ACS ES&T Water

Electrochemical advanced oxidation processes (EAOPs) are increasingly investigated for tertiary wastewater treatment, yet their short-term effects on real wastewater effluent remain poorly constrained. This study investigated real wastewater treatment plant (WWTP) effluent following short-term boron-doped diamond (BDD) electrochemical treatment using an interdigitated double-diamond electrode (iDDE), combined with hydrochemical, stable-isotope and shotgun metagenomic analyses. After 10 min at 7.0 V and 1.02 A, physicochemical parameters and bulk carbon pools remained largely unchanged. In contrast, d18O-DO decreased by 31.3 +/-0.28 permille alongside a small increase in dissolved oxygen (0.16 +/-0.03 mmol/L), indicating substantial oxygen consistent with electrochemical oxygen generation. Shotgun metagenomics revealed pronounced restructuring of the recovered microbial DNA pool, including increases in Comamonadaceae (~12.3% vs. ~25.1%) and Zoogloeaceae (~3.3% vs. ~8.4%), whereas the oxidative-stress-defence gene repertoire showed no coherent response. These taxonomic changes cannot be interpreted as selective survival, as metagenomics do not resolve viability or intra-/extracellular DNA contribution. Overall, short-term iDDE treatment affected the microbial component of real WWTP effluent despite comparatively limited changes in bulk chemistry, highlighting that EAOP effects extend beyond conventional disinfection.

Per- and polyfluoroalkyl substances (PFAS) are present throughout the environment due to decades of use in many commercial products and industrial processes. These molecules contain a hydrophobic fluorocarbon tail and a functional headgroup (typically a carboxylic or sulfonic acid) that allows hydrophilic interactions with the surrounding environment. These properties make them inherently resistant to degradation, difficult to track through the environment, readily bioaccumulative, and likely to impact human health. Through collaboration with town officials, multiple meetings with local sustainability committee members and school officials, and direct contact with individual homeowners, we conducted community-driven sampling of PFAS in well water from 105 houses in Tremont, ME, a rural coastal town that relies on private wells for drinking water. The rate of PFAS detection for one or more analytes across all wells (N=105) was 69%. Of all wells tested, 16 (15%) exceeded the current public drinking water standard in Maine (20 ng/L for 6 PFAS), and an additional 17 (totaling 31% of all wells tested) exceeded Maine's newly adopted public drinking water standards for the state (based on EPA 2024 standards). Because private wells are unregulated, homeowners may use these results as a guide for taking action to maintain safe drinking water. Both hierarchical cluster analyses and pairwise Spearman's rank correlation of PFAS test results revealed similar profiles in households near and far from one another. Along with the absence of obvious, large-scale potential point sources of PFAS contamination, these findings support septic systems as the primary source of PFAS contamination in drinking water across this town. The community-driven approach to the sampling effort has enabled information sharing on the importance of water filtration options, discussion of long-term pollution abatement strategies for individual households and the town, and focused efforts for future well water testing and PFAS mitigation.

Wastewater contains a complex mixture of nucleic acids derived from human cells, microorganisms, diets, and agricultural or industrial inputs. Metatranscriptomics has the potential to broaden the understanding of wastewater viromes, but its utility is constrained by high-abundance RNA molecules that prevent the detection of lower-abundance signals, including human pathogens. Two major sources of this interfering background signal are ribosomal RNAs (rRNAs) from various organisms, and plant-infecting tobamoviruses from human diets, which are often the most abundant RNA viruses in wastewater. These rRNA molecules and RNA genomes can account for upwards of 95% of total RNA sequences, thereby obscuring the detection of lower-abundance viruses of public health importance. This study evaluated whether combined depletion of ribosomal RNAs and genomic RNA from abundant plant tobamoviruses can improve the recovery of underrepresented viral sequences from wastewater. RNA extracts from a large municipal wastewater treatment plant in Calgary, Canada, were processed with or without probe-based depletion of rRNAs and eight abundant tobamoviruses. Metatranscriptomes revealed that depletion reduced rRNA reads by 90%. Reads assigned to tobamoviruses, which accounted for up to 25% of viral reads in untreated samples, were reduced to <2% of viral reads after depletion. This decrease in undesired RNA signal led to an order of magnitude increase in viral read signals and improved assembled viral genome quality. Furthermore, the number of reads assigned to human and animal pathogens increased 42-fold after depletion, enabling the detection of important pathogens like influenza, RSV, and hepatitis A, which were undecteable in untreated samples. Together, these findings demonstrate that targeted depletion of abundant background RNAs improves the detection of low abundance viruses, and enhances the utility of metatranscriptomic approaches for wastewater viral surveillance.

Wastewater sequencing using probe-capture enrichment of public health-relevant viruses is a powerful, non-intrusive approach to monitor infectious diseases, but performance depends on sample type and processing approach. We compared two processing pipelines from wastewater influent samples collected at three timepoints across three wastewater treatment facilities. Each sample was split for parallel processing, with viruses concentrated (1) directly from whole influent using nano-sized affinity particles and (2) by settling and dewatering the solids fraction. Combined DNA/RNA extracts were prepared into libraries using a pre-commercial Illumina protocol, then probe-capture enrichment was performed with Illumina Viral Surveillance Panel (VSP) v1 (66 targets) and v2 (242 targets). Sequencing depth was increased for VSP v2 to accommodate the expanded target set. On average, 160 +/- 146 viral species were detected per sample, with wastewater fraction (sample type) explaining 19% of the variance in viral abundances. Solids contained a higher abundance of DNA viruses compared to RNA viruses, while influent exhibited the inverse. Metagenomic analysis of the VSP v2 sequencing data detected more viral species in influent (median 358) than in solids (median 184), but probe-capture enrichment produced more comparable target-virus readouts across fractions, supporting the use of either fraction for wastewater-based epidemiology.

The anaerobic biotransformation of lindane ({gamma}-hexachlorocyclohexane), a persistent legacy pesticide and challenging subsurface contaminant was modelled using an integrated Monod biokinetics framework. Calibrated against batch experimental data, this model accounted for the dissolution of solid phase lindane and the thermodynamics of reactions involved. The study focused on a methanogenic, lindane-degrading enrichment culture (GT2) that used ethanol as the electron donor, where ethanol fermentation supplied hydrogen to support reductive dechlorination of lindane. A Dehalobacter strain was identified as the lindane-degrading organism, while a member of bacterial order Geobacterales carried out ethanol fermentation. First-order decay constants for lindane-degrading Dehalobacter were estimated from experimental data and showed variation across different growth conditions. The biokinetics model captured interactions among key microbial processes, namely lindane dechlorination, ethanol fermentation, hydrogenotrophic methanogenesis, and acetoclastic methanogenesis. Thirteen biokinetics parameters including maximum substrate utilization rates (k), half-velocity constants (Ks), and decay constants for these microbial processes were estimated through a Bayesian optimization approach. Global sensitivity analysis showed that eight parameters, primarily k and Ks, had significant linear impact on the model output, whereas responses to decay constants were more variable and potentially non-linear. This study proposed an effective modelling approach for characterizing syntrophic microbial growth within enrichment cultures and provides valuable biokinetics parameters estimates for real-world applications of anaerobic lindane bioremediation.

Groundwater contamination by lead and nitrate exceeding the permissible limits of the World Health Organization (WHO) and the Bureau of Indian Standards (BIS) of 10 ppb lead and 45 to 50 ppm nitrate poses a severe health risk of systemic toxicity in humans and animals. Microbial nitrate reduction coupled with microbiologically induced calcite precipitation (MICP) proved to be a novel bioremediation strategy to transform soluble lead nitrate into insoluble lead biominerals. This study presents statistical optimization of operational parameters for nitrate reduction and co-precipitation of biogenic lead carbonate-calcium carbonate by nitrate-reducing bacterial consortia. The SEM, TEM with SEAD, and XRD analysis of the biomineral confirmed lead carbonate co-precipitation with CaCO3. A two-level factorial design was applied to screen significant variables and interactive factors that influence nitrate reduction and biomineral precipitation. The highest nitrate reduction, maximum ammonia production, and biogenic Pb-mineralization efficiency of > 90% were obtained at the optimum conditions (initial nitrate requirement of 600 ppm, temperature 37 oC, pH 8, and carbon content of 5.5 g/L) compared to those obtained under the common process parameters. ANOVA with a high correlation coefficient (R2 > 0.99) corroborated the linear model for the biogenic mineralization process. This study on the biogenic removal of Pb by nitrate-reducing bacterial consortia revealed possible opportunities for large-scale treatment of Pb-contaminated surface and subsurface water bodies.

Coastal waters often experience fecal contamination from human (e.g., septic systems, wastewater treatment catchments and outfalls) and non-human (e.g., wildlife, domesticated pets, and livestock) origins, posing human health risks through contaminated fisheries and recreational waters. Quantitative PCR (qPCR) methods can identify and quantify fecal origins for their management by targeting Bacteroides, a dominant group of gastrointestinal bacteria with high host specificity. This study identified major contributors and drivers of fecal contamination to Mobile Bay, Alabama, an urbanized estuary in the southern United States. We utilized a multi-species qPCR source tracking approach targeting total Bacteroides and human-, pig-, dog-, cow-, and fowl-associated Bacteroides. Water samples and environmental parameters were collected over one year from 13 sites that represent a range of potential fecal inputs common to coastal waters. Human-associated feces were a consistent major contributor to the system (positive in 82% of samples), with localized inputs from pig (wild or domestic) and dog. Freshwater discharge and nitrogen constituents were good predictors of Bacteroides concentrations in this system, while areas with impervious surfaces and developed land cover may be contributing elevated fecal indicator levels. The multi-species qPCR source tracking approach described in this study can be implemented in other watersheds to support the development of better detection, identification, and fecal management strategies for community health of coastal areas.

The unprecedented rise in greenhouse gases such as carbon dioxide (CO2), and their detrimental effects on the atmosphere have intensified the current climate emergency. This challenge has inspired the development of novel carbon capture and utilisation (CCU) technologies, specifically microbial electrosynthesis (MES), for the bioelectrochemical fixation of CO2 into commodity chemical compounds (CCCs) using microbial biocatalysts. In this study, we systematically enriched and maintained robust mixed microbial communities of electroactive bacteria (EAB) from wastewater treatment sludge to serve as biocatalysts for MES. The MES performance of these biocatalysts under different operational conditions in bioelectrochemical systems (BES) reactors were investigated. BES reactors with enriched biocatalysts incubated and maintained under controlled conditions produced higher CCCs yields than those developed at ambient temperatures, while strict anaerobic operations further improved the MES efficiency compared to aerobic conditions. At an applied potential of -1000 mV vs Ag/AgCl, CO2 conversion reached up to 88.11%, significantly outperforming the lower potential (-600 mV) operation. These findings underscore the importance of systematic development of robust and stable biocatalysts, as well as maintaining strict anaerobic conditions, for achieving efficient MES performance, thereby demonstrating the potential of our approach for scalable industrial CCU applications.

Wastewater-based epidemiology uses both targeted and non-targeted bioanalytical approaches to detect and track disease-causing microbes in wastewater. Targeted approaches such as quantitative polymerase chain reaction (qPCR) remain widely used while sequencing-based approaches have slowly gained popularity. However, concordance between established targeted molecular assays and sequencing approaches remains incompletely characterized. To assess the validity of sequencing as an alternative to qPCR, we compared outcomes of both methods targeting five respiratory viruses in wastewater samples collected across 16 Texas cities over 4 years (May 2022-July 2026) of continuous sampling. We found positive correlations between qPCR and sequencing measurements, with both methods independently correlating with statewide clinical emergency department visits. qPCR positivity also increased with genomic sequencing coverage, and sequencing demonstrated greater potential than qPCR as an early respiratory virus outbreak detection method. Our findings support metagenomic sequencing as a sensitive and highly specific alternative to targeted qPCR for wastewater surveillance, and reinforce its utility towards long-term longitudinal monitoring of respiratory viruses.

Subsurface tile drainage rapidly transports nutrients and biological contaminants to surface waters, yet multi-year evaluations of drainage water management (DWM) effects on nutrients, Escherichia coli, and antibiotic resistance genes (ARGs) remain limited. A paired-field design with high-frequency soil moisture monitoring was used to quantify water yield, dissolved nitrogen, phosphorus, and Escherichia coli (E. coli) over seven and eight water years, and ARGs (tetM, tetW, ermB, sul1) over two years in southern Michigan. Soil moisture at tile depth (~75 cm) was higher under controlled drainage, confirming that DWM increases field water-holding capacity. Compared to the free drainage field, DWM reduced cumulative tile discharge by 43% and annual nutrient yields by 46-83% for nitrate and 12-82% for soluble reactive phosphorus, but effects on ammonium were variable. Average daily nutrient yields declined by ~50% over the study period, driven primarily by managed periods. Nutrient concentrations did not differ between treatments, indicating that yield reductions resulted from decreased water loss. Mean daily E. coli yields declined by 71% over the study period, and concentrations were 46% lower under controlled drainage. Controlled drainage reduced tetM and sul1 ARG concentrations and yields during managed periods and throughout the study; however, sensitivity analyses showed that these effects were strongly influenced by a high-precipitation event. After excluding the event, differences in ARG concentrations were no longer detected, although ARG yields remained 43% lower during managed periods. These findings highlight the potential for DWM to mitigate nutrient, microbial, and antibiotic resistance transport from agricultural drainage systems.

Biological filtration in newly established aquaria depends on the establishment of nitrifying microorganisms that prevent accumulation of toxic ammonia and nitrite. Many aquarists use commercial nitrifying supplements to accelerate biofilter establishment and reduce ammonia and nitrite accumulation. However, the microbial composition and in situ efficacy of these products remain poorly characterized. We evaluated the ability of enrichment cultures containing comammox Nitrospira (CX) and the ammonia-oxidizing archaeon Ca. Nitrosotenuis aquarius (AQ) to serve as aquarium nitrifying supplements, tested alongside a conventional supplement (CS) containing ammonia-oxidizing bacteria. Inoculation of new aquaria containing zebrafish (Danio rerio) with nitrifying supplements resulted in distinct nitrifier profiles within biofilters of each treatment group corresponding with significant reductions in peak ammonia and nitrite concentrations, which were most evident when applied at the maximum tested supplement dose. Treating aquaria with CX, AQ, or CS maintained ammonia and nitrite at low concentrations, and CX effectively prevented ammonia and nitrite accumulation through all stages of aquarium operation and biofilter establishment. After removing all fish from the aquaria, ammonia "spike-in" experiments demonstrated biofilters treated with CX and AQ conferred long-term protection against nitrite accumulation following acute ammonia loading events. Whereas 16S rRNA gene sequences associated with CS were rarely detected within biofilter profiles, those associated with CX and AQ established early and persisted temporally. Overall, results demonstrate how nitrifiers might be differentially adapted to home aquarium conditions and the potential of AOA and comammox Nitrospira to improve biofilter establishment and maintaining safe water quality in newly established freshwater aquaria.

Wastewater-based epidemiology (WBE) has become a cornerstone of public health surveillance, yet routine regulatory monitoring remains largely focused on respiratory viruses and selecting antimicrobial resistant bacteria. Emerging foodborne parasites, such as Cyclospora cayetanensis, remain excluded from standard environmental surveillance panels despite increasing domestic outbreaks. This study presents a laboratory-based pilot protocol evaluating the feasibility of extracting and quantifying C. cayetanensis from urban wastewater across the Chicago metropolitan area. Using sample concentration via centrifugation and mechanical bead-beating, automated magnetic bead nucleic acid extraction, and digital PCR (dPCR), we demonstrate high-sensitivity detection in municipal influents and sewer nodes. These findings establish a proof-of-concept laboratory methodology to support the expansion of regulatory public health surveillance frameworks to include non-routine protozoan pathogens.

Antibiotic resistance genes (ARGs) travel from cities into rivers and coastal waters through wastewater treatment plants. Monitoring them normally requires metagenomic sequencing, which is sufficiently costly that most utilities can sample only occasionally. Weather and location data are freely available on a daily basis for virtually any location worldwide, making them attractive predictors for identifying where limited sequencing resources should be prioritized. However, whether such models generalize to previously unseen cities remains largely untested, as most published studies train and evaluate models within the same catchments, thereby assessing interpolation rather than geographic transferability. To address this gap, we paired 235 wastewater metagenomes collected from five European cities with 23 abiotic predictors spanning geospatial, meteorological, hydrological, radiative, and temporal domains. Model performance was evaluated using leave-one-group-out (LOGO) cross-validation, in which all samples from one city were withheld for testing while the remaining cities were used for training. CatBoost achieved a median LOGO ROC-AUC of 0.929 and a median F1-score of 0.750. Using freely available environmental reanalysis predictors (meteorological, hydrological, radiative, geospatial, and temporal) - without any metagenomic sequencing - CatBoost achieved a mean ROC-AUC of 0.722, recovering 78% of the predictive performance of the full omics-integrated model. Removing latitude and longitude reduced ROC-AUC by only 0.003, whereas replacing random cross-validation with city-wise validation reduced ROC-AUC by 0.052. Predictive performance varied across ARG classes, ranging from a ROC-AUC of 0.981 for {beta}-lactam resistance genes to 0.762 for glycopeptide resistance genes. These findings demonstrate that freely available environmental reanalysis predictors - spanning meteorological, hydrological, radiative, geospatial, and temporal domains - recover 78% of the predictive signal for antibiotic resistance gene occurrence in urban wastewater, allowing scarce sequencing capacity to be directed to the catchments where it changes a decision.

Eutrophication-driven hypoxia and harmful algal blooms are an expanding threat to water quality in estuaries. Engineered aeration is a technology used in freshwater and, to a lesser extent, estuarine systems to mitigate both hypoxia and algal blooms. However, the efficacy of engineered aeration has not been well studied within estuarine ecosystems and very little is known about the impact of aeration on microbial communities in either estuarine or freshwater environments. Here we present a time series investigation of microbial community response to aeration and cessation of aeration in a eutrophic estuary. Samples for 16S rRNA gene analysis were collected over about a month-long period during which time the aerators transitioned from being "on" to "off" to "on" again. Cessation of aeration selected for the dominance of a eukaryotic algae similar to Heterosigma akashiwo. After the resumption of aeration, H. akashiwo dominance decreased rapidly. Cessation of aeration also shifted the structure of the entire microbial community, where diversity decreased and the structure of microbial communities at the surface diverged from those from the bottom of the water column. These changes did not occur at non-aerated control sites. Distinct groups of taxa responded to the disruption in a successional pattern through time, with most taxa never returning to their pre-aeration-cessation abundance during the observational period. These findings demonstrate the complex community response to anthropogenic interventions and provide support for bloom suppression through aeration.

With the increase of recycled water to offset water demand, the potential possibility to spread contagious RNA viruses, such as Coxsackievirus B3, increases. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. Robust monitoring is needed with low cost and low infrastructure technologies to increase accessibility of monitoring technologies worldwide. Novel viral detection methods have been developed, based on synthetic riboswitches that bind to the target virus and trigger a reporter gene, thus amplifying the detection signals. Such monitoring technologies can leverage machine learning to optimize the candidate nucleic acid sequences used for detection. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features based on free energy calculations and parameters that represent single strandedness. This model uses a sparsely gated Mixture of Experts (MoE) architecture to route sequence and thermodynamic features to specialized experts, achieving strong generalization performance across cross-validation and held-out testing. When evaluated against baseline Decision Tree, Random Forest, Gaussian Naive Bayes, and Dense Neural Network classifiers, the MoE model demonstrated near-zero classification errors. Furthermore, post-hoc feature importance and k-mer analyzes reveal that near-perfect predictive performance in silico is strongly driven by sequence constructs in addition to generalizable folding mechanics. An ablation study of numerical feature and sequence inputs showed that while the Dense Neural Network also achieved high accuracy across all ablation conditions, the MoE architecture was retained as the primary model because its specialized subnetworks do not require all model parameters to be activated offering a potential computational advantage.

An increasing number of researchers, educators, and at-home growers are turning to hydroponics as an alternative method of food production, diversifying and strengthening the food supply chain. This has led to a substantial increase in the amount of academic work involving hydroponic production of plants for a variety of purposes, including but not limited to a more controlled and customizable environment than conventional production methods, utilizing hydroponics as a method of wastewater treatment, and investigating the vertical production of plants on a much smaller footprint than traditional commercial-scale production. Because so many unique stakeholders are interested in hydroponics for a variety of reasons, academic studies use a wide variety of hydroponic systems with varying levels of technology and automation, making comparisons across studies and systems challenging. A few institutions have sophisticated plant growth and research facilities, like those used by commercial companies, but these systems are often prohibitively expensive, costing upwards of $10,000 USD for a very limited amount of growing space for experimental trials. To enable more researchers, educators, and citizen scientists to conduct experiments and easily compare their results, this study details three pilot-scale hydroponic production systems (Kratky, NFT, and DWC), utilizing entirely off-the-shelf components, for under $5,000 USD each, that are capable of maintaining the desired consistent temperature and relative humidity levels, and have space for 216, 108 (6 irrigation loops), and 64 (16 irrigation bins) plants, respectively. The material lists and prices are provided along with assembly instructions and general guidelines for each hydroponic system. The testing and optimization process of environmental conditions within each growth system is also detailed, with suggested customizations for potential experimental setups. The authors developed these systems during their Masters and Doctoral degree programs, enabling improved hydroponic research at the University of Illinois Urbana-Champaign. They hope that other aspiring hydroponic researchers can benefit from this work in the development and operation of their hydroponic systems, increasing research efficiency and collaboration opportunities in this field of study.

Background: Chronic arsenic exposure in drinking water causes skin lesions and cancers and raises infant, cardiovascular and all-cause mortality in Bangladesh. National surveys tested source water in 2012-13, 2019 and 2025 with different kits, so the trend is uncertain. Objectives: We estimated how arsenic in source water changed after laboratory adjustment of 2012-13 readings, and how many people remained exposed. We also assessed whether districts high in earlier surveys held most people exposed in 2025. Methods: We analysed 11,624 households in three Multiple Indicator Cluster Surveys. We counted arsenic strictly above 10 and 50 parts per billion (ppb). Results: Estimates reproduced published 2019 and 2025 tables. After laboratory adjustment of 2012-13, the share above 10 ppb fell from 24.0% to 14.4% (adjusted ratio 0.61, sampling 95% CI 0.55 to 0.67; 0.69 with interview month). Above 50 ppb it fell from 10.9% to 8.6% (0.80, 0.69 to 0.93; 0.93 with month). Most of the fall at 10 ppb occurred within source types. Without false positives, it would vanish only if later kits detected 61% of sources above 10 ppb. About 26 million people used such water in 2025 (kit scenarios 23 to 32 million). The 25 districts high in 2012-13 and 2019 held 39% of the population but 72% of those exposed (bootstrap 95% interval 60 to 76). Clustering was evident, but no district was a significant hotspot. Conclusions: Arsenic above 10 ppb was lower in 2025 than in 2012-13, but the size of the fall depends on unverified later kits. Change at 50 ppb and since 2019 was unclear after month adjustment. One household in seven exceeded the WHO guideline in 2025. Districts found high earlier held most exposed people, supporting prioritised well testing there alongside broader surveillance. Future surveys need one kit with a laboratory cross-check.

Microalgae are promising biological platforms for sustainable energy production because they can convert photosynthetic activity into electricity in biophotovoltaic (BPV) systems. Identifying strains that grow well at elevated temperatures and under reduced irradiance is an important step toward BPV applications in seasonally variable environments. Microalgae were isolated from Seondong Lake (Seoul, Republic of Korea) during three sampling periods and screened for growth at different temperatures. Among 45 isolates, KHUC-11 showed the highest weighted growth index based on growth rates at 25, 30, and 35 under dark conditions and was selected for further characterization. Phylogenetic reconstruction of the 45S rRNA gene cluster, internal transcribed spacer 2 (ITS2) secondary-structure comparison, and morphological characterization supported the description of KHUC-11 as Desmodesmus ecotolerans sp. nov. D. ecotolerans produced more biomass than Chlamydomonas reinhardtii CC-125 under both moderate- and low-light conditions. Under low light, its final dry biomass concentrations were 1.81 and 1.58 times those of CC-125 under mixotrophic and photoautotrophic conditions, respectively. Despite its smaller cell size, D. ecotolerans showed higher photosynthetic performance than CC-125, and screen-printed electrode (SPE)-based electrochemical assays demonstrated enhanced anodic current generation and CV-derived estimated power output. Taken together, these findings identify D. ecotolerans as a novel climate-resilient Desmodesmus species with bioelectrochemical properties that support further evaluation for BPV applications.

Background: In coastal Bangladesh, saline drinking water has been linked to raised blood pressure. The 2025 Multiple Indicator Cluster Survey (MICS) measured electrical conductivity (EC), a salinity indicator, in source water, but its report gave no population counts or district estimates. Objectives: To estimate how many people live in households whose tested source has high EC, and to describe patterns by place, household characteristics, source type and arsenic. Methods. EC was measured in source water of 6,040 households in 64 districts, February to June. In 14.5% of households the tested source was not the main drinking-water source. Primary outcomes were EC above 1,500 and 3,000 microsiemen/cm, survey risk classes that are not health-based standards. Estimates were survey weighted; comparisons were observational. Results: EC exceeded 1,500 microsiemen/cm in 5.3% of households (95% CI 4.7 to 5.9), about 9.0 million people, including 1.8 million above 3,000 microsiemen/cm. Coastal-zone districts held 26% of households but 70% of those above 1,500 microsiemen/cm. Shares varied little by wealth or in households with young children or pregnant women. Nationally, deep tube wells more often had high EC because most are coastal. In the coastal zone, deep and shallow wells had similar shares (16.1% and 17.9%). After district adjustment, EC was about 18% lower in deep wells (geometric mean ratio 0.82, 0.73 to 0.92). Within upazilas (restricted sample) it was 0.93 (0.80 to 1.07). Arsenic above 10 ppb was more common where EC was high, but this link weakened after district adjustment. Conclusions: Millions of people live in households whose tested source has high EC, mostly in coastal and south-central districts. Deep wells had modestly lower EC after geographic adjustment, but the difference shrank within upazilas and depended on the handling of implausibly low readings. The findings support measuring EC alongside arsenic, with documented meter calibration.

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.