Photosynthetic pretreatment increases membrane-based rejection of boron and arsenic.

The metalloids boron and arsenic are ubiquitous and difficult to remove during water treatment. As chemical pretreatment using strong base and oxidants can increase their rejection during membrane-based nanofiltration (NF), we examined a nature-based pretreatment approach using benthic photosynthetic processes inherent in a unique type of constructed wetland to assess whether analogous gains can be achieved without the need for exogenous chemical dosing. During peak photosynthesis, the pH of the overlying clear water column above a photosynthetic microbial mat (biomat) that naturally colonizes shallow, open water constructed wetlands climbs from circumneutral to approximately 10. This biological increase in pH was reproduced in a laboratory bioreactor and resulted in analogous increases in NF rejection of boron and arsenic that is comparable to chemical dosing. Rejection across the studied pH range was captured using a monoprotic speciation model. In addition to this mechanism, the biomat accelerated the oxidation of introduced arsenite through a combination of abiotic and biotic reactions. This resulted in increases in introduced arsenite rejection that eclipsed those achieved solely by pH. Capital, operation, and maintenance costs were used to benchmark the integration of this constructed wetland against chemical dosing for water pretreatment, manifesting long-term (sub-decadal) economic benefits for the wetland-based strategy in addition to social and environmental benefits. These results suggest that the integration of nature-based pretreatment approaches can increase the sustainability of membrane-based and potentially other engineered treatment approaches for challenging water contaminants.

Scalable and customizable parallel flow-through reactors to quantify biological processes related to contaminant attenuation by photosynthetic wetland microbial mats.

Shallow, unit process open water wetlands harbor a benthic microbial mat capable of removing nutrients, pathogens, and pharmaceuticals at rates that rival or exceed those of more traditional systems. A deeper understanding of the treatment capabilities of this non-vegetated, nature-based system is currently hampered by experimentation limited to demonstration-scale field systems and static lab-based microcosms that integrate field-derived materials. This limits fundamental mechanistic knowledge, extrapolation to contaminants and concentrations not present at current field sites, operational optimization, and integration into holistic water treatment trains. Hence, we have developed stable, scalable, and tunable laboratory reactor analogs that offer the capability to manipulate variables such as influent rates, aqueous geochemistry, light duration, and light intensity gradations within a controlled laboratory environment. The design is composed of an experimentally adaptable set of parallel flow-through reactors and controls that can contain field-harvested photosynthetic microbial mats ("biomat") and could be adapted for analogous photosynthetically active sediments or microbial mats. The reactor system is contained within a framed laboratory cart that integrates programable LED photosynthetic spectrum lights. Peristaltic pumps are used to introduce specified growth media, environmentally derived, or synthetic waters at a constant rate, while a gravity-fed drain on the opposite end allows steady-state or temporally variable effluent to be monitored, collected, and analyzed. The design allows for dynamic customization based on experimental needs without confounding environmental pressures and can be easily adapted to study analogous aquatic, photosynthetically driven systems, particularly where biological processes are contained within benthos. The diel cycles of pH and dissolved oxygen (DO) are used as geochemical benchmarks for the interplay of photosynthetic and heterotrophic respiration and likeness to field systems. Unlike static microcosms, this flow-through system remains viable (based on pH and DO fluctuations) and has at present been maintained for more than a year with original field-based materials.•Lab-scale flow-through reactors enable controlled and accessible exploration of shallow, open water constructed wetland function and applications.•The footprint and operating parameters minimize resources and hazardous waste while allowing for hypothesis-driven experiments.•A parallel negative control reactor quantifies and minimizes experimental artifacts.

Statistical Learning and Uncommon Soil Microbiota Explain Biogeochemical Responses after Wildfire.

Wildfires are a perennial event globally, and the biogeochemical underpinnings of soil responses at relevant spatial and temporal scales are unclear. Soil biogeochemical processes regulate plant growth and nutrient losses that affect water quality, yet the response of soil after variable intensity fire is difficult to explain and predict. To address this issue, we examined two wildfires in Colorado, United States, across the first and second postfire years and leveraged statistical learning (SL) to predict and explain biogeochemical responses. We found that SL predicts biogeochemical responses in soil after wildfire with surprising accuracy. Of the 13 biogeochemical analytes analyzed in this study, 9 are best explained with a hybrid microbiome + biogeochemical SL model. Biogeochemical-only models best explain 3 features, and 1 feature is explained equally well with the hybrid and biogeochemical-only models. In some cases, microbiome-only SL models are also effective (such as predicting NH4+). Whenever a microbiome component is employed, selected features always involve uncommon soil microbiota (i.e., the "rare biosphere" [existing at <1% mean relative abundance]). Here, we demonstrate that SL paired with DNA sequence and biogeochemical data predicts environmental features in postfire soils, although this approach could likely be applied to any biogeochemical system. IMPORTANCE Soil biogeochemical processes are critical to plant growth and water quality and are substantially disturbed by wildfire. However, soil responses to fire are difficult to predict. To address this issue, we developed a large environmental data set that tracks postfire changes in soil and used statistical learning (SL) to build models that exploit complex data to make predictions about biogeochemical responses. Here, we show that SL depends upon uncommon microbiota in soil (the "rare biosphere") to make surprisingly accurate predictions about soil biogeochemical responses to wildfire. Using SL to explain variation in a natively chaotic environmental system is mechanism independent. Likely, the approach that we describe for combining SL with microbiome and biogeochemical parameters has practical applications across a range of issues in the environmental sciences where predicting responses would be useful.