Environmental impact assessment of multi-source solid waste based on a life cycle assessment, principal component analysis, and random forest algorithm

Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model

With the accelerated urbanization and economic development in Northwest China, the efficiency of urban wastewater treatment and the importance of water quality management have become increasingly significant. This work aims to explore urban wastewater treatment and carbon reduction mechanisms in Northwest China to alleviate water resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis of the impacts of different wastewater treatment processes on water quality parameters. This work pays particular attention to their impact on key indicators such as Chemical Oxygen Demand (COD), NH4+-N, Total Phosphorus (TP), and Total Nitrogen (TN), and the application of predictive models. The work first establishes a Random Forest Regression (RFR) model. The RFR algorithm integrates Bagging ensemble learning and random subspace theory to construct multiple decision trees and aggregate their predictions, thereby enhancing the model's prediction accuracy and stability. Using bootstrap sampling, the RFR model generates multiple training subsets from the original data and randomly selects subsets of variables to construct regression trees. Its performance in predicting various water quality indicators is then evaluated. The results show that the RFR model exhibits excellent performance, achieving high levels of prediction accuracy and stability for all indicators. For example, the R2 for COD prediction is 0.99954, while the R2 values for NH4+-N, TP, and TN predictions reach 0.99989. Compared to five other models, the RFR model demonstrates the best performance across all water quality indicator predictions. This work provides critical support for optimizing wastewater treatment technologies and developing water resource management policies. These findings also offer essential theoretical and empirical insights for the future improvement of urban wastewater treatment technologies and water resource management decision-making.

Simplification and simulation of evaluation process for low efficiency constructed wetlands based on principal component analysis and machine learning

The existing performance evaluation process of constructed wetlands (CWs) is complex, with shortcomings in both simplification of method and construction of simulation model, especially for low-efficiency CWs (LECWs, with an average close-degree calculated by the entropy weight method being <0.6). This study presents a case study of LECWs in the Ningxia region (comprising 13 subsurface flow constructed wetlands (SSF CWs) and 7 surface flow constructed wetlands (SF CWs)), employs the entropy weight method (EWM) to construct an evaluation of CW operational efficiency, simplifies evaluation indicators through principal component analysis (PCA), develops two random forest (RF) models to validate the rationality of the simplified indicators, and establishes simulation models by logistic regression (LR). The results demonstrate that the evaluation indicators of CWs can be simplified to chemical oxygen demand (COD) and total nitrogen (TN), with no significant difference observed between the evaluation results and the original model (P < 0.05), thereby indicating reliability. Moreover, the simulation model performs well with R2 values for fitting SSF CWs and SF CWs exceeding 0.8. According to the simulated results of the model, the operational efficiency of LECWs is more significantly affected by the COD removal rates compared to the TN removal rates. In comparison to influent with 0 < COD/TN < 3 and 5 < COD/TN < 8, the operational efficiency of SSF CWs and SF CWs is optimal when COD/TN is between 3 and 5. These research findings may provide valuable support for streamlining evaluation processes and daily management for LECWs.

Life cycle assessment of plastic waste in Suzhou, China: Management strategies toward sustainable express delivery

The explosive growth of China's express delivery industry has greatly increased plastic waste, with low-value plastics not effectively utilized, such as PE packaging bags, which are often not recycled and end up in landfills or incinerators, causing significant resource waste and severe plastic pollution. A gate -to- grave life cycle assessment was adopted to assess the impacts of express delivery plastic waste (EDPW) management models (S1, landfill; S2, incineration; S3, mechanical pelletization), with Suzhou, China as a case. Results showed that mechanical pelletization, was the most environmentally advantageous, exhibiting a comprehensive environmental impact potential of -215.54 Pt, significantly lower than that of landfill (S1, 78.45 Pt) and incineration (S2, -121.77 Pt). The analysis identified that the end-of-life disposal and sorting stages were the principal contributors to environmental impacts in all three models, with transportation and transfer stages of residual waste having minimal effects. In terms of all environmental impact categories, human carcinogenic toxicity (HTc) emerged as the most significant contributor in all three scenarios. Specifically, S1 exhibited the most detrimental effect on human health, while S2 and S3 showed positive environmental impacts. Based on these findings, it is recommended that the application and innovation in mechanical recycling technologies be enhanced, the promotion of the eco-friendly transformation of packaging materials be pursued, and a sustainable express delivery packaging recycling management system be established. These strategies are essential for achieving more eco-friendly management of EDPW, reducing its environmental pollution, and moving towards more sustainable express delivery management practices.

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

A critical review for the impact of anaerobic digestion on the sustainable development goals

Anaerobic Digestion (AD) technology emerges as a viable solution for managing municipal organic waste, offering pollution reduction and the generation of biogas and fertilisers. This study reviews the research works for the advancements in AD implementation to effectively impact the UN Sustainable Development Goals (SDGs). Furthermore, the study critically analyses responsible waste management that contributes to health and safety, elevating quality of life in both rural and urban areas and, finally, creates a map of AD outputs onto all 17 SDGs. Finally, the assessment employs the three sustainability pillars (i.e., economic, environmental, and social perspectives) to examine the direct and indirect links between AD and all 17 UN SDGs. The findings reveal substantial progress, such as poverty reduction through job creation, bolstering economic growth (SDGs 1, 8, 10, 12), enhancing agricultural productivity (SDG 2), advancing renewable energy usage and diminishing reliance on fossil fuels (SDG 7), fostering inclusive education and gender equality (SDGs 4, 5, 9), combating climate change (SDG 13), transforming cities into sustainable and harmonious environments (SDGs 11, 16, 17), and curbing environmental pollution (SDGs 3, 6, 12, 14, 15). Nonetheless, the study highlights the need for further efforts to achieve the SDG targets, particularly in part of liquid and solid fertilisers as the AD outputs.

Effect of granulation on chlorine-release behavior during municipal solid waste incineration

The preparation of refuse-derived fuel (RDF) is an effective and simple means of rural municipal solid waste utilization. The release of chlorine during RDF combustion is important as it causes high-temperature corrosion and pollutants emission such as HCl, dioxins, etc. In this paper, constant-temperature and increasing-temperature combustion experiments were carried out using an electrically heating furnace to analyse the effects of granulation (pressure and additives) on the release of chlorine in particles. During the constant-temperature combustion below 800 °C, only organic chlorine was released from the RDF. The increase of granulation pressure from 1 MPa to 10 MPa did not affect the total amount of chlorine release, but delayed the organic chlorine release by increasing the gas diffusion resistance. During the constant-temperature combustion above 900 °C, inorganic chlorine was released as well. The increase of granulation pressure enhanced the inorganic chlorine release significantly by promoting the reactants contact. During the increasing-temperature combustion, the increase of granulation pressure delayed the organic chlorine release as well but inhibited the inorganic chlorine release. This was mainly attributed to the slow temperature rise to 900 °C, during which the inherent calcium in the RDF reacted with silicon and aluminium, resulting in less reactants for an inorganic chlorine release reaction. Three calcium-based additives were used to inhibit chlorine release. CaCO3 showed no dechlorination effect, and CaO showed better dechlorination effect than Ca(OH)2. For the constant-temperature combustion at 900 °C, the addition of CaO with a Ca/Cl ratio of 2 achieved a dechlorination efficiency of over 90%, with little influence from the granulation pressure. For the increasing-temperature combustion, the granulation pressure had a significant influence on CaO dechlorination effectiveness. Only at a granulation pressure as high as 10 MPa, did the addition of CaO with the Ca/Cl ratio of 2.5 achieve a dechlorination efficiency of 95%.