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Assessing the presence of waterborne pathogens and antibiotic resistance genes (ARGs) is crucial for managing the environmental quality of drinking water sources. However, detecting low abundance pathogens in such settings is challenging. In this study, a workflow was developed to enrich for broad spectrum pathogens from drinking water samples. A mock community was used to evaluate the effectiveness of various enrichment broths in detecting low-abundance pathogens. Monthly metagenomic surveillance was conducted in a drinking water source from May to September 2021, and water samples were subjected to five enrichment procedures for 6 h to recover the majority of waterborne bacterial pathogens. Oxford Nanopore Technology (ONT) was used for metagenomic sequencing of enriched samples to obtain high-quality pathogen genomes. The results showed that selective enrichment significantly increased the proportions of targeted bacterial pathogens. Compared to direct metagenomic sequencing of untreated water samples, targeted enrichment followed by ONT sequencing significantly improved the detection of waterborne pathogens and the quality of metagenome-assembled genomes (MAGs). Eighty-six high-quality MAGs, including 70 pathogen MAGs, were obtained from ONT sequencing, while only 12 MAGs representing 10 species were obtained from direct metagenomic sequencing of untreated water samples. In addition, ONT sequencing improved the recovery of mobile genetic elements and the accuracy of phylogenetic analysis. This study highlights the urgent need for efficient methodologies to detect and manage microbial risks in drinking water sources. The developed workflow provides a cost-effective approach for environmental management of drinking water sources with microbial risks. The study also uncovered pathogens that were not detected by traditional methods, thereby advancing microbial risk management of drinking water sources. [Display omitted] • An enrichment workflow targeted on enteric pathogens was developewd for uncovering pathogen genomes from drinking water. • Targeted enrichment followed ONT sequencing significantly improved the detection of waterborne pathogens. • This workflow also improved the recovery of mobile genetic elements and assessed the transferability of majority of ARGs. [ABSTRACT FROM AUTHOR]

The degradable protective articles made of thermoplastic polyvinyl alcohol (TPVA) are widely used in nuclear power plants, and they are thermally decomposed after use to reduce solid waste. However, in the real decomposition of TPVA, the temperature in the oxidation reactor is not self-sustaining; as a result, the degradation products contain a lot of CO, resulting in more pollution and energy waste. In this paper, jet stirred reactor (JSR) and Chemkin software were used to study the reaction kinetics characteristics of the oxidation process of degradation products from TPVA in the range of 550 °C–700 °C. Both experiments and kinetic simulation show that a higher average temperature of the oxidation reactor is needed to achieve lower CO emissions. When using 5% or 10% TPVA degradation solution, the average temperature should not befall below 625 °C or 675 °C. The corresponding residence time should be greater than 6 s and 5 s respectively. The combination of research findings and engineering practice provides great help to the optimization of the actual work process. • The temperature and residence time required for the complete degradation of thermoplastic polyvinyl alcohol after preliminary oxidation with the Fenton method were studied in a jet stirred reactor. • Ketones, the main products of TPVA initial degradation, are used as combustible materials after solution gasification to simulate the oxidation process simulation. • The simulated data are in good agreement with the experimental data. • This article produces cloud maps of operating conditions corresponding to different CO and CH 4 emission standards to guide similar engineering applications. [ABSTRACT FROM AUTHOR]

Since China's speedy economic development, the problems of energy consumption and environmental pollution have become increasingly prominent. A question worth in-depth study is whether ecological civilization (EC) construction, one of the main policies to solve China's severe environmental and ecological problems, affects carbon dioxide emissions (CDE). In this study, we use a novel method proposed by Hsiao et al. (2012) to estimate the average treatment effect by using panel data to study the impact of establishing the EC pilot zones of China on CDE. The results show that, although EC construction is effective in reducing CDE, the impact varies from region to region. The study findings can provide a scientific basis to promote the experience of EC construction better and improve its CDE effect. • Impact of ecological civilization pilot zones on carbon emissions is studied. • An empirical analysis is conducted based on the HCW method. • Effects of ecological civilization on reducing carbon emissions vary by area. [ABSTRACT FROM AUTHOR]

Does energy-saving have a positive effect on the long-term development of enterprises? To answer this question, this study uses the propensity score matching (PSM) method to determine the impact of enterprises completing energy-saving objectives on their financial performance based on data from an industrial enterprise database in China. The results show that industrial enterprises that have completed the energy-saving target have advantages in profitability, operational and financial indicators but have deficiencies in debt indicators, and there is no significant difference in the ratio of profits to cost and expense. The research results of high energy-consuming industry are the same as the overall sample of industrial enterprises in terms of operational and financial indicators, and debt ratio indicators. Nevertheless, energy saving has no significant impact on profit and ratio of profits to cost of high energy-consuming industry. • Impact of energy saving on financial performance of industrial enterprises in China was studied. • An empirical analysis was conducted based on propensity score matching method. • Energy saving positively influences profitability, operational and financial indicators. [ABSTRACT FROM AUTHOR]

The occurrence and removal of 38 antibiotics from nine classes in two drinking water treatment plants (WTPs) were monitored monthly over one year to evaluate the efficiency of typical treatment processes, track the source of antibiotics in tap water and assess their potential risks to ecosystem and human health. In both source waters, 18 antibiotics were detected at least once, with average total antibiotic concentrations of 538.5 ng/L in WTP1 and 569.3 ng/L in WTP2. The coagulation/flocculation and sedimentation, sand filtration and granular activated carbon processes demonstrated limited removal efficiencies. Chlorination, on the other hand, effectively eliminated antibiotics by 48.7 ± 11.9%. Interestingly, negative removal was observed along the distribution system, resulting in a significant antibiotic presence in tap water, with average concentrations of 131.5 ng/L in WTP1 and 362.8 ng/L in WTP2. Source tracking analysis indicates that most antibiotics in tap water may originate from distribution system. The presence of antibiotics in raw water and tap water posed risks to the aquatic ecosystem. Untreated or partially treated raw water could pose a medium risk to infants under six months. Water parameters, for example, temperature, total nitrogen and total organic carbon, can serve as indicators to estimate antibiotic occurrence and associated risks. Furthermore, machine learning models were developed that successfully predicted risk levels using water quality parameters. Our study provides valuable insights into the occurrence, removal and risk of antibiotics in urban WTPs, contributing to the broader understanding of antibiotic pollution in water treatment systems. [Display omitted] • 18 antibiotics were detected in raw water from two drinking water systems. • Temperature, TN and TOC could be used to estimate antibiotic occurrence. • A majority of antibiotics in tap water could originate from the distribution systems. • Antibiotics in raw water and tap water posed medium to high level of risks. • Machine learning models were developed successfully to predict the antibiotics risk levels. [ABSTRACT FROM AUTHOR]