Comprehensive study on parameter sensitivity for flow and nutrient modeling in the Hydrological Simulation Program Fortran model

Numerous parameters are used to construct the HSPF (Hydrological Simulation Program Fortran) model, which results in significant difficulty in calibrating the model. Parameter sensitivity analysis is an efficient method to identify important model parameters. Through this method, a model’s calibration process can be simplified on the basis of understanding the model’s structure. This study investigated the sensitivity of the flow and nutrient parameters of HSPF using the DSA (differential sensitivity analysis) method in the Xitiaoxi watershed, China. The results showed that flow was mostly affected by parameters related to groundwater and evapotranspiration, including DEEPFR (fraction of groundwater inflow to deep recharge), LZETP (lower-zone evapotranspiration parameter), and AGWRC (base groundwater recession), and most of the sensitive parameters had negative and nonlinear effects on flow. Additionally, nutrient components were commonly affected by parameters from land processes, including MON-SQOLIM (monthly values limiting storage of water quality in overland flow), MON-ACCUM (monthly values of accumulation), MON-IFLW-CONC (monthly concentration of water quality in interflow), and MON-GRND-CONC (monthly concentration of water quality in active groundwater). Besides, parameters from river systems, KATM20 (unit oxidation rate of total ammonia at 20 °C) had a negative and almost linear effect on ammonia concentration and MALGR (maximal unit algal growth rate for phytoplankton) had a negative and nonlinear effect on ammonia and orthophosphate concentrations. After calibrating these sensitive parameters, our model performed well for simulating flow and nutrient outputs, with R ² and E_NS (Nash–Sutcliffe efficiency) both greater than 0.75 for flow and greater than 0.5 for nutrient components. This study is expected to serve as a valuable complement to the documentation of the HSPF model to help users identify key parameters and provide a reference for performing sensitivity analyses on other models.     

Heterogeneous Fenton-like discoloration of Rhodamine B using natural schorl as catalyst: optimization by response surface methodology

A cationic dye, Rhodamine B (RhB), could be efficiently discolored by heterogeneous Fenton-like reaction catalyzed by natural schorl. In this work, with the main goal of the optimization for RhB discoloration, central composite design under the response surface methodology (RSM) was employed for the experiment design and process optimization. The significance of a second-order polynomial model for predicting the optimal values of RhB discoloration was evaluated by the analysis of variance and 3D response surface and counter plots for the interactions between two variables were constructed. The Pareto graphic analysis of the discoloration process indicated that, among all the variables, solution pH (X ₃, 47.95 %) and H₂O₂ concentration (X ₁, 24.39 %) had the largest influences on the heterogeneous Fenton-like discoloration of RhB. Based on the model prediction, the optimum conditions for RhB discoloration were determined to be 45 mM H₂O₂ concentration, 2.5 g/L schorl dosage, solution pH 2, and 110 min reaction time, with the maximum RhB discoloration ratio of 98.86 %. The corresponding experimental value of RhB discoloration ratio under the optimum conditions was determined as 99.31 %, which is very close to the optimized one, implying that RSM is a powerful and satisfactory strategy for the process optimization.     

Evaluating the sensitivity of a subsurface multicomponent reactive transport model with respect to transport and reaction parameters.

The input variables for a numerical model of reactive solute transport in groundwater include both transport parameters, such as hydraulic conductivity and infiltration, and reaction parameters that describe the important chemical and biological processes in the system. These parameters are subject to uncertainty due to measurement error and due to the spatial variability of properties in the subsurface environment. This paper compares the relative effects of uncertainty in the transport and reaction parameters on the results of a solute transport model. This question is addressed by comparing the magnitudes of the local sensitivity coefficients for transport and reaction parameters. General sensitivity equations are presented for transport parameters, reaction parameters, and the initial (background) concentrations in the problem domain. Parameter sensitivity coefficients are then calculated for an example problem in which uranium(VI) hydrolysis species are transported through a two-dimensional domain with a spatially variable pattern of surface complexation sites. In this example, the reaction model includes equilibrium speciation reactions and mass transfer-limited non-electrostatic surface complexation reactions. The set of parameters to which the model is most sensitive includes the initial concentration of one of the surface sites, the formation constant (Kf) of one of the surface complexes and the hydraulic conductivity within the reactive zone. For this example problem, the sensitivity analysis demonstrates that transport and reaction parameters are equally important in terms of how their variability affects the model results.     

Investigating the potential of Morris algorithm for improving the computational constraints of global sensitivity analysis

Environmental Science and Pollution Research - Sensitivity analysis (SA) is widely acknowledged as advantageous and worthwhile in recognizing parameters for model calibration and optimization,...     

Photocatalytic discoloration of Methyl Orange by anatase/schorl composite: optimization using response surface method

The anatase/schorl composites were prepared and employed for the photocatalytic discoloration of an azo dye, Methyl Orange (MO). X-ray diffraction results indicated that TiO₂ existed in the form of anatase phase and no diffraction peaks of schorl could be observed for all the composite samples. Scanning electron micrographs showed that the particles of anatase were well deposited and dispersed on the surface of schorl. Photocatalytic experiments revealed that the anatase/schorl composites exhibited higher photocatalytic activity for MO discoloration than pure TiO₂ and more than 90 % discoloration ratio could be obtained within 60 min UV irradiation when the sample containing 3 wt.% of schorl as TiO₂ support was used. Then, the central composite design (CCD) under the response surface methodology (RSM) was employed for the experiment design and process optimization. The significance of a second-order polynomial model for predicting the optimal values of MO discoloration was evaluated by the analysis of variance (ANOVA) and 3D response surface plots for the interactions between two variables were constructed. Based on the model prediction, the optimum conditions for the photocatalytic discoloration of MO by TiO₂/schorl composite were determined to be 15?×?10^?3 mM MO initial concentration, 2.7 g/l photocatalyst dosage, solution pH 6.6 and 43 min reaction time, with a maximum MO discoloration ratio of 98.6 %. Finally, a discoloration ratio of 94.3 % was achieved for the real sample under the optimum conditions, which was very close to the predicted value, implying that RSM is a powerful and satisfactory strategy for the process optimization.     

Parameter precision in the HBV-NP model and impacts on nitrogen scenario simulations in the Rönneå river, southern Sweden

The HBV-NP model is a newly developed water quality model that describes the turnover and fluxes of both nitrogen and phosphorous. It is based on the conceptual precipitation/runoff HBV model. The HBV-NP model was applied for simulation of nitrogen for the R?nne? catchment in southern Sweden. The catchment was divided into 64 subcatchments in the model. Discharge measurements from six stations and nitrogen measurements from 12 stations were used in the calibration of parameters in the model. Eight automatic calibrations were performed with different combinations of time periods, objective functions, and levels of the nitrogen load in the model. A regionally extended interpretation of the Nash-Sutcliffe R2 criterion was used in the calibration. In the evaluation of the criterion, the errors were summed over both time steps and sampling points. Scenario simulations of combined measures for reduction of nitrogen load into the sea by 30% were thereafter performed with the eight sets of parameters established by calibration. The model parameters were not uniquely defined by the calibration. However, the simulated relative reduction of nitrogen load into the sea was relatively insensitive to the choice of parameter set, given the available input sources, variables, and data.     

Pesticide pressure and fish farming in barrage pond in northeastern France. Part III: how management can affect pesticide profiles in edible fish?

Purpose

The quality of fish produced in ponds needs to be ensured. Indeed, pond is often strongly connected to an agricultural watershed, and pesticides are a main health and environmental issue of concern. In this context, the purpose of this study is to highlight the management practices which could impact the pesticide contamination profiles in edible fish and to give recommendations for better practices.

Methods

A principal component analysis, coupled to a hierarchical cluster analysis, was performed to evaluate temporal evolution of contamination profiles and to assess variability among fish species and among sites according to watershed characteristics. The explicative variables correspond to muscular concentrations of pesticides (azoxystrobin, clomazone, diflufenican, carbendazim, isoproturon, metazachlor, napropamid) in three species of fish (Perca fluviatilis, Cyprinus carpio and Rutilus rutilus), caught in five ponds during two sampling campaigns. Management data are added variables in order to discuss about parameters suspected to be implicated in the contamination profiles recorded.

Results

This work shows that high amounts of pesticides applied, short crop rotation durations and bare soil practices led to contamination of sediments and fish and were associated to a “bad” management of watershed. Breeding fish that had low masses and establishing the fishing period at the end of winter seemed to be “bad” management of pond. Aggravating topological parameters were big watershed coupled to small pond and high proportions of sand soils in the watershed.

Conclusions

Reducing amounts of pesticide used (e.g. policy agency plans, farmer acceptance), favouring long-term rotations and inter-cultures, adapting pond creation and fish farming practices to watershed management and topography all could reduce pesticide levels in edible fish and contribute to a better sustainability of the extensive fish farming in pond.     

水质模型参数识别的遗传算法

Ｏ’ｃｏｎｎｏｒ模型是一个比广泛应用的Ｓｔｒｅｅｔｅｒ－Ｐｈｉｌｉｐｓ模型更精确的水质模型,由于该模型参数率定的困难性,限制它在实际中应用。     

Field calibration of surface: a model of agricultural chemicals in surface waters

Agricultural chemicals sporadically occur at detectable levels in the surface waters of intensively farmed watersheds. HSPF, a previously released model of agricultural chemicals in surface water, had been used to predict concentrations which were much higher (10 X) than those actually observed during monitoring studies. A new model, SURFACE, is described here which is much simpler than HSPF and gives better predictions of surface water concentrations. SURFACE uses PRZM, an EPA model, to calculate edge-of-field runoff losses and simple hydraulic routing algorithms to determine concentrations at the bottom of large river basins. In water systems sampled during 1985 and 1986, SURFACE predictions of annualized mean concentrations for alachlor, atrazine, cyanazine and metolachlor were within 0.09 ppb half of the time.     

Field calibration of surface: A model of agricultural chemicals in surface waters

Abstract

Agricultural chemicals sporadically occur at detectable levels in the surface waters of intensively farmed watersheds. HSPF, a previously released model of agricultural chemicals in surface water, had been used to predict concentrations which were much higher (10 X) than those actually observed during monitoring studies. A new model, SURFACE, is described here which is much simpler than HSPF and gives better predictions of surface water concentrations. SURFACE uses PRZM, an EPA model, to calculate edge‐of‐field runoff losses and simple hydraulic routing algorithms to determine concentrations at the bottom of large river basins. In water systems sampled during 1985 and 1986, SURFACE predictions of annualized mean concentrations for alachlor, atrazine, cyanazine and metolachlor were within 0.09 ppb half of the time.     

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles

Purpose

The present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium.

Method

Iron?Csilver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique.

Results

The optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10?mg?g^?1, respectively, as compared to the experimental value of 54.0?mg?g^?1 with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set.

Conclusion

Predictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

Calibration of lichen transplants considering faint memory effects

Biomonitoring, among other purposes, can be used as a tool to study the dispersion of trace elements through the atmosphere and back to the earth's surface again. In this sense it has been used for more than 20 years now. Yet, the calibration of biomonitors elemental contents against other atmospheric element dispersion variables like deposition or airborne concentration is still an open subject. The knowledge of a calibration nevertheless represents a qualitative improvement upon the use of biomonitors. Biomonitors elemental contents are usually assumed to depend in a linear way on the average values of atmospheric element dispersion variables, thus a linear regression is assumed to provide a good calibration. In this work: the element dispersion variables like deposition or airborne concentration will be referred in general as availability variables due to reasons that will be clarified; an uptake experiment using transplants of lichen Parmelia sulcata carried out in Portugal during a 2-year period (1994/1996) is described; and new concepts and calibration methods are presented. Lichen and aerosol samples were analysed by nuclear analytical techniques PIXE and INAA, and total deposition dry residue samples were analysed by ICP-MS. A database of roughly 70,000 values was thus created and biomonitor calibration carried out considering that biomonitors are not mechanical instruments but that they rather describe their exposure with a non-perfect memory. Data shows that surveying lichens four times within 1 year, with a period of 3 months in between sampling campaigns, allows the recovery of availability mean, maxima and standard deviation for many elements.     

响应面法和神经网络优化Acinetobacter sp. DNS32发酵基质

为了提高阿特拉津降解菌Acinetobacter sp.DNS32的产量,分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分(玉米粉、豆饼粉、K2HPO4)进行优化研究。响应曲面法确定3种成分的含量为玉米粉39.494 g/L,豆饼粉25.638 g/L和K2HPO43.265 g/L时,预测发酵活菌最大生物量为7.079×108CFU/mL,实测量为7.194×108CFU/mL;人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L时,预测最大值为7.199×108CFU/mL,实测量为7.244×108CFU/mL;最终确定培养基配方:玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L,CaCO3为3.000 g/L,MgSO4.7H2O和NaCl均为0.200 g/L;优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36.6%。结果表明,在阿特拉津降解菌DNS32发酵培养基组分优化方面,响应面法和基于人工神经网络的遗传算法都是可行的,基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。     

响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．DNS32的产量，分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分（玉米粉、豆饼粉、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L，豆饼粉25．638g／L和K。HPO。3．265g／L时，预测发酵活菌最大生物量为7．079×10^8CFU／mL，实测量为7．194×10^8CFU／mL；人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L时，预测最大值为7．199×10^8CFU／mL，实测量为7．244×10。CFU／mL；最终确定培养基配方：玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L，CaCO3为3．000g／L，MgSO4·7H2O和NaCl均为0．200g／L；优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36．6％。结果表明，在阿特拉津降解菌DNS32发酵培养基组分优化方面，响应面法和基于人工神经网络的遗传算法都是可行的，基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。     

Laccase production by Phomopsis liquidambari B3 cultured with food waste and wheat straw as the main nitrogen and carbon sources

Food waste is the most difficult waste to manage because of the high treatment costs and the risk of environmental contamination. Wheat straw burning has become an increasingly discussed topic in recent years because of the air pollution it causes. However, food waste and wheat straw contain high amounts of nutrient elements; efficient utilization of these wastes can improve the environment by lessening airborne pollutant emission and the amount of waste that must be landfilled. It is well known that use of low-cost and abundant waste materials in microbial fermentations can reduce product costs. We aimed to use these resources while improving laccase production by the endophytic fungus Phomopsis liquidambari B3. We cultured P. liquidambari B3 in medium containing food waste and wheat straw as the main nitrogen and carbon sources, respectively. We optimized the fermentation conditions by response surface methodology (RSM), using a Box–Behnken design for RSM I and a central composite design for RSM II. Optimization resulted in an 11.07-fold (1.98-fold RSM I; 5.59-fold RSM II) increase in laccase yield compared with that before optimization. The model was validated by mathematical evaluations and by comparisons between predicted and experimental values. Under optimized conditions, 53.76% of lignin in wheat straw was degraded. By optimizing fermentation conditions and using multiple bioresources, laccase production by this fungus was increased. These results provide the foundation for future research and for scaled-up laccase production.

Implications:Food waste and wheat straw are waste products, but they could be bioresources if they were managed properly. In this work, we innovatively used a mixture of food waste and wheat straw as a substrate for the novel strain Phomopsis liquidambari B3 to produce laccase. This process, which provided the foundation for the subsequent research and for scaled-up laccase production, offers solutions for both rural and urban pollution problems: that is, it reduces the amount of waste material that needs to be disposed of by burning or dumping, and it also produces a valuable product.     

A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations

The occurrence of Dense Non-Aqueous Phase Liquid (DNAPL) contaminations in the subsurface is a threat for drinkwater resources in the western world. Surfactant-Enhanced Aquifer Remediation (SEAR) is widely considered as one of the most promising techniques to remediate DNAPL contaminations in-situ, be it with considerable additional costs compared to classical pump-and-treat remediations. A cost-effective design of the remediation set-up is therefore essential. In this work, a pilot SEAR test is executed at a DNAPL contaminated site in Belgium in order to collect data for the calibration of a multi-phase multi-component model. The calibrated model is used to assess a series of scenario-analyses for the full-scale remediation of the site. The remediation variables that were varied were the injection and extraction rate, the injection and extraction duration, and the surfactant injection concentrations. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL and (ii) a total minimal cost. These Pareto curves can help decision makers to select an optimal remediation strategy in terms of cost and remediation efficiency. The Pareto front shows a considerable trade-off between the total remediation cost and the removed DNAPL mass.     

Influence of the operation conditions on CO2 capture by CaO-derived sorbents prepared from synthetic CaCO3

In this work, a statistical experimental design is performed in order to prepare CaCO₃ materials for use as CaO-based CO₂ sorbent precursors. The influence of different operational parameters such as synthesis temperature (ST), stirring rate (SR) and surfactant percent (SP) on CO₂ capture is studied by applying Response Surface Methodology (RSM). The samples were characterized using different analytical techniques including X-ray diffraction, N₂ adsorption isotherm analysis and Scanning Electron Microscopy–X-ray Energy Dispersive Spectroscopy (SEM-EDX). CO₂ capture capacity was determined by means of a thermogravimetric analyzer which recorded the mass uptake of the samples when these were exposed to a gas stream containing diluted (15%) CO₂. The statistical approach used in this work provides a rapid way of predicting and optimizing the main preparation variables of CaO-derived sorbents for CO₂ sorption. The results obtained clearly indicate that four parameters statistically influence CO₂ uptake: SR, the square of SR, its interaction with SP and the square of SP.     

基于响应曲面法优化烧结烟气脱硫灰改性工艺

采用BOX-Behnken的中心组合实验设计及响应面分析方法对半干法烧结烟气脱硫灰进行改性研究,得到脱硫灰转化率的预测模型。结果表明:通过该预测模型可以很好地描述脱硫灰的转化率与反应温度、反应时间和气固比等重要操作参数之间的关系,R2=0.9903。因素分析表明,反应温度对脱硫灰的转化率影响最大,同时反应温度和气固比的交互作用与反应温度和时间的交互作用对脱硫灰的转化率的影响作用相同。利用得到的改进预测模型可以计算脱硫灰的转化率。