A combination of denitrification and pesticide sorption with the biodegradable polymer poly(-caprolactone) (PCL) was examined. The function of PCL is to act as carbon source and carrier for the bacteria and simultaneously as sorbent for the pesticide endosulfan. In a short-term examination (1 month) the addition of the pesticide endosulfan to a continuous-flow fixed-bed reactor resulted in an inhibition of biomass production without reduction of the denitrification performance. However in a long-term semi-batch reactor test (6 months) biomass production and partly denitrification rates were affected. No significant differences in microbial composition between the reactors were observed. Regardless of the type of reactor or presence of endosulfan, Acidovorax facilis was the main constituent. Denitrifikation und Pestizidelimination im Trinkwasser mit dem biologisch abbaubaren Polymer Poly(-caprolacton) (PCL)Es wurde eine Kombination aus Denitrifikation und Pestizidelimination mit dem bioabbaubaren Polymer Poly(-caprolacton) (PCL) untersucht. PCL dient dabei als Kohlenstoffquelle und Trägermaterial für die Bakterien und gleichzeitig als Sorbens für das Pestizid Endosulfan. Bei einem Kurzzeittest (1 Monat) wurde bei Zudosierung des Pestizids Endosulfan zu einem kontinuierlich betriebenen Festbettreaktor eine Hemmung der Biomasseproduktion festgestellt, ohne dass die Denitrifikationsleistung vermindert war. In einem Langzeit-Semi-BatchReaktor-Test (6 Monate) wurden dagegen die Biomasseproduktion und teilweise auch die Denitrifikationsgeschwindigkeit beeinträchtigt. Dabei wurden zwischen den beiden Reaktoren keine signifikanten Unterschiede der mikrobiellen Zusammensetzung gefunden. Unabhängig vom Reaktortyp oder der Anwesenheit von Endosulfan war Acidovorax facilis der dominante Stamm.
Abstract. The widely applied geostatistical interpolation methods of ordinary kriging (OK) or external drift kriging (EDK) interpolate the variable of interest to the unknown location, providing a linear estimator and an estimation variance as measure of uncertainty. The methods implicitly pose the assumption of Gaussianity on the observations, which is not given for many variables. The resulting “best linear and unbiased estimator” from the subsequent interpolation optimizes the mean error over many realizations for the entire spatial domain and, therefore, allows a systematic under-(over-)estimation of the variable in regions of relatively high (low) observations. In case of a variable with observed time series, the spatial marginal distributions are estimated separately for one time step after the other, and the errors from the interpolations might accumulate over time in regions of relatively extreme observations. Therefore, we propose the interpolation method of quantile kriging (QK) with a two-step procedure prior to interpolation: we firstly estimate distributions of the variable over time at the observation locations and then estimate the marginal distributions over space for every given time step. For this purpose, a distribution function is selected and fitted to the observed time series at every observation location, thus converting the variable into quantiles and defining parameters. At a given time step, the quantiles from all observation locations are then transformed into a Gaussian-distributed variable by a 2-fold quantile–quantile transformation with the beta- and normal-distribution function. The spatio-temporal description of the proposed method accommodates skewed marginal distributions and resolves the spatial non-stationarity of the original variable. The Gaussian-distributed variable and the distribution parameters are now interpolated by OK and EDK. At the unknown location, the resulting outcomes are reconverted back into the estimator and the estimation variance of the original variable. As a summary, QK newly incorporates information from the temporal axis for its spatial marginal distribution and subsequent interpolation and, therefore, could be interpreted as a space–time version of probability kriging. In this study, QK is applied for the variable of observed monthly precipitation from raingauges in South Africa. The estimators and estimation variances from the interpolation are compared to the respective outcomes from OK and EDK. The cross-validations show that QK improves the estimator and the estimation variance for most of the selected objective functions. QK further enables the reduction of the temporal bias at locations of extreme observations. The performance of QK, however, declines when many zero-value observations are present in the input data. It is further revealed that QK relates the magnitude of its estimator with the magnitude of the respective estimation variance as opposed to the traditional methods of OK and EDK, whose estimation variances do only depend on the spatial configuration of the observation locations and the model settings.
Abstract. The widely applied geostatistical interpolation methods of Ordinary Kriging (OK) or External Drift Kriging (EDK) interpolate the variable of interest to the unknown location, providing a linear estimator and an estimation variance as measure of uncertainty. The methods implicitly pose the assumption of Gaussianity on the observations, which is not given for many variables. The resulting 'best linear and unbiased estimator' from the subsequent interpolation optimizes the mean error over many realizations for the entire spatial domain and, therefore, allows a systematic under-(over-) estimation of the variable in 5 regions of relatively high (low) observations. In case of a variable with observed time-series, the spatial marginal distributions are estimated separately for one time step after the other, and the errors from the interpolations might accumulate over time in regions of relatively extreme observations. Therefore, we propose the interpolation method of Quantile Kriging (QK) with a two step procedure prior to interpolation: we firstly estimate distributions of the variable over time at the observation locations and then estimate the marginal 10 distributions over space for every given time step. For this purpose, a distribution function is selected and fitted to the observed time-series at every observation location, thus converting the variable into quantiles and defining parameters. At a given time step, the quantiles from all observation locations are then transformed into a Gaussian-distributed variable by a twofold quantile-quantile transformation with the Beta-and the Normal-distribution function. The spatio-temporal description of the proposed method accommodates skewed marginal distributions and resolves the spatial non-stationarity of the original vari-15 able. The Gaussian-distributed variable and the distribution parameters are now interpolated by OK and EDK. At the unknown location, the resulting outcomes are reconverted back into the estimator and the estimation variance of the original variable.As a summary, QK newly incorporates information from the temporal axis for its spatial marginal distribution and subsequent interpolation and, therefore, could be interpreted as a space-time version of Probability Kriging.In this study, QK is applied for the variable of observed monthly precipitation from raingauges in South Africa. The es-
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.