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

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.