Source appointment at large-scale and ungauged catchment using physically-based model and dynamic export coefficient

Iron-based multi-carbon composite and Pseudomonas furukawaii ZS1 co-affect nitrogen removal, microbial community dynamics and metabolism pathways in low-temperature aquaculture wastewater

Aerobic denitrification is the key process in the elimination of nitrogen from aquaculture wastewater, especially for wastewater with high dissolved oxygen and low carbon/nitrogen (C/N) ratio. However, a low C/N ratio, especially in low-temperature environments, restricts the activity of aerobic denitrifiers and decreases the nitrogen elimination efficiency. In this study, an iron-based multi-solid carbon source composite that immobilized aerobic denitrifying bacteria ZS1 (IMCSCP) was synthesized to treat aerobic (DO > 5 mg/L), low temperature (<15 °C) and low C/N ratio (C/N = 4) aquaculture wastewater. The results showed that the sequencing batch biofilm reactor (SBBR) packed with IMCSCP exhibited the highest nitrogen removal performance, with removal rates of 95.63% and 85.44% for nitrate nitrogen and total nitrogen, respectively, which were 33.03% and 30.75% higher than those in the reactor filled with multi-solid carbon source composite (MCSC). Microbial community and network analysis showed that Pseudomonas furukawaii ZS1 successfully colonized the SBBR filled with IMCSCP, and Exiguobacterium, Cellulomonas and Pseudomonas were essential for the nitrogen elimination. Metagenomic analysis showed that an increase in gene abundance related to carbon metabolism, nitrogen metabolism, extracellular polymer substance synthesis and electron transfer in the IMCSCP, enabling denitrification in the SBBR to be achieved via multiple pathways. The results of this study provided new insights into the microbial removal mechanism of nitrogen in SBBR packed with IMCSCP at low temperatures.

Analysis of future nitrogen and phosphorus loading in watershed and the risk of lake blooms under the influence of complex factors: Implications for management

For the management of eutrophic lakes, watershed nitrogen and phosphorus control is oriented to future water quality. Assessing future nutrient dynamics and the risk of lake eutrophication is necessary. However, current assessments often lack integrated consideration of socioeconomic and climatic factors, which reduces the reference value of the results. In this study, a typical large shallow lake Chaohu, which is highly influenced by human activities, was selected as the study area, and the current and future total nitrogen (TN) and total phosphorus (TP) loading in the basin were analysed using the improved MARINA model, and the risk of water bloom were assessed. The results showed that socioeconomic factors alone varied future TN and TP loading by -24% to 32% and -40% to 34%, respectively, under different development patterns. After considering the effect of increased precipitation, the changes of TN and TP loading became -10% to 163% and -29% to 108%, respectively. The effect on loading reduction under the sustainable development pattern was weakened (58% and 28% for TN and TP loading, respectively) and the increase in loading under the brutal development pattern was significantly amplified (409% and 215% for TN and TP loading, respectively). The adoption of active environmental policies remained an effective way of loading control. However, the risk of water bloom in local lake areas might persist due to factors such as urbanization. Timely and comprehensive assessments can provide managers with more information to identify key factors that contribute to the risk of water blooms and to develop diverse water quality improvement measures. The insights from our study are applicable to other watersheds around the world with similar socio-economic background and climatic conditions.

High-resolution risk mapping of heavy metals in soil with an integrated static-dynamic interaction model: A case study in an industrial agglomeration area in China

Heavy metal pollution of soils in industrial agglomeration areas is an increasing concern worldwide. In this study, we traced the sources of heavy metal emissions using a positive matrix factorization (PMF) model. Accordingly, we proposed a novel static-dynamic risk interaction model incorporating multiple risk-related factors to quantify the spatial interaction of emission sources and the probability of accumulation of heavy metals on a large scale. This model was further classified using the Jenks optimization technique to predict the spatial distribution of high-risk hotspots. Our results determined four primary emission sources of heavy metals: industrial (35.01 %), natural (28.61 %), agricultural (26.07 %), and traffic (10.31 %) sources. Five levels were classified by the integrated risk coefficient (IRC), namely, from extremely high to extremely low risk. The extremely high- and high-risk hotspots constituting 41.52 % of the total area of the Zhenhai District, with IRC values ranging from 0.221 to 0.413, were mainly generated by multiple sources linked to PMF-based factors. This quantitative evaluation framework can generate a high-resolution spatially distributed pollution risk map at the grid scale (1 km), which can provide a relatively precise basis for policymaking for point-to-point soil pollution management.