Acceptable Parameter Sets Research Articles

A Bayesian updating framework for calibrating the hydrological parameters of road networks using taxi GPS data

Abstract. Hydrological parameters should pass through a careful calibration procedure before being used in a hydrological model that aids decision making. However, significant difficulty is encountered when applying existing calibration methods to regions in which runoff data are inadequate. To achieve accurate hydrological calibration for ungauged road networks, we propose a Bayesian updating framework that calibrates hydrological parameters based on taxi GPS data. Hydrological parameters were calibrated by adjusting their values such that the runoff generated by acceptable parameter sets corresponded to the road disruption periods during which no taxi points are observed. The proposed method was validated on 10 flood-prone roads in Shenzhen and the results revealed that the trends of runoff could be correctly predicted for 8 of 10 roads. This study demonstrates that the integration of hydrological models and taxi GPS data can provide viable alternative measures for model calibration to derive actionable insights for flood hazard mitigation.

AutoQS v1: automatic parametrization of QuickSampling based on training images analysis

Abstract. Multiple-point geostatistics are widely used to simulate complex spatial structures based on a training image. The practical applicability of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. While methods for automatically selecting training images are available, parametrization can be cumbersome. Here, we propose to find an optimal set of parameters using only the training image as input. The difference between this and previous work that used parametrization optimization is that it does not require the definition of an objective function. Our approach is based on the analysis of the errors that occur when filling artificially constructed patterns that have been borrowed from the training image. Its main advantage is to eliminate the risk of overfitting an objective function, which may result in variance underestimation or in verbatim copy of the training image. Since it is not based on optimization, our approach finds a set of acceptable parameters in a predictable manner by using the knowledge and understanding of how the simulation algorithms work. The technique is explored in the context of the recently developed QuickSampling algorithm, but it can be easily adapted to other pixel-based multiple-point statistics algorithms using pattern matching, such as direct sampling or single normal equation simulation (SNESIM).

Ambient Conditions of Winter Thunderstorms in Japan to Reproduce Observed Gamma‐Ray Glow Energy Spectra

AbstractElectric field of thunderclouds modifies components and energy spectra of the cosmic‐ray air shower. In particular, thunderstorms accelerate charged particles, resulting in an enhancement of gamma‐ray fluxes on the ground, known as a gamma‐ray glow. This phenomenon has been observed in recent years by the Gamma‐Ray Observation of Winter THunderclouds collaboration from winter thunderstorms in the Hokuriku area of Japan. The present work examines the ambient conditions required to produce spectral features of the previously detected gamma‐ray glows, by using Monte Carlo simulations of particle interactions in the atmosphere. We focus on three parameters, the strength and length of the electric field, and the length of a null‐field attenuation region below the electrified region. The average spectrum of observed gamma‐ray glows in winter thunderstorms of Japan requires an electric field intensity close to 0.31 MV/m, slightly exceeding the Relativistic Runaway Electron Avalanche threshold of 0.284 MV/m. The vertical size of the electric field region should be comparable to 1 km. The estimated attenuation region size is 300–500 m, necessary to reduce the low‐energy photon flux of the average gamma‐ray glows. There is still a wide range of acceptable parameter sets with degeneracy to make a similar spectrum.

Impact of land use land cover changes on flow uncertainty in Siliana watershed of northwestern Tunisia

It is widely admitted that changes in land use land cover (LULC) influence the hydrology of the catchment. However, how these changes affect hydrological model prediction uncertainty is still a raising question. In this paper we addressed this question by investigating the impacts of the rapid change in LULC in the Siliana catchment in Tunisia on monthly flow and magnitude of flow extremes using the SWAT hydrological model while quantifying the contribution of LULC to the model parameter and prediction uncertainty. At a first step, the SWAT model parameter and prediction uncertainty were estimated using the GLUE method and acceptable parameter sets were identified. Subsequently, the SWAT model was fed with historical LULC as derived from Landsat 5 and 8 satellite images for the years 1990, 2000, 2013 and 2019, and run with the acceptable parameter sets. The results show that the increase in olive plantations (+380 %), urban area (+200 %), and irrigated lands (+309 %) from 1990 to 2019, has LULC decreased monthly flow, high flows magnitude but did not impact low flows in particular over the previous two decades. The findings also suggest that model prediction uncertainty can mask LULC effects, suggesting that model results can be misleading without explicit consideration of uncertainty when assessing the hydrological impacts of changes in LULC.

Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed

Water cycling within agricultural watersheds includes high uncertainty because of natural and anthropogenic factors (e.g., cultivation practices). Remotely sensed evapotranspiration products (RS-ET) have been adopted as an additional constraint on watershed modeling to enhance the accuracy of water cycling predictions while reducing uncertainty. However, plant parameters affecting evapotranspiration (ET) in watershed models are poorly calibrated without the use of appropriate constraints. The goal of this study is to assess the predictive uncertainty of the Soil and Water Assessment Tool (SWAT), depending on the inclusion or exclusion of annual crop yield as an additional constraint for an agricultural watershed. We analyzed the simulated results with acceptable performance measures depending on a varying degree of model constraints: one constraint (streamflow), two constraints (streamflow and RS-ET) and three constraints (streamflow, RS-ET, and crop yield). The three performance measures used were Nash-Sutcliffe Efficiency (NSE), Percent bias (P-bias), and Kling-Gupta Efficiency (KGE). As the number of model’s constraints increased, the number of acceptable parameter sets were substantially reduced from 180 (acceptable for streamflow) to 116 (acceptable for streamflow and RS-ET) and 2 (acceptable for streamflow, RS-ET, and crop yield). In addition, overall model performance measures for ET were greatest in the simulation results with three constraints representing 0.02–0.2 and 0.04–0.05 greater NSE and KGE values than those of one constraint and two constraints, respectively. The parameter set with the best ET performance measures was also acceptable for predicting crop yield. Based on these results, we conclude that this crop yield data can be adopted as a model constraint for agricultural watersheds to reduce model uncertainty in ET simulations and to increase model prediction accuracy.

Microstructural and Mechanical Properties of a Solid-State Additive Manufactured Magnesium Alloy

Abstract In recent years, additive manufacturing (AM) has gained prominence in rapid prototyping and production of structural components with complex geometries. Magnesium alloys, which have a strength-to-weight ratio that is superior compared with steel and aluminum alloys, have shown potential in lightweighting applications. However, commercial beam-based AM technologies have limited success with magnesium alloys due to vaporization and hot cracking. Therefore, as an alternative approach, we propose the use of a near net-shape solid-state additive manufacturing process, additive friction stir deposition (AFSD), to fabricate magnesium alloys in bulk. In this study, a parametric investigation was performed to quantify the effect of process parameters on AFSD build quality including volumetric defects and surface quality in magnesium alloy AZ31B. In order to understand the effect of the AFSD process on structural integrity in the magnesium alloy AZ31B, in-depth microstructure and mechanical property characterization was conducted on a bulk AFSD build fabricated with a set of acceptable process parameters. Results of the microstructure analysis of the as-deposited AFSD build revealed bulk microstructure similar to wrought magnesium alloy AZ31 plate. Additionally, similar hardness measurements were found in AFSD build compared with control wrought specimens. While tensile test results of the as-deposited AFSD build exhibited a 20% drop in yield strength (YS), nearly identical ultimate strength was observed compared with the wrought control. The experimental results of this study illustrate the potential of using the AFSD process to additively manufacture Mg alloys for load bearing structural components with achieving wrought-like microstructure and mechanical properties.

Reach-Scale Model of Aquatic Vegetation Quantifies N Fate in a Bedrock-Controlled Karst Agroecosystem Stream

In-stream fate of nutrients in karst agroecosystems remains poorly understood. The significance of these streams is recognized given spring/surface water confluences have been identified as hotspots for biogeochemical transformations. In slow-moving streams high in dissolved inorganic nutrients, benthic and floating aquatic macrophytes are recognized to proliferate and drastically impact nutrient fate; however, models that quantify coupled interactions between these pools are limited. We present a reach-scale modeling framework of nitrogen dynamics in bedrock-controlled streams that accounts for coupled interactions between hydrology, hydraulics, and biotic dynamics and is validated using a multi-year, biweekly dataset. A fluvial N budget with uncertainty was developed to quantify transformation dynamics for the dissolved inorganic nitrogen (DIN) pool using a GLUE-like modeling framework, and scenario analyses were run to test for model function over variable environmental conditions. Results from a 10,000 run uncertainty analysis yielded 195 acceptable parameter sets for the calibration period (2000–2002), 47 of which were acceptable for the validation period (2003) (Nash-Sutcliffe Efficiency (NSE) > 0.65; percent bias (PBIAS) < ±15), with significantly different posterior parameter spaces for parameters including denitrification coefficients and duckweed growth factors. The posterior solution space yielded model runs with differing biomass controls on DIN, including both algae and duckweed, but suggested duckweed denitrifies at a rate that would place the bedrock agroecosystem stream on the high-end of rates reported in the literature, contradicting the existing paradigm about bedrock streams. We discuss broader implications for watershed-scale water quality modeling and implementation strategies of management practices for karst agroecosystems, particularly with respect to stream restoration.

Динамический и прочностной анализ системы главного привода при проектировании моечной машины нового типа для овощей и фруктов

This paper describes a new drum-type fruit-vegetable washer, which is created on the basis of manual vegetable washing method. The main driving part of the washer is a crank mechanism with springs. The main drive design process is proposed to be carried out in two stages: dynamic research and strength analysis. The results of dynamic research show the effect of spring stiffness on reducing dynamic reactions in the crank mechanism as well as the required engine power, in other words, on reducing washer vibration and energy consumption. Stress analysis was performed using the finite element software package ABAQUS. The design process allows selecting a set of acceptable parameters for the main drive of the washer. This result is used to create a prototype of an innovative fruit-vegetable washer.

Assessing the effectiveness of riparian buffers for reducing organic nitrogen loads in the Coastal Plain of the Chesapeake Bay watershed using a watershed model

Riparian buffers are an important conservation practice to mitigate water quality degradation in the Coastal Plain of the Chesapeake Bay watershed (CBW). Although forested and grassed riparian buffers have been implemented in this region through government programs, the impacts of riparian buffers on water quality have been rarely examined. The objective of this study was to assess the long-term effects of riparian buffers to improve water quality in the Coastal Plain of the CBW. A watershed model, Soil and Water Assessment Tool (SWAT), was employed for this study. Considering impacts of model uncertainty (i.e., equifinality) on the effectiveness of riparian buffers, we adopted all parameter sets that produced acceptable simulation results. Multiple riparian buffer implementation scenarios were developed to generate the baseline condition on total organic nitrogen (TON) loads without riparian buffers and examine variation of TON loads with areal coverage of riparian buffers. Through the calibration processes, a total of 235 acceptable parameter sets were identified and used to simulate TON loads. The simulation results indicated that riparian buffers significantly reduce TON loads. Without riparian buffers, annual TON loads from the 220 km2 study watershed were 18 to 34 metric tons, but declined to 8 to 21 metric tons with riparian buffers. The effectiveness of riparian buffers on reducing annual TON loads increased from 17% to 45% with an increase in the extent of riparian buffer implementation. The effectiveness of riparian buffers tended to be higher during early spring than other seasons as high soil water conditions promote occurrence of surface water flow and thus TON loads. Riparian buffers were more efficient on croplands than other land use types due to high soil nutrient levels caused by fertilizer applications. The effectiveness of riparian buffers differed considerably by parameter set. Thus, efforts to consider model uncertainty are important to provide better insight into the impacts of conservation practices. This study supports ongoing riparian buffer programs for the Mid-Atlantic Coastal Plain by demonstrating the effectiveness of riparian buffers and informing implementation guidelines.

An Algorithm for Generating Sets of Acceptable Energy Supply Values for Intelligent Systems for Carrying Heavy Trains

This article deals with an algorithm for generating a set of acceptable values of energy supply indicators. This algorithm has been designed for use in intelligent systems to make decisions on the carrying of heavy trains on limiting sections. Using the generated sets of permissible parameters of operational indicators for weight, speed, and location of oncoming and passing trains will allow sending trains according to energy-efficient train paths. The algorithm contains sections of energy supply and cargo turnover, assessment of the acceptable parameters of electricity supply, and obtaining a set of acceptable parameters of operational parameters of train accommodation.

Problems of developing the model for forming parent machine-building enterprise order portfolio under cooperation conditions

The article deals with the feature’s projects portfolio formation of head enterprise that produces high-tech products. Methods the projects portfolio formation are considered: mathematical, simulation and economic-statistical. The factors that influence the choice of the model are illustrated.It is proposed to carry out research on the basis of creation complexes ofinterrelated, multi-level models and optimization-simulation approach. Themeans of implementing this approach are human-machine iterative,interactive procedures. To find the optimal solution by the decision maker,procedures should be based on the identification of the main goals andpreferences used in the creation of an optimal of the project portfolio, thestudy of a set of acceptable parameters and actions. The head enterprise is considered as a complex system described by severalmodels, to which the control scheme with direct and feedback is applicable,where: functional breakdown of the process of forming the projects portfolio into separate blocks justifies the use of multi-level modeling of a complex production system; in long-term planning and forecasting, it is necessary to solve both the direct problem of system consequences for a given “input”, and the inverse problem of planning, which has a significant spread of output parameters. In order to optimize the method, it is proposed to create iteration- optimization analytical models using the two-point boundary value method, including planning from the achieved and planning from the final result. Indicated the advantages of using a specialized mathematical apparatus inthe formation of the projects portfolio.

Relativistic parameterizations of neutron matter and implications for neutron stars

We construct parameter sets of the relativistic mean-field model fitted to the recent constraints on the asymmetry energy $J$ and the slope parameter $L$ for pure neutron matter. We find cases of unphysical behaviour, i.e.\ the appearance of negative pressures, for stiff parameter sets with low values of the effective mass $m^*/m$. In some cases the equation of state of pure neutron matter turns out to be outside the allowed band given by chiral effective field theory. The mass-radius relations of neutron stars for all acceptable parameter sets shows a maximum mass in excess of $2M_\odot$ being compatible with pulsar mass measurements. Given the constraints on the model in the low-density regime coming from chiral effective theory, we find that the radius of a $1.4M_\odot$ neutron star is nearly independent on the value of $L$. This is in contrast to some previous claims for a strong connection of the slope parameter with the radius of a neutron star. In fact, the mass-radius relation turns out to depend only on the isoscalar parameters of symmetric matter. The constraints of GW170817 on the tidal deformability and on the radius are also discussed.

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

Abstract. Distributed catchment models are widely used tools for predicting hydrologic behavior. While distributed models require many parameters to describe a system, they are expected to simulate behavior that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several behavioral sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modeling application. We outline a hierarchical approach to reduce the number of behavioral sets based on regional, observation-driven, and expert-knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of behavioral parameter sets and altered distributions of spatiotemporal simulations, simulating a well-studied headwater catchment, Stringer Creek, Montana, using the distributed hydrology–soil–vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10 000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series reduced the number of behavioral parameter sets but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioral parameter sets can reduce equifinality and bolster more careful application and simulation of spatiotemporal processes via distributed modeling at the catchment scale.

Modeling the Agility MLC in the Monaco treatment planning system.

We investigate the relationship between the various parameters in the Monaco MLC model and dose calculation accuracy for an Elekta Agility MLC. The vendor‐provided MLC modeling procedure — completed first with external vendor participation and then exclusively in‐house — was used in combination with our own procedures to investigate several sets of MLC modeling parameters to determine their effect on dose distributions and point‐dose measurements. Simple plans provided in the vendor procedure were used to elucidate specific mechanical characteristics of the MLC, while ten complex treatment plans — five IMRT and five VMAT — created using TG‐119‐based structure sets were used to test clinical dosimetric effects of particular parameter choices. EDR2 film was used for the vendor fields to give high spatial resolution, while a combination of MapCHECK and ion chambers were used for the in‐house TG‐119‐based procedures. The vendor‐determined parameter set provided a reasonable starting point for the MLC model and largely delivered acceptable gamma pass rates for clinical plans — including a passing external evaluation using the IROC H&N phantom. However, the vendor model did not provide point‐dose accuracy consistent with that seen in other treatment systems at our center. Through further internal testing it was found that there existed many sets of MLC parameters, often at opposite ends of their allowable ranges, that provided similar dosimetric characteristics and good agreement with planar and point‐dose measurements. In particular, the leaf offset and tip leakage parameters compensated for one another if adjusted in opposite directions, which provided a level curve of acceptable parameter sets across all plans. Interestingly, gamma pass rates of the plans were less dependent upon parameter choices than point‐dose measurements, suggesting that MLC modeling using only gamma evaluation may be generally an insufficient approach. It was also found that exploring all parameters of the very robust MLC model to find the best match to the vendor‐provided QA fields can reduce the pass rates of the TG‐119‐based clinical distributions as compared to simpler models. A wide variety of parameter sets produced MLC models capable of meeting RPC passing criteria for their H&N IMRT phantom. The most accurate models were achievable using a combination of vendor‐provided and in‐house procedures. The potential existed for an over‐modeling of the Agility MLC in an effort to obtain the fine structure of certain quality assurance fields, which led to a reduction in agreement between calculation and measurement of more typical clinical dose distributions.PACS number(s): 87.56.nk, 87.53.Kn, 87.55.km, 87.55.Qr

A Simple Method for Optimization of Reference Gene Identification and Normalization in DNA Microarray Analysis.

BackgroundComparative DNA microarray analyses typically yield very large gene expression data sets that reflect complex patterns of change. Despite the wealth of information that is obtained, the identification of stable reference genes is required for normalization of disease- or drug-induced changes across tested groups. This is a prerequisite in quantitative real-time reverse transcription-PCR (qRT-PCR) and relative RT-PCR but rare in gene microarray analysis. The goal of the present study was to outline a simple method for identification of reliable reference genes derived from DNA microarray data sets by comparative statistical analysis of software-generated and manually calculated candidate genes.Material/MethodsDNA microarray data sets derived from whole-blood samples obtained from 14 Zucker diabetic fatty (ZDF) rats (7 lean and 7 diabetic obese) were used for the method development. This involved the use of software-generated filtering parameters to accomplish the desired signal-to-noise ratios, 75th percentile signal manual normalizations, and the selection of reference genes as endogenous controls for target gene expression normalization.ResultsThe combination of software-generated and manual normalization methods yielded a group of 5 stably expressed, suitable endogenous control genes which can be used in further target gene expression determinations in whole blood of ZDF rats.ConclusionsThis method can be used to correct for potentially false results and aid in the selection of suitable endogenous control genes. It is especially useful when aimed to aid the software in cases of borderline results, where the expression and/or the fold change values are just beyond the pre-established set of acceptable parameters.

Simulated crepitus and its reality-based specification using wearable patient dummy

Physical therapists are trained in manual examination techniques to test the impaired motor functions of patients. In this study, we have introduced a wearable robotic dummy joint to simulate disordered joint resistances or behaviors to support physical therapists in learning such techniques. We developed a discontinuous joint friction model based on a stick-slip phenomenon to simulate knee joint resistances caused by crepitus, a typical symptom accompanied by osteoarthritis. Practicing therapists participated in a reality-based evaluation test and specified acceptable parameter sets to adjust the simulated crepitus for the exoskeletal patient robot. The simulated crepitus and wearable dummy joint are expected to support the training of physical therapists.

Desorption mass spectrometry: Revisiting the in-situ calibration technique for mixed group-V alloy MBE growth of ~3.3 µm diode lasers

We apply the desorption mass spectrometry (DMS) technique and analyze the desorbed Sb species in-situ during MBE growth of mixed As/Sb heterostructures. We demonstrate how DMS is useful in pre-growth calibration of the V/III ratio, the group-III ratio, as well as the Sb-content in quaternary or quinary mixed As/Sb alloys. We also apply DMS to the digital alloy growth method. For demonstration purposes, we start with an un-calibrated MBE system, use the DMS technique to calibrate all of the previously undetermined MBE parameters and grow a ~3.3µm diode laser heterostructure in only one attempt. The results demonstrate that the DMS technique will allow the MBE to quickly converge toward a set of acceptable growth parameters without the need for ex-situ calibration of alloy composition.

Modelling pesticide leaching under climate change: parameter vs. climate input uncertainty

Abstract. Assessing climate change impacts on pesticide leaching requires careful consideration of different sources of uncertainty. We investigated the uncertainty related to climate scenario input and its importance relative to parameter uncertainty of the pesticide leaching model. The pesticide fate model MACRO was calibrated against a comprehensive one-year field data set for a well-structured clay soil in south-western Sweden. We obtained an ensemble of 56 acceptable parameter sets that represented the parameter uncertainty. Nine different climate model projections of the regional climate model RCA3 were available as driven by different combinations of global climate models (GCM), greenhouse gas emission scenarios and initial states of the GCM. The future time series of weather data used to drive the MACRO model were generated by scaling a reference climate data set (1970–1999) for an important agricultural production area in south-western Sweden based on monthly change factors for 2070–2099. 30 yr simulations were performed for different combinations of pesticide properties and application seasons. Our analysis showed that both the magnitude and the direction of predicted change in pesticide leaching from present to future depended strongly on the particular climate scenario. The effect of parameter uncertainty was of major importance for simulating absolute pesticide losses, whereas the climate uncertainty was relatively more important for predictions of changes of pesticide losses from present to future. The climate uncertainty should be accounted for by applying an ensemble of different climate scenarios. The aggregated ensemble prediction based on both acceptable parameterizations and different climate scenarios has the potential to provide robust probabilistic estimates of future pesticide losses.

Predicting pesticide leaching under climate change: Importance of model structure and parameter uncertainty

The assessment of climate change impacts on pesticide leaching requires a careful consideration of both parameter uncertainty and model structural error. Our aim was to assess (i) model structure uncertainty, with specific focus on the effects of temperature on pesticide sorption and diffusion, and its relation to parameter uncertainty and (ii) the importance of these sources of uncertainty in long-term predictions of pesticide leaching in the perspective of climate change. We introduced optional functions describing the effects of temperature on sorption and diffusion into the pesticide fate and transport model MACRO5.2, which resulted in four structurally different versions. The generalised likelihood uncertainty estimation (GLUE) method was applied to these four structurally different model versions in order to generate an ensemble of acceptable parameter combinations for long-term predictions of pesticide leaching under climate change. The analysis was performed in a two-step procedure. First, for each model version, 80,000 parameter combinations were tested against a comprehensive field data set of bromide and bentazone concentrations in drainflow and in the soil profile to identify acceptable parameter combinations. In a second step, we performed 30-year predictions based on these acceptable parameter sets for three hypothetical compounds that differed in their sorption strength. The period 1970–1999 was used to represent the current climate for a region in south-west Sweden. The future climate time series was derived for the period 2070–2099 by perturbing the observed time series based on monthly change factors calculated from climate projections of the regional climate model RCA3 with boundary conditions of the global circulation model ECHAM5 forced by the A2-emission scenario. For all three pesticide compounds, total losses were predicted to increase for autumn applications due to projected increases in winter rainfall, but decrease for spring applications. Differences between model versions were found, despite the large uncertainty in parameter estimates. Thus, temperature dependent diffusion was shown to be important for weakly sorbed compounds, while inclusion of temperature dependent sorption significantly affected the predictions for moderately and strongly sorbed compounds. The response to climate change was consistent for the different model versions, but the models that included temperature dependent sorption showed relatively more leaching in a future climate. Our findings stressed the importance of considering temperature dependent processes when modelling future pesticide losses.

Impact of policy-relevant scenarios on ozone in southern England: Influence of chemical mechanism choice

Monte Carlo sampling of pre-specified parameter ranges has been used to replace single ‘best estimate’ Photochemical Trajectory Model runs with 9006 ‘acceptable’ model runs that were each consistent with the observations of elevated O3 during July 2006 at a rural location in southern England. These acceptable parameter sets were then used for probabilistic evaluation of policy relevant photochemical oxidant control strategies, based on 30% reductions in VOC and NOx precursor emissions and the geographic locations where the emission reductions were carried out. Indicator species ratios were used to complete a diagnostic evaluation of model performance. The chemical mechanism employed in the base case model was replaced with four other mechanisms from the literature. Probabilistic uncertainty analysis showed that the results of the policy-relevant scenarios showed little or no sensitivity to chemical mechanism choice. A large degree of error compensation could be achieved through the selection of acceptable parameter sets and this hid the differences between the five chemical mechanisms studied.