scholarly journals Monitoring and Modelling of Butyltin Compounds in Finnish Inland Lake

2021 ◽  
Author(s):  
Heidi Ahkola ◽  
Janne Juntunen ◽  
Kirsti Krogerus ◽  
Timo Huttula
Keyword(s):  
Passive Sampling ◽  
Computational Modelling ◽  
Passive Samplers ◽  
Extreme Value Analysis ◽  
Sampling Techniques ◽  
Grab Sampling ◽  
Value Analysis ◽  
Wood Processing ◽  
Inland Lake ◽  
Butyltin Compounds

Abstract Butyltin compounds (BTCs) in surface waters is seldom studied due to their low concentrations and limitations of analytical techniques. In this study we measured total concentration of BTCs with grab water sampling, dissolved concentration with passive samplers and particle bound fraction with sedimentation traps in Finnish inland lake. The sampling was conducted from May to September during two study years. The differences between sampling techniques and the concentrations were obvious. E.g. tributyltin (TBT) was detected only in 4-24 % of the grab samples when the detection with passive samplers was 93% and with sedimentation traps 50%. The dissolved BTC concentrations measured with grab and passive sampling suggested hydrological differences between the study years. This was confirmed with flow velocity measurements. However, the annual difference was not observed in BTC concentrations of settled particle.The extreme value analysis suggested that grab sampling and sedimentation trap sampling results contain more extreme peak values than passive sampling. This indicates that BTCs are present in surface water in trace concentrations despite they are not detected with all the sampling techniques. The assumption that WWTP, located in the study area, was the source of BTCs was not valid as elevated BTC concentrations were detected also at the reference site, located upstream of WWTP. Computational modelling and back tracking simulations also supported the concept that WWTP cannot be the only source but BTCs can come even from upstream of the sampling area where there is e.g. wood processing industry.

scholarly journals Comparison of sampling methodologies for nutrient monitoring in streams: uncertainties, costs and implications for mitigation

2014 ◽  
Vol 18 (11) ◽  
pp. 4721-4731 ◽  
Author(s):  
J. Audet ◽  
L. Martinsen ◽  
B. Hasler ◽  
H. de Jonge ◽  
E. Karydi ◽  
...  
Keyword(s):  
Aquatic Ecosystems ◽  
Passive Sampling ◽  
Mitigation Strategies ◽  
Passive Samplers ◽  
Nutrient Load ◽  
Monitoring Methods ◽  
Nitrogen And Phosphorus ◽  
Grab Sampling ◽  
Reliable Monitoring ◽  
Phosphorus Losses

Abstract. Eutrophication of aquatic ecosystems caused by excess concentrations of nitrogen and phosphorus may have harmful consequences for biodiversity and poses a health risk to humans via water supplies. Reduction of nitrogen and phosphorus losses to aquatic ecosystems involves implementation of costly measures, and reliable monitoring methods are therefore essential to select appropriate mitigation strategies and to evaluate their effects. Here, we compare the performances and costs of three methodologies for the monitoring of nutrients in rivers: grab sampling; time-proportional sampling; and passive sampling using flow-proportional samplers. Assuming hourly time-proportional sampling to be the best estimate of the "true" nutrient load, our results showed that the risk of obtaining wrong total nutrient load estimates by passive samplers is high despite similar costs as the time-proportional sampling. Our conclusion is that for passive samplers to provide a reliable monitoring alternative, further development is needed. Grab sampling was the cheapest of the three methods and was more precise and accurate than passive sampling. We conclude that although monitoring employing time-proportional sampling is costly, its reliability precludes unnecessarily high implementation expenses.

scholarly journals Comparison of sampling methodologies for nutrient monitoring in streams: uncertainties, costs and implications for mitigation

2014 ◽  
Vol 11 (7) ◽  
pp. 7585-7614 ◽  
Author(s):  
J. Audet ◽  
L. Martinsen ◽  
B. Hasler ◽  
H. de Jonge ◽  
E. Karydi ◽  
...  
Keyword(s):  
Aquatic Ecosystems ◽  
Passive Sampling ◽  
Mitigation Strategies ◽  
Passive Samplers ◽  
Nutrient Load ◽  
Monitoring Methods ◽  
Nitrogen And Phosphorus ◽  
Grab Sampling ◽  
Reliable Monitoring ◽  
Phosphorus Losses

Abstract. Eutrophication of aquatic ecosystems caused by excess concentrations of nitrogen and phosphorus may have harmful consequences for biodiversity and poses a health risk to humans via the water supplies. Reduction of nitrogen and phosphorus losses to aquatic ecosystems involves implementation of costly measures, and reliable monitoring methods are therefore essential to select appropriate mitigation strategies and to evaluate their effects. Here, we compare the performances and costs of three methodologies for the monitoring of nutrients in rivers: grab sampling, time-proportional sampling and passive sampling using flow proportional samplers. Assuming time-proportional sampling to be the best estimate of the "true" nutrient load, our results showed that the risk of obtaining wrong total nutrient load estimates by passive samplers is high despite similar costs as the time-proportional sampling. Our conclusion is that for passive samplers to provide a reliable monitoring alternative, further development is needed. Grab sampling was the cheapest of the three methods and was more precise and accurate than passive sampling. We conclude that although monitoring employing time-proportional sampling is costly, its reliability precludes unnecessarily high implementation expenses.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

Spatial stochastic simulation to aid local extreme value analysis of cyclone-induced wave heights when numerical hydrodynamic simulations are scarce

2021 ◽  
Author(s):  
Jeremy Rohmer ◽  
Rodrigo Pedreros ◽  
Yann Krien
Keyword(s):  
Confidence Interval ◽  
Stochastic Simulation ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Percentage Error ◽  
Value Analysis ◽  
Return Levels ◽  
Wave Heights ◽  
Local Extreme

<p>To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically >1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years – Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).</p>

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