Recovery of Clean Water and Ammonia from Domestic Wastewater: Impacts on Embodied Energy and Greenhouse Gas Emissions.

Treatment of domestic wastewater can recover valuable resources, including clean water, energy, and ammonia. Important metrics for these systems are greenhouse gas (GHG) emissions and embodied energy, both of which are location- and technology-dependent. Here, we determine the embodied energy and GHG emissions resulting from a conventional process train, and we compare them to a nonconventional process train. The conventional train assumes freshwater conveyance from a pristine source that requires energy for pumping (US average of 0.29 kWh/m3), aerobic secondary treatment with N removal as N2, and Haber-Bosch synthesis of ammonia. Overall, we find that this process train has an embodied energy of 1.02 kWh/m3 and a GHG emission of 0.77 kg-CO2eq/m3. We compare these metrics to those of a nonconventional process train that features anaerobic secondary treatment technology followed by further purification of the effluent by reverse osmosis and air stripping for ammonia recovery. This "short-cut" process train reduces embodied energy to 0.88 kWh/m3 and GHG emissions to 0.42 kg-CO2eq/m3, while offsetting demand for ammonia from the Haber-Bosch process and decreasing reliance upon water transported over long distances. Finally, to assess the potential impacts of nonconventional nitrogen removal technology, we compared the embodied energy and GHG emissions resulting from partial nitritation/anammox coupled to anaerobic secondary treatment. The resulting process train enabled a lower embodied energy but increased GHG emissions, largely due to emissions of N2O, a potent greenhouse gas.

PEZy-miner: An artificial intelligence driven approach for the discovery of plastic-degrading enzyme candidates.

Plastic waste has caused a global environmental crisis. Biocatalytic depolymerization mediated by enzymes has emerged as an efficient and sustainable alternative for plastic treatment and recycling. However, it is challenging and time-consuming to discover novel plastic-degrading enzymes using conventional cultivation-based or omics methods. There is a growing interest in developing effective computational methods to identify new enzymes with desirable plastic degradation functionalities by exploring the ever-increasing databases of protein sequences. In this study, we designed an innovative machine learning-based framework, named PEZy-Miner, to mine for enzymes with high potential in degrading plastics of interest. Two datasets integrating information from experimentally verified enzymes and homologs with unknown plastic-degrading activity were created respectively, covering eleven types of plastic substrates. Protein language models and binary classification models were developed to predict enzymatic degradation of plastics along with confidence and uncertainty estimation. PEZy-Miner exhibited high prediction accuracy and stability when validated on experimentally verified enzymes. Furthermore, by masking the experimentally verified enzymes and blending them into homolog dataset, PEZy-Miner effectively concentrated the experimentally verified entries by 14∼30 times while shortlisting promising plastic-degrading enzyme candidates. We applied PEZy-Miner to 0.1 million putative sequences, out of which 27 new sequences were identified with high confidence. This study provided a new computational tool for mining and recommending promising new plastic-degrading enzymes.

Microbial Water Quality through a Full-Scale Advanced Wastewater Treatment Demonstration Facility.

The fates of viruses, bacteria, and antibiotic resistance genes during advanced wastewater treatment are important to assess for implementation of potable reuse systems. Here, a full-scale advanced wastewater treatment demonstration facility (ozone, biological activated carbon filtration, micro/ultrafiltration, reverse osmosis, and advanced oxidation) was sampled over three months. Atypically, no disinfectant residual was applied before the microfiltration step. Microbial cell concentrations and viability were assessed via flow cytometry and adenosine triphosphate (ATP). Concentrations of bacteria (16S rRNA gene), viruses (human adenovirus and JC polyomavirus), and antibiotic resistance genes (sul1 and bla TEM ) were assessed via quantitative PCR following the concentration of large sample volumes by dead-end ultrafiltration. In all membrane filtration permeates, microbial concentrations were higher than previously reported for chloraminated membranes, and log10 reduction values were lower than expected. Concentrations of 16S rRNA and sul1 genes were reduced by treatment but remained quantifiable in reverse osmosis permeate. It is unclear whether sul1 in the RO permeate was from the passage of resistance genes or new growth of microorganisms, but the concentrations were on the low end of those reported for conventional drinking water distribution systems. Adenovirus, JC polyomavirus, and bla TEM genes were reduced below the limit of detection (∼10-2 gene copies per mL) by microfiltration. The results provide insights into how treatment train design and operation choices affect microbial water quality as well as the use of flow cytometry and ATP for online monitoring and process control.

Identification of sewage markers to indicate sources of contamination: Low cost options for misconnected non-stormwater source tracking in stormwater systems.

There has been increasing research focusing on the detection and occurrence of wastewater contamination in urban water systems. To find suitable markers to indicate industrial and domestic sewage flows inappropriately entering storm drains, this study investigated the occurrence and fate of 52 chemical markers through wastewater treatment facilities of manufacturers of agricultural and sideline products, beverage products, and pharmaceutical products, which are also consumed in our daily life. Of the 52 candidate markers, sodium, chloride, potassium, isomalto-oligosaccharide, acesulfame, theanine, glycerol, and clarithromycin were found to be conservative markers, with an average change in concentrations through the wastewater treatment processes of <30%. These markers are useful in identifying industrial and domestic sewage flow contamination in urban sewers. Specially, sodium, chloride, potassium, isomalto-oligosaccharide, acesulfame, and clarithromycin exhibited higher concentrations in blackwater than in greywater, with detected average concentrations of 43.8 mg/L, 189 mg/L, 37.3 mg/L, 123 µg/L, 37.2 µg/L, and 0.99 µg/L in blackwater, respectively. In contrast, theanine and glycerol were observed with higher concentrations in greywater than in blackwater (average 10.1 µg/L and 19.5 µg/L in greywater, respectively). The benchmark concentrations to discriminate between industrial and domestic sewage were also presented. A study in a storm drainage system of downstream Taihu catchment, China demonstrated the usefulness of the markers as low-cost options to trace and quantify misconnected wastewater entries into storm drains, while denoting priority areas for misconnected entries correction.

[Endoscopic transnasal orbital decompression for dysthyroid orbitopathy].

OBJECTIVE: To evaluate the effect of endoscopic transnasal orbital decompression for patients with dysthyroid orbitopathy. METHODS: Nine cases (15 eyes) of dysthyroid orbitopathy were included in this study. All patients were treated by transnasal endoscopic orbital decompression. RESULTS: The follow-up ranged from 6 months to 2 years. Proptosis measured' by exophthalmoter reduced from (21.93 +/- 1.49) mm to (16.87 +/- 1.25) mm after operation. The visual acuity improved from 0.57 +/- 0.12 to 0.69 +/- 0.12, the palperbral fissures reduced from (11.07 +/- 1.44) mm to (8.20 +/- 1.15) mm. Postoperatively, the orbital pressure was significantly decreased as compared with the preoperative result (P < 0.001). Diplopia was cured in 3 of 7 cases. CONCLUSION: Endoscopic transnasal orbital decompression is an effective method for the treatment of dysthyroid orbitopathy.