Efluentes de laticínios: uma abordagem cienciométrica e metanalítica

Abstract

The dairy sector is very important for the global economy; however, dairy wast are significant polluters. Several treatments can be applied to minimize the environmental impacts caused by dairy effluents, which constitutes a highly relevant research theme. To contribute to this theme, a systematic review of dairy effluents was conducted, comprising scientometric and meta-analytic approaches. For the scientometric analysis, the terms "dairy effluent" and "dairy wastewater" were searched in the Web of Science (WoS) database for the period from 1945 to 2021. The search yielded 1258 documents, which were manually filtered to result in 1191 articles. The documents were categorized based on language of publication, number of publications and citations over time, publications by country, areas of knowledge, keywords, and most relevant publications based on citation count. The works were further separated according to the method used (physicochemical or biological) and finally grouped by treatment method. It was observed that English was the predominant language used in publications, with the majority being articles; India had the highest number of publications. The highest peak in publications occurred in 2021, and there is an estimated growth in the number of publications and citations up to 2030. Most of the works are related to effluent treatment, with biological processes being the primary focus (70%). Within the biological treatments, processes utilizing bioreactors, cultivation of microalgae or aquatic plants, anaerobic digestion, microbial fuel cells, enzymes, biofilters, and microorganism cultivation were highlighted. For the metaanalysis, considering microbiomes of dairy effluents, searches were conducted in PubMed and ENA in public nucleotide databases, using the terms "dairy wastewater" and "dairy effluent". Out of the 11 studies found, 6 were selected, encompassing a total of 126 samples. The studies were selected based on the following criteria: being related to dairy effluent, having gene sequencing of the 16S ribosomal subunit (rDNA) region conducted on the Illumina platform, and having access to physicochemical data of the samples. Processing of raw sequencing data was performed using the QIIME 2 software package, and sequences were clustered into ASVs and classified using the SILVA database. Analyses of alpha- and beta-diversity, relative abundance, linear discriminant analysis (LDA) effect size (LEfSe), and core microbial analysis were conducted. The dataset was categorized based on effluent type, sample origin, treatment type, and reactor type. The results identified some of the most abundant genera in these categories, including Bacteroidetes, Streptococcus, Lactococcus, and Smithella. Interestingly, the same bacterial genera found in the categories were also the most prevalent in the core, indicating that they play a key role in dairy effluents.