Development and validation of a biomonitoring method to measure As, Cr, and Ni in human urine samples by ICP-UCT-MS.

We developed an inductively coupled plasma mass spectrometry (ICP-MS) method using Universal Cell Technology (UCT) with a PerkinElmer NexION ICP-MS, to measure arsenic (As), chromium (Cr), and nickel (Ni) in human urine samples. The advancements of the UCT allowed us to expand the calibration range to make the method applicable for both low concentrations of biomonitoring applications and high concentrations that may be observed from acute exposures and emergency response. Our method analyzes As and Ni in kinetic energy discrimination (KED) mode with helium (He) gas, and Cr in dynamic reaction cell (DRC) mode with ammonia (NH3) gas. The combination of these elements is challenging because a carbon source, ethanol (EtOH), is required for normalization of As ionization in urine samples, which creates a spectral overlap (40Ar12C+) on 52Cr. This method additionally improved lab efficiency by combining elements from two of our previously published methods(Jarrett et al., 2007; Quarles et al., 2014) allowing us to measure Cr and Ni concentrations in urine samples collected as part of the National Health and Nutrition Examination Survey (NHANES) beginning with the 2017-2018 survey cycle. We present our rigorous validation of the method selectivity and accuracy using National Institute of Standards and Technology (NIST) Standard Reference Materials (SRM), precision using in-house prepared quality control materials, and a discussion of the use of a modified UCT, a BioUCell, to address an ion transmission phenomenon we observed on the NexION 300 platform when using higher elemental concentrations and high cell gas pressures. The rugged method detection limits, calculated from measurements in more than 60 runs, for As, Cr, and Ni are 0.23 µg L-1, 0.19 µg L-1, and 0.31 µg L-1, respectively.

Assessing fuel treatment effectiveness using satellite imagery and spatial statistics.

Understanding the influences of forest management practices on wildfire severity is critical in fire-prone ecosystems of the western United States. Newly available geospatial data sets characterizing vegetation, fuels, topography, and burn severity offer new opportunities for studying fuel treatment effectiveness at regional to national scales. In this study, we used ordinary least-squares (OLS) regression and sequential autoregression (SAR) to analyze fuel treatment effects on burn severity for three recent wildfires: the Camp 32 fire in western Montana, the School fire in southeastern Washington, and the Warm fire in northern Arizona. Burn severity was measured using differenced normalized burn ratio (dNBR) maps developed by the Monitoring Trends in Burn Severity project. Geospatial data sets from the LANDFIRE project were used to control for prefire variability in canopy cover, fuels, and topography. Across all three fires, treatments that incorporated prescribed burning were more effective than thinning alone. Treatment effect sizes were lower, and standard errors were higher in the SAR models than in the OLS models. Spatial error terms in the SAR models indirectly controlled for confounding variables not captured in the LANDFIRE data, including spatiotemporal variability in fire weather and landscape-level effects of reduced fire severity outside the treated areas. This research demonstrates the feasibility of carrying out assessments of fuel treatment effectiveness using geospatial data sets and highlights the potential for using spatial autoregression to control for unmeasured confounding factors.

Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens.

BACKGROUND: Disease maps are used increasingly in the health sciences, with applications ranging from the diagnosis of individual cases to regional and global assessments of public health. However, data on the distributions of emerging infectious diseases are often available from only a limited number of samples. We compared several spatial modelling approaches for predicting the geographic distributions of two tick-borne pathogens: Ehrlichia chaffeensis, the causative agent of human monocytotropic ehrlichiosis, and Anaplasma phagocytophilum, the causative agent of human granulocytotropic anaplasmosis. These approaches extended environmental modelling based on logistic regression by incorporating both spatial autocorrelation (the tendency for pathogen distributions to be clustered in space) and spatial heterogeneity (the potential for environmental relationships to vary spatially). RESULTS: Incorporating either spatial autocorrelation or spatial heterogeneity resulted in substantial improvements over the standard logistic regression model. For E. chaffeensis, which was common within the boundaries of its geographic range and had a highly clustered distribution, the model based only on spatial autocorrelation was most accurate. For A. phagocytophilum, which has a more complex zoonotic cycle and a comparatively weak spatial pattern, the model that incorporated both spatial autocorrelation and spatially heterogeneous relationships with environmental variables was most accurate. CONCLUSION: Spatial autocorrelation can improve the accuracy of predictive disease risk models by incorporating spatial patterns as a proxy for unmeasured environmental variables and spatial processes. Spatial heterogeneity can also improve prediction accuracy by accounting for unique ecological conditions in different regions that affect the relative importance of environmental drivers on disease risk.