Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach.

Lacking unit process data is a major challenge for developing life cycle inventory (LCI) in life cycle assessment (LCA). Previously, we developed a similarity-based approach to estimate missing unit process data, which works only when less than 5% of the data are missing in a unit process. In this study, we developed a more flexible machine learning model to estimate missing unit process data as a complement to our previous method. In particular, we adopted a decision tree-based supervised learning approach to use an existing unit process dataset (ecoinvent 3.1) to characterize the relationship between the known information (predictors) and the missing one (response). The results show that our model can successfully classify the zero and nonzero flows with a very low misclassification rate (0.79% when 10% of the data are missing). For nonzero flows, the model can accurately estimate their values with an R2 over 0.7 when less than 20% of data are missing in one unit process. Our method can provide important data to complement primary LCI data for LCA studies and demonstrates the promising applications of machine learning techniques in LCA.

Spatial variation of pharmaceuticals in the unit processes of full-scale municipal wastewater treatment plants in Korea.

Several reports have elucidated the removal of pharmaceutical residues in municipal wastewater treatment plants (WWTPs). However, there remains a need to determine the spatial distribution of pharmaceuticals in the unit processes of full-scale municipal WWTPs. Herein, spatial variations of fifteen pharmaceuticals in the unit processes of four full-scale municipal WWTPs were assessed by analyzing both solid and liquid samples. Furthermore, different pathways of each pharmaceutical such as biodegradation, adsorption, deconjugation, and electrostatic interaction were investigated. Pharmaceutical mass loading were measured at various points for the different unit process and evaluated using liquid chromatography-tandem mass spectrometry. The average mass loading of acetaminophen and caffeine decreased tremendously in the first biological treatment process regardless of the process configuration. In contrast, a temporary increase was observed in the mass loading of ibuprofen in the anaerobic and/or anoxic processes, which was presumably caused by deconjugation. Additionally, the adverse effect of coagulation on ibuprofen removal was validated. The major removal mechanism for the selected antibiotics, except for sulfamethoxazole, was the adsorption by biosolids due to electrostatic interaction. Subsequently, a drastic decrease was observed in their mass loadings in the solid-liquid separation process of the WWTPs. The membrane bioreactor (MBR) shows excellent capability for mitigation of pharmaceuticals in municipal wastewater because it comprises a high concentration of biosolids that act as adsorbents. The evaluation of the spatial variations of the selected pharmaceuticals in different unit processes provides valuable information on their behavior and removal mechanisms.

Mathematically formulated key performance indicators for design and evaluation of treatment trains for resource recovery from urban wastewater.

While urban wastewater infrastructure is aging and no longer adequate, climate change and sustainability are urging the transition from pollution management to resource recovery. Lacking evidence-based quantitative evaluation of the potential benefits and consequences of resource recovery from wastewater hinders the negotiation amongst stakeholders and slows down the transition. This study proposes mathematical formulations for technical, environmental, economic, and social key performance indicators (KPIs) that can be used to quantify the benefits and the risks of resource recovery. The proposed formulations are derived from the literature and validated with stakeholders. Each KPI is mathematically formulated at treatment train level by considering: (1) the characteristics of individual unit processes (UPs) in the treatment train (TT), (2) the context in which the TT is installed, and (3) the resources to be recovered. The mathematical formulations of the KPIs proposed in this study enable a transparent, consistent and informative evaluation of existing treatment trains, as well as support the (computer aided) design of new ones. This could aid the transition from urban wastewater treatment to resource recovery from urban wastewater.

Detection of microplastic traces in four different types of municipal wastewater treatment plants through FT-IR and TED-GC-MS.

Large amounts of microplastics are discharged into wastewater treatment plants (WWTPs), from where some of them are released into natural waterbodies on account of their not being fully eliminated by WWTPs. To investigate the behavior and emission of microplastics from WWTPs, we selected four WWTPs with different treatment technologies, including anaerobic-anoxic-aerobic (A2O), sequence batch reactor (SBR), media, and membrane bioreactor (MBR). The number of microplastics detected using Fourier transform infrared (FT-IR) spectroscopy ranged from 520 to 1820 particles/L in influent and from 0.56 to 2.34 particles/L in effluent. The microplastic removal efficiencies of four WWTPs were over 99%, indicating that the type of treatment technologies did not significantly affect the removal rate of microplastics. In the unit process for each WWTP, the major stages relating to microplastic removal were the secondary clarifier and tertiary treatment processes. Most microplastics detected were categorized as fragments and fibers, while other types were hardly detected. The size of more than 80% of microplastic particles detected in WWTPs ranged between 20 and 300 µm, indicating that they were significantly smaller than the size threshold defined for microplastics. Therefore, we used thermal extraction-desorption coupled with gas chromatography-mass spectroscopy (TED-GC-MS) to evaluate the microplastic mass content in all four WWTPs, and the results were compared with those of the FT-IR analysis. In this method, only four components, namely polyethylene, polypropylene, polystyrene, and polyethylene terephthalate, were analyzed because of the analysis limitation, and the total microplastic concentration represented the sum of four components concentrations. The influent and effluent microplastic concentrations estimated by TED-GC-MS ranged from not detectable to 160 µg/L and 0.04-1.07 µg/L, respectively, indicating a correlation coefficient of 0.861 (p < 0.05) between the TED-GC-MS and FT-IR results, when compared to the combined abundance of the four microplastic components by FT-IR analysis.

Concentration and reduction efficiency of vancomycin-resistant heterotrophic bacteria and vanA and vanB genes in wastewater treatment unit processes.

OBJECTIVES: This study elucidated the distribution and fate of vancomycin (VCM)-resistant heterotrophic bacteria (HTB) and resistance genes, vanA and vanB, during each treatment unit process of a wastewater treatment plant (WWTP). METHODS: Several bacterial counts as well as copy numbers of vanA and vanB genes were determined in each wastewater and sludge sample. In addition, HTB strains isolated from wastewater and sludge were analyzed for VCM susceptibility. Then, the fate and reduction ratios of each bacterial count, copy number of vanA and vanB genes, and the existence ratio of VCM-resistant HTB strains in the wastewater treatment unit process were evaluated. RESULTS: VCM-resistant HTB were detected in all wastewater and sludge samples, and their existence ratio decreased along the treatment process (92.9% in influent wastewater to 39.4% in chlorinated water). Notably, most of the HTB isolated from the influent wastewater were resistant to 8.0 µg/mL of VCM, strongly suggesting that a significant number of antibiotic-resistant bacteria are flowing into the WWTP from urban areas through the sewage system. The vanA and vanB genes were also detected in all wastewater and sludge, with high copy numbers (102-104 copies/mL) even in chlorinated water samples. CONCLUSIONS: Results revealed that residual VCM-resistant HTB, and resistance genes, which could not be completely removed, were ubiquitously released into the aquatic environment. Furthermore, a high existence ratio of VCM-resistant HTB and high copy numbers of resistance genes were also detected in the sludge, indicating that they are constantly circulating in the WWTP via the returned sludge.

[Distribution of Heavy Metals and Their Corresponding Nanoparticles in Different Treatment Unit Processes in the Sewage Treatment Plant].

Total heavy metal concentration, heavy metal nanoparticle concentration, particle size, and the removal effect of different treatment unit processes on heavy metals and heavy metal nanoparticles were analyzed in this study. Inductively coupled plasma mass spectrometry (ICP-MS) and single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) were applied in nine treatment units performing continuous wastewater treatment processes in the Chengdu Shuangliu International Airport sewage treatment plant. Results showed that different treatment unit processes had different effects on the removal of different total heavy metal elements, with the effects on Fe being the most significant; Fe was mainly removed in the secondary sedimentation tank at a rate of 98.53%. The removal effects of different heavy metal nanoparticles varied in different treatment unit processes, with the effects of Ni, Pd, and Fe being the most significant. Heavy metal nanoparticles removal varied by treatment unit processes (aeration grit tank, secondary sedimentation tank, and high-efficiency sedimentation tank). The particle size distribution of heavy metal nanoparticles in different treatment unit processes was 23.28-147.83 nm, and different treatment unit processes did not have a significant impact on the particle size of each heavy metal nanoparticle. In addition, pH exhibited a significant negative correlation with Fe and Fe nanoparticles. Excluding Fe and Fe nanoparticles, other heavy metals and their nanoparticles were not significantly related; thus, different processing unit processes exhibited different removal mechanisms for heavy metals and their corresponding nanoparticles.