Isolation and characterization of denitrifiers from woodchip bioreactors for bioaugmentation application.

AIMS: This study was done to obtain denitrifiers that could be used for bioaugmentation in woodchip bioreactors to remove nitrate from agricultural subsurface drainage water. METHODS AND RESULTS: We isolated denitrifiers from four different bioreactors in Minnesota, and characterized the strains by measuring their denitrification rates and analysing their whole genomes. A total of 206 bacteria were isolated from woodchips and thick biofilms (bioslimes) that formed in the bioreactors, 76 of which were able to reduce nitrate at 15°C. Among those, nine potential denitrifying strains were identified, all of which were isolated from the woodchip samples. Although many nitrate-reducing strains were isolated from the bioslime samples, none were categorized as denitrifiers but instead as carrying out dissimilatory nitrate reduction to ammonium. CONCLUSIONS: Among the denitrifiers confirmed by 15 N stable isotope analysis and genome analysis, Cellulomonas cellasea strain WB94 and Microvirgula aerodenitrificans strain BE2.4 appear to be promising for bioreactor bioaugmentation due to their potential for both aerobic and anaerobic denitrification, and the ability of strain WB94 to degrade cellulose. SIGNIFICANCE AND IMPACT OF THE STUDY: Denitrifiers isolated in this study could be useful for bioaugmentation application to enhance nitrate removal in woodchip bioreactors.

A geostatistical approach to identify and mitigate agricultural nitrous oxide emission hotspots.

Anthropogenic emissions of nitrous oxide (N2O), a trace gas with severe environmental costs, are greatest from agricultural soils amended with nitrogen (N) fertilizer. However, accurate N2O emission estimates at fine spatial scales are made difficult by their high variability, which represents a critical challenge for the management of N2O emissions. Here, static chamber measurements (n=60) and soil samples (n=129) were collected at approximately weekly intervals (n=6) for 42-d immediately following the application of N in a southern Minnesota cornfield (15.6-ha), typical of the systems prevalent throughout the U.S. Corn Belt. These data were integrated into a geostatistical model that resolved N2O emissions at a high spatial resolution (1-m). Field-scale N2O emissions exhibited a high degree of spatial variability, and were partitioned into three classes of emission strength: hotspots, intermediate, and coldspots. Rates of emission from hotspots were 2-fold greater than non-hotspot locations. Consequently, 36% of the field-scale emissions could be attributed to hotspots, despite representing only 21% of the total field area. Variations in elevation caused hotspots to develop in predictable locations, which were prone to nutrient and moisture accumulation caused by terrain focusing. Because these features are relatively static, our data and analyses indicate that targeted management of hotspots could efficiently reduce field-scale emissions by as much 17%, a significant benefit considering the deleterious effects of atmospheric N2O.

Calculating the detection limits of chamber-based soil greenhouse gas flux measurements.

Renewed interest in quantifying greenhouse gas emissions from soil has led to an increase in the application of chamber-based flux measurement techniques. Despite the apparent conceptual simplicity of chamber-based methods, nuances in chamber design, deployment, and data analyses can have marked effects on the quality of the flux data derived. In many cases, fluxes are calculated from chamber headspace vs. time series consisting of three or four data points. Several mathematical techniques have been used to calculate a soil gas flux from time course data. This paper explores the influences of sampling and analytical variability associated with trace gas concentration quantification on the flux estimated by linear and nonlinear models. We used Monte Carlo simulation to calculate the minimum detectable fluxes (α = 0.05) of linear regression (LR), the Hutchinson/Mosier (H/M) method, the quadratic method (Quad), the revised H/M (HMR) model, and restricted versions of the Quad and H/M methods over a range of analytical precisions and chamber deployment times (DT) for data sets consisting of three or four time points. We found that LR had the smallest detection limit thresholds and was the least sensitive to analytical precision and chamber deployment time. The HMR model had the highest detection limits and was most sensitive to analytical precision and chamber deployment time. Equations were developed that enable the calculation of flux detection limits of any gas species if analytical precision, chamber deployment time, and ambient concentration of the gas species are known.