Treatment efficiency and greenhouse gas emissions of non-floating and floating bed activated sludge system with acclimatized sludge treating landfill leachate

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD - chemical oxygen demand, NH4-N - ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

Assessing the social cost of municipal solid waste management in Beijing: A systematic life cycle analysis

The increased municipal solid waste generation poses ponderous pressure on the economy, environment, and public health. The current waste treatment process has multiple limitations. To inform policymakers on the best practices and feasibility, we develop a more comprehensive social costing model to assess the impacts of municipal solid waste management throughout its life cycle. The prominent findings show that the life cycle social cost of municipal solid waste in Beijing in 2021 is 12.4 billion yuan. Incineration has the highest social cost, totaling 10.172 billion yuan. The social cost per unit of waste incineration is 2,045 yuan/t, which is higher than that of landfill (1,288 yuan/t), composting (1,132 yuan/t), anaerobic digestion (1,057 yuan/t), and recyclables resource utilization (-344 yuan/t). The life cycle assessment results show that economic costs, including collection, transportation, and treatment costs, account for about 61%, and health loss costs account for about 37%. The scenario analysis suggests a significant potential for social cost savings from food waste and recyclables utilization. Ideally, a social cost reduction of almost 38% could be achieved. Error analysis examines the influence of variation in uncertain parameters on the evaluation results. This paper provides scientific strategies for optimal investment and decision-making on the comprehensive municipal solid waste management. These findings could provide an essential reference for policymakers and stakeholders in municipal solid waste management, replicated in different cities and other emerging economies.

Effects of high-strength landfill leachate effluent on stress-induced microalgae lipid production and post-treatment micropollutant degradation

This research investigates the effects of landfill leachate effluent concentrations from moving bed biofilm reactor (MBBR) on stress-induced Chlorella vulgaris and Scenedesmus armatus lipid production and post-treatment micropollutant degradation. The effluent concentrations were varied between 25%, 50%, 75%, and 100% (v/v). The landfill leachate influent was treated using two-stage moving bed biofilm reactor under 24 h and 18 h hydraulic retention time (HRT). The results indicated that the effluent concentration was positively correlated with the stress-induced microalgae lipid production in the post-treatment of residual micropollutants. C. vulgaris and S. armatus completely remove residual micropollutants in the effluent. The superoxide dismutase and peroxidase activity were positively correlated with the cellular lipid content. The lipid content of C. vulgaris and S. armatus cultivated in the 18 h HRT effluent were 31-51% and 51-64%, while those in the 24 h HRT effluent were 15-16% and 5-19%. The optimal condition of microalgae cultivation for the post-treatment of residual micropollutants was 50-75% (v/v) effluent concentrations under 18 h HRT, achieving the highest lipid production of 113-116 mg/L for C. vulgaris and 74-75 mg/L for S. armatus. Essentially, the MBBR landfill leachate effluent holds promising potential as a substrate for microalgae lipid production.

Propionate-cultured sludge bioaugmentation to enhance methane production and micropollutant degradation in landfill leachate treatment

This research investigates the use of propionate-cultured sludge to enhance methane (CH₄) production and micropollutant biodegradation in biochemical methane potential (BMP) experiment treating landfill leachate. The experiments were carried out using non-acclimatized and acclimatized seed sludge with variable food to microorganism ratios of 1:1 and 1:2. Under the propionate-cultured sludge bioaugmentation, the concentrations of propionate-cultured sludge were varied between 10, 20, and 30 % (v/v). The acclimatized seed sludge exhibited high microbial abundance and diversity which promoted the CH₄ production and micropollutant biodegradation. The modified Gompertz model indicated that the optimal condition was the acclimatized seed sludge with 30% (v/v) propionate-cultured sludge, achieving the lag time (λ), maximum CH₄ production rate (R_max), and maximum CH₄ potential yield (P_max) of 0.57 day, 17.35 NmL/h, and 140.58 NmL/g COD. The research novelty lies in the use of propionate-cultured sludge bioaugmentation in landfill leachate treatment to enhance CH₄ production and micropollutant biodegradation.

Degradation of Landfill Leachate Using UV-TiO2 Photocatalysis Combination with Aged Waste Reactors

This study explored the performance of TiO2 nanoparticles in combination with aged waste reactors to treat landfill leachate. The optimum conditions for synthesis of TiO2 were determined by a series of characterizations and removal rates of methyl orange. The effect of the ultraviolet irradiation time, amount of the catalyst, and pH on the removal efficiency for the chemical oxygen demand (COD) and color in the leachate was explored to determine the optimal process conditions, which were 500 min, 4 g/L and 8.88, respectively. The removal rates for COD and chroma under three optimal conditions were obtained by the single factor control method: 89% and 70%; 95.56% and 70%; and 85% and 87.5%, respectively. Under optimal process conditions, the overall average removal rates for ammonium nitrogen (NH4+–N) and COD in the leachate for the combination of TiO2 nanoparticles and an aged waste reactor were 98.8% and 32.5%, respectively, and the nitrate (NO3−–N) and nitrite nitrogen (NO2–N) concentrations were maintained at 7–9 and 0.01–0.017 mg/L, respectively. TiO2 nanoparticles before and after the photocatalytic reaction were characterized by emission scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction, and Fourier transform infrared spectrometry. In addition, TiO2 nanoparticles have excellent recyclability, showing the potential of the photocatalytic/biological combined treatment of landfill leachate. This simulation of photocatalysis-landfilling could be a baseline study for the implementation of technology at the pilot scale.