DOI: 10.25338/b82906
title: Environmental metadata for: 2019/2020 biomineral sampling of drainpipes from California public rest areas

description: 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 &lt;
 0.001), annual users per rest area (β = 0.5, P &lt; 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
 &lt; 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.