Animal carcass burial management: implications for sustainable biochar use

Adsorption of antibiotics onto low-grade charcoal in the presence of organic matter: Batch and column tests

Antibiotics contaminate diverse ecosystems and threaten human health. In ecosystems including water, sediment, and soil, the amount of antibiotics present is tiny compared to the amount of natural organic matter. However, most studies have ignored the co-presence of natural organic matter in the adsorption of target antibiotics. In this study, we quantitatively evaluated the effect of co-presenting natural organic matter on the adsorption of sulfamethazine (SMZ) through batch and column experiments using low-grade charcoal, an industrial by-product. SMZ was used as a model antibiotic compound and humic acid (HA) was used to represent natural organic matter. The co-presence of 2000 mg/L HA (400 times the concentration of SMZ) lowered the adsorption rate of SMZ from 0.023 g/mg·min to 0.007 g/mg·min, and the maximum adsorption capacity from 39.8 mg/g to 15.6 mg/g. HA blocked the charcoal's pores and covered its surface adsorption sites, which dramatically lowered its capacity to adsorb SMZ. Similar results were obtained in the flow-through column experiments, where the co-presence of natural organic matter shortened the lifetime of the charcoal. As a result, the co-presence of a relatively high concentration of natural organic matter can inhibit the adsorption of SMZ and likely other antibiotic compounds, and thus the presence of natural organic matter should be accounted for in the design of adsorption processes to treat antibiotics in water.

Composition and functional diversity of bacterial communities during swine carcass decomposition

OBJECTIVE

This study investigated the changes in bacterial communities within decomposing swine microcosms, comparing soil with or without intact microbial communities, and under aerobic and anaerobic conditions.

METHODS

The experimental microcosms consisted of four conditions: UA, unsterilized soil-aerobic condition; SA, sterilized soil-aerobic condition; UAn, unsterilized soil-anaerobic condition; and San, sterilized soil-anaerobic condition. The microcosms were prepared by mixing 112.5 g of soil and 37.5 g of ground carcass, which were then placed in sterile containers. The carcass-soil mixture was sampled at day 0, 5, 10, 30, and 60 of decomposition, and the bacterial communities that formed during carcass decomposition were assessed using Illumina MiSeq sequencing of the 16S rRNA gene.

RESULTS

A total of 1,687 amplicon sequence variants representing 22 phyla and 805 genera were identified in the microcosms. The Chao1 and Shannon diversity indices varied in between microcosms at each period (p<0.05). Metagenomic analysis showed variation in the taxa composition across the burial microcosms during decomposition, with Firmicutes being the dominant phylum, followed by Proteobacteria. At the genus level, Bacillus and Clostridium were the main genera within Firmicutes. Functional prediction revealed that the most abundant Kyoto encyclopedia of genes and genomes metabolic functions were carbohydrate and amino acid metabolisms.

CONCLUSION

This study demonstrated a higher bacteria diversity in UA and UAn microcosms than in SA and SAn microcosms. In addition, the taxonomic composition of the microbial community also exhibited changes, highlighting the impact of soil sterilization and oxygen on carcass decomposition. Furthermore, this study provided insights into the microbial communities associated with decomposing swine carcasses in microcosm.

A supervised machine learning approach to discriminate the effect of carcass leachate on shallow groundwater quality around on-farm livestock mortality burial sites

The evaluation of leachate leakage at livestock mortality burial sites is challenging, particularly when groundwater is previously contaminated by agro-livestock farming. Supervised machine learning was applied to discriminate the impacts of carcass leachate from pervasive groundwater contamination in the following order: data labeling, feature selection, synthetic data generation, and classification. Physicochemical data of 359 water samples were collected from burial pits (LC), monitoring wells near pits (MW), pre-existing shallow household wells (HW), and background wells with pervasive contamination (BG). A linear classification model was built using two representative groups (LC and BG) affected by different pollution sources as labeled data. A classifier was then applied to assess the impact of leachate leakage in MW and HW. As a result, leachate impacts were observed in 40% of MW samples, which indicates improper construction and management of some burial pits. Leachate impacts were also detected in six HW samples, up to 120 m downgradient, within one year. The quantitative decision-making tool to diagnose groundwater contamination with leachate leakage can contribute to ensuring timely responses to leakage. The proposed machine learning approach can also be used to improve the environmental impact assessment of water pollution by improper disposal of organic waste.