# Data Integrity in Water and Environmental Microbiology Testing Data integrity in water and environmental microbiology testing is the discipline of making every reported number traceable to an original, contemporaneous, complete, and unaltered observation. A count of 3 colony-forming units (CFU) per 100 mL in a distribution sample, a most probable number (MPN) from a surface water survey, or an intact cell concentration from a flow cytometer is only as valuable as the records behind it. When those records hold up, the laboratory can defend its result through an audit, an outbreak investigation, or a regulatory review. When they do not, the result is guesswork with a decimal point. This guide sets out how to build data integrity into a water microbiology workflow, from the sampling bottle to the archived record. It applies to any laboratory producing microbial data on drinking water, groundwater, recreational water, wastewater, irrigation water, food processing wash water, or pharmaceutical water systems, whether or not the laboratory holds accreditation. Building the framework takes roughly 3 to 5 working days of combined bench and supervisory time, spread over 4 to 6 weeks while standard operating procedures (SOPs) are revised and staff are trained. Daily operation adds 10 to 15 minutes per analytical batch for controls and record checks, plus a few hours each quarter for trending and management review. External proficiency testing typically arrives twice per year [1]. ## Why Data Integrity Carries Public Health Weight Pathogenic contamination of drinking water poses the most significant health risk to humans on a global scale, and the classical risk assessment paradigm, running from source identification through exposure assessment to risk characterization, was laid out in the perspective prepared for the Walkerton inquiry as the framework for reducing that risk [2]. Water microbiology results sit at the decision end of that paradigm. A false negative can leave a community drinking contaminated water. A false positive can trigger an unnecessary boil water advisory. Surveillance programs multiply the stakes because they turn thousands of individual results into policy. Between 2017 and 2024, Bhutan's national program compiled 35,343 samples from 273 surveillance sites into a single Water Quality Monitoring Information System and found that only 52.8 percent of urban samples met the microbial standard of 0 CFU/100 mL, while residual chlorine compliance fell to 11.9 percent, prompting a national recommendation to standardize testing methodology and audit water safety plans [3]. Every percentage in that sentence depends on the integrity of the underlying records. Field programs translate data into action. Water quality testing across 65 schools in Belize found that 83 percent had at least one sample below the WHO-recommended free chlorine residual level, coliforms in 66 percent of schools, and Escherichia coli in 22 percent, and those findings were used to trigger timely public health responses [4]. Accuracy also differs between laboratories and between the methods they use, which is why participation in external quality assessment (EQA), including proficiency testing (PT), is described as a necessary risk management resource for environmental microbiology testing [1]. Data integrity is what makes all three layers trustworthy: the single result, the surveillance dataset, and the inter-laboratory comparison. ## The Principle: ALCOA+ at the Water Microbiology Bench Data integrity means the degree to which a recorded result is attributable, legible, contemporaneous, original, and accurate. Those five attributes form the ALCOA framework, extended in laboratory quality management to ALCOA+, which adds complete, consistent, enduring, and available. In plain terms, a good record shows who did what, when, to which sample, using which method and reagent lot, with the original observation preserved and the calculation traceable back to it. Water microbiology has failure points that analytical chemistry laboratories do not face. The analyte is alive. Concentrations change between collection and analysis, so holding time and transport temperature are part of the result, not paperwork around it. Counts depend on analyst technique, and many readings are still transcribed by hand from a counting grid or a colony counter display. Incubation runs overnight or longer, so a mistake made on Monday surfaces on Wednesday, after the sample is long gone. Microbiology also has an unusual raw data problem. In chemistry, the vial can be re-injected. In culture, the plate is read once, photographed perhaps, and then autoclaved. The recorded count becomes the only permanent raw record, which raises the bar for capturing it correctly the first time. Raw data in this context means the original observation (the count, the image, the instrument output), while metadata means the context that makes it interpretable (sample identity, time, temperature, media lot, analyst). The data lifecycle runs from sampling through transport, receipt, analysis, raw record, calculation, review, release, retention, and sometimes reuse years later in trend analyses. Each handoff is a chance to lose or alter information. A quality management system (QMS) treats the whole chain as one process. In wastewater surveillance, QMS principles are applied across sample collection, laboratory analysis, data interpretation, and reporting to ensure reliability, reproducibility, and cross-site comparability [5]. The same architecture applies to any environmental microbiology laboratory. Two ideas anchor everything that follows. First, data are generated once and must be captured at the point of generation, in a form that cannot be silently altered. Second, every correction must be visible, attributed, and justified, whether the record is paper or electronic. ## Materials, Equipment, and Records | Item | Working specification | Role in data integrity | |, - |, - |, - | | Sterile sample bottles, 100 to 120 mL | Pre-dosed with sodium thiosulfate (0.1 mL of 10 percent w/v solution per 120 mL bottle, or a manufacturer pre-dosed bottle) | Neutralizes residual chlorine so counts reflect the sample at collection, not ongoing disinfection during transport | | Transport cooler with wet ice or gel packs | Target near 4 °C, with a temperature indicator or data logger | Transport temperature is an analytical condition and must be recorded | | Phosphate-buffered dilution water | pH 7.2, sterilized, checked for background growth before use | Diluent for filtration and plating, a contaminated blank invalidates the batch | | Membrane filtration apparatus | 0.45 µm pore size, 47 mm filters, sterile funnels and forceps | Standard hardware for coliform and E. coli membrane filtration | | Culture media | Chromogenic or defined-substrate media for total coliforms and E. coli, or membrane filter media per your method, stored per manufacturer, each lot logged with expiry and growth or inhibition checks | Media lot failures masquerade as sample results unless lots are controlled | | Incubators | 35 ± 0.5 °C for total coliforms and heterotrophic plate counts, 44.5 ± 0.2 °C where the method requires, mapped for uniformity and continuously monitored with a calibrated thermometer or data logger | Temperature excursions invalidate incubation conditions retroactively | | Control organisms | A nonpathogenic E. coli strain as positive control and a non-target relative such as Enterobacter sp. as negative control, mirroring the organism pairs used in drinking water PT challenges [1] | Prove the method detects the target and discriminates against close relatives | | Flow cytometry viability reagents | SYBR Green I and propidium iodide (PI), PI final 6 µM, staining at 35 °C, stocks prepared in TRIS buffer rather than DMSO, validated window 3 to 12 µM PI (below 3 µM leaves damaged cells unstained, above 12 µM falsely labels intact cells, 25 °C stains slowly and incompletely, 44 °C damages cells) [6] | Staining conditions are analytical conditions, artifacts here corrupt viability data | | Electronic records system | [Laboratory information management system](/knowledge/bioinformatics/lims-explained-lab-information-management-systems) (LIMS) or validated electronic capture with individual user accounts and a computer-generated audit trail, or controlled paper worksheets in bound books [7] | Captures raw data contemporaneously and makes every change visible | | Documentary records | Chain of custody forms, holding time log, media and calibration logs, deviation and corrective action forms | The documentary skeleton of ALCOA+ | | Decontamination | Autoclave and biohazard waste containers | Safe disposal of cultures, filters, and contaminated consumables | **Safety note.** Environmental concentrates, wastewater, and wash water can contain enteric pathogens. Process them in a designated containment area with gloves and laboratory coat, and autoclave cultures, filters, and waste before disposal. Propidium iodide and SYBR Green I are potential mutagens, weigh and stain with gloves and eye protection, and collect stained waste separately for hazardous disposal. ## Data Integrity in Water and Environmental Microbiology Testing, Step by Step The sequence below follows the life of a sample. Each step lists the action and the reason it protects the record. 1. **Write the data integrity policy and map the lifecycle.** Adopt ALCOA+ in a one-page policy, then list every step from bottle to archive with a named owner for each. **Why:** You cannot protect a data path you have not mapped, and the policy gives staff and auditors a single definition of a good record. 2. **Assign unique sample identifiers at collection.** Every bottle gets an identifier at the moment of collection, linked to location, date, time, collector name, and, for chlorinated systems, the residual chlorine measured on site. **Why:** Attributability starts at the source. An unidentified bottle cannot be defended no matter how good the bench work is. 3. **Control transport temperature and enforce holding times.** Ship chilled near 4 °C and dark, record the collection time and the receipt time, compute the elapsed time, and flag any exceedance. The stakes are quantified in holding time trials that tested intervals from 0 to 216 hours under three storage treatments: canal water held fecal indicator bacteria near baseline for at least 72 hours under controlled cold (about 4 °C), at least 48 hours under changing 4 to 21 °C conditions, and at least 30 hours under controlled ambient (about 21 °C), while river water samples stayed stable for shorter intervals [8]. Compliance methods set maximum hold times far shorter than these research intervals, so record the actual times and reject or flag samples that exceed the limit. **Why:** Concentrations drift with time and temperature, so an out-of-hold sample analyzed anyway produces a number that no longer describes the water. 4. **Verify sample receipt and log deviations.** On receipt, check bottle integrity, transport temperature, elapsed time, and documentation completeness, and log every deviation against the sample identifier. **Why:** Silent acceptance of a compromised sample is the most common origin of an indefensible result. 5. **Control media, reagents, and equipment.** Log every media and reagent lot with receipt date, expiry, and growth or inhibition checks (a target strain must grow, a non-target strain must be suppressed). Calibrate pipettes, thermometers, and balances on schedule, and keep incubator temperature records continuous rather than spot-checked. **Why:** A failed media lot or a drifting incubator produces systematic error that looks like a trend in your samples. 6. **Run microbiology quality control with every batch.** Filter a method blank (sterile dilution water through the full apparatus), run the positive control and the non-target control, and analyze duplicates on a defined fraction of samples. **Safety note:** Treat every environmental sample as potentially infectious, use gloves and coat, work in containment, and autoclave all cultures, filters, and waste before disposal. **Why:** Controls are the only way to distinguish a bad batch from a bad sample. 7. **Capture raw data at the point of generation.** Record counts as they are read, on controlled paper worksheets or in a validated electronic system with individual accounts and an audit trail. Paperless capture through instruments that combine incubation and enumeration removes an entire transcription step [7]. **Why:** Contemporaneous capture is the "contemporaneous" in ALCOA, and every manual transcription is an error opportunity that no later review can fully repair. 8. **Count within the defined window and apply conventions.** Read plates within the reading window your method specifies after incubation. Record original observations, including crowded filters as too numerous to count (TNTC) with a documented estimate, rather than inventing a precise number. **Why:** Colonies keep growing and changing after the ideal reading window, and a TNTC estimate recorded as an exact count overstates the precision of the method. 9. **Verify calculations and report the detection limit honestly.** Check dilution factors and filtered volumes before release. Worked example: filter 100 mL and count 3 coliform colonies, report 3 CFU/100 mL. Filter 10 mL and count 1 colony, report 10 CFU/100 mL. Filter 100 mL and find no colonies, report less than 1 CFU/100 mL, never 0, because the detection limit is one colony per volume filtered. **Why:** A reported 0 implies the organism was absent, when the method can only say none were detected in the volume examined. 10. **Apply second-person review before release.** A second qualified person checks the [batch record](/knowledge/molecular-biology/batch-record) against the raw data, the controls, the calculations, and any deviations, then signs. **Why:** Self-review misses self-consistent errors, a second reader catches transcription slips and logic gaps. 11. **Participate in external proficiency testing.** Subscribe to an accredited PT provider and treat each round as a graded exam. Proficiency Testing Canada and the Canadian Microbiological Proficiency Testing program, both ISO/IEC 17043:2010-accredited EQA providers, have run a drinking water PT program since 1996, issuing challenges twice per year, each with four samples containing unknown concentrations of E. coli and Enterobacter spp., and a cross-sectional evaluation of 150 Canadian environmental testing laboratories found that detection accuracy depended on the testing method used and the laboratory's accreditation status [1]. **Why:** Internal controls show your batch was internally consistent. PT shows your result agrees with the outside world, which is what the public health decision actually needs. 12. **Trend, audit, and correct.** Plot control results over time, review audit trails on a schedule, hold management review, and log corrective actions to closure. Plan for missing data before it happens. In one surface water monitoring dataset of 2,118,844 data points, 9.69 percent of values were missing, and an iterative structural expectation maximization approach outperformed simpler dynamic imputation methods, improving imputation accuracy by roughly 50 percent on some error metrics [9]. For compliance data, flag and document gaps and report observed values only, reserving validated imputation for research interpretation. **Why:** Laboratory quality management is a cycle, not a certificate. Trends reveal slow drifts that single batches hide, and a pre-agreed missing data plan prevents improvised fixes during a crisis. ## Controls and Expected Results Microbiology quality control is where data integrity becomes visible on the bench. Each control has an expected result, and anything else is a signal to stop. **Method blank.** Expected result: no growth. Any colony means contamination somewhere in the dilution water, apparatus, or technique, and the whole batch is invalid. Never subtract the blank count from sample counts, find and fix the source and repeat the batch. **Positive and non-target controls.** Expected result: the positive control grows with correct colony morphology and the target reaction, the non-target control either does not grow or grows without the target reaction. Drinking water PT challenges deliberately pair the target organism with a close non-target relative, because discrimination between close relatives is exactly where methods fail [1]. A non-target that lights up with the target reaction means the media lot or incubation conditions need investigation before any sample result from that batch is reported. **Duplicate precision.** Expected result: replicate filters agree within the precision criterion stated in your method. When they do not, suspect mixing, pipetting, or filtration technique before suspecting the water. **Proficiency testing.** Expected result: results within the provider's acceptance limits. A single failure demands documented root-cause analysis. A pattern across rounds demands method review, retraining, or revalidation before the next round. **Staining and instrument controls (flow cytometry).** Expected result: intact and permeabilized cell controls resolve into distinct populations, and daily instrument checks (flow rate, alignment, background) stay within limits. Viability fractions are only as good as the staining, which is why the validated dye concentration, buffer, and temperature window exist [6]. **Temperature and hold time records.** Expected result: continuous records within method limits with no gaps. A gap in the incubator record is itself a deviation to document, because it leaves incubation conditions unverifiable. ## How to Read and Report Your Data ![Worked calculation showing how filtered volume and colony count convert to CFU per 100 mL, including the less than 1 result when no colonies grow.](/article-images/illustrations/data-integrity-water-environmental-microbiology-testing-cfu-report.webp) Report culture results as CFU per 100 mL for water, with the detection limit stated whenever no colonies are found. Report MPN results as the MPN index per 100 mL with the confidence interval the method provides. Report flow cytometry as total cell concentration and intact cell concentration, or as the fraction of intact cells. Compliance decisions compare these numbers to a standard. Many drinking water programs set the microbial limit at 0 CFU/100 mL for E. coli, as applied in national compliance assessment [3], so a single confirmed detection fails the sample. That is why the detection limit and the honesty of a "less than 1 CFU/100 mL" report carry real consequences. Read microbial results next to the chemistry that governs them. In the Harare water system, elevated ammonia and reduced free residual chlorine across sections of the distribution network coincided with detectable heterotrophic bacterial growth, the kind of coincidence that only appears when microbial and physicochemical data are captured and reviewed together [10]. A disinfection residual that disappears tells you the microbial result is a system problem, not a sampling fluke. For flow cytometry, interpret the intact cell fraction as an early warning on disinfection. In kinetic tests with six oxidants, cells lost membrane integrity at low exposures without a detectable lag phase while total cell concentrations fell only under ozonation, and membrane integrity was judged a conservative, and therefore safe, parameter for disinfection control [11]. A declining intact fraction can signal effective treatment even while total counts look unchanged. ## Troubleshooting Data Integrity Problems | Symptom | Likely cause | Fix | |, - |, - |, - | | Colonies on the method blank | Contaminated dilution water, filter, funnel, or technique | Reject the batch, sterilize or replace components, filter a fresh blank, repeat, document the deviation | | Positive control fails or reacts weakly | Expired or overheated media, wrong incubation temperature, degraded control strain | Check media lot records and incubator logs, replace media, refresh the control culture, re-run | | Non-target control shows the target reaction | Chromogen cross-reaction, media lot defect, incubation above the specified band | Confirm organism identity, verify incubator uniformity, quarantine the media lot | | Duplicate counts disagree widely | Poor sample mixing, pipetting error, uneven filter wetting | Mix vigorously per the method, calibrate pipettes, re-filter in duplicate | | Counts drop between collection and analysis | Hold time too long or transport temperature abuse | Enforce hold limits, ship chilled with a temperature record, flag or reject out-of-hold samples [8] | | High counts in chlorinated water | Missing or exhausted thiosulfate, chlorine dissipated during transport | Verify bottle dosing, measure residual chlorine at collection, review transport time | | PT result outside acceptance limits | Method-specific weakness, analyst training gap, counting error | Perform root-cause analysis, retrain, review the method before the next round [1] | | Counts differ between analysts or counters | Subjective manual counting, poor counter accuracy | Qualify the counting method, prefer automated capture with multiple reads [7] | | Trend analysis distorted by data gaps | Missed sampling, instrument downtime, unrecorded rejected samples | Adopt a documented [data management](/blog/guides/data-management-basics-principles-processes-and-best-practices) plan, flag gaps, use validated imputation only for research analysis [9] | | Flow cytometry viability fractions look implausible | Staining artifacts or instrument drift | Follow the validated staining pipeline, run intact and permeabilized controls, perform daily instrument QC [6] | | Electronic records show no author or change history | Shared accounts or an unvalidated system | Issue individual accounts, validate the system, review audit trails on a schedule [7] | ## Variations and Alternative Workflows ### Culture-based method choices Membrane filtration, MPN, defined substrate tests, and heterotrophic plate count all sit on the same data integrity chain, only the endpoint changes. MPN yields a statistical estimate with [confidence intervals](/blog/data-analysis/confidence-interval-formula-examples) rather than a direct count, so report it as an index per 100 mL with the interval the method provides. Whichever method the laboratory chooses, the controls, hold times, and record rules above do not change. ### Automated incubation and enumeration Automated counters that incubate plates and enumerate them repeatedly through the incubation phase were qualified across eight pharmaceutical microbiology laboratories for environmental monitoring, bioburden, and water system testing, with comparable analytical performance across sites, and the paperless workflow was adopted specifically to minimize data integrity issues, since manual counting poses validation challenges due to poor counting accuracy [7]. Qualification still has to demonstrate equivalence for your own methods and sample types before the system replaces a validated manual read. ### Flow cytometry with viability staining Flow cytometry (FCM) with SYBR Green I and PI delivers total and intact cell counts in minutes. Because staining conditions are analytical conditions, follow a validated pipeline. The published development pipeline for this dye pair walked through dye solvent, dye concentration, temperature, and time, and found that the solvent itself (DMSO) could impair membrane integrity, requiring a switch to TRIS buffer for stock preparation [6]. Disinfection kinetics measured this way showed membrane damage rate constants spanning seven orders of magnitude across oxidants, in the order ozone, then chlorine, then chlorine dioxide and ferrate at similar rates, then permanganate, then chloramine [11]. **Safety note:** PI and SYBR Green I are potential mutagens, handle stained samples and waste with gloves and dispose of them as hazardous waste. ### Molecular tiers for wastewater surveillance Wastewater surveillance programs layer reverse transcription quantitative PCR (RT-qPCR), digital PCR, and sequencing, and a quality-managed operational architecture integrates sampling design, analytical workflow, and interpretation so that heterogeneous, low-prevalence signals remain comparable across sites [5]. The data integrity chain extends to molecular results, where extraction controls, inhibition controls, and run records play the role that method blanks play in culture. ### Field testing and secondary data use Point-of-use field testing feeds surveillance in low-resource settings, as in Kampala, where a Portable Microbiology Laboratory graded the risk of spring and borehole water and ten years of archived inpatient typhoid records were correlated with monthly rainfall to show a weather-sensitive, predictable pattern [12]. Archived data are only reusable if the original records were complete, which is the enduring attribute of ALCOA+ doing its work years later. ### Statistical treatment of monitoring data Environmetric methods such as [principal component analysis](/blog/data-analysis/principal-component-analysis-explained) (PCA) can identify which parameters drive contamination patterns, as applied to drinking water data in Perak, Malaysia, where PCA flagged turbidity, total coliform, E. coli, sodium, and aluminium as the major factors at the river intake [13]. [Statistical power](/blog/guides/statistical-power-what-it-is-and-why-it-matters-in-research) does not substitute for data quality, it depends on it. ## Storage, Stability, and Record Retention **Samples.** Transport chilled near 4 °C and dark, never frozen, and analyze within the maximum hold time your method or permit sets, recording the actual collection and analysis times. The holding time behavior described in the step-by-step section is the reason the clock is part of the result. **Media and reagents.** Store per the manufacturer, protect chromogenic media from light, log every lot with expiry, and discard on schedule. Prepare flow cytometry dye stocks in TRIS buffer per the validated pipeline rather than solvents that damage cell membranes [6]. **Records.** Retention periods are set by your accreditation body and legal requirements, commonly measured in years. Electronic records need scheduled backups with tested restores and retained audit trails. Paper records need bound books or controlled worksheets and secured storage. Retention is what satisfies the enduring and available attributes of ALCOA+, and it is what makes trend analyses and outbreak reconstructions possible long after the plates are gone. ## Common Mistakes and Limitations ![Four record-keeping failures in water microbiology: counting from memory, overwriting values, shared logins, and recording requested hold times.](/article-images/illustrations/data-integrity-water-environmental-microbiology-testing-record-mistakes.webp) These are the errors that most often destroy the defensibility of water microbiology data, seen repeatedly in audits and PT investigations. 1. **Recording counts from memory hours after reading the plate.** The record is no longer contemporaneous, and the plate is already autoclaved. 2. **Overwriting an original value** instead of a single-line strikethrough with initials, date, and reason on paper, or an audit-trailed change in an electronic system. 3. **Shared logins or uncontrolled spreadsheets,** so no one can later say who changed what, when, or why. 4. **Recording the requested hold time instead of the actual times,** which hides the exact condition the result depends on. 5. **Repeating a test until a passing result appears** without documenting the earlier attempts, sometimes called testing into compliance. 6. **Skipping the method blank to save a filter,** which removes the only evidence that the batch apparatus was clean. 7. **Reporting 0 CFU/100 mL** when the honest report is less than 1 CFU/100 mL at the volume examined. 8. **Correcting numbers in a downstream spreadsheet** while the raw record stays wrong, so the released report and the evidence diverge. 9. **Treating PT as a pass or fail event** instead of trending performance across rounds, which is where method drift shows first. 10. **Closing a deviation without a documented root cause and corrective action,** which guarantees the same deviation returns. Limitations deserve equal space. Culture methods detect cells that can grow on the media used, so viable but non-culturable (VBNC) organisms, meaning cells that are alive but will not divide under standard conditions, escape the count, and no growth is not proof of safety. The flow cytometry intact cell fraction measures membrane integrity, not infectivity, so it complements rather than replaces culture and regulatory testing. Imputation produces modeled values, not observations, and must be labeled as modeled wherever it is used. Data integrity controls cannot rescue a poorly designed sampling plan, and individual results, especially failures in distribution systems or during outbreaks, need interpretation by qualified professionals within a water safety plan. ## Frequently Asked Questions ### What does data integrity mean in water and environmental microbiology testing? It means every reported result traces to an original, contemporaneous, complete, and unaltered observation, from the sampling bottle through analysis, review, and archived storage. ### How quickly must a water sample reach the laboratory for microbiology testing? As soon as practical and always within the maximum holding time in your method or permit, transported chilled and dark, with the actual collection and analysis times recorded. ### What is the difference between quality control and proficiency testing? Quality control is internal and runs with every batch, such as method blanks, positive controls, and duplicates. Proficiency testing is external, with blind samples graded by an accredited provider to compare your laboratory against others. ### Can automated colony counters replace manual counting for compliance reporting? Yes, once the system is qualified and validated for your methods and sample types, and paperless capture reduces transcription errors, though flagged plates still deserve human review. ### What should I do if the method blank shows colonies? Reject the batch, investigate the contamination source, correct it, repeat the analysis, and document the deviation. Never subtract blank counts from sample results. ### How should missing values be handled in a water quality dataset? Flag and document the gaps, report observed values as collected, and reserve validated imputation methods for research interpretation, never for silent gap filling in compliance data. ### Do flow cytometry counts replace culture-based counts? No, they complement culture by delivering rapid total and intact cell counts, while most compliance standards remain culture based. ### Which records must a water microbiology laboratory keep? Chain of custody, receipt conditions, media and reagent lot logs, calibration records, raw counts, calculations, review signatures, proficiency testing results, deviations, and corrective actions, retained for the period your accreditation body requires. ## Sources 1. [Improving drinking water quality through proficiency testing-the impact of testing method and accreditation status on Escherichia coli detection by Canadian environmental testing laboratories.](https://pubmed.ncbi.nlm.nih.gov/38756531/) 2. [Sources, pathways, and relative risks of contaminants in surface water and groundwater: a perspective prepared for the Walkerton inquiry.](https://pubmed.ncbi.nlm.nih.gov/11809004/) 3. [Drinking water quality surveillance in Bhutan: trend and compliance (2017-2024).](https://pubmed.ncbi.nlm.nih.gov/41026136/) 4. [Assessment of Water Quality among Handwashing and Drinking Water Stations in Schools in Belize, 2022.](https://pubmed.ncbi.nlm.nih.gov/40199266/) 5. [Toward a quality-managed operational architecture for wastewater surveillance of zoonotic and emerging pathogens.](https://pubmed.ncbi.nlm.nih.gov/42429767/) 6. [A pipeline for developing and testing staining protocols for flow cytometry, demonstrated with SYBR Green I and propidium iodide viability staining.](https://pubmed.ncbi.nlm.nih.gov/27810378/) 7. [Multisite Qualification of an Automated Incubator and Colony Counter for Environmental and Bioburden Applications in Pharmaceutical Microbiology.](https://pubmed.ncbi.nlm.nih.gov/36379594/) 8. [Evaluation of fecal indicator bacteria concentration in different water sources, storage treatments, and holding times.](https://pubmed.ncbi.nlm.nih.gov/42563276/) 9. [Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation.](https://pubmed.ncbi.nlm.nih.gov/38608909/) 10. [The Environmental Occurrence of Pharmaceutical Residues, Agrochemical Contaminants, and Antimicrobial Resistance in a Wastewater-Impacted Urban Water System: A One Health Assessment.](https://pubmed.ncbi.nlm.nih.gov/42513088/) 11. [Kinetics of membrane damage to high (HNA) and low (LNA) nucleic acid bacterial clusters in drinking water by ozone, chlorine, chlorine dioxide, monochloramine, ferrate(VI), and permanganate.](https://pubmed.ncbi.nlm.nih.gov/21146846/) 12. [Assessing the environmental and climatic influences on the incidence of severe typhoid in Kampala, Uganda.](https://pubmed.ncbi.nlm.nih.gov/40245013/) 13. [From tap to table: An assessment of drinking water quality in Perak, Malaysia.](https://pubmed.ncbi.nlm.nih.gov/38555880/)