Simultaneous annihilation of microorganisms and volatile organic compounds from municipal solid waste storage rooms with slightly acidic electrolyzed water

Release characteristics of volatile organic compounds at residential garbage collection points: a case study of Hangzhou, China

With the implementation of garbage classification, perishable waste has become increasingly concentrated. This has led to a significant change in the VOC release characteristics at residential garbage collection points, posing a potential risk with unknown characteristics. This study investigated the release characteristics, odor pollution, and health risks of VOCs at garbage collection points under different classification effectiveness, seasons, garbage drop-off periods, and garbage collection point types. The results showed that the average concentration of VOCs released from the garbage sorting collection points (SPs) was 341.43 ± 261.16 μg/m3, and oxygenated compounds (e.g., ethyl acetate and acetone) were the main VOC components. The VOC concentration increased as the community classification effectiveness improved, and it was higher in summer (followed by spring, autumn, and winter). Moreover, the VOC concentrations were higher in the evenings than in the mornings and at centralized garbage collection points (CPs) than at SPs. Further, odor activity value (OAV) assessments indicated that acrolein, styrene, and ethyl acetate were the critical odorous components, with an average OAV of 0.87 ± 0.85, implying marginal odor pollution in some communities. Health risk assessments further revealed that trichloroethylene, benzene, and chlorotoluene were the critical health risk substances, with an average carcinogenic risk (CR) value of 10-6-10-4, and a non-carcinogenic risk (HI) value < 1. These results indicated that HIs were acceptable, but potential CRs existed in the communities. Therefore, VOC pollution prevention and control measures should be urgently strengthened at the garbage collection points in high pollution risk scenarios.

Effectiveness of Nitazoxanide and Electrolyzed Oxiding Water in Treating Chagas Disease in a Canine Model

Chagas disease (CD) is caused by the protozoan Trypanosoma cruzi, and affects seven million people in Latin America. Side effects and the limited efficacy of current treatment have led to new drug research. The objective of this work was to evaluate the effectiveness of nitazoxanide (NTZ) and electrolyzed oxidizing water (EOW) in a canine model of experimental CD. Náhuatl dogs were infected with the T. cruzi H8 strain and NTZ- or EOW-treated orally for 10 days. Seronegativity was shown at 12 months post-infection (mpi) in the NTZ-, EOW-, and benznidazole (BNZ)-treated groups. The NTZ and BNZ groups had high levels of IFN-γ, TNF-α, IL-6, IL-12B, and IL-1β at 1.5 mpi and low levels of IL-10. Electrocardiographic studies showed alterations from 3 mpi and worsening at 12 mpi; NTZ treatment produced fewer cardiac pathomorphological changes compared to EOW, similar to BNZ treatment. There was no cardiomegaly in any group. In conclusion, although NTZ and EOW did not prevent changes in cardiac conductivity, they were able to avoid the severity of heart damage in the chronic phase of CD. NTZ induced a favorable proinflammatory immune response after infection, being a better option than EOW as a possible treatment for CD after BNZ.

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available in the online version of this article at 10.1007/s11783-023-1677-1 and is accessible for authorized users.