Dataset Information

Digital Proxy of a Bio-Reactor (DIYBOT) combines sensor data and data analytics to improve greywater treatment and wastewater management systems.

ABSTRACT: Technologies to treat wastewater in decentralized systems are critical for sustainable development. Bioreactors are suitable for low-energy removal of inorganic and organic compounds, particularly for non-potable applications where a small footprint is required. One of the main problems associated with bioreactor use is sporadic spikes of chemical toxins, including nanoparticles. Here, we describe the development of DIYBOT (Digital Proxy of a Bio-Reactor), which enables remote monitoring of bioreactors and uses the data to inform decisions related to systems management. To test DIYBOT, a household-scale membrane aerated bioreactor with real-time water quality sensors was used to treat household greywater simulant. After reaching steady-state, silver nanoparticles (AgNP) representative of the mixture found in laundry wastewater were injected into the system to represent a chemical contamination. Measurements of carbon metabolism, effluent water quality, biofilm sloughing rate, and microbial diversity were characterized after nanoparticle exposure. Real-time sensor data were analyzed to reconstruct phase-space dynamics and extrapolate a phenomenological digital proxy to evaluate system performance. The management implication of the stable-focus dynamics, reconstructed from observed data, is that the bioreactor self-corrects in response to contamination spikes at AgNP levels below 2.0?mg/L. DIYBOT may help reduce the frequency of human-in-the-loop corrective management actions for wastewater processing.

SUBMITTER: McLamore ES

PROVIDER: S-EPMC7229150 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Digital Proxy of a Bio-Reactor (DIYBOT) combines sensor data and data analytics to improve greywater treatment and wastewater management systems.

McLamore Eric S ES Huffaker Ray R Shupler Matthew M Ward Katelyn K Datta Shoumen Palit Austin SPA Katherine Banks M M Casaburi Giorgio G Babilonia Joany J Foster Jamie S JS

Scientific reports 20200515 1

Technologies to treat wastewater in decentralized systems are critical for sustainable development. Bioreactors are suitable for low-energy removal of inorganic and organic compounds, particularly for non-potable applications where a small footprint is required. One of the main problems associated with bioreactor use is sporadic spikes of chemical toxins, including nanoparticles. Here, we describe the development of DIYBOT (Digital Proxy of a Bio-Reactor), which enables remote monitoring of bior ...[more]

PMID: 32415099

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Project description:As decentralized water reuse continues to gain popularity, risk-based treatment guidance is increasingly sought for the protection of public health. However, effort s to evaluate pathogen risks and log-reduction requirements have been hindered by an incomplete understanding of pathogen occurrence and densities in locally-collected wastewaters (i.e., from decentralized collection systems). Of particular interest is the potentially high enteric pathogen concentration in small systems with an active infected excreter, but generally lower frequency of pathogen occurrences in smaller systems compared to those with several hundred contributors. Such variability, coupled with low concentrations in many source streams (e.g., sink, shower/bath, and laundry waters), has limited direct measurement of pathogens. This study presents an approach to modeling pathogen concentrations in variously sized greywater and combined wastewater collection systems based on epidemiological pathogen incidence rates, user population size, and fecal loadings to various residential wastewater sources. Pathogen infections were modeled within various population sizes (5-, 100-, and 1,000-person) for seven reference pathogens (viruses: adenoviruses, Norovirus, and Rotavirus; bacteria: Campylobacter and Salmonella spp.; and protozoa: Cryptosporidium and Giardia spp.) on each day of 10,000 possible years, accounting for intermittent infection and overlap of infection periods within the population. Fecal contamination of fresh greywaters from bathroom sinks, showers/baths, and laundry, as well as combined greywater and local combined wastewater (i.e., including toilets), was modeled based on reported fecal indicators in the various sources. Simulated daily infections and models of fecal contamination were coupled with pathogen shedding characteristics to generate distributions of pathogen densities in the various waters. The predicted frequency of pathogen occurrences in local wastewaters was generally low due to low infection incidence within small cohort groups, but increased with collection scale (population size) and infection incidence rate (e.g., Norovirus). When pathogens did occur, a decrease in concentrations from 5- to 100- and from 100- to 1,000-person systems was observed; nonetheless, overall mean concentrations (i.e., including non-occurrences) remained the same due to the increased number of occurrences. This highlights value of the model for characterizing scaling effects over averaging methods, which overestimate the frequency of pathogen occurrence in small systems while underestimating concentration peaks that likely drive risk periods. Results of this work will inform development of risk-based pathogen reduction requirements for decentralized water reuse.

Project description:We assessed the annual probability of infection resulting from non-potable exposures to distributed greywater and domestic wastewater treated by an aerobic membrane bioreactor (MBR) followed by chlorination. A probabilistic quantitative microbial risk assessment was conducted for both residential and office buildings and a residential district using Norovirus, Rotavirus, Campylobacter jejuni, and Cryptosporidium spp. as reference pathogens. A Monte Carlo approach captured variation in pathogen concentration in the collected water and pathogen (or microbial surrogate) treatment performance, when available, for various source water and collection scale combinations. Uncertain inputs such as dose-response relationships and the volume ingested were treated deterministically and explored through sensitivity analysis. The predicted 95th percentile annual risks for non-potable indoor reuse of distributed greywater and domestic wastewater at district and building scales were less than the selected health benchmark of 10-4 infections per person per year (ppy) for all pathogens except Cryptosporidium spp., given the selected exposure (which included occasional, accidental ingestion), dose-response, and treatment performance assumptions. For Cryptosporidium spp., the 95th percentile annual risks for reuse of domestic wastewater (for all selected collection scenarios) and district-collected greywater were greater than the selected health benchmark when using the limited, available MBR treatment performance data; this finding is counterintuitive given the large size of Cryptosporidium spp. relative to the MBR pores. Therefore, additional data on MBR removal of protozoa is required to evaluate the proposed MBR treatment process for non-potable reuse. Although the predicted Norovirus annual risks were small across scenarios (less than 10-7 infections ppy), the risks for Norovirus remain uncertain, in part because the treatment performance is difficult to interpret given that the ratio of total to infectious viruses in the raw and treated effluents remains unknown. Overall, the differences in pathogen characterization between collection type (i.e., office vs. residential) and scale (i.e., district vs. building) drove the differences in predicted risk; and, the accidental ingestion event (although modeled as rare) determined the annual probability of infection. The predicted risks resulting from treatment malfunction scenarios indicated that online, real-time monitoring of both the MBR and disinfection processes remains important for non-potable reuse at distributed scales. The resulting predicted health risks provide insight on the suitability of MBR treatment for distributed, non-potable reuse at different collection scales and the potential to reduce health risks for non-potable reuse.

Dataset Information

Digital Proxy of a Bio-Reactor (DIYBOT) combines sensor data and data analytics to improve greywater treatment and wastewater management systems.

Publications

Digital Proxy of a Bio-Reactor (DIYBOT) combines sensor data and data analytics to improve greywater treatment and wastewater management systems.

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