Stormwater data management connects laboratory results with the information needed to interpret them: sampling locations, collection times, analytical methods, reporting limits, quality-control results and review decisions. A useful system preserves these relationships as data move from laboratory delivery through review, approval, analysis and reporting.
The distinction between receiving results and accepting them for use is fundamental. A laboratory file can import successfully while still containing results that need investigation, qualification or rejection.
A Quality Assurance Project Plan (QAPP) prepared by Herrera Environmental Consultants for the City of Tacoma illustrates how this process can be organised. The May 2025 plan for Stormwater Contaminants of Emerging Concern in Western Washington specifies ESdat for managing most analytical laboratory data, alongside documented checks, approval stages and data qualifiers. It offers a practical example of how environmental data management software fits within a wider quality assurance process. [1]
Key takeaways
- Successful data import does not establish scientific suitability. Results still need review against project requirements and quality objectives.
- Electronic data deliverables (EDDs) provide structured laboratory information. Their value depends on preserving sample identifiers, methods, limits and quality-control information.
- Review status and data qualifiers serve different purposes. Status records progress through a workflow; qualifiers explain conditions affecting individual results.
- ESdat has a defined role in the Washington QAPP. It is specified for most analytical laboratory data, while other systems handle continuous monitoring data, specialist outputs and statistical analysis.
- Non-detects must remain distinguishable from measured concentrations. Their reporting limits and qualifiers affect interpretation and statistical treatment.
- Software supports accountable review. Project personnel remain responsible for deciding whether data are suitable for their intended use.
Why stormwater data need more than a concentration
A result becomes useful when its context is clear.
Before comparing two stormwater concentrations, a reviewer may need to establish whether they represent the same parameter, matrix, analytical method and sampling approach. A grab sample and a flow-weighted composite sample can describe different aspects of a storm event. A reported detection and a non-detect at the same numerical value also carry different meanings.
The Washington study demonstrates this complexity. Its QAPP describes monitoring at 16 locations and includes flow-weighted composite sampling, grab sampling and settled stormwater suspended particulate matter. Target contaminants include 6PPD-quinone, microplastics and tire wear particles, and per- and polyfluoroalkyl substances (PFAS). [1, abstract and sections 2–4]
For data managers, the practical implication is to maintain enough information to answer three questions:
- What does this result represent?
- What quality limitations affect it?
- Is it suitable for the proposed analysis?
The broader governance benefits are explored in How Governments Use Environmental Data Management Systems to Improve Monitoring, Compliance and Public Trust. Here, the focus is the operational path from laboratory delivery to usable data.
What the Washington stormwater QAPP documents
A QAPP sets out how a project intends to generate, manage and assess information of suitable quality.
The Washington document specifies a workflow in which analytical laboratory data, except high-resolution mass spectrometry outputs, will be stored and managed in ESdat. It describes initial checks, more detailed quality review, corrective action, approval status and result qualifiers. [1, section 10.1]
This is evidence of a specified project workflow. The plan alone does not demonstrate that every procedure was completed or establish measured improvements in efficiency or environmental outcomes.
That distinction allows the document to be used appropriately: as a detailed example of data-management design.
The laboratory data workflow
The QAPP’s process can be expressed as five stages:
Laboratory EDD → initial checks → unapproved results → detailed quality review and corrective action → approval for use
Qualifiers accompany results where necessary, preserving limitations identified during review. [1, section 10.1]
1. Receive a structured electronic data deliverable
An electronic data deliverable is a laboratory data file supplied in an agreed structure for electronic processing.
The QAPP requires laboratory results, including quality-control sample results, to be delivered in Excel or comma-separated value (CSV) format. Final laboratory reports are also required as PDFs. These formats serve complementary purposes: the report preserves the laboratory’s presentation, while the EDD supplies structured information for data handling. [1, sections 10.2–10.3]
An Excel file is therefore not automatically a suitable EDD. The project and laboratory need an agreed structure that identifies what each field means and how results, samples and quality information relate.
Before sampling begins, teams should agree on:
- Sample identifiers and naming conventions.
- Parameter names and analytical methods.
- Units, detection limits and reporting limits.
- Qualifier definitions.
- Quality-control data requirements.
- Procedures for correcting or replacing a delivery.
These are practical specification questions for any laboratory-to-database workflow.
2. Check the delivery before accepting it
The Washington plan describes an initial review in which the data manager checks basic EDD information, including sample names and laboratory methods against the chain-of-custody form. [1, section 10.1]
These checks address whether the delivery corresponds to the intended sampling and analytical work.
A file may be readable and correctly formatted but still contain an unexpected sample identifier or method. Resolving that discrepancy before acceptance helps prevent uncertain records from moving into routine analysis.
3. Keep imported results visibly unapproved
After initial acceptance and upload, the QAPP specifies that results are available in ESdat with an unapproved status. [1, section 10.1]
This separates availability from acceptance for use.
For a team managing several laboratories or sampling events, visible status helps reviewers distinguish newly received information from reviewed data. Reports and exports should make that distinction clear so users understand which records they are viewing.
4. Review results against quality objectives
The plan then assigns Herrera staff a more detailed review to determine whether laboratory analyses meet the project’s measurement quality objectives.
Where objectives are not met, the data manager works with the laboratory to identify corrective steps. These may include sample reanalysis, appropriate flagging or both. The workflow concludes with approval for use in ESdat. [1, section 10.1]
Section 12 describes checks covering completeness, methodology, holding times, blanks, reporting limits, duplicates, matrix spikes, control standards and sample representativeness. [1, section 12]
These checks answer different questions. Duplicates help assess precision. Blanks help identify possible contamination. Holding-time review examines whether relevant timing requirements were met. Completeness review considers whether enough valid information is available.
Together, they assess more than whether a database accepted the file.
5. Preserve the review outcome
The decision to use a result should remain connected to any qualifications affecting it.
The QAPP states that flagging and qualification will be tracked in ESdat, with the usability of qualified data documented in the project report. It also requires reasons for rejected data to be recorded. [1, sections 13.1 and 13.5]
A later analyst should be able to understand why a value was retained as an estimate, excluded or subjected to further investigation.
Data verification, validation and approval: what is the difference?
These terms describe related activities, but they should not be treated as interchangeable.
| Term | Main question |
|---|---|
| Import checks | Can the delivery be processed, and does its basic information match expectations? |
| Data verification | Are the data complete, correct and consistent with specified methods and procedures? |
| Data validation | What does the assessed quality mean for the intended use of the results? |
| Approval status | Has the record completed the organisation’s required review stage? |
| Data usability assessment | Does the dataset adequately support the project’s objectives? |
EPA guidance distinguishes verification against specified requirements from validation that evaluates data quality in relation to intended use. [2]
The Washington QAPP describes verification and validation activities across its data-management and review sections, although its separate section 12.3, “Validation Requirements,” is marked “Not applicable.” The specific checks and responsibilities should therefore be described directly, without assuming a separate independent validation service. [1, sections 10–13]
Approval is a workflow decision. It does not remove qualifications or make a result suitable for every future purpose.
Understanding stormwater data qualifiers
Qualifiers communicate conditions that affect interpretation. Table 15 of the Washington QAPP defines four codes. [1, printed page 87]
| Qualifier | Meaning in this QAPP | Implication for users |
|---|---|---|
| J | The result is an estimate. | It may be used for evaluation with its limitations understood. |
| R | The result is rejected because of major analytical quality problems. | It is excluded from the project’s evaluation. |
| U | The result is below the detection and/or reporting limit. | It must remain identifiable as a non-detect. |
| UJ | The result is below the reporting limit and has an estimated qualification. | Both the non-detect status and quality limitation need to remain visible. |
Qualifier definitions and application rules should be checked against the relevant project plan and laboratory documentation before datasets are combined.
Why status and qualifiers must stay separate
A result can complete review and still carry an estimated-value qualifier.
“Approved” communicates that the prescribed review has been completed and a use decision made. “J” communicates a limitation affecting the result itself. Retaining both gives analysts a more informative record than a single approved-or-rejected field.
Why non-detects should not become zeros
A non-detect means that an analyte was not reported as detected under the applicable analytical conditions. It does not establish a concentration of zero.
Consider two illustrative records:
| Record | Reported information | Interpretation |
|---|---|---|
| A | Detected concentration of 5 ng/L | A measured detection at the reported concentration. |
| B | Non-detect with a reporting limit of 5 ng/L | A result reported below that limit. |
They cannot be interpreted as equivalent measurements simply because both records contain the number 5.
The Washington QAPP requires appropriate qualifiers for estimated detections and non-detects. For general summary statistics involving non-detected values, it specifies a Kaplan–Meier approach rather than a substitution method. [1, section 13.2]
That is the project’s selected approach, rather than a universal prescription. The data-management requirement is to retain the limits and qualifiers needed for a suitable statistical method to be applied.
Where ESdat fits in the wider software workflow
The QAPP describes ESdat as the consultant’s online chemistry database and a central repository capable of holding laboratory data across several environmental matrices. That capability description does not mean this stormwater study sampled every matrix listed.
The project’s information is distributed across several tools and destinations:
| Information or activity | Arrangement specified in the QAPP |
|---|---|
| Most analytical laboratory data | ESdat |
| Continuous monitoring data | Aquarius |
| HRMS charts and tables | Project SharePoint storage, followed by the SAM project webpage |
| Statistical analysis and graphing | R |
| Public communication and access | Planned dashboards and project data publication |
[1, sections 10 and 13.3]
The HRMS arrangement is specific to the outputs and workflow described in this plan. It should not be generalised into a claim about every possible HRMS dataset or current software configuration.
The wider lesson is that a coherent data workflow can involve multiple specialist systems. Their responsibilities and handover requirements need to be explicit.
ESdat’s documented laboratory integration capabilities
ESdat’s current laboratory integration documentation describes automated laboratory report loading, data-quality checks, review and approval within the system, audit history and QA reporting against configurable objectives. [3]
This vendor documentation describes available capabilities. The QAPP provides a separate source showing how ESdat was specified within a particular project.
Organisations evaluating the software should confirm which capabilities, laboratory connections and configuration arrangements apply to their own proposed workflow.
What to look for in stormwater data management software
A useful evaluation follows real data from delivery to reporting. The following questions translate the QAPP’s workflow into practical requirements.
| Requirement | Question to ask |
|---|---|
| Laboratory data intake | Can the system receive the required EDDs while retaining relevant laboratory reports? |
| Sample identity | Can reviewers reconcile delivered samples with sampling and chain-of-custody records? |
| Analytical context | Are methods, units, reporting limits and qualifiers preserved? |
| Quality-control relationships | Can reviewers connect relevant QC information to affected results? |
| Review status | Can users distinguish incoming, unapproved and reviewed data? |
| Qualification | Can limitations and review decisions remain attached to results? |
| Corrections | Can amended deliveries be handled without losing the history needed to interpret changes? |
| Data reuse | Do exports preserve enough context for statistical analysis, reporting and future review? |
These are evaluation questions, not a claim that every platform implements them in the same way.
A practical demonstration should use a representative laboratory package containing detections, non-detects and at least one quality issue. Reviewers can then follow the records through import, assessment and export.
From reviewed results to defensible reporting
Reporting should communicate the meaning of results as well as their values.
For each output, teams should define which review statuses are included, how estimated values are displayed, how rejected records are excluded and how non-detects are represented. These rules should remain consistent between database extracts, statistical analyses, tables and public communication.
The Washington QAPP assigns project personnel responsibility for assessing the overall dataset against project objectives. It also specifies reporting and public-access arrangements. [1, sections 12–13]
For environmental teams, the practical objective is a reusable record in which another person can understand what was measured, what was checked, what limitations remain and why the data were considered suitable for use.
Frequently asked questions
What is stormwater data management?
Stormwater data management is the organised handling of sampling information, laboratory results, monitoring records and quality assessments so they remain interpretable through analysis, reporting and reuse.
What is an electronic data deliverable?
An EDD is a structured electronic file supplied for processing or import. In the Washington QAPP, laboratory results and QC results are required in Excel or CSV format, alongside PDF laboratory reports. [1, sections 10.2–10.3]
Does successful EDD import mean the results are validated?
No. Successful import establishes that a delivery can be processed. The Washington workflow separately specifies initial checks, unapproved status, detailed quality review and approval. [1, section 10.1]
How is ESdat used in the Washington stormwater QAPP?
ESdat is specified for storing and managing most analytical laboratory data, supporting the review workflow and tracking data flags and qualifications. Continuous monitoring data and specialist HRMS outputs have separate arrangements. [1, sections 10 and 13.5]
Can an approved result still be qualified?
Yes. Completing review does not remove a result’s limitations. An estimated result may be accepted for a defined evaluation while retaining its qualifier. [1, section 10.1 and Table 15]
What is the difference between a detection limit and a reporting limit?
A detection limit concerns the ability to distinguish an analyte from background under specified conditions. A reporting limit is the threshold used for reporting results under the applicable method and laboratory arrangements. Their exact definitions and treatment should be confirmed in the project and laboratory documentation.
Can spreadsheets be used for stormwater data?
Yes. The QAPP explicitly allows Excel EDDs. The wider question is how the team controls review status, qualifiers, corrections and reuse after delivery. A spreadsheet file format alone does not define that workflow. [1, section 10.3]
Does environmental data management software replace scientific review?
No. The Washington plan assigns review, corrective action and usability decisions to project personnel. Software supports the process by organising information and preserving its status and qualifications. [1, sections 10–13]
Glossary
| Term | Meaning |
|---|---|
| 6PPD-quinone (6PPDQ) | A transformation product of the tire additive 6PPD, included among the study’s target contaminants. |
| Chain of custody | Documentation identifying samples and recording their handling and transfer. |
| Contaminant of emerging concern (CEC) | A substance receiving attention because of developing evidence or concern about its occurrence or effects. |
| Electronic data deliverable (EDD) | A structured electronic delivery of results and associated information. |
| High-resolution mass spectrometry (HRMS) | An analytical technique used in the study for non-targeted chemical investigation. |
| Holding time | The specified period allowed before a sample must undergo a relevant preparation or analytical step. |
| Measurement quality objective (MQO) | A defined performance requirement used to assess measurement quality. |
| Non-detect | A result not reported as detected under the applicable analytical conditions and limits. |
| PFAS | Per- and polyfluoroalkyl substances, a group of chemicals targeted in the study. |
| QA/QC | Quality assurance and quality control: planned processes and checks for assessing and maintaining data quality. |
| Quality Assurance Project Plan (QAPP) | A project document setting out objectives, methods, responsibilities and quality procedures. |
| Qualifier | A code communicating a condition or limitation affecting a result. |
| Reporting limit | The threshold applied when reporting an analytical result under specified arrangements. |
References
- Herrera Environmental Consultants, Inc. (14 May 2025). Quality Assurance Project Plan: Stormwater Contaminants of Emerging Concern in Western Washington, prepared for the City of Tacoma. Principal sections used: abstract and background; section 10, Data Management Procedures, printed pages 86–88; section 12, Data Verification, pages 90–94; and section 13, Data Quality (Usability) Assessment, pages 95–97. Table 15 defines the project’s data qualifiers.
- United States Environmental Protection Agency (December 2002). Guidance for Quality Assurance Project Plans, EPA QA/G-5, EPA/240/R-02/009. Section 2.4.1 explains data review, verification and validation. Used here for conceptual definitions.
- ESdat. Laboratory Integration. Product documentation describing laboratory data exchange, quality checks, review and approval, audit history and QA reporting.
- Environmental Standards. How Governments Use Environmental Data Management Systems to Improve Monitoring, Compliance and Public Trust. Related reading on the broader governance context.
