1. Inputs
One row per site × campaign × analyte. Required columns: site_id, campaign, analyte_std, result_value, detect_flag, rl.
Recognized columns include Site ID/site_id, Lat, Lon, Reach_group, Bracket_role, USGS_streamflow_gage.
Recommended schema: experiment_id, reach_group, experiment_type, downstream_site, upstream_sites, tributary_sites, baseline_sites, all_sites. Multiple sites are pipe-delimited.
Upload PFAS CSV or run the demo data. All computation is local to your browser.
Use statement. This is a screening and prioritization tool. It does not provide regulatory compliance determinations or source apportionment. The f_trib diagnostic is not a flow or PFAS load fraction.
2. What the tool computes
ΣPFAS40 is computed in each imputation replicate as the sum of the 40 target PFAS analytes. Non-detects are left-censored at their reporting limits (RLs).
- Censoring-aware totals. Analytes with ≥2 detections use a fitted lognormal distribution truncated at [0, RL]; analytes with <2 detections use Uniform(0, RL).
- RL/2 sensitivity. MI site totals are compared against one-half reporting-limit substitution and detect-only sums.
- Machine-readable reach bracketing. Reach objects are read from the reach table rather than hard-coded into the script.
- ftrib diagnostic. f_trib = (C_dn - C_up)/(C_trib - C_up) is reported only when the downstream concentration lies within the upstream–tributary end-member range.
- Reproducibility outputs. Download CSV outputs and a validation report for manuscript or SI archiving.
Python edition available
Download Python script
Download the companion script for Spyder/Anaconda, Jupyter, or Google Colab workflows. It implements the same manuscript-aligned MI and reach-bracketing logic as the browser tool.
The browser and Python editions are bundled together for public release; both run locally and do not transmit user data.
Live tool: GitHub Pages. Source code: GitHub repository. Archival DOI: 10.5281/zenodo.20464907.
Methodological basis and references
This tool operationalizes established methods for censored environmental data, multiple imputation, reproducible computational workflows, EPA Method 1633A PFAS analysis, rank-based sensitivity checks, and synoptic PFAS loading/source-screening concepts. These references are included to acknowledge the methodological basis of the workflow and to direct users to the underlying literature.
- Helsel, D. R. Statistics for Censored Environmental Data Using Minitab and R, 2nd ed.; Wiley: Hoboken, NJ, 2012.
- Rubin, D. B. Multiple Imputation for Nonresponse in Surveys; Wiley: New York, 1987.
- Lubin, J. H.; Colt, J. S.; Camann, D.; Davis, S.; Cerhan, J. R.; Severson, R. K.; Bernstein, L.; Hartge, P. Epidemiologic Evaluation of Measurement Data in the Presence of Detection Limits. Environmental Health Perspectives 2004, 112 (17), 1691–1696. https://doi.org/10.1289/ehp.7199.
- Helsel, D. R. Fabricating Data: How Substituting Values for Nondetects Can Ruin Results. Chemosphere 2006, 65 (11), 2434–2439. https://doi.org/10.1016/j.chemosphere.2006.04.051.
- Antweiler, R. C.; Taylor, H. E. Evaluation of Statistical Treatments of Left-Censored Environmental Data Using Coincident Uncensored Data Sets: I. Summary Statistics. Environmental Science & Technology 2008, 42 (10), 3732–3738. https://doi.org/10.1021/es071301c.
- Wilkinson, M. D.; Dumontier, M.; Aalbersberg, I. J.; Appleton, G.; Axton, M.; Baak, A.; et al. The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data 2016, 3, 160018. https://doi.org/10.1038/sdata.2016.18.
- Goble, C. A.; Soiland-Reyes, S.; Bacall, F.; Garijo, D.; Gil, Y.; Ferreira da Silva, R.; et al. Applying the FAIR Principles to Computational Workflows. Scientific Data 2025, 12, 337. https://doi.org/10.1038/s41597-025-04451-9.
- U.S. EPA. Method 1633: Analysis of Per- and Polyfluoroalkyl Substances (PFAS) in Aqueous, Solid, Biosolids, and Tissue Samples by LC-MS/MS, Revision A; EPA Office of Water: Washington, DC, December 2024.
- Spearman, C. The Proof and Measurement of Association between Two Things. American Journal of Psychology 1904, 15 (1), 72–101. https://doi.org/10.2307/1412159.
- Woodward, E. E.; Senior, L. A.; Fleck, J. A.; Barber, L. B.; Hansen, A. M.; Duris, J. W. Using a Time-of-Travel Sampling Approach to Quantify Per- and Polyfluoroalkyl Substances (PFAS) Stream Loading and Source Inputs in a Mixed-Source, Urban Catchment. ACS ES&T Water 2024, 4 (10), 4356–4367. https://doi.org/10.1021/acsestwater.4c00288.
- Asare, P. T.; Kharel, G.; Nice, M. S.; Lavy, B. L.; Birmingham, M.; Harvey, O. R. Handling Left-Censored PFAS Data in Surface-Water Reconnaissance: A Reproducible Workflow Applied to Ten Sites in the Trinity River Headwaters, Texas. PLOS Water 2026, e0000554. https://doi.org/10.1371/journal.pwat.0000554.
3. Results
Results will appear here after running the analysis or manuscript demo data.
How to read this table: Each row is a machine-readable reach object. delta_SigmaPFAS40_95II is the downstream-minus-upstream contrast, and f_trib_or_mix_median is reported only for applicable concentration-space tributary or mixing diagnostics. Values are blank when the diagnostic is not applicable.