ArticleACS ES&T water2024
Predictive Modeling Reveals Elevated Conductivity Relative to Background Levels in Freshwater Tributaries within the Chesapeake Bay Watershed, USA.
Article in ACS ES&T water, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- From indicators to governance: a pressure-condition-response framework reveals nonlinear ecological responses to multiple stressors in an agricultural-urban basin.Environmental monitoring and assessment · 2026Article
- Regional high-frequency monitoring revealed chloride concentrations in exceedance of ecological benchmarks in urban streams across the Delaware River Basin, USA.Environmental monitoring and assessment · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Elevated conductivity (i.e., specific conductance or SC) causes osmotic stress in freshwater aquatic organisms and may increase the toxicity of some contaminants. Indices of benthic macroinvertebrate integrity have declined in urban areas across the Chesapeake Bay watershed (CBW), and more information is needed about whether these declines may be due to elevated conductivity. A predictive SC model for the CBW was developed using monitoring data from the National Water Quality Portal. Predictor variables representing SC sources were compiled for nontidal reaches across the CBW. Random forests modeling was conducted to predict SC at four time periods (1999-2001, 2004-2006, 2009-2011, and 2014-2016), which were then compared to a national data set of background SC to quantify departures from background SC. Carbonate geology, impervious cover, forest cover, and snow depth were the most important variables for predicting SC. Observations and modeled results showed snow depth amplified the effect of impervious cover on SC. Elevated SC was predicted in two-thirds of reaches in the CBW, and these elevated conditions persisted over time in many areas. These results can be used in stressor identification assessments to prioritize future monitoring and to determine where management activities could be implemented to reduce salinization.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.