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.

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.

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.