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David L. Courtemanch

Publications and source records attributed to David L. Courtemanch.

2 recordsLinked to original sources

Estimating pesticide sampling rates by the polar organic chemical integrative sampler (POCIS) in the presence of natural organic matter and varying hydrodynamic conditions

The polar organic chemical integrative sampler (POCIS) was calibrated to monitor pesticides in water under controlled laboratory conditions. The effect of natural organic matter (NOM) on the sampling rates ( R s ) was evaluated in microcosms containing <0.1&ndash;5 mg L -1 of total organic carbon (TOC). The effect of hydrodynamics was studied by comparing R s values measured in stirred (SBE) and quiescent (QBE) batch experiments and a flow-through system (FTS). The level of NOM in the water used in these experiments had no effect on the magnitude of the pesticide sampling rates ( p > 0.05). However, flow velocity and turbulence significantly increased the sampling rates of the pesticides in the FTS and SBE compared to the QBE ( p < 0.001). The calibration data generated can be used to derive pesticide concentrations in water from POCIS deployed in stagnant and turbulent environmental systems without correction for NOM.

Environmental Pollution↗

An algal model for predicting attainment of tiered biological criteria of Maine's streams and rivers

State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1–6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant model will broaden detection of biological impairment and further diagnose sources of impairment. The algal discriminant model is specific to Maine, but our approach of explicitly tying an assessment tool to tiered aquatic-life goals is widely transferrable to other regions, taxonomic groups, and waterbody types.

Freshwater Science↗