment, are discussed. We conclude this article with the research needs of attaining a better understanding of different mineral scaling types, aiming to inspire researchers to take scale formation mechanism into consideration when developing more effective approaches of scaling control in membrane desalination.
Developing an anthropogenic carbon dioxides (CO2) emissions monitoring and verification support (MVS) capacity is essential to support the Global Stocktake (GST) and ratchet up Nationally Determined Contributions (NDCs). The 2019 IPCC refinement proposes top-down inversed CO2 emissions, primarily from fossil fuel (FFCO2), as a viable emission dataset. Despite substantial progress in directly inferring FFCO2 emissions from CO2 observations, substantial challenges remain, particularly in distinguishing local CO2 enhancements from the high background due to the long atmospheric lifetime. Alternatively, using short-lived and co-emitted nitrogen dioxide (NO2) as a proxy in FFCO2 emission inversion has gained prominence. This methodology is broadly categorized into plume-based and emission ratios (ERs)-based inversion methods. In the plume-based methods, NO2 observations act as locators, constraints, and validators for deciphering CO2 plumes downwind of sources, typically at point source and city scales. The ERs-based inversion approach typically consists of two steps: inferring NO2-based nitrogen oxides (NOx) emissions and converting NOx to CO2 emissions using CO2-to-NOx ERs. While integrating NO2 observations into FFCO2 emission inversion offers advantages over the direct CO2-based methods, uncertainties persist, including both structural and data-related uncertainties. Addressing these uncertainties is a primary focus for future research, which includes deploying next-generation satellites and developing advanced inversion systems. Besides, data caveats are necessary when releasing data to users to prevent potential misuse. Advancing NO2-based CO2 emission inversion requires interdisciplinary collaboration across multiple communities of remote sensing, emission inventory, transport model improvement, and atmospheric inversion algorithm development.
● 3D printing enables rapid prototyping and optimisation of MES reactors.
● 3D-printed electrodes improve electron transfer and biocompatibility.
● Tailored ink materials boost conductivity for sustainable energy.
● Bioprinting refines biofilm stability and microbial-electrode interactions.
Microbial electrochemical system (MES) offers sustainable solutions for environmental applications such as wastewater treatment, energy generation, and chemical synthesis by leveraging microbial metabolism and electrochemical processes. This review explores the transformative role of 3D printing in MES research, focusing on reactor body design, electrode fabrication, and bioprinting applications. Rapid prototyping facilitated by 3D printing expedites MES development while unlocking design flexibility, which enhances performance in optimising fluid dynamics and mass transfer efficiency. Tailored ink materials further improve the conductivity and biocompatibility of electrodes, paving the way for environmental applications. 3D-printed bio-anodes and bio-cathodes offer enhanced electrogenesis and boosted electron acceptance processes, respectively, by fine-tuning electrode architectures. Additionally, 3D bioprinting presents opportunities for scaffold fabrication and bioink formulation, enhancing biofilm stability and electron transfer efficiency. Despite current challenges, including material selection and cost, the integration of 3D printing in MES holds immense promise for advancing energy generation, wastewater treatment, resource recovery, carbon utilisation, and biosensing technologies.
