Bias Uncertainty Research Articles

Calibration Probe Uncertainty and Validation for the Hypersonic Material Environmental Test System

This paper presents an uncertainty analysis of the stagnation-point calibration probe surface predictions for conditions that span the performance envelope of the Hypersonic Materials Environmental Test System facility located at NASA Langley Research Center. A second-order stochastic expansion was constructed over 47 uncertain parameters to evaluate the sensitivities, identify the most significant uncertain variables, and quantify the uncertainty in the stagnation-point heat flux and pressure predictions of the calibration probe for low- and high-enthalpy test conditions. A sensitivity analysis showed that measurement bias uncertainty is the most significant contributor to the stagnation-point pressure and heat flux variance for the low-enthalpy condition. For the high-enthalpy condition, a paradigm shift in sensitivities revealed the computational fluid dynamics model input uncertainty as the main contributor. A comparison between the prediction and measurement of the stagnation-point conditions under uncertainty showed that there was evidence of statistical disagreement. A validation metric was proposed and applied to the prediction uncertainty to account for the statistical disagreement when compared with the possible stagnation-point heat flux and pressure measurements.

Reconciling the water balance of large lake systems

Water balance models are commonly employed to improve understanding of drivers behind changes in the hydrologic cycle across multiple space and time scales. Generally, these models are physically-based, a feature that can lead to unreconciled biases and uncertainties when a model is not encoded to be faithful to changes in water storage over time. Statistical methods represent one approach to addressing this problem. We find, however, that there are very few historical hydrological modeling studies in which bias correction and uncertainty quantification methods are routinely applied to ensure fidelity to the water balance. Importantly, we know of none (aside from preliminary applications of the model we advance in this study) applied specifically to large lake systems. We fill this gap by developing and applying a Bayesian statistical analysis framework for inferring water balance components specifically in large lake systems. The model behind this framework, which we refer to as the L2SWBM (large lake statistical water balance model), includes a conventional water balance model encoded to iteratively close the water balance over multiple consecutive time periods. Throughout these iterations, the L2SWBM can assimilate multiple preliminary estimates of each water balance component (from either historical model simulations or interpolated in situ monitoring data, for example), and it can accommodate those estimates even if they span different time periods. The L2SWBM can also be executed if data for a particular water balance component are unavailable, a feature that underscores its potential utility in data scarce regions. Here, we demonstrate the utility of our new framework through a customized application to the Laurentian Great Lakes, the largest system of lakes on Earth. Through this application, we find that the L2SWBM is able to infer new water balance component estimates that, to our are knowledge, are the first ever to close the water balance over a multi-decadal historical period for this massive lake system. More specifically, we find that posterior predictive intervals for changes in lake storage are consistent with observed changes in lake storage across this period over simulation time intervals of both 6 and 12 months. In additional to introducing a framework for developing definitive long-term hydrologic records for large lake systems, our study provides important insights into the origins of biases in both legacy and state-of-the-art hydrological models, as well as regional and global hydrological data sets.

Cluster cosmology with the NIKA2 SZ Large Program

The main limiting factor of cosmological analyses based on thermal Sunyaev-Zel’dovich (SZ) cluster statistics comes from the bias and systematic uncertainties that affect the estimates of the mass of galaxy clusters. High-angular resolution SZ observations at high redshift are needed to study a potential redshift or morphology dependence of both the mean pressure profile and of the mass-observable scaling relation used in SZ cosmological analyses. The NIKA2 camera is a new generation continuum instrument installed at the IRAM 30-m telescope. With a large field of view, a high angular resolution and a high-sensitivity, the NIKA2 camera has unique SZ mapping capabilities. In this paper, we present the NIKA2 SZ large program, aiming at observing a large sample of clusters at redshifts between 0.5 and 0.9, and the characterization of the first cluster oberved with NIKA2.

Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests

In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here, we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a random forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-mean-square error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

Sensitivity of flood frequency analysis to data record, statistical model, and parameter estimation methods: An evaluation over the contiguous United States

ABSTRACTThe current statistical methods applied in flood frequency analysis require long data records to obtain reliable estimates, particularly for long return periods. Moreover, the choice of the statistical model and the parameter estimation procedure may introduce uncertainty in the estimates. In this work, we investigate the sensitivity of flood frequency analysis to various sample sizes, statistical models, and parameter estimation methods over six major hydrological regions in the contiguous United States. Results show that flood frequency estimates based on annual maximum series approach convergence to the reference values (estimates derived from 70 years record) in terms of median for 35‐year or longer records. However, the uncertainty remains significant and a record of 35 years (20 years) is associated with ~50% (100%) larger uncertainty on the estimated 100‐year flood. The generalised extreme value distribution combined with maximum likelihood estimation method is associated with the largest uncertainty, while the log‐Pearson type III exhibits comparable bias and smaller uncertainty. Application of the partial duration series approach to 20‐year records shows no significant advantage. Our findings suggest that the hydroclimatic characteristics of the catchments exhibit limited impact on the uncertainty.

A web-based tailored nursing intervention (TAVIE en m@rche) aimed at increasing walking after an acute coronary syndrome: Multicentre randomized trial.

Evaluate a web-based tailored nursing intervention, TAVIE en m@rche, on increasing daily steps after an acute coronary syndrome. Parallel two-group multicentre randomized trial. An experimental group receiving TAVIE en m@rche, was compared to a control group receiving hyperlinks to public websites. Acute coronary syndrome patients who were insufficiently active were recruited from three coronary care units. Daily steps at 12weeks were the primary outcome. Secondary outcomes included self-reported walking and moderate to vigorous physical activity (MVPA). Exploratory outcomes were angina frequency, emergency department visits, hospitalizations and secondary prevention programme attendance. Primary data were analysed for 39 participants. No significant effects were found. At 12weeks 275.9 more dailysteps and 1,464.3 more energy expenditure in MVPA were found in the experimental group relative to the control. No effects were found for angina frequency, emergency department visits, hospitalizations and secondary prevention programme attendance. The lack of effect on our primary result may be explained by the intervention goal that was mismatched to the needs of our mostly sufficiently active sample at randomization, resulting in no meaningful change in dailysteps. Although the non-significantly greater increase in self-reported MVPA may represent gains in health among the participants that accessed TAVIE en m@rche, this result should be interpreted with caution. From 40%-60% of acute coronary syndrome patients self-report insufficient levels of physical activity. No effect was found on the primary outcome of daily steps. Although not significant, a greater increase in MVPA was found at 12weeks. The primary outcome can be explained by most of the sample having attained the physical activity recommendation at randomization. Caution in interpreting the non-significant increase in MVPA is warranted due to attrition bias and statistical uncertainty. Future directions may consider the timing of randomization in relation to meeting the needs of insufficiently active acute coronary syndrome patients.

The Foundation Supernova Survey: Measuring Cosmological Parameters with Supernovae from a Single Telescope

Abstract Measurements of the dark energy equation-of-state parameter, w, have been limited by uncertainty in the selection effects and photometric calibration of z < 0.1 Type Ia supernovae (SNe Ia). The Foundation Supernova Survey is designed to lower these uncertainties by creating a new sample of z < 0.1 SNe Ia observed on the Pan-STARRS system. Here we combine the Foundation sample with SNe from the Pan-STARRS Medium Deep Survey and measure cosmological parameters with 1338 SNe from a single telescope and a single, well-calibrated photometric system. For the first time, both the low-z and high-z data are predominantly discovered by surveys that do not target preselected galaxies, reducing selection bias uncertainties. The z > 0.1 data include 875 SNe without spectroscopic classifications, and we show that we can robustly marginalize over CC SN contamination. We measure Foundation Hubble residuals to be fainter than the preexisting low-z Hubble residuals by 0.046 ± 0.027 mag (stat + sys). By combining the SN Ia data with cosmic microwave background constraints, we find w = −0.938 ± 0.053, consistent with ΛCDM. With 463 spectroscopically classified SNe Ia alone, we measure w = −0.933 ± 0.061. Using the more homogeneous and better-characterized Foundation sample gives a 55% reduction in the systematic uncertainty attributed to SN Ia sample selection biases. Although use of just a single photometric system at low and high redshift increases the impact of photometric calibration uncertainties in this analysis, previous low-z samples may have correlated calibration uncertainties that were neglected in past studies. The full Foundation sample will observe up to 800 SNe to anchor the LSST and WFIRST Hubble diagrams.

A Rainfall Data Intercomparison Dataset of RADKLIM, RADOLAN, and Rain Gauge Data for Germany

Quantitative precipitation estimates (QPE) derived from weather radars provide spatially and temporally highly resolved rainfall data. However, they are also subject to systematic and random bias and various potential uncertainties and therefore require thorough quality checks before usage. The dataset described in this paper is a collection of precipitation statistics calculated from the hourly nationwide German RADKLIM and RADOLAN QPEs provided by the German Weather Service (Deutscher Wetterdienst (DWD)), which were combined with rainfall statistics derived from rain gauge data for intercomparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The resulting two point shapefiles are readable with all common GIS and constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 to 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, the publication of this data collection has the potential to benefit other users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing.

Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites

Abstract Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

Automated reduction of statistical errors in the estimated correlation function matrix for operational modal analysis

In operational modal analysis, the correlation function matrix is treated as multiple free decays from which system parameters are extracted. The finite time length of the measured system response, however, introduces statistical errors into the estimated correlation function matrix. These errors cause both random and bias errors that transfer to an identification process of the modal parameters. The bias error is located on the envelope of the modal correlation functions, thus violating the assumption that the correlation function matrix contains multiple free decays. Therefore, the bias error transmits to the damping estimates in operational modal analysis. In this paper, we show an automated algorithm that reduces the bias error caused by the statistical errors. This algorithm identifies erratic behaviour in the tail region of the modal correlation function and reduces this noise tail. The algorithm is tested on a simulation case and experimental data of the Heritage Court Building, Canada. Based on these studies, the algorithm reduces bias error and uncertainty on the damping estimates and increases stability in the identification process.

Particle dispersion analysis in an 11-row Boeing 767 cabin mockup

This study investigated dispersion of particles in the longitudinal direction of a Boeing 767 aircraft cabin mockup. The results of the experimental testing would help validate computational fluid dynamics (CFD) models for research inside aircraft cabins. The objective of the study was to check on the longitudinal dispersion of instantly released powder particles inside the cabin, simulating a sneeze from an infected seated passenger. Particles with diameters between 0.5 and 5 μm are the most dangerous to the human health; thus, this range was sampled and analyzed. An 11-row B-767 cabin mockup was used with actual dimensions of a similar aircraft cabin. Inside the mockup cabin, actual aircraft equipment such as seats and air diffusers were used. Particles were released from the center seat of the second row of the cabin and collected in rows 1 through 11, with more focus on the samples collected in rows 1 through 5. Normalized particle counts and time needed to sample specific number of particles were investigated. Normalized counts for collected particles decreased at a rate of 20%–35% per row away from the release source. The relative uncertainty including random and bias uncertainty totaled to a maximum of ±38%. The highest uncertainty was in the second and third rows, which was in the same row of particle release and one row behind it. Collection times to sample 500, 1000, and 2000 particles, with diameters between 0.5 and 5 μm, increased exponentially along the length of the cabin. The increase in collection time to achieve 1000 particles compared to 500 particles was 1.3 times higher in rows 2 and 3, 1.6 times higher in row 4, and 1.8 times higher in row 5.

Long‐Period Ground Motions from Past and Virtual Megathrust Earthquakes along the Nankai Trough, Japan

Abstract Long‐period ground motions from large (Mw≥7.0) subduction‐zone earthquakes are a real threat for large‐scale human‐made structures. The Nankai subduction zone, Japan, is expected to host a major megathrust earthquake in the near future and has therefore been instrumented with offshore and onshore permanent seismic networks. We use the ambient seismic field continuously recorded at these stations to simulate the long‐period (4–10 s) ground motions from past and future potential offshore earthquakes. First, we compute impulse response functions (IRFs) between an ocean‐bottom seismometer of the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) network, which is located offshore on the accretionary wedge, and 60 onshore Hi‐net stations using seismic interferometry by deconvolution. As this technique only preserves the relative amplitude information of the IRFs, we use a moderate Mw 5.5 event to calibrate the amplitudes to absolute levels. After calibration, the IRFs are used together with a uniform stress‐drop source model to simulate the long‐period ground motions of the 2004 Mw 7.2 intraplate earthquake. For both events, the residuals of the 5% damped spectral acceleration (SA) computed from the horizontal and vertical components of the observed and simulated waveforms exhibit almost no bias and acceptable uncertainties. We also compare the observed SA values of the Mw 7.2 event to those from the subduction‐zone BC Hydro ground‐motion model (GMM) and find that our simulations perform better than the model. Finally, we simulate the long‐period ground motions of a hypothetical Mw 8.0 subduction earthquake that could occur along the Nankai trough. For this event, our simulations generally exhibit stronger long‐period ground motions than those predicted by the BC Hydro GMM. This study suggests that the ambient seismic field recorded by the ever‐increasing number of ocean‐bottom seismometers can be used to simulate the long‐period ground motions from large megathrust earthquakes.

Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction

Because of the harsh working condition in electrified vehicles, the measured current and voltage signals typically contain non-ignorable noises and bias, which potentially decline the accuracy of state-of-charge estimation. In this regard, the noise and bias corruption should be well addressed to maintain sufficient accuracy and robustness. This paper improves the existing methods in the literature from two aspects: (a) A novel offset-free equivalent circuit model is developed to remove the current bias; and (b) based on the offset-free equivalent circuit model, a two-layer estimator is proposed to estimate the state of charge using real-time identified model parameters. The robustness of the two-layer estimator against model uncertainties and the aging effect is further evaluated. Simulation and experimental results show that the proposed two-layer estimator can effectively attenuate the current bias and estimate the state of charge accurately with the error confined to ±4% under different levels of current bias and model uncertainties.

Uncertainty quantification of LWR-PROTEUS Phase II experiments using CASMO-5

This paper presents an improved uncertainty quantification technique for the validation of CASMO-5 on the spent fuel reactivity worth experiments of the LWR-PROTEUS Phase II program. In the program, eleven spent fuel samples manufactured from rods irradiated in a Swiss PWR (discharge burnups of 20–120 MWd/kg) were measured in the PROTEUS research reactor. In this work, both irradiation and reactivity worth measurement steps were modeled with CASMO-5 and the uncertainty on the code prediction were calculated using SHARK-X. For the first time in this work, we propagated the nuclear data uncertainties coming from cross-sections and fission yields for all samples in both fuel irradiations and the reactivity worth experiment models. We found that the fission yield and cross section uncertainties have similar contributions to the uncertainty of the reactivity worth prediction and that the reactivity worth probability distribution is non-normal because of the non-normal distribution of the perturbed fission yield data produced by the GEF code. Propagating input uncertainties through fuel irradiation as well as considering fission yield uncertainty led to larger computational uncertainty than previously reported. However, the observed trends with respect to exposure, fuel type, and moderating conditions are similar. In particular, a linear regression analysis showed that the predictions of reactivity with exposure by CASMO-5 are very accurate for various moderating conditions and for very high burnup.Finally, we estimated the effects of the irradiation history specifications on the relative reactivity worth bias and its uncertainty using two independent irradiation histories. While the bias uncertainty coming from uncertain cross sections was similar when considering either irradiation histories, we observed differences in the bias value. The irradiation history specification is a significant source of modeling bias and should be further investigated.

A laboratory-study on the analytical determination and removal processes of THC-COOH and bezoylecgonine in the activated sludge reactor

The present study focused on 11-nor-9carboxy-Δ9-THC (THC-COOH) and Benzoylecgonine (BE), the most common metabolites of cannabis and cocaine, respectively, present in the domestic sewage entering the wastewater treatment plants. The aims of the study were: (1) to validate the analytical method of detection in wastewater and sludge; (2) to determine contribution of biodegradation and other processes to the removal in the biological reactor of the wastewater treatment plant (WWTP) and the response of biomass to different drug concentrations. The Ultra-Performance Liquid Chromatography coupled to tandem Mass Spectrometry method showed to be repeatable and reliable (recovery>75%; repeatability<10–15%; bias uncertainty<10) for measurements in wastewater; the ultrasound assisted extraction (USE) demonstrated to be reliable as pre-treatment of activated sludge solid phase. Both drugs were fully removed from the liquid phase in the lab-scale biological reactor within 24 h. Biodegradation was the main BE removal mechanism, and the first order kinetic model provided the best fitting of the experimental data. THC-COOH was mainly removed due to a combination of adsorption and biodegradation; adsorption was better described by the pseudo-second order kinetic model and the Freundlich isotherm. Both drugs at the higher concentrations caused inhibition of nitrogen oxidation and carbon removal.

Hydrologic Observation, Model, and Theory Congruence on Evapotranspiration Variance: Diagnosis of Multiple Observations and Land Surface Models

AbstractThis paper reconciles the state‐of‐the‐art observations and simulations of evapotranspiration (ET) temporal variability through a diagnostic framework composed of an observation‐model‐theory triplet. Specifically, a confirmed theoretical tool, Evapotranspiration Temporal VARiance Decomposition (EVARD), is used as a benchmark to estimate ET monthly variance ( ) across the contiguous United States (CONUS) with inputs including hydroclimatic observations, Gravity Recovery and Climate Experiment‐based terrestrial water storage, four observation‐based products (ETRSUW by the University of Washington, ETRSMOD16 from MOD16 Global Terrestrial ET Data Set, ETFLUXNET upscaled from of fluxtower observations, and ETGLEAM from Global Land Evaporation Amsterdam Model), and four operational land surface models (LSMs: MOSAIC, NOAH, NOAH‐MP, and VIC). Five experiments are systematically designed to evaluate and diagnose possible errors and uncertainties in ET temporal variance estimated by the four observation‐based ET products and the four LSM simulations. Based on the results of these experiments, the following diagnostic hypotheses regarding the uncertainty of the observation‐based ET products are illustrated: ETRSUW captures the high signals in the Midwest with negligible bias and moderate uncertainty over the contiguous United States; ETFLUXNET systematically underestimates over CONUS but with the lowest level of uncertainty; ETRSMOD16 has medium bias with the highest level of uncertainty, and the spatial distribution of high signal from ETRSMOD16 is different from other estimates; ETGLEAM has slight negative bias and medium uncertainty, and in the West Coast is smaller than that from ETVARD. Regarding the LSMs, it is found that any of the four LSMs can be the best depending on a certain set of reference observations. The study reveals that LSMs have shown a reasonably worthy, though not perfect, capability in estimating ET and its variability in regions/aquifers with limited human interference. However, RS‐based observations and theoretical estimates suggest that all the four LSMs examined in this study are not able to accurately predict the ET variability in regions/aquifers heavily influenced by human activities like Central Valley and High Plains aquifers; they all underestimate ET variability along the West Coast due to seasonal vegetation responses to Mediterranean climate and human water use. In addition, LSMs underestimate intraannual ET variance in California and the High Plains with underestimated terrestrial storage change components in ET variance, due to the inappropriate representation of groundwater pumping and its impact on ET and other hydrologic processes. This paper urges advancing hydrologic knowledge by finding congruence among models, data, and theories.

The estimation of uncertainty of measurement of glycated hemoglobin as an analytical performance specification and in the interpretation of its results

BackgroundGlycated hemoglobin (HbA1c) plays a key role in monitoring the glycemic state of an individual. Uncertainty of measurement (U) indicates the magnitude of the doubt about a measurement result. To properly classify an individual as under either good or poor glycemic control, it has been suggested that U of an HbA1c result should not exceed ±0.5%. MethodsThe statistical method used to calculate uncertainty of measurement was the “top down” approach suggested by EURACHEM/CITAC. This approach allows the inclusion of imprecision, bias, uncertainty of bias and uncertainty of the calibration of the HbA1c method. The value of bias was obtained using data generated from the external quality assessment of the Randox International Quality Assessment Scheme and that of the Unity data management software system. Imprecision was calculated after the daily analysis of two levels of control sera. ResultsCalculation of uncertainty of measurement of HbA1c was a straightforward procedure used to calculate U. Due to the different bias results obtained using two different external quality programs, the results of U were significantly different (±0.19% vs ± 0.43%) from each other; however, in both cases, the U results were below the maximal suggested uncertainty of ±0.5%. ConclusionsThe calculation of U of HbA1c by the EURACHEM/CITAC method is a practical approach that can be used as an additional analytical goal in the measurement of HbA1c. In addition, this information can aid clinicians to determine the level of confidence that can be placed in the test results.

Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth

Exposure estimation of fine particulate matter with diameter < 2.5 μm (PM2.5) at high spatiotemporal resolution is crucial to epidemiological studies that examine acute or sub-chronic health outcomes of PM2.5. However, exposure assessment of PM2.5 has been negatively affected by sparsely distributed monitoring stations. In addition, several limitations exist among the existing methods for high spatiotemporal resolution PM2.5 estimation, including ignorance or limited use of spatial autocorrelation, single-model methods, and use of aerosol optical depth data with non-random missingness. These limitations probably introduce bias or high uncertainty in model estimation. In this paper, we proposed an approach of constrained mixed-effect bagging models to leverage advanced algorithm of the high-resolution AOD retrieved by Multi-Angle Implementation of Atmospheric Correction (MAIAC), with other spatiotemporal predictors and spatial autocorrelation to reliably estimate PM2.5 at a high spatiotemporal resolution. Our base model was a daily mixed-effect spatial model that accounted for spatial autocorrelation using embedded structured and unstructured spatial random effects. Point estimates from the base models were then averaged based on the bootstrap aggregating (bagging) to reduce variance in prediction. Then, constrained optimization was developed to minimize the impact of missing AOD and to capture a full time-series of PM2.5 concentration. Our daily-level bagging allowed AOD-PM2.5 association and spatial autocorrelation to vary daily, which substantially improved the model performance. As a case study of daily PM2.5 predictions in 2014 in Shandong Province, China, our approach achieved R2 of 0.87 (RMSE: 18.6 μg/m3) in cross validation, and R2 of 0.75 (RMSE: 20.6 μg/m3) in an independent test, similar to or better than most existing methods. We further extended the 2014 models to simulate 2014–2016 full time-series of biweekly average PM2.5 concentrations with no use of covariates in 2015–2016 but constrained optimization over 2014 daily point estimates; the results showed well-captured temporal trend with a total correlation of 0.81 between the simulated and observed values from 2015 to 2016. Our approach can be applied for other regions for exposure estimation of PM2.5 when measurements alone are not able to capture the desirable spatial and temporal resolutions.

Supply Chain Visibility and Social Responsibility: Investigating Consumers’ Behaviors and Motives

Consumers increasingly want to know more about where and how the products they purchase are being made. To create transparency requires a company to both gain visibility into its supply chain and disclose information to consumers. In this paper, we focus on the dimension of visibility and investigate when companies can benefit from greater supply chain visibility. To do so, we design an incentivized human–subject experiment to study two key questions: (i) How does supply chain visibility impact consumers’ valuations of a company’s social responsibility (SR) practices in its upstream supply chain? (ii) What roles do indirect reciprocity and consumers’ prosociality play in affecting their valuations under different levels of visibility? In our design, greater visibility is represented by lower uncertainty in the outcomes of a company’s SR efforts. Our results show that consumers value greater visibility regarding a company’s SR practices in the upstream supply chain. This is especially true if consumers exhibit a self-serving bias and use uncertainty as an excuse not to pay for SR. We also observe that high prosocial consumers do not exhibit strong indirect reciprocity. Conversely, indirect reciprocity significantly increases low prosocial consumers’ valuations under High visibility. Our work adds to the experimental literature focusing on transparency and SR (which has primarily studied disclosure) by examining the equally important but understudied dimension of visibility. Furthermore, our results on consumer heterogeneity offer insights into what SR information resonates with a company’s target consumers. The online appendix is available at https://doi.org/10.1287/msom.2017.0685 .

Bed-mounted Electro Magnetic meters: Implications for robust velocity measurement in Urban Drainage systems

Flow monitoring in Urban Drainage Systems (UDS) is required for a successful system control and operational assessment. Commonly used methods can lead to erroneous results in partially filled pipes and hostile environmental conditions, normally encountered in UDS. Recent studies focused on the flow rate measurements in UDS revealed that the capability of acoustic Doppler velocimeters to estimate mean flow velocity is impeded by several factors. Most prominent issues are the operation under low flow depths and velocities, as well as in the case of the sedimentation at low flow velocities. This study is focused on an alternative method for the velocity measurements in the UDS, based on Electro-Magnetic Velocity (EMV) meters. The study also determines the sensor’s capacity to operate when covered by a porous sediment layer, using a newly developed procedure. A brief theoretical background is given to support the idea behind the usage of EMV in UDS. Measurement uncertainties were firstly benchmarked in the laboratory flume without sediment. After local, site-specific (re)calibration, EMV operated with combined uncertainty of only few cm/s. Furthermore, the EMV measured the flow rates with depths low as 4 cm and velocities bellow 5 cm/s. Additionally, a series of tests were performed with sediment layers above the EMV meter, varying in height from 0 to 80 mm. Observational uncertainty analysis showed that EMV meter can be used even in these conditions. Since the bias uncertainty increased with the rise of the sediment depth, a correction function model was derived for the transformation of the output signal, reducing the observational uncertainties below 5 cm/s. Subsequently, practical implications of the EMV usage in the UDS are considered.