Environmental metadata for: 2019/2020 biomineral sampling of drainpipes from California public rest areas
Cite this dataset
Lim, Kahui et al. (2021). Environmental metadata for: 2019/2020 biomineral sampling of drainpipes from California public rest areas [Dataset]. Dryad. https://doi.org/10.25338/B82906
This environmental data set corresponds to a two part study. The first part details a multiple regression analysis of measured and categorical parameters that influence biomineral urease activity. Using an expanded version of the dateset used in the first part, a second and separate microbial ecology study focuses on the bacterial community structure related to both biomineral and liquid associated bacteria. The expanded data set includes liquid samples obtained from urine drainage systems.
Part 1: Multiple Regression Analysis submitted to Sustainable Environment Research
Clogging and odor is strongly associated with ureolytic biomineralization in waterless and low-flow urinal drainage systems in high usage settings. These blockages continue to hinder widespread waterless and low-flow urinal adoption due to subsequent high maintenance requirements and hygiene concerns. Through field observations, hypothesis testing, and multiple regression analysis, this study attempts to characterize, for the first time, the ureolytic activity of the biomineralization found in alternative cated at 9 State-owned restrooms. Multiple regression analysis (n = 55, df = 4, R2 = 0.665) suggests that intrasystem sampling location (β = 1.23, P < 0.001), annual users per rest area (β = 0.5, P < 0.004), and the organic/inorganic mass fraction (β = 0.59, P = 0.003 ), are statistically significant influencers of the ureolytic activity of biomineral samples (p < 0.05). Conversely, ureC gene abundance (P = 0.551), urinal type (P = 0.521) and sampling season (P = 0.956) are not significant predictors of biomineral ureolytic activity. We conclude that high concentrations of the urease alpha subunit, ureC, which can be interpreted as proxy measure of a strong, potentially ureolytic community, does not necessarily mean that the gene is being expressed. Future studies should assess ureC transcriptional activity to measure gene expression rather than gene abundance to assess the relationship between environmental conditions, their role in transcription, and urease activities. In sum, this study presents a method to characterize biomineral ureolysis and establishes baseline values for future ureolytic inhibition treatment studies that seek to improve the usability of urine collection and related source separation technologies.
Part 2: Microbial Ecology Study submitted to PLOS ONE
In this study, we examined the total bacterial community associated with ureolytic biomineralization from urine drainage systems. Biomineral samples were obtained from 11 California Department of Transportation public restrooms fitted with waterless, low-flow, or conventional urinals in 2019. Following high throughput 16S rRNA Illumina sequences processed using the DADA2 pipeline, the microbial diversity assessment of 169 biomineral and urine samples resulted in 3,869 reference sequences aggregated as 598 operational taxonomic units (OTUs). Using PERMANOVA testing, we found strong, significant differences between biomineral samples grouped by intrasystem sampling location and urinal type. Biomineral microbial community profiles and alpha diversities differed significantly when controlling for sampling season. Observational statistics revealed that biomineral samples obtained from waterless urinals contained the largest ureC/16S gene copy ratios and were the least diverse urinal type in terms of Shannon indices. Waterless urinal biomineral samples were largely dominated by the Bacilli class (86.1%) compared to low-flow (41.3%) and conventional samples (20.5%), and had the fewest genera that account for less than 2.5% relative abundance per OTU. A Mantel test suggests that the environmental variables monitored in this study were moderately correlated with the microbial community (r = 0.351), but a test using the Haversine distance suggests that geographic distances had greater correlations with the community structure (r = 0.685). Our findings are useful for future microbial ecology studies of urine source-separation technologies, as we have established a comparative basis using a large sample size and study area. For this study, the FASTQ sequencing files can be found on NCBI with the BioProject Accession number PRJNA699694.
California Department of Transportation, Award: Agreement Number 65A0734