Who Smells? Forecasting Taste and Odor in a Drinking Water Reservoir

Kehoe, Michael; Chun, Kwok Pan; Baulch, Helen M.

doi:10.1021/acs.est.5b00979

Cited by 67 publications

(39 citation statements)

References 43 publications

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“…Despite relying on underlying correlations, ML algorithms can improve substantially on traditional correlative analyses (Kehoe et al . , ; Rivero‐Calle et al . ).…”

Section: Introductionmentioning

confidence: 99%

The predictability of a lake phytoplankton community, over time‐scales of hours to years

et al. 2018

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Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here, we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time-scales. Communities were highly predictable over hours to months: model R decreased from 0.89 at 4 hours to 0.74 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell densities were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can set prediction targets for process-based models and help elucidate the mechanisms underlying ecological dynamics.

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“…Despite relying on underlying correlations, ML algorithms can improve substantially on traditional correlative analyses (Kehoe et al . , ; Rivero‐Calle et al . ).…”

Section: Introductionmentioning

confidence: 99%

The predictability of a lake phytoplankton community, over time‐scales of hours to years

et al. 2018

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“…This technique allows for non-linear responses and estimate the relative importance of each predictor variable on the response variable. Random forest models were selected because they have previously been shown to be useful for complex environmental data [ 34 , 59 , 60 , 61 ].…”

Section: Methodsmentioning

confidence: 99%

Metabolomic Profiles of a Midge (Procladius villosimanus, Kieffer) Are Associated with Sediment Contamination in Urban Wetlands

Jeppe

Kouremenos²,

Townsend

et al. 2017

Metabolites

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Metabolomic techniques are powerful tools for investigating organism-environment interactions. Metabolite profiles have the potential to identify exposure or toxicity before populations are disrupted and can provide useful information for environmental assessment. However, under complex environmental scenarios, metabolomic responses to exposure can be distorted by background and/or organismal variation. In the current study, we use LC-MS (liquid chromatography-mass spectrometry) and GC-MS (gas chromatography-mass spectrometry) to measure metabolites of the midge Procladius villosimanus inhabiting 21 urban wetlands. These metabolites were tested against common sediment contaminants using random forest models and metabolite enrichment analysis. Sediment contaminant concentrations in the field correlated with several P. villosimanus metabolites despite natural environmental and organismal variation. Furthermore, enrichment analysis indicated that metabolite sets implicated in stress responses were enriched, pointing to specific cellular functions affected by exposure. Methionine metabolism, sugar metabolism and glycerolipid metabolism associated with total petroleum hydrocarbon and metal concentrations, while mitochondrial electron transport and urea cycle sets associated only with bifenthrin. These results demonstrate the potential for metabolomics approaches to provide useful information in field-based environmental assessments.

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“…Over 95% of the drainage basin is agricultural land [26] suggesting that non-point nutrient sources (diffuse pollution, overland run-off) may factor significantly in nutrient loading to BPL. Water quality issues such as eutrophication remain a challenge, and the reservoir has persistent problems with taste, odour, and algal blooms [27,28]. …”

Section: Site Descriptionmentioning

confidence: 99%

Modelling Dissolved Oxygen/Sediment Oxygen Demand under Ice in a Shallow Eutrophic Prairie Reservoir

Terry

Sadeghian

Lindenschmidt

2017

Water

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Dissolved oxygen is an influential factor of aquatic ecosystem health. Future predictions of oxygen deficits are paramount for maintaining water quality. Oxygen demands depend greatly on a waterbody's attributes. A large sediment-water interface relative to volume means sediment oxygen demand has greater influence in shallow systems. In shallow, ice-covered waterbodies the potential for winter anoxia is high. Water quality models offer two options for modelling sediment oxygen demand: a zero-order constant rate, or a sediment diagenesis model. The constant rate is unrepresentative of a real system, yet a diagenesis model is difficult to parameterise and calibrate without data. We use the water quality model CE-QUAL-W2 to increase the complexity of a zero-order sediment compartment with limited data. We model summer and winter conditions individually to capture decay rates under-ice. Using a semi-automated calibration method, we find an annual pattern in sediment oxygen demand that follows the trend of chlorophyll-a concentrations in a shallow, eutrophic Prairie reservoir. We use chlorophyll-a as a proxy for estimation of summer oxygen demand and winter decay. We show that winter sediment oxygen demand is dependent on the previous summer's maximum chlorophyll-a concentrations.

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Who Smells? Forecasting Taste and Odor in a Drinking Water Reservoir

Cited by 67 publications

References 43 publications

The predictability of a lake phytoplankton community, over time‐scales of hours to years

The predictability of a lake phytoplankton community, over time‐scales of hours to years

Metabolomic Profiles of a Midge (Procladius villosimanus, Kieffer) Are Associated with Sediment Contamination in Urban Wetlands

Modelling Dissolved Oxygen/Sediment Oxygen Demand under Ice in a Shallow Eutrophic Prairie Reservoir

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