Predicting biochemical oxygen demand in European freshwater bodies

Vigiak, Olga; Grizzetti, Bruna; Údias, Ángel; Zanni, Michela; Dorati, Chiara; Bouraoui, F.; Pistocchi, Alberto

doi:10.1016/j.scitotenv.2019.02.252

Cited by 73 publications

(48 citation statements)

References 54 publications

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“…Despite the fact that the application of the GLW methodology has become robust and its application frequent (e.g. [20,[25][26][27][28]), its vulnerability to MAUP has not yet been directly investigated. Previous studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Gilbert

Chaiban

et al. 2020

PLoS ONE

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The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson's r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson's r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products. OPEN ACCESS Citation: Da Re D, Gilbert M, Chaiban C, Bourguignon P, Thanapongtharm W, Robinson TP, et al. (2020) Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem. PLoS ONE 15(1): e0221070. https://doi.org/10.

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Section: Introductionmentioning

confidence: 99%

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Gilbert

Chaiban

et al. 2020

PLoS ONE

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“…Removal efficiencies per treatment level were adopted from literature 3,19-21 ( Table 2). BOD efficiencies were set after calibration of BOD fluxes in Europe 22 within the range of literature values. WWTP treatment types are reported in the UWWTD database 10 , however only nutrient removal technologies were considered to assign tertiary treatment level.…”

Section: Domestic Pollutants Loads (Quantity)mentioning

confidence: 99%

Domestic waste emissions to European waters in the 2010s

et al. 2020

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Estimation of domestic waste emissions to waters is needed for pollution assessment and modelling. We assessed quantity and location of domestic waste emissions to European waters for the 2010s. Specifically, we considered discharges of domestic waste Population Equivalent (PE, the amount of waste that equals to 60 g per day of Biochemical Oxygen Demand), and mean annual loads (t/y) of total nitrogen, total phosphorus, and 5-days Biochemical Oxygen Demand. The spatial resolution and extent of the analysis corresponded to the CCM2 River and Catchment Database for Europe, for catchments of mean area of 6.4 km 2. The assessment is based on available European databases that allowed pinpointing waste emissions to a high spatial and conceptual resolution. Content gaps, particularly concerning domestic waste from isolated dwellings, were filled through alternative sources of information, exploiting population density and national statistics data. The dataset is of interest for assessing waste emissions to and fate through European fresh and marine waters also beyond the three pollutants evaluated in this study.

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“…The statistical modeling method has widely been applied in the Americas [25][26][27] or Asia [28][29][30] but less frequently in Europe. A few European examples are [31,32].…”

Section: Type #mentioning

confidence: 99%

Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods

Kardos

Clement

2020

Open Geosciences

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AbstractWatershed area and a bunch of relief, land use, and wastewater characteristics for 32 upland and 33 lowland small river courses are generated. Based on these characteristics, logistic binary regression models are trained to predict if the river achieves the good physico-chemical status, and discriminant analysis models are trained to predict the physico-chemical status class on a five-class scale.Univariate models revealed that elevation (for upland rivers), the share of artificial surfaces (for lowland rivers) along with forests, and wastewater quality variables such as biochemical oxygen demand, chemical oxygen demand, and phosphorus are the most significant predictors. Discriminant analysis models performed better on upland than on lowland rivers. Achievement of good status could be predicted with an accuracy of ~90% (with 2 to 4 variable logit models), whereas the status class with an accuracy of 63/48% (with 2 to 4 variable discriminant analysis models) for upland and lowland rivers, respectively. This contribution uses Hungary as a case study.

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Predicting biochemical oxygen demand in European freshwater bodies

Cited by 73 publications

References 54 publications

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

Domestic waste emissions to European waters in the 2010s

Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods

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