Environmental Science and Ecotechnology最新文献

Although phosphorus (P) recovery and management from sewage sludge are practiced in North America and Europe, such practices are not yet to be implemented in China. Here, we evaluated the environmental sustainability opportunity and socio-economic costs of recovering P from sewage sludge by replacing the current-day treatments (CT; sludge treatment and landfill) and P chemical fertilizer application (CF) in China using life cycle assessment and life cycle costing methods. Three potential P recovery scenarios (PR₁‒PR₃: struvite, vivianite, and treated sludge) and corresponding current-day scenarios (CT₁‒CT₃ and CF) were considered. Results indicated that PR₁ and PR₂ have smaller environmental impacts than the current-day scenarios, whereas PR₃ has larger impacts in most categories. PR₃ has the lowest net costs (sum of internal costs and benefits, 39.1–54.7 CNY per kg P), whereas PR₂ has the lowest external costs (366.8 CNY per kg P). Societal costs for production and land use of 1 kg P by P recovery from sewage sludge (e.g., ∼527 CNY for PR₁) are much higher than those of P chemical fertilizers (∼20 CNY for CF). However, considering the costs in the current-day treatments (e.g., ∼524 CNY for CT₁), societal costs of P recovery scenarios are close to or slightly lower than those of current-day scenarios. Among the three P recovery scenarios, we found that recovering struvite as P fertilizer has the highest societal feasibility. This study will provide valuable information for improved sewage sludge management and will help promote the sustainable supply of P in China.

Sustainable water management is essential to guaranteeing access to safe water and addressing the challenges posed by climate change, urbanization, and population growth. In a typical household, greywater, which includes everything but toilet waste, constitutes 50–80% of daily wastewater generation and is characterized by low organic strength and high volume. This can be an issue for large urban wastewater treatment plants designed for high-strength operations. Segregation of greywater at the source for decentralized wastewater treatment is therefore necessary for its proper management using separate treatment strategies. Greywater reuse may thus lead to increased resilience and adaptability of local water systems, reduction in transport costs, and achievement of fit-for-purpose reuse. After covering greywater characteristics, we present an overview of existing and upcoming technologies for greywater treatment. Biological treatment technologies, such as nature-based technologies, biofilm technologies, and membrane bioreactors (MBR), conjugate with physicochemical treatment methods, such as membrane filtration, sorption and ion exchange technologies, and ultraviolet (UV) disinfection, may be able to produce treated water within the allowable parameters for reuse. We also provide a novel way to tackle challenges like the demographic variance of greywater quality, lack of a legal framework for greywater management, monitoring and control systems, and the consumer perspective on greywater reuse. Finally, benefits, such as the potential water and energy savings and sustainable future of greywater reuse in an urban context, are discussed.

Multifunctional and diversified agriculture can address diverging pressures and demands by simultaneously enhancing productivity, biodiversity, and the provision of ecosystem services. The use of digital technologies can support this by designing and managing resource-efficient and context-specific agricultural systems. We present the Digital Agricultural Knowledge and Information System (DAKIS) to demonstrate an approach that employs digital technologies to enable decision-making towards diversified and sustainable agriculture. To develop the DAKIS, we specified, together with stakeholders, requirements for a knowledge-based decision-support tool and reviewed the literature to identify limitations in the current generation of tools. The results of the review point towards recurring challenges regarding the consideration of ecosystem services and biodiversity, the capacity to foster communication and cooperation between farmers and other actors, and the ability to link multiple spatiotemporal scales and sustainability levels. To overcome these challenges, the DAKIS provides a digital platform to support farmers' decision-making on land use and management via an integrative spatiotemporally explicit approach that analyses a wide range of data from various sources. The approach integrates remote and in situ sensors, artificial intelligence, modelling, stakeholder-stated demand for biodiversity and ecosystem services, and participatory sustainability impact assessment to address the diverse drivers affecting agricultural land use and management design, including natural and agronomic factors, economic and policy considerations, and socio-cultural preferences and settings. Ultimately, the DAKIS embeds the consideration of ecosystem services, biodiversity, and sustainability into farmers' decision-making and enables learning and progress towards site-adapted small-scale multifunctional and diversified agriculture while simultaneously supporting farmers' objectives and societal demands.

The presence of organic matter in lakes profoundly impacts drinking water supplies, yet treatment processes involving coagulants and disinfectants can yield carcinogenic disinfection by-products. Traditional assessments of organic matter, such as chemical oxygen demand (COD_Mn) and biochemical oxygen demand (BOD₅), are often time-consuming. Alternatively, optical measurements of dissolved organic matter (DOM) offer a rapid and reliable means of obtaining organic matter composition data. Here we employed DOM optical measurements in conjunction with parallel factor analysis to scrutinize COD_Mn and BOD₅ variability. Validation was performed using an independent dataset encompassing six lakes on the Yungui Plateau from 2014 to 2016 (n = 256). Leveraging multiple linear regressions (MLRs) applied to DOM absorbance at 254 nm (a₂₅₄) and fluorescence components C1–C5, we successfully traced COD_Mn and BOD₅ variations across the entire plateau (68 lakes, n = 271, R² > 0.8, P < 0.0001). Notably, DOM optical indices yielded superior estimates (higher R²) of COD_Mn and BOD₅ during the rainy season compared to the dry season and demonstrated increased accuracy (R² > 0.9) in mesotrophic lakes compared to oligotrophic and eutrophic lakes. This study underscores the utility of MLR-based DOM indices for inferring COD_Mn and BOD₅ variability in plateau lakes and highlights the potential of integrating in situ and remote sensing platforms for water pollution early warning.

Effective monitoring of atmospheric concentrations is vital for assessing the Stockholm Convention's effectiveness on persistent organic pollutants (POPs). This task, particularly challenging in polar regions due to low air concentrations and temperature fluctuations, requires robust sampling techniques. Furthermore, the influence of temperature on the sampling efficiency of polyurethane foam discs remains unclear. Here we employ a flow-through sampling (FTS) column coupled with an active pump to collect air samples at varying temperatures. We delved into breakthrough profiles of key pollutants, such as polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs), and organochlorine pesticides (OCPs), and examined the temperature-dependent behaviors of the theoretical plate number (N) and breakthrough volume (V_B) using frontal chromatography theory. Our findings reveal a significant relationship between temperature dependence coefficients (K_TN, K_TV) and compound volatility, with decreasing values as volatility increases. While distinct trends are noted for PAHs, PCBs, and OCPs in K_TN, K_TV values exhibit similar patterns across all chemicals. Moreover, we establish a binary linear correlation between log (V_B/m³), 1/(T/K), and N, simplifying breakthrough level estimation by enabling easy conversion between N and V_B. Finally, an empirical linear solvation energy relationship incorporating a temperature term is developed, yielding satisfactory results for N at various temperatures. This approach holds the potential to rectify temperature-related effects and loss rates in historical data from long-term monitoring networks, benefiting polar and remote regions.

Microbial electrosynthesis (MES) converts CO₂ into value-added products such as volatile fatty acids (VFAs) with minimal energy use, but low production titer has limited scale-up and commercialization. Mediated electron transfer via H₂ on the MES cathode has shown a higher conversion rate than the direct biofilm-based approach, as it is tunable via cathode potential control and accelerates electrosynthesis from CO₂. Here we report high acetate titers can be achieved via improved in situ H₂ supply by nickel foam decorated carbon felt cathode in mixed community MES systems. Acetate concentration of 12.5 g L⁻¹ was observed in 14 days with nickel-carbon cathode at a poised potential of −0.89 V (vs. standard hydrogen electrode, SHE), which was much higher than cathodes using stainless steel (5.2 g L⁻¹) or carbon felt alone (1.7 g L⁻¹) with the same projected surface area. A higher acetate concentration of 16.0 g L⁻¹ in the cathode was achieved over long-term operation for 32 days, but crossover was observed in batch operation, as additional acetate (5.8 g L⁻¹) was also found in the abiotic anode chamber. We observed the low Faradaic efficiencies in acetate production, attributed to partial H₂ utilization for electrosynthesis. The selective acetate production with high titer demonstrated in this study shows the H₂-mediated electron transfer with common cathode materials carries good promise in MES development.

Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.

Land-sea atmosphere interaction (LSAI) is one of the important processes affecting ozone (O₃) pollution in coastal areas. The effects of small-scale LSAIs like sea-land breezes have been widely studied. However, it is not fully clear how and to what extent the large-scale LSAIs affect O₃ pollution. Here we explored an O₃ episode to illuminate the role of large-scale LSAIs in O₃ pollution over the Bohai–Yellow Seas and adjacent areas through observations and model simulations. The results show that the northern Bohai Sea's coastal region, influenced by the Mongolian High, initially experienced a typical unimodal diurnal O₃ variation for three days, when O₃ precursors from Beijing–Tianjin–Hebei, Shandong, and Northeast China were transported to the Bohai–Yellow Seas. Photochemical reactions generated O₃ within marine air masses, causing higher O₃ levels over the seas than coastal regions. As the Mongolian High shifted eastward and expanded, southerly winds on its western edge transported O₃-rich marine air masses toward the coast, prolonging pollution for an additional three days and weakening diurnal variations. Subsequently, emissions from the Korean Peninsula and marine shipping significantly affected O₃ levels in the northern Bohai Sea (10.7% and 13.7%, respectively). Notably, Shandong's emissions played a substantial role in both phases (27.5% and 26.1%, respectively). These findings underscore the substantial impact of large-scale LSAIs driven by the Mongolian High on O₃ formation and pollution duration in coastal cities. This insight helps understand and manage O₃ pollution in northern Bohai Sea cities and broadly applies to temperate coastal cities worldwide.

Neighborhood gardens serve as sensitive sites for human microbial encounters, with phyllosphere microbes directly impacting our respiratory health. Yet, our understanding remains limited on how factors like season, garden age, and land use shape the risk of respiratory diseases (RDs) tied to these garden microbes. Here we examined the microbial communities within the phyllosphere of 72 neighborhood gardens across Shanghai, spanning different seasons (warm and cold), garden ages (old and young), and locales (urban and rural). We found a reduced microbial diversity during the cold season, except for Gammaproteobacteria which exhibited an inverse trend. While land use influenced the microbial composition, urban and rural gardens had strikingly similar microbial profiles. Alarmingly, young gardens in the cold season hosted a substantial proportion of RDs-associated species, pointing towards increased respiratory inflammation risks. In essence, while newer gardens during colder periods show a decline in microbial diversity, they have an increased presence of RDs-associated microbes, potentially escalating respiratory disease prevalence. This underscores the pivotal role the garden age plays in enhancing both urban microbial diversity and respiratory health.

When organophosphate pesticides (OPs) are not used and handled in accordance with the current rules and standards, it results in serious threats to the aquatic environment and human health. Phaeodactylum tricornutum is a prospective microalgae-based system for pollutant removal and carbon sequestration. Genetically engineered P. tricornutum, designated as the OE line (endogenously expressing purple acid phosphatase 1 [PAP1]), can utilize organic phosphorus for cellular metabolism. However, the competencies and mechanisms of the microalgae-based system (namely the OE line of P. tricornutum) for metabolizing OPs remain to be addressed. In this study, the OE line exhibited the effective biodegradation competencies of 72.12% and 68.2% for 30 mg L⁻¹ of dichlorvos and 50 mg L⁻¹ of glyphosate, accompanied by synergistic accumulations of biomass (0.91 and 0.95 g L⁻¹) and lipids (32.71% and 32.08%), respectively. Furthermore, the biodiesel properties of the lipids from the OE line manifested a high potential as an alternative feedstock for microalgae-based biofuel production. A plausible mechanism of OPs biodegraded by overexpressed PAP1 is that sufficient inorganic P for adenosine triphosphate and concurrent carbon flux for the reduced form of nicotinamide adenine dinucleotide phosphate biosynthesis, which improved the OP tolerance and biodegradation competencies by regulating the antioxidant system, delaying programmed cell death and accumulating lipids via the upregulation of related genes. To sum up, this study demonstrates a potential strategy using a genetically engineered strain of P. tricornutum to remove high concentrations of OPs with the simultaneous production of biomass and biofuels, which might provide novel insights for microalgae-based pollutant biodegradation.