Water reuse for irrigation is increasingly recognized as an essential and economical strategy in areas with water scarcity. A simple, low-cost, low-maintenance, and highly efficient Pond-In-Pond (PIP) treatment system can be used for wastewater reuse. PIP is a treatment technology in which two types of ponds -- anaerobic and aerobic -- are combined into a single pond and consist of a deeper inner section entirely submerged within the outer pond. Previous studies on PIPs and PIP-like systems have reinforced the potential for reuse through promising performance results with BOD removal of over 80% and a reduction in land area requirements by approximately 40%. Yet, no prior efforts have been made to understand the performance mechanism of such systems. This study makes use of two, 2-D modeling tools in developing a fundamental understanding of PIP flow dynamics and the expected performance. The modeling results showed that the PIP configuration offers improved flow diversion along with reduced flow velocity. Additionally, the PIP retained approximately 17% more (p < 0.05) particles than the traditional pond with most of the particles concentrated within the inner pond. Lower velocity and higher solids retention in the PIP thus allowed for better treatment performance compared to traditional ponds. The findings from this study can be used as preliminary data for future in-depth investigations of the PIP system leading toward effective and optimal designs. This will help address the major societal concern of water scarcity with low-cost and effective wastewater treatment.

Microalgae-based nutrient recovery represents an emerging technology to reduce effluent phosphate concentrations beyond conventionally-used enhanced biological phosphorus removal. The resulting biomass can be valorized in the form of various end-products, including bio-based plastics, foams, and dyes, thus reducing overall operational costs through the sale of algal biomass. To mitigate the frequency and severity of disruptive events (i.e., culture crashes), accurate and timely monitoring is essential. Standard microalgal monitoring which relies on bulk measurements (e.g., total chlorophyll, total suspended solids) lacks the resolution to detect changes in microalgal community composition, while manual microscopy-based techniques are time-intensive and require a trained specialist. Benchtop imaging flow cytometers can automate the collection of single cell resolution data, but are prohibitively expensive (>$70,000 per instrument). These barriers preclude process operators from tracking and responding to changes in microalgal community structure without a significant resource investment. To overcome these barriers, we have developed the ARTiMiS: a low-cost flow imaging microscopy-based platform with onboard software capable of providing species-level quantitation of microalgal communities in (near) real-time. The ARTiMiS leverages novel multi-modal imaging and a deep learning-based image processor optimized on a curated database of industrially relevant microalgae. We have successfully used the ARTiMiS for (near) real-time monitoring of mixed microalgal communities at a 0.15 MGD water and resource recovery facility. With high classification accuracy (>90%) on a device an order-of-magnitude lower cost than commercially-available solutions, ARTiMiS-based monitoring provided critical insights into the relationships between water quality parameters, microalgal community structure (taxonomy and phenotype), and process performance.

Intensive microalgal cultivation using photobioreactors (PBRs) can recover nutrients from wastewater beyond the current limit of technology while addressing climate change concerns. This technology can improve on low-rate cultivation systems (e.g., ponds) by reducing the physical footprint and increasing biomass while using operational parameters (e.g., solids residence time (SRT)) to select for specific communities. To understand the temporal variation in microbial community structure and functional dynamics in such systems, we conducted a 16-month monitoring campaign of a full-scale microalgal wastewater treatment system (0.15 MGD) that leverages rapid nutrient assimilation in PBRs (EcoRecover, CLEARAS Water Recovery) and separation of HRT/SRT. Long-term samples spanning from November 2021 to February 2023 captured periods of stable performance and upset. Community structure analyses (via 18S and 16S rRNA gene sequencing) revealed that high Scenedesmus sp., balanced ammonia and nitrite oxidizing bacteria (AOB and NOB), high alkalinity, and high influent ammonium were the drivers of stable performance. Distinct shifts in microbial community with loss of Scenedesmus sp., increased AOB compared to NOB leading to partial nitrification, high ambient temperature, and spikes in influent orthophosphate were indicators of process upsets. Insights into these periods of stability and upset help identify operational changes, including changes to PBR configurations, reduced SRT, and higher alkalinity, to facilitate stable community and system performance. Metagenomics and metatranscriptomics further revealed functional dynamics in response to operational changes distinct from population dynamics.

Water stress and associated issues have led a push for expanded water reuse. Advanced treatment trains that couple advanced oxidation processes (AOPs), such as ozone, with biologically active carbon (BAC) filtration are of interest as a lower cost, membrane-free technology. However, little is known about the microbial communities that are the fundamental drivers of AOP-BAF treatment. The overarching objective of this study was to demonstrate the potential for microbial community profiling as a diagnostic tool for assessing the functionality and resilience of sequential processes employed in a water reuse treatment train. We utilized 16S rRNA gene amplicon sequencing to profile the bacterial microbiota characteristic of each stage of treatment with time in a reuse train employing coagulation, flocculation, sedimentation, ozonation, BAC filtration, granular activated carbon (GAC) adsorption, and UV disinfection. It was found that there was a distinct baseline microbiota associated with each stage of treatment and a succession with time. Oxidative treatments, such as O₃, drove the sharpest shifts and greatest variance in microbial community composition. Other operational changes, such as adjustment in O3:TOC, temperature, filter aid polymer, chlorine quenching agent, and EBCT also shaped the microbial community composition of each process. Of these, supplementation of nitrogen and phosphorus resulted in the strongest bifurcation and was particularly noted in the analysis of microbial communities inhabiting the BAC and GAC units. The findings of this study improve understanding of bacterial dynamics occurring in advanced water treatment trains and help to factor this understanding into improved system design and operation.

Acid fermenters are an emerging biotechnology that harnesses microbial communities to process organic wastes into chemical precursors that can replace many of the compounds traditionally produced by the fossil fuel industry. Synthetic biologists have recently developed tools that give us unprecedented, bottom-up control of microbial communities and which are potentially deployable in acid fermenters to manipulate the often-wide ranging profile of simple, organic products. Better control of this profile will reduce downstream processing costs and increase the efficiency of any ensuing valorization process paired with an acid fermenter.

Specifically, we use conjugation to deliver a recently isolated, transposon-encoded CRISPR-Cas system (INTEGRATE) to native recipients in an acid fermenter community. This system interrupts the acetate pathway causing metabolites to funnel down alternative metabolic pathways, namely the butyrate pathway. INTEGRATE will cause interruption by insertion of a beta-glucosidase gene (BglC), into acetate pathway genes. BglC results in a protein that can degrade cellobiose, a rate limiting step in the overall degradation of cellulose. Our approach will reduce acetate production, increase butyrate production, and improve degradation of cellulosic wastes in an acid fermenter community.

​We demonstrate this by first isolating and expressing BglC from Thermobifida fusca YX in E. coli. Then we incorporate BglC into INTEGRATE and use it to interrupt acetate pathway genes in E. coli. We next add the customized INTEGRATE system onto a transmissible vector and demonstrate transfer from E. coli and successful expression in Clostridium acetobutylicum, a common microorganism found in acid fermenter communities.