Magnesium ammonium phosphate (MAP) precipitation generally requires an external phosphorus source to increase the recovery of NH₄⁺-N from biogas slurry. However, P-rich piggery biogas residue has been ignored as a phosphorus source. In this study, biogas residue was carbonized into biogas residue biochar (BRC), followed by acid leaching to synthesize functionalized BRC and release PO₄³⁻-P from its ash as the phosphorus source. The effects of different acids on the leaching efficiency and morphological changes of P in BRC were investigated, and NH₄⁺-N and PO₄³⁻-P in the biogas slurry were recovered with functionalized BRC and MAP precipitation. The results showed that oxalic acid-hydrochloric acid mixed acid could leach more than 96% of P in BRC, while weakening the inhibitory effect of Ca²⁺ on MAP precipitation. The BRC was mainly composed of inorganic P, and most nonapatite IP and apatite P (Ca₃(PO₄)₂) were leached during acid leaching, with the latter more easily leached. Under optimal recovery conditions, the method had a significant recovery effect on NH₄⁺-N (96.4%) and PO₄³⁻-P (99.3%) in biogas slurry. The recovery of NH₄⁺-N and PO₄³⁻-P by functionalized BRC was mainly through chemical precipitation (forming NH₄MgPO₄·H₂O precipitate) while bonding with -OH, C = O and C-H functional groups. The final recovery product was also a BRC-based slow-release N-P fertilizer rich in struvite. This study solved the disposal problems of P-rich biogas residue and N-rich biogas slurry while providing an innovative technology for the resource utilization of faecal sewage at pig farms. Graphical Abstract

A novel biological nitrogen removal system based on nitritation coupled with thiosulfate-driven denitritation (Nitritation-TDD) was developed to achieve a high nitrogen removal rate and low sludge production. A nitritation sequential batch reactor (nitritation SBR) and an anoxic up-flow sludge bed (AnUSB) reactor were applied for effective nitritation and denitritation, respectively. Above 75% nitrite was accumulated in the nitritation SBR with an influent ammonia loading rate of 0.43 kg N/d/m³. During Nitritation-TDD operation, particle sizes (d₅₀) of the sludge decreased from 406 to 225 um in nitritation SBR and from 327–183 um in AnUSB reactor. Pyrosequencing tests revealed that ammonium-oxidizing bacteria (AOB) population was stabilized at approximately 7.0% (calculated as population of AOB-related genus divided by the total microbial population) in the nitritation SBR. In contrast, nitrite-oxidizing bacteria (NOB) population decreased from 6.5–0.6% over the same time, indicating the effective nitrite accumulation in the nitritation SBR. Thiobacillus, accounting for 34.2% in the AnUSB reactor, was mainly responsible for nitrogen removal via autotrophic denitritation, using an external source of thiosulfate as electron donor. Also, it was found that free nitrous acid could directly affect the denitritation activity.

Climate change is one of the most pressing global challenges that could potentially threaten ecosystems, human populations, and weather patterns over time. Impacts including rising sea levels and soil salinization are caused by climate change, primarily driven by human activities such as fossil fuel combustion for energy production. The resulting greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) emissions, amplify the greenhouse effect and accelerate global warming, underscoring the urgent need for effective mitigation strategies. This study investigates the performance and outcomes of various machine learning regression models for predicting CO₂ emissions. A comprehensive overview of performance metrics, including R², mean absolute error, mean squared error, and root-mean-squared error, and cross-validation scores for decision tree, random forest, multiple linear regression, k-nearest neighbors, gradient boosting, and support vector regression models was conducted. The biggest source of CO₂ emissions was coal (46.11%), followed by natural gas (25.49%) and electricity (26.70%). Random forest and gradient boosting both performed well, but multiple linear regression had the highest prediction accuracy among machine learning models (R² = 0.98 training, 0.99 testing). Support vector regression (SVR) and k-nearest neighbors (KNN) demonstrated lower accuracies, whereas decision tree displayed overfitting. The decision tree, random forest, multiple linear regression, and gradient boosting models were found to be extremely sensitive to coal, natural gas, and petroleum (transportation sector) based on sensitivity analysis. Random forest and gradient boosting demonstrated the most sensitivity to coal usage, whereas KNN and SVR maintained excellent R² scores (0.94–0.98) but were less susceptible to changes in the variables. This analysis provides insights into the agreement and discrepancies between predicted and actual CO₂ emissions, highlighting the models’ effectiveness and potential limitations.

The massive loss of nitrogen (N) and phosphorus (P) from farmland ditches contributes to non-point source pollution, posing a significant global environmental challenge. Effectively removing these nutrients remains difficult in intensive agricultural systems. To address this, a novel composite ecological ditch system (CEDS) was developed by modifying traditional drainage ditches to integrate a grit chamber, zeolite, and ecological floating beds. Dynamic monitoring of N and P levels in water, plants, and zeolite was conducted to evaluate the system’s nutrient interception performance and mechanisms. The results showed the following: (1) Water quality improved markedly after passing through the CEDS, with nutrient concentrations decreasing progressively along the flow path. The system intercepted 41.0% of N and 31.9% of P, with inorganic N and particulate P as the primary forms of nutrient loss. (2) Zeolite removes N primarily through ion exchange, and P likely through chemical reactions, with maximum capacities of 3.47 g/kg for N and 1.83 g/kg for P. (3) Ecological floating beds with hydroponic cultivation enhanced nutrient uptake by the roots of Canna indica and Iris pseudacorus, with N uptake surpassing P. (4) Nutrient interception efficiency was positively correlated with temperature, ditch inlet concentrations, and rice runoff concentrations, but negatively with precipitation. This study demonstrates the CEDS’s potential for improving farmland water quality and suggests further enhancements in design and management to increase its economic and aesthetic value.