Residual-based Method Research Articles

Improved a posteriori error bounds for reduced port-Hamiltonian systems

Projection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error can be bounded by residual-based methods, which are typically known to be highly pessimistic in the sense of largely overestimating the true error. This work applies two improved error bounding techniques, namely (a) a hierarchical error bound and (b) an error bound based on an auxiliary linear problem, to the case of port-Hamiltonian systems. The approaches rely on a secondary approximation of (a) the dynamical system and (b) the error system. In this paper, these methods are adapted to port-Hamiltonian systems. The mathematical relationship between the two methods is discussed both theoretically and numerically. The effectiveness of the described methods is demonstrated using a challenging three-dimensional port-Hamiltonian model of a classical guitar with fluid–structure interaction.

Arsenic Exposure and Epigenetic Aging: The Association with Cardiovascular Disease and All-Cause Mortality in the Strong Heart Study.

Inorganic arsenic (As) may increase the risk of cardiovascular disease (CVD) and all-cause mortality through accelerated aging, which can be estimated using epigenetic-based measures. We evaluated three DNA methylation-based aging measures (PhenoAge, GrimAge, DunedinPACE) (epigenetic aging measures) as potential mediators of the previously reported association of As exposure with CVD incidence, CVD mortality, and all-cause mortality in the Strong Heart Study (SHS), an epidemiological cohort of American Indian adults. Blood DNA methylation and urinary As levels were measured in 2,323 SHS participants (41.5% men, mean age of 55 years old). PhenoAge and GrimAge values were calculated using a residual-based method. We tested the association of urinary As with epigenetic aging measures using linear regression, the association of epigenetic aging measures with the three health outcomes using additive hazards models, and the mediation of As-related CVD incidence, CVD mortality, and all-cause mortality by epigenetic aging measures using the product of coefficients method. SHS participants with higher vs. lower urinary As levels had similar PhenoAge age, older GrimAge age, and faster DunedinPACE. An interquartile range increase in urinary As was associated with higher of PhenoAge age acceleration [ (0.17, 0.80) years], GrimAge age acceleration [0.80 (0.60, 1.00) years], and DunedinPACE [0.011 (0.005, 0.018)], after adjusting for age, sex, center location, genetic components, smoking status, and body mass index. Of the 347 incident CVD events per 100,000 person-years associated with a doubling in As exposure, 21.3% (9.1, 57.1) and 22.6% (9.5, 56.9), were attributable to differences in GrimAge and DunedinPACE, respectively. Arsenic exposure was associated with older GrimAge and faster DunedinPACE measures of biological age. Furthermore, accelerated biological aging measured from DNA methylation accounted for a relevant fraction of As-associated risk for CVD, CVD mortality, and all-cause mortality in the SHS, supporting the role of As in accelerated aging. Research of the biological underpinnings can contribute to a better understanding of the role of aging in arsenic-related disease. https://doi.org/10.1289/EHP11981.

Comparison of Residual-based Methods on Fault Detection

An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over time, and isolate faults. As faults are typically rare occurrences, it is essential to perform this monitoring in an unsupervised manner. Various approaches have been proposed not only to detect faults in an unsupervised manner but also to distinguish between different potential fault types. In this study, we perform a comprehensive comparison between two residual-based approaches: autoencoders, and theinput-output models that establish a mapping between operating conditions and sensor readings. We explore the sensorwise residuals and aggregated residuals for the entire system in both methods. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. To perform the comparison, we utilize the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model, specifically a subset of the turbofan engine dataset containing three different fault types. All models are trained exclusively on healthy data. Fault detection is achieved by applying a threshold that is determined based on the healthy condition. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate. While the fault detection performance is similar for both models, the input-output model provides better interpretability regarding potential fault types and the possible faulty components.

Anomaly Detection in Power System State Estimation: Review and New Directions

Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.

Deep convolutional Ritz method: parametric PDE surrogates without labeled data

The parametric surrogate models for partial differential equations (PDEs) are a necessary component for many applications in computational sciences, and the convolutional neural networks (CNNs) have proven to be an excellent tool to generate these surrogates when parametric fields are present. CNNs are commonly trained on labeled data based on one-to-one sets of parameter-input and PDE-output fields. Recently, residual-based deep convolutional physics-informed neural network (DCPINN) solvers for parametric PDEs have been proposed to build surrogates without the need for labeled data. These allow for the generation of surrogates without an expensive offline-phase. In this work, we present an alternative formulation termed deep convolutional Ritz method (DCRM) as a parametric PDE solver. The approach is based on the minimization of energy functionals, which lowers the order of the differential operators compared to residual-based methods. Based on studies involving the Poisson equation with a spatially parameterized source term and boundary conditions, we find that CNNs trained on labeled data outperform DCPINNs in convergence speed and generalization abilities. The surrogates generated from the DCRM, however, converge significantly faster than their DCPINN counterparts, and prove to generalize faster and better than the surrogates obtained from both CNNs trained on labeled data and DCPINNs. This hints that the DCRM could make PDE solution surrogates trained without labeled data possibly.

Cross-sectional associations between drinking water arsenic and urinary inorganic arsenic in the United States: NHANES 2003–2014

BackgroundInorganic arsenic is a potent carcinogen and toxicant associated with numerous adverse health outcomes. The contribution of drinking water from private wells and regulated community water systems (CWSs) to total inorganic arsenic exposure is not clear. ObjectivesTo determine the association between drinking water arsenic estimates and urinary arsenic concentrations in the 2003–2014 National Health and Nutrition Examination Survey (NHANES). MethodsWe evaluated 11,088 participants from the 2003–2014 NHANES cycles. For each participant, we assigned private well and CWS arsenic levels according to county of residence using estimates previously derived by the U.S. Environmental Protection Agency and U.S. Geological Survey. We used recalibrated urinary dimethylarsinate (rDMA) to reflect the internal dose of estimated water arsenic by applying a previously validated, residual-based method that removes the contribution of dietary arsenic sources. We compared the adjusted geometric mean ratios and corresponding percent change of urinary rDMA across tertiles of private well and CWS arsenic levels, with the lowest tertile as the reference. Comparisons were made overall and stratified by census region and race/ethnicity. ResultsOverall, the geometric mean of urinary rDMA was 2.52 (2.30, 2.77) μg/L among private well users and 2.64 (2.57, 2.72) μg/L among CWS users. Urinary rDMA was highest among participants in the West and South, and among Mexican American, Other Hispanic, and Non-Hispanic Other participants. Urinary rDMA levels were 25% (95% confidence interval (CI): 17–34%) and 20% (95% CI: 12–29%) higher comparing the highest to the lowest tertile of CWS and private well arsenic, respectively. The strongest associations between water arsenic and urinary rDMA were observed among participants in the South, West, and among Mexican American and Non-Hispanic White and Black participants. DiscussionBoth private wells and regulated CWSs are associated with inorganic arsenic internal dose as reflected in urine in the general U.S. population.

A Brand-New Simple, Fast, and Effective Residual-Based Method for Radial Basis Function Neural Networks Training

The radial basis function (RBF) neural network is a type of universal approximator, and has been widely used in various fields. Improving the training speed and compactness of RBF networks are critical for promoting their applications. In the present study, we propose a simple, fast, and effective RBF networks training method, which is based on the residual extreme points and their neighborhoods (thus called the REN method for short in this paper). The REN method calculates RBF centers and widths through a two-level iterative process, and realizes two main functionalities, namely 1) adding multiple centers within one pass through the whole data set, and 2) calculating RBF widths specifically for each center. The use of this algorithm does not need any parameter adjustments, and the models for approximation or classification can be obtained by only one run. The performance of the proposed REN algorithm is compared with the classic and powerful orthogonal least squares (OLS) algorithm. By reaching the same accuracies, the REN algorithm trains RBF networks 50 and 320 times faster, in the chirp (0˜50 Hz, 2 s, 1 kHz, 2001 samples) and two-dimensional peaks (2401 samples) signal approximation tasks respectively, than the OLS algorithm does, and the number of centers obtained by the REN algorithm is reduced by half. When incorporating the same number of centers, the REN algorithm achieves accuracies up to 3 orders of magnitude higher than the best results obtained by the OLS algorithm. In the classification task of a real discrete breast cancer data, both methods result in accuracies comparable to many existent methods, but the REN algorithm has the advantages of fast training speeds and no requirements for parameter adjustments. The REN algorithm proposed in this study may potentially be used for tasks with large scale of data or applications that require high model performances.

Variance function estimation in regression model via aggregation procedures

In the regression problem, we consider the problem of estimating the variance function by means of aggregation methods. We focus on two particular aggregation setting: Model Selection aggregation (MS) and Convex aggregation (C), where the goal is to select the best candidate and to build the best convex combination of candidates, respectively, among a collection of candidates. In both cases, the construction of the estimator relies on a two-step procedure and requires two independent samples. The first step exploits the first sample to build the candidate estimators for the variance function by the residual-based method and then the second dataset is used to perform the aggregation step. We show the consistency of the proposed method with respect to the -error both for MS and C aggregations. We evaluate the performance of these two methods in the heteroscedastic model and illustrate their interest in both the regression problem with reject option and the quantile regression.

Impact of long-term exposures of PM2.5 components and sources on cause-specific mortality in a US Medicare cohort: results from two-stage residual analysis

Background: Our understanding of the impact of long-term exposures to PM2.5 constituents and sources on mortality is limited due in large part to the often strong correlations between individual PM2.5 constituent concentrations. Methods: We examined associations between long-term exposures to PM2.5 constituents and sources and cause-specific mortality in US older adults using a two-stage residual method. We did so using demographic and mortality data for 15.4 million Medicare beneficiaries living within the conterminous United States (US) between 2000 and 2008. We assessed PM2.5 constituents exposures for each beneficiary and used factor analysis and residual-based methods to characterize PM2.5 sources and mixtures, respectively. In age-, sex-, race- and site- stratified Cox proportional hazard models adjusted for neighborhood socio-economic status (SES), we assessed associations of individual PM2.5 constituents, sources, and mixtures and cause-specific mortality and examined modification of these associations by participant demographics and location of residence. Results: Hazard ratios (HR) were highest for all causes of death, except COPD, for coal-related PM2.5 constituents and sources. We found Pb, nitrate, Si, and metal-, traffic-, and soil-related components to be significantly associated with increased HR for most causes of death. Using residual-based methods, we found attenuation in HRs to be greatest for all causes of death when variability from sulfate, Se, or coal-related PM2.5 was removed from the PM2.5 mixture. While attenuated, HRs for mortality from all-cause, CVD, and IHD remained statistically significant and positive when variability from NO3–, OC, Si, and traffic- and soil-related PM2.5 were removed from the analyses. Removal of variability due to oil and metal-related sources had no discernable effect on HRs. Discussion: PM2.5 components related to coal combustion, traffic, and to a lesser extent, soil were strongly associated with mortality from CVD, respiratory disease, and cancer.

Issues and recommendations for the residual approach to quantifying cognitive resilience and reserve

BackgroundCognitive reserve and resilience are terms used to explain interindividual variability in maintenance of cognitive health in response to adverse factors, such as brain pathology in the context of aging or neurodegenerative disorders. There is substantial interest in identifying tractable substrates of resilience to potentially leverage this phenomenon into intervention strategies. One way of operationalizing cognitive resilience that has gained popularity is the residual method: regressing cognition on an adverse factor and using the residual as a measure of resilience. This method is attractive because it provides a statistical approach that is an intuitive match to the reserve/resilience conceptual framework. However, due to statistical properties of the regression equation, the residual approach has qualities that complicate its interpretation as an index of resilience and make it statistically inappropriate in certain circumstances.Methods and resultsWe describe statistical properties of the regression equation to illustrate why the residual is highly correlated with the cognitive score from which it was derived. Using both simulations and real data, we model common applications of the approach by creating a residual score (global cognition residualized for hippocampal volume) in individuals along the AD spectrum. We demonstrate that in most real-life scenarios, the residual measure of cognitive resilience is highly correlated with cognition, and the degree of this correlation depends on the initial relationship between the adverse factor and cognition. Subsequently, any association between this resilience metric and an external variable may actually be driven by cognition, rather than by an operationalized measure of resilience. We then assess several strategies proposed as potential solutions to this problem, such as including both the residual and original cognitive measure in a model. However, we conclude these solutions may be insufficient, and we instead recommend against “pre-regression” strategies altogether in favor of using statistical moderation (e.g., interactions) to quantify resilience.ConclusionsCaution should be taken in the use and interpretation of the residual-based method of cognitive resilience. Rather than identifying resilient individuals, we encourage building more complete models of cognition to better identify the specific adverse and protective factors that influence cognitive decline.

An Improved Residual-Based Detection Method for Stealthy Anomalies on Mobile Robots

With the expansion of the cyber-physical system (CPS) application area, its importance has become more and more prominent. As one of the typical applications of CPS, the anomaly detections of mobile robots have attracted the attention of all parties. As part of the CPS, mobile robots face the problem that conventional residual-based detection methods cannot identify stealthy anomalies. The conventional residual-based detection methods mainly use the residual signal calculated from the control signal and measure output for detection, which is widely used in fault diagnosis. Still, it is difficult to be useful in deceptive stealthy anomalies purposefully imposed on mobile robots, which are designed to evade the conventional detections by tampering with measure output. Furthermore, they can control the system to deviate from the expected operations, causing degradation of control performance or even damage without being detected. Based on this, by analyzing the system model of CPS and the stealthy conditions of anomalies, the improved residual-based detection method is proposed in this paper. Moreover, three stealthy anomalies purposefully imposed on an omnidirectional mobile robot (OMR) are detected by using the conventional residual-based methods and the improved residual-based method. Finally, the experimental results show that the method proposed can effectively detect the stealthy anomalies purposefully imposed on the OMR.

A Residual-Based Subgrid-Scale Method Combined with Spherical Harmonics Angular Approximation for Solving the Boltzmann Transport Equation

This paper presents a stabilized finite element method (FEM) and a spherical harmonics method to discretize the space and angle of the Boltzmann transport equation. The FEM is based on the subgrid-scale (SGS) model, which decomposes the unknowns into resolvable scale and SGS with an approximation for the SGS and then embeds it into a resolvable scale formulation, which yields a stabilized variational formula with only a resolvable scale. In this method, the SGS is identified as the residual of the flux, which represents the indistinguishable high-frequency component. This method is characterized by a residual equation proposed on the subgrid, thus reflecting the relationship between the residual of the flux and the residual of the source. A simple assumption is proposed that the residual of the flux is the scaling of the residual of the source. The scaling parameter is identified as a stabilization parameter, and it takes the inverse of the norm of the transport operator. This method has been verified by various benchmark problems, and the numerical results show that it has high accuracy, stability, and void applicability.

Rigorous Predictive Noise Modeling Approach for Model-Based Onset Detection and Enhanced Picking of P-Waves in Seismic Signals

Detecting and picking the onset of P-waves in seismic signals has a fairly rich literature, among which model-based (predictive) approaches hold immense promise. A majority of these models are usually built on certain critical assumptions, namely, stationarity, linearity, and Gaussianity. Despite their criticality, very little reported literature exists on validating these assumptions on real seismic data. Furthermore, the predictive capabilities of these models are not utilized to their best potential in detection. Therefore, there exists a strong need for a rigorous approach to model seismic noise and develop a method that builds on the statistical properties of residuals resulting from the noise models. The objectives of this work are (i) to critically study certain long-held assumptions in seismic noise modeling, (ii) to develop rigorous time-series models for background noise that are commensurate with the noise properties, so as to (iii) devise a residual-based method for detection and enhanced picking of P-waves. An important finding of this work, arising from our study on 185 historical data sets, is that these standard assumptions do not hold for most of the data sets under study; rather, they exhibit additional special features such as heteroskedasticity and integrating effects. Consequent to these novel discoveries, we develop auto-regressive integrated moving average- generalized auto-regressive conditionally heteroskedastic (ARIMA-GARCH) models for seismic noise. The proposed residual-based detector and picker is found to be highly effective with a 90% detection rate while picking 91% of the events with an accuracy of ≤ 0.625 seconds based on tests with 100 historical data sets. Further, when the noise model is used in combination with the existing AIC-based pickers, the number of events picked with an accuracy of ≤ 0.625 seconds is 50% more than the existing AR-AIC picker. Therefore, the proposed method can be considered highly competitive and effective in P-wave detection and picking.

The impact of Long-Term PM2.5 constituents and their sources on specific causes of death in a US Medicare cohort.

Our understanding of the impact of long-term exposures to PM2.5 constituents and sources on mortality is limited. To examine associations between long-term exposures to PM2.5 constituents and sources and cause-specific mortality in US older adults. We obtained demographic and mortality data for 15.4 million Medicare beneficiaries living within the conterminous United States (US) between 2000 and 2008. We assessed PM2.5 constituents exposures for each beneficiary and used factor analysis and residual-based methods to characterize PM2.5 sources and mixtures, respectively. In age-, sex-, race- and site- stratified Cox proportional hazard models adjusted for neighborhood socio-economic status (SES), we assessed associations of individual PM2.5 constituents, sources, and mixtures and cause-specific mortality and examined modification of these associations by participant demographics and location of residence. We assessed the robustness of our findings to additional adjustment for behavioral risk factors and to alternate exposure definitions and exposure windows. Hazard ratios (HR) were highest for all causes of death, except COPD, for PM2.5 constituents and the coal combustion-related PM2.5 components, with no evidence of confounding by behavioral covariates. We further found Pb and metal-related PM2.5 components to be significantly associated with increased HR of all causes of death, except COPD and lung cancer mortality, and nitrate (NO3-) and silicon (Si) and associated source-related PM2.5 components (traffic and soil, respectively) to be significantly associated with increased all-cause, CVD, respiratory and all cancer-related mortality HR. Associations for other examined constituents and mortality were inconsistent or largely null. Our analyses of mixtures were generally consistent with these findings. Mortality HRs were greatest for minority, especially Black, low-income urban, younger, and male beneficiaries. PM2.5 components related to coal combustion, traffic, and to a lesser extent, soil were strongly associated with mortality from CVD, respiratory disease, and cancer.

Water as a relevant source of inorganic arsenic exposure in U.S. cities

BACKGROUND AND AIM: Water arsenic represents a health concern for US residents served by community water systems (CWS) exceeding the Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL) of 10 µg/L, and for residents reliant on unregulated private wells. Little is known on the contribution of CWS arsenic exposures below the MCL, particularly in urban areas. We evaluated whether water arsenic is a relevant source of urinary arsenic in urban settings in the Multi-Ethnic Study of Atherosclerosis (MESA). METHODS: We examined the association of CWS arsenic with urinary arsenic concentrations (µg/L) for 910 MESA participants from Baltimore, Chicago, Los Angeles, New York City, St. Paul, MN, and Winston-Salem, NC. We assigned three-year population weighted averages of CWS arsenic concentrations using 2006-2008 EPA data (before MCL implementation) to participants by zip code. Dietary information and urinary samples were collected in 2000-2002. Urinary arsenic species were measured using HPLC-ICPMS. We accounted for seafood intake using a residual based method. Linear models adjusted for race/ethnicity, income, smoking status, diet (rice) and urinary creatinine to account for urine dilution. RESULTS:CWS arsenic levels ranged from 0.5 µg/L (LOD) in several cities to 3.05 µg/L in Chicago (median 0.44 µg/L). CWS arsenic was positively correlated (p 0.05) with urinary inorganic arsenic (Spearman’s rho 0.11), dimethylarsinate (DMA) (0.09), monomethylarsonate (MMA) (0.16) and their sum (0.10). In adjusted models, a 1 µg/L change in CWS arsenic was associated with 1.29 (95%CI 1.01, 1.64) higher inorganic arsenic and 1.19 (1.03, 1.38) higher MMA. The association with DMA, sum of inorganic and methylated species was not statistically significant. CONCLUSIONS:Drinking water arsenic is a relevant source of exposure and internal dose in US urban areas, determined by urinary arsenic biomarkers. Research is needed to determine health implications of chronic exposure to water arsenic below the MCL, including vulnerable subpopulations. KEYWORDS: arsenic, drinking water, exposure assessment, environmental epidemiology

Water leakage detection and localization using hydraulic modeling and classification

Abstract A significant percentage of treated water is lost due to leakage in water distribution systems. The state-of-the-art leak detection and localization schemes use a hybrid approach to hydraulic modeling and data-driven techniques. Most of these works, however, focus on single leakage detection and localization. In this research, we propose to use combined pressure and flow residual data to detect and localize multiple leaks. The proposed approach has two phases: detection and localization. The detection phase uses the combination of pressure and flow residuals to build a hydraulic model and classification algorithm to identify leaks. The localization phase analyzes the pattern of isolated leak residuals to localize multiple leaks. To evaluate the performance of the proposed approach, we conducted experiments using Hanoi Water Network benchmark and a dataset produced based on LeakDB benchmark's dataset preparation procedure. The result for a well-calibrated hydraulic model shows that leak detection is 100% accurate while localization is 90% accurate, thereby outperforming minimum night flow and raw- and residual-based methods in localizing leaks. The proposed approach performed relatively well with the introduction of demand and noise uncertainty. The proposed localization approach is also able to locate two to four leaks that existed simultaneously.

A New Method to Bound the Integrity Risk for Residual-Based ARAIM.

This paper develops a tight integrity risk bound for Residual-Based (RB) Advanced Receiver Autonomous Integrity Monitoring (ARAIM). ARAIM measurement models include nominal biases accounting for unknown but bounded errors, and faults of unbounded magnitude. In RB methods, upper bounding the integrity risk requires that one finds the worst-case directions of both the multi-satellite fault vector and of the all-in-view nominal bias vector. Previous methods only account for the worst-case fault direction assuming zero nominal bias. To address this issue, in this paper, we derive a new bounding method in parity space. The method establishes a direct relationship between mean estimation error and RB test statistic non-centrality parameter, which accounts for both faults and nominal errors. ARAIM performance is evaluated to quantify the improvement provided by the proposed method over previous approaches.

An Efficient Residual-Based Method for Railway Image Dehazing.

Trains shuttle in semiopen environments, and the surrounding environment plays an important role in the safety of train operation. The weather is one of the factors that affect the surrounding environment of railways. Under haze conditions, railway monitoring and staff vision could be blurred, threatening railway safety. This paper tackles image dehazing for railways. The contributions of this paper for railway video image dehazing are as follows: (1) this paper proposes an end-to-end residual block-based haze removal method that consists of two subnetworks, namely fine-grained and coarse-grained network can directly generate the clean image from input hazy image, called RID-Net (Railway Image Dehazing Network). (2) The combined loss function (per-pixel loss and perceptual loss functions) is proposed to achieve both low-level features and high-level features so to generate the high-quality restored images. (3) We take the full-reference criterion (PSNR&SSIM), object detection, running time, and sensory vision to evaluate the proposed dehazing method. Experimental results on railway synthesized dataset, benchmark indoor dataset, and real-world dataset demonstrate our method has superior performance compared to the state-of-the-art methods.

Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells

Fuel cells are considered a highly efficient and eco-friendly alternative for energy production. However, for successful commercialization of fuel cells, it is necessary to ensure the reliability of fuel cell systems. This study proposes a real-time fault detection and diagnosis method for the thermal management system, one of the important balance of plants in proton exchange membrane fuel cell systems. A hierarchical method is developed for fault detection and diagnosis using the residuals which are differences between the analytical redundancies and the measured values as indicators. The analytical redundancies are calculated with regression models. The proposed method consists of two steps. After faults are classified into the thermal management system at the subsystem level diagnosis in the first step, specific component faults are diagnosed in the second step. The residual of the heat transfer rate is used in the first step, and the additional residuals related to two coolant flow rates and one coolant temperature are used in the second step. When faults occurred in the pumps, sensors, and heat exchanger, the experimental results showed that the residual-based method accurately diagnosed the causes and locations of problems in fuel cell systems.

Urinary Arsenic Species are Detectable in Urban Underserved Hispanic/Latino Populations: A Pilot Study from the Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS).

Background: Hispanics/Latinos represent >15% of the United States (US) population and experience a high burden of cardiovascular disease (CVD) and diabetes. Dietary exposure, particularly to arsenic (As), may be associated with CVD and diabetes in Hispanics/Latinos. Rural populations in the US exposed to As in drinking water have increased risk of diabetes and CVD; however, little is known about the risk among urban populations with low As in water who are mostly exposed to As through food. Methods: To explore the levels of inorganic arsenic exposure (the sum of inorganic and methylated arsenic species in urine, ∑As, corrected by a residual-based method) in persons of Hispanic/Latino origin, we conducted a pilot study quantifying urinary arsenic levels among 45 participants in the Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS). Results: The median (interquartile range) of the urinary arsenic species (µg/L) were as follows: inorganic As 0.6 (0.4, 1.0), monomethylarsonic acid 1.2 (0.7, 1.9), dimethylarsinic acid 7.2 (4.3, 15.3), and ∑As 6.0 (4.3, 10.5). Conclusions: This study adds to the existing evidence that harmful forms of arsenic are present in this group of Hispanics/Latinos.