After evaluating and selecting the most parsimonious multiple linear regression model composed of categorical and quantitative environmental variables, the observed influence, or lack thereof, of these variables will be discussed in the context of biomineral ureolytic activity.

Multiple linear model and validation

The multiple linear model composed of 55 observations is described in Tables 1 and 2. As shown in correlation heatmaps and residual analysis from Figs. S2, S3, and S4, the linear model is in agreement with the Gauss-Markov OLS regression assumptions, which require that: a) the expected value of the regression residuals tends towards zero, b) the residuals are homoscedastic, c) there is no autocorrelation between the regressors and the residuals such that exogeneity is upheld, d) the predictors are not multicollinear, and e) the residuals are also normal [30]. The residuals shown in Fig. S2 do not appear to have a trend based on the index plot, do not exhibit any correlation with each other from the autocorrelation plot, and appear homoscedastic from the fitted values vs. residuals plot. Finally, the residuals also appear normally distributed from the quantile-quantile plot in Fig. S2. As such, it was concluded that the natural log-log linear model appropriately describes natural logarithmically transformed data and that the model fits well with the data. The AICC model selection results are shown in Table S2, suggesting that the most parsimonious and probable model is Model 3 [29, 31].

Table 1 Summary of effect sizes of significant predictors on biomineral ureolytic activity
Table 2 Multiple regression summary of model predicting biomineral ureolytic activity

The regression results describing the most probable model (Model 3) is shown in Tables 1 and 2, which also depicts the regression results from other tested models. The results presented in Tables 1 and 2 suggest that ureC gene concentrations (Model 4, p = 0.551), sampling season (Model 5, p = 0.956), and urinal types were statistically insignificant predictors of ureolytic activity (p > 0.05) and of low practical significance as indicated by the relatively small regression coefficients (see Table 1). From Table 1, the strongest predictor of biomineral ureolytic activity was the sampling location, namely, those sampled from the main urinal drainage pipes exhibited the greatest enzymatic activity. In Model 3, the second strongest predictor was the VS/TS ratio. Annual number of users at a given rest area also positively influenced urease activity likely due to the increased loading and usage frequency resulting in a semi-constant stream of nutrients necessary for a strong ureolytic community to develop and thrive.

The influence of organic matter on ureolytic activity

Organic content is shown to be a significant (p = 0.003) and of sizeable effect (( hat{upbeta} ) = 0.59) in predicting ureolytic activity (Table 1). This observation may be consistent with past findings from soil research which found correlations between organic matter concentrations and urease activity [13, 14, 32]. Others also observed that increased carbohydrate availability at neutral pH was correlated with increased Actinomyces naeslundii and Sporosarcina pasteurii urease activity [14, 17]. Liu et al., however, noticed that carbohydrate availability had no effect on ureC gene expression marked by through reverse-transcriptase quantitative real-time PCR (RT-qPCR) mRNA transcripts [17]. Liu et al. hypothesizes that these observations were due to carbohydrate availability and pH modulation affecting the expression of genes other than ureC responsible for urease synthesis or apoenzyme activation [17].

Increasing the biomass of the inoculum by providing a carbon source in microbial induced calcite precipitation studies has been reported to promote the ureolytic activity [14]. Tobler et al. concluded that molasses supplementation selected for a larger microbial community that obtains their nitrogen from ureolysis, though there is no nitrogen limitation in urinals [14]. Others, who studied the environmental factors affecting microbially induced calcium precipitation concluded that increasing biomass may also increase ureolytic activity as there could be more active cells present [33]. Extracellular urease has also been suggested to be stabilized by adsorption to soil colloids, particularly organic matter, which may be similar to that observed in biomineral samples obtained from urine drain pipes [19].

One limitation of this study is that it is unclear what component of the organic fraction is correlated with increased ureolytic activity as VS is a bulk measurement encompassing any organic mass. Within the biomineral/stone matrix is also an organic fraction composed of carbohydrates, proteins, lipids, and dead cell mass that binds the mineral fraction of the precipitate [4]. Therefore, future research could evaluate different organic components such as carbohydrates, proteins, and exopolysaccharide substances.

The non-effect of urinal type and seasonality on ureolytic activity

In addition to the linear regression results, Kruskal-Wallis testing for biomineral ureolytic activity between waterless and low-flow urinals provides evidence that waterless and low-flow do not significantly differ in terms of biomineral activity (p = 0.47). While urinal type is not a statistically significant predictor of ureolytic activity, biomineral samples from waterless urinals have exhibited a greater maximum ureolytic activity than any biomineral sample obtained from low-flow urinals in this study, as shown in Fig. 3.

Fig. 3
figure3

Descriptive statistics on the effects of urinal type on natural log-transformed ureC gene copies and biomineral activity

Finally, sampling season (as shown in Table 2) demonstrated no statistical (p = 0.956) or practical significance (( hat{upbeta} ) = 0.01) in predicting biomineral activity. This may explain why fouling is a year-round phenomenon, as the biomineral ureolytic activity remains unaffected by seasonality, as the high urease activities year-round facilitate conditions necessary for precipitation to occur. Because seasonality does not seem to impact biomineral activity, future observations on the ureolytic activity of urine drainpipes may be performed without temporal confounding effects. Future microbial ecology studies can reveal more about the response of the bacterial community structure to seasonality, which can then be cumulatively related to the biomineral ureolytic activity.

Effects of intrasystem sampling location on ureolytic activity

While the ureolytic activity of biomineral samples obtained from the drainage pipes immediately following the drain traps were not significantly different from those corresponding to samples obtained from waterless urinal cartridges (Pairwise Wilcoxon Rank Sum: p = 0.053), samples taken from the main drain lines which contacts handwashing water were significantly non-identical in terms of ureolytic activity (Kruskal-Wallis: p < 0.001; Pairwise Wilcoxon Rank Sum: p < 0.001). Within one system, cartridges and gallery drain lines immediately succeeding the urinal are exposed to the same urine feed without mixing with potable water and thus face similar environmental conditions that influence ureolytic activity [13]. Because drain line samples directly follow cartridge samples and are exposed to the same urine, the relative similarity in environmental conditions between cartridge and drain line samples may explain their different ureolytic rates compared to main drainpipe samples but not with each other.

Biomineral ureolytic activity may be predicted by transcriptional activity more than by urec and 16S rRNA gene abundance

Kruskal-Wallis testing results suggest that the ureC abundance between low-flow and waterless urinals are significantly nonidentical (p < 0.001), but there was no detected significant effect on biomineral ureolytic activity as suggested by the multiple regression results shown in Table 2. The lack of statistical significance describing the relationship between ureC gene copies and ureolytic activities disagrees with bivariate correlation studies done by Sun et al. and Fisher et al., where it was found that soil ureolysis rates were significantly correlated with ureC gene copies [34, 35]. Notably, neither studies discussed effect size and used a small sample size (n < 12) for analyses describing individual soil horizons [34, 35]. Conversely, other soil urease studies have also found that ureolytic activities are correlated with total nitrogen, total carbon, and soil organic carbon concentrations, but not the abundance of ureC genes as in agreement with our study [36]. The regression results suggest that ureolytic gene abundance is insufficient in predicting ureolytic activity in a linear model.

Greater abundances of potentially ureolytic bacteria indicated by proxy of sample ureC gene concentrations, may not be correlated with biomineral ureolytic rates as suggested by the regression results. That ureC was detectable indicates that part of the bacterial community in the biomineral samples has the urease-positive genotype, but not all bacteria with the ureC may be displaying a urease-positive phenotype [37]. This is because urease activity may not be expressed under the growth conditions found in urine drain pipes, and may explain why urease activities did not differ significantly when grouped by urinal type [37]. Expression of the urease-positive genotype and the eventual translation into the urease protein is regulated at the transcriptional level rather than at the genomic level [38,39,40].

That ureC gene abundance is not a statistically significant predictor of biomineral ureolytic activity is likely due to the need for environmental conditions that would induce certain microbial transcriptional responses that cause an increase in urease activity. When comparing ureC copies per g VS, values grouped by intrasystem sampling location differed significantly between cartridge vs. gallery drain (Kruskal-Wallis: p < 0.001; Wilcoxon Rank Sum: p < 0.001) and cartridge vs. gallery main drain (Kruskal-Wallis: p < 0.001; Wilcoxon Rank Sum: p < 0.001). However, Fig. 3 reinforces hypothesis testing results in that samples from the main drain with the lowest functional gene concentrations exhibited maximal ureolytic activity of all samples as predicted by the multiple regression model. One possible explanation is that the main drains and low-flow urinal drain lines are exposed to flush and sink water, which leads to a decrease in nitrogen concentrations in the stream contacting the biofilm due to dilution. In response, the ureolytic ammonia oxidizing bacterial community may be upregulating ureC transcription to produce more urease to convert the urea into ammonia at a faster rate for pH regulation or to acquire ammonia for biomass production or energy generation [41].

Further regression testing by adding the 16S rRNA gene concentration as a variable to the most probable model (Model 3) also suggests that the 16S rRNA gene concentrations in the biomineral samples are not a strong (( hat{upbeta} ) = 0.13) or significant (p = 0.127) predictor of ureolytic activity. This suggests that a greater bacterial load within a sample, estimated by proxy of gene concentration may not correspond to greater ureolytic rates in a given biomineral sample. Our observations on the lack of correlation between 16S rRNA gene abundance and ureolytic activity disagrees with Wang et al.’s study, where they found a statistically significant correlation between urease activity and 16S rRNA copies via automatic linear modeling [32]. However, such discrepancies in results may be due to distinct environmental conditions between soil samples and ureolytic biomineralization from drain pipes which could influence the expression of the urease gene and its eventual translation.

From Fig. 4, the observation that conventional and low-flow urinals can have similar in situ ureolytic rates with those from waterless urinals is consistent with the regression results where it was found that urinal type is neither a significant (p = 0.521) and practical (( hat{upbeta} ) = − 0.12) predictor of the in vitro biomineral ureolytic activity. While low-flow urinals constitute most fixtures described in Fig. 4 due to drain trap inaccessibility for other urinal types, Fig. 4 demonstrates that Dunnigan northbound, a waterless urinal site, exhibited the greatest maximum in situ ureolytic rates of all drain traps tested. Conversely, the standard urinal at Tejon Pass ranked 2nd of all sites screened for in situ ureolytic rate. The Tejon Pass urinal exhibited a similar rate (115 μS cm− 1 min− 1 g− 1 VS) compared to that of the Dunnigan northbound standard height urinal (118 μS cm− 1 min− 1 g− 1 VS). Urinals in the same study sites also appear to exhibit different urea conversion rates. One possible explanation is that there simply may be less ureolytically active biomineral mass in one drain trap compared to the urinal adjacent to it at the time of sampling. It cannot be guaranteed that there is sufficient biomineral mass within a given drain trap at any given time, which could be affecting the in situ ureolytic rates. Ideally, a larger sample size for the in-situ tests could alleviate any ambiguity from this confounding factor, and so further research with increased sample size is needed. Regardless of this confounding factor, the in-situ tests demonstrate that it is possible for the ureolytic activity of biomineral samples from urinals with high flush water volumes to match that from waterless urinals. Raw urease activity values grouped by sampling sites can be found in Table S1.

Fig. 4
figure4

Comparison of in situ trap urea conversion rate for various rest areas with trap-type urinals

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