As the “carbon peaking and carbon neutrality” strategy advances, carbon emissions have gradually become a significant indicator in selecting and evaluating sewage and sludge treatment solutions. This study compared the carbon footprints of different digested sludge post-treatment routes, taking the Lu’an project in China as an example. Considering anaerobic digestion and digested sludge post-treatment options, the carbon footprints are as follows: 347.7 kg CO₂ (land application) < 459.7 kg CO₂ (composting-involved land application) < 858.4 kg CO₂ (brickmaking). In general, land application was superior to brickmaking from the perspective of carbon footprints. The power consumption incurred by aerating and turning and the direct N₂O and CH₄ emissions during composting increase the composting-involved land application carbon footprint. However, digested sludge that is not subject to high-temperature sterilization and compost is phytotoxic and can be fetid, which is a limitation of its applicability. And the composted sludge has a lower N ratio and water content, so the same N input means more sludge usage, which is conducive to solving the disposal problem of large amounts of sludge. Thus, if possible, composting-involved land application should be a preference, and improvements to the technique are required to minimize energy consumption and direct N₂O and CH₄ emissions.

Water quality monitoring (WQM) of urban rivers has been a reliable method to supervise the urban water environment. Indiscriminate WQM strategies can hardly emphasize the concerning pollution and usually require high costs of money, time, and manpower. To tackle these issues, this work carried out a multi-dimensional study (large spatial scale, multiple monitoring parameters, and long time scale) on the water quality of two urban rivers in Jiujiang City, China, which can provide indicative information for the optimization of WQM. Of note, the spatial distribution of NH₃-N concentration varied significantly both in terms of the two different rivers as well as the different sections (i.e., much higher in the northern section), with a maximal difference, on average greater, than five times. Statistical methods and machine learning algorithms were applied to optimize the monitoring objects, parameters, and frequency. The sharp decrease in water quality of adjacent sections was identified by Analytical Hierarchy Process of water quality assessment indexes. After correlation analysis, principal component analysis, and cluster analysis, the various WQM parameters could be divided into three principal components and four clusters. With the machine learning algorithm of Random Forest, the relation between concentration of pollutants and rainfall depth was fitted using quadratic functions (calculated Pearson correlation coefficients ≥ 0.89), which could help predict the pollution after precipitation and further determine the appropriate WQM frequency. Generally, this work provides a novel thought for efficient, smart, and low-cost water quality investigation and monitoring strategy determination, which contributes to the construction of smart water systems and sustainable water source management.

In this study, urban wastewater was treated by flocculation, ozone pre-oxidation and denitrification for efficient purification. Polymeric aluminum chloride (PAC) and polymeric aluminum ferric sulfate (PAFS) were added to the wastewater at different levels to remove the COD, turbidity, TP and TN of the wastewater. A better flocculant was selected and its optimum ozone pre-oxidation concentration was determined by changing the ozone concentration and measuring the effluent quality. Denitrification was further enhanced by varying the C/N ratio of the wastewater. The results show that, with the increase in flocculant dosage, the removal rates of COD, turbidity, TP and TN by PAC and PAFS were improved. The purification effect of PAC was better than that of PAFS and the optimum removal of COD, turbidity, TP and TN was obtained at a dosage of 80 mg L⁻¹ by PAC, at 55.9%, 55.6%, 90.0% and 13.3%, respectively. Ozone pre-oxidation enhanced the removal of COD, turbidity and TN by PAC, and the optimal ozone dosage was 1.2 mg L⁻¹, which resulted in 64.8%, 57.1% and 24.8% removal of COD, turbidity and TN, respectively. With the increase in the C/N ratio from 2.0 to 4.0, the NO₃⁻-N concentration of PAC-treated water gradually decreased, but when the C/N ratio was 4.0, the COD concentration increased, so the optimal C/N ratio should be 3.5. Overall, the combination of ozone pre-oxidation, denitrification and flocculation was an effective method to treat urban wastewater, which has a strong application prospect.

With the increasing frequency of desilting in urban drainage systems, the safe disposal and resource reuse of sewer sediments have become a prominent practical challenge. Screened sand, the most promising component for resource recovery from sewer sediments, still lacks systematic insight into its pollution risks and the necessity of pretreatment. In this study, 120 raw sewer sediment samples were collected from sanitary, storm, and illicitly connected (IC) storm sewers in Shanghai, alongside seasonal screened sand samples. We systematically characterized their physicochemical properties and heavy metal and antibiotic pollution profiles, and evaluated the purification performance of ultrasonic treatment, sodium hexametaphosphate (SHMP) washing, and their coupled processes. Results revealed significant differences in sediment properties across pipeline types. Screened sand, dominated by SiO₂ and CaO, shows preliminary potential for reuse as a low-grade bulk building material, but its organic loss on ignition (LOI) of 5.29–13.42% exceeded the reuse limit. Concentrations of heavy metals and antibiotics were generally higher in screened sand than in raw sediments, with further enrichment in the fine sand fractions, indicating that screening only redistributed contaminants rather than eliminated them. The coupled ultrasonic–SHMP process, applied for the first time to screened sand from sewer sediments, achieved optimal performance, with a maximum LOI reduction and over 85% removal of certain antibiotics, without damaging the sand’s mineral skeleton. This study provides a scientific basis for the safe resource reuse of screened sand.