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Aichivirus 1 (AiV-1) is an enteric virus that has been documented to be the causative agent of diarrhea in humans. It is transmitted by fecal–oral route, through person-to-person contact, consumption of contaminated food or water, or recreation of contaminated water. AiV-1 is highly prevalent in water samples and has been proposed as a potential indicator of fecal contamination in water reservoirs. This study aimed to investigate the prevalence and genetic diversity of AiV-1 in environmental water samples in Thailand. A total of 126 samples were collected monthly from November 2016 to July 2018 from various sources of environmental water including irrigation water, reservoir, river, and wastewater. The presence of AiV-1 was detected by RT-nested PCR of the 3CD region and further analyzed by phylogenetic analysis. The AiV-1 was detected in 28 out of 126 (22.2%) of tested samples. A high frequency of AiV-1 detection was in wastewater (52.4%). All 28 AiV-1 strains detected in this study belonged to the genotype B and were closely related to AiV strains detected previously in environmental waters and in humans worldwide. This study demonstrated, for the first time, the contamination of AiV-1 in various sources of water samples in Thailand and provided a better insight into the prevalence of AiV-1 in environmental waters and its potential risk of human health.

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Global warming represents one of the most critical internationally perceived environmental issues. The growing, and increasingly global, wine sector is one of the industries which is under increasing pressure to adopt approaches for environmental assessment and reporting of product-related greenhouse gas emissions. The International Organization for Vine and Wine has recently recognized the need to develop a standard and objective methodology and a related tool for calculating carbon footprint (CF). This study applied this tool to a wine previously analyzed using the life cycle assessment (LCA) methodology. The objective was to test the tool as regards both its potential and possible limitations, and thus to assess its suitability as a standard tool. Despite the tool's user-friendliness, a number of limitations were noted including the lack of accurate baseline data, a partial system boundary and the impossibility of dealing with the multi-functionality issue. When the CF and LCA results are compared in absolute terms, large discrepancies become obvious due to a number of different assumptions, as well as the modeling framework adopted. Nonetheless, in relative terms the results seem to be quite consistent. However, a critical limitation of the CF methodology was its focus on a single issue, which can lead to burden shifting. In conclusion, the study confirmed the need for both further improvement and adaptation to additional contexts and further studies to validate the use of this tool in different companies.  相似文献   
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