Hierarchical bivariate time series models: a combined analysis of the effects of particulate matter on morbidity and mortality

In this paper we develop a hierarchical bivariate time series model to characterize the relationship between particulate matter less than 10 microns in aerodynamic diameter (PM10) and both mortality and hospital admissions for cardiovascular diseases. The model is applied to time series data on mortality and morbidity for 10 metropolitan areas in the United States from 1986 to 1993. We postulate that these time series should be related through a shared relationship with PM10. At the first stage of the hierarchy, we fit two seemingly unrelated Poisson regression models to produce city-specific estimates of the log relative rates of mortality and morbidity associated with exposure to PM10 within each location. The sample covariance matrix of the estimated log relative rates is obtained using a novel generalized estimating equation approach that takes into account the correlation between the mortality and morbidity time series. At the second stage, we combine information across locations to estimate overall log relative rates of mortality and morbidity and variation of the rates across cities. Using the combined information across the 10 locations we find that a 10 microg/m3 increase in average PM10 at the current day and previous day is associated with a 0.26% increase in mortality (95% posterior interval -0.37, 0.65), and a 0.71% increase in hospital admissions (95% posterior interval 0.35, 0.99). The log relative rates of mortality and morbidity have a similar degree of heterogeneity across cities: the posterior means of the between-city standard deviations of the mortality and morbidity air pollution effects are 0.42 (95% interval 0.05, 1.18), and 0.31 (95% interval 0.10, 0.89), respectively. The city-specific log relative rates of mortality and morbidity are estimated to have very low correlation, but the uncertainty in the correlation is very substantial (posterior mean = 0.20, 95% interval -0.89, 0.98). With the parameter estimates from the model, we can predict the hospitalization log relative rate for a new city for which hospitalization data are unavailable, using that city's estimated mortality relative rate. We illustrate this prediction using New York as an example.     

A measurement error model for time-series studies of air pollution and mortality

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.     

A multilevel mixed effects varying coefficient model with multilevel predictors and random effects for modeling hospitalization risk in patients on dialysis

For patients on dialysis, hospitalizations remain a major risk factor for mortality and morbidity. We use data from a large national database, United States Renal Data System, to model time-varying effects of hospitalization risk factors as functions of time since initiation of dialysis. To account for the three-level hierarchical structure in the data where hospitalizations are nested in patients and patients are nested in dialysis facilities, we propose a multilevel mixed effects varying coefficient model (MME-VCM) where multilevel (patient- and facility-level) random effects are used to model the dependence structure of the data. The proposed MME-VCM also includes multilevel covariates, where baseline demographics and comorbidities are among the patient-level factors, and staffing composition and facility size are among the facility-level risk factors. To address the challenge of high-dimensional integrals due to the hierarchical structure of the random effects, we propose a novel two-step approximate EM algorithm based on the fully exponential Laplace approximation. Inference for the varying coefficient functions and variance components is achieved via derivation of the standard errors using score contributions. The finite sample performance of the proposed estimation procedure is studied through simulations.     

An estimate of avian mortality at communication towers in the United States and Canada

Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action.     

Estimating Mortality Derived from Indoor Exposure to Particles of Outdoor Origin

Following an extensive review of the literature, we further analyze the published data to examine the health effects of indoor exposure to particulate matter (PM) of outdoor origin. We obtained data on all-cause, cardiovascular, and respiratory mortality per 10 μg/m³ increase in outdoor PM₁₀ or PM_2.5; the infiltration factors for buildings; and estimated time spent outdoors by individuals in the United States, Europe, China, and globally. These data were combined log-linear exposure–response model to estimate the all-cause, cardiovascular, and respiratory mortality of exposure to indoor PM pollution of outdoor origin. Indoor PM pollution of outdoor origin is a cause of considerable mortality, accounting for 81% to 89% of the total increase in mortality associated with exposure to outdoor PM pollution for the studied regions. The findings suggest that enhancing the capacity of buildings to protect occupants against exposure to outdoor PM pollution has significant potential to improve public health outcomes.     

大气颗粒物致细胞损伤效应的分子机制

大气颗粒物(PM)是大气中各种具有不同化学组分的颗粒状物质的混合体。其中,空气动力学直径≤10μm和≤2.5μm的可吸入颗粒物(PM10和PM2.5)由于吸附有重金属、多环芳烃等有机物及细菌、病毒等有害成分,并能够通过呼吸系统进入体内,因此对人体健康具有极大的危害作用。流行病学研究数据显示,大气中PM含量增加会显著提高呼吸道和心血管疾病的发病率和致死率。因此,揭示PM诱导呼吸系统和心血管系统损伤的分子机制,对于制定相应防治策略、减低环境相关疾病的危害具有重要意义和医学价值。我们根据目前有限的研究线索,对PM诱导细胞损伤反应中涉及的氧化应激、内质网应激和炎性反应等机制做一综述。     

Spatial Dynamics of Lyme Disease: A Review

Lyme disease (LD), the most frequently reported vector-borne disease in the United States, requires that humans, infected vector ticks, and infected hosts all occur in close spatial proximity. Understanding the spatial dynamics of LD requires an understanding of the spatial determinants of each of these organisms. We review the literature on spatial patterns and environmental correlates of human cases of LD and the vector ticks, Ixodes scapularis in the northeastern and midwestern United States and Ixodes pacificus in the western United States. The results of this review highlight a need for a more standardized and comprehensive approach to studying the spatial dynamics of the LD system. Specifically, we found that the only environmental variable consistently associated with increased LD risk and incidence was the presence of forests. However, the reasons why some forests are associated with higher risk and incidence than others are still poorly understood. We suspect that the discordance among studies is due, in part, to the rapid developments in both conceptual and technological aspects of spatial ecology hastening the obsolescence of earlier approaches. Significant progress in identifying the determinants of spatial variation in LD risk and incidence requires that: (1) existing knowledge of the biology of the individual components of each LD system is utilized in the development of spatial models; (2) spatial data are collected over longer periods of time; (3) data collection and analysis among regions are more standardized; and (4) the effect of the same environmental variables is tested at multiple spatial scales.     

Estimating allowable take for an increasing bald eagle population in the United States

Effectively managing take of wildlife resulting from human activities poses a major challenge for applied conservation. Demographic data essential to decisions regarding take are often expensive to collect and are either not available or based on limited studies for many species. Therefore, modeling approaches that efficiently integrate available information are important to improving the scientific basis for sustainable take thresholds. We used the prescribed take level (PTL) framework to estimate allowable take for bald eagles (Haliaeetus leucocephalus) in the conterminous United States. We developed an integrated population model (IPM) that incorporates multiple sources of information and then use the model output as the scientific basis for components of the PTL framework. Our IPM is structured to identify key parameters needed for the PTL and to quantify uncertainties in those parameters at the scale at which the United States Fish and Wildlife Service manages take. Our IPM indicated that mean survival of birds >1 year old was high and precise (0.91, 95% CI = 0.90–0.92), whereas mean survival of first-year eagles was lower and more variable (0.69, 95% CI = 0.62–0.78). We assumed that density dependence influenced recruitment by affecting the probability of breeding, which was highly imprecise and estimated to have declined from approximately 0.988 (95% CI = 0.985–0.993) to 0.66 (95% CI = 0.34–0.99) between 1994 and 2018. We sampled values from the posterior distributions of the IPM for use in the PTL and estimated that allowable take (e.g., permitted take for energy development, incidental collisions with human made structures, or removal of nests for development) ranged from approximately 12,000 to 20,000 individual eagles depending on risk tolerance and form of density dependence at the scale of the conterminous United States excluding the Southwest. Model-based thresholds for allowable take can be inaccurate if the assumptions of the underlying framework are not met, if the influence of permitted take is under-estimated, or if undetected population declines occur from other sources. Continued monitoring and use of the IPM and PTL frameworks to identify key uncertainties in bald eagle population dynamics and management of allowable take can mitigate this potential bias, especially where improved information could reduce the risk of permitting non-sustainable take.     

Hazards Affecting Grizzly Bear Survival in the Greater Yellowstone Ecosystem

Abstract: During the past 2 decades, the grizzly bear (Ursus arctos) population in the Greater Yellowstone Ecosystem (GYE) has increased in numbers and expanded its range. Early efforts to model grizzly bear mortality were principally focused within the United States Fish and Wildlife Service Grizzly Bear Recovery Zone, which currently represents only about 61% of known bear distribution in the GYE. A more recent analysis that explored one spatial covariate that encompassed the entire GYE suggested that grizzly bear survival was highest in Yellowstone National Park, followed by areas in the grizzly bear Recovery Zone outside the park, and lowest outside the Recovery Zone. Although management differences within these areas partially explained differences in grizzly bear survival, these simple spatial covariates did not capture site-specific reasons why bears die at higher rates outside the Recovery Zone. Here, we model annual survival of grizzly bears in the GYE to 1) identify landscape features (i.e., foods, land management policies, or human disturbances factors) that best describe spatial heterogeneity among bear mortalities, 2) spatially depict the differences in grizzly bear survival across the GYE, and 3) demonstrate how our spatially explicit model of survival can be linked with demographic parameters to identify source and sink habitats. We used recent data from radiomarked bears to estimate survival (1983–2003) using the known-fate data type in Program MARK. Our top models suggested that survival of independent (age ≥ 2 yr) grizzly bears was best explained by the level of human development of the landscape within the home ranges of bears. Survival improved as secure habitat and elevation increased but declined as road density, number of homes, and site developments increased. Bears living in areas open to fall ungulate hunting suffered higher rates of mortality than bears living in areas closed to hunting. Our top model strongly supported previous research that identified roads and developed sites as hazards to grizzly bear survival. We also demonstrated that rural homes and ungulate hunting negatively affected survival, both new findings. We illustrate how our survival model, when linked with estimates of reproduction and survival of dependent young, can be used to identify demographically the source and sink habitats in the GYE. Finally, we discuss how this demographic model constitutes one component of a habitat-based framework for grizzly bear conservation. Such a framework can spatially depict the areas of risk in otherwise good habitat, providing a focus for resource management in the GYE.     

Air pollution combustion emissions: characterization of causative agents and mechanisms associated with cancer, reproductive, and cardiovascular effects

Combustion emissions account for over half of the fine particle (PM(2.5)) air pollution and most of the primary particulate organic matter. Human exposure to combustion emissions including the associated airborne fine particles and mutagenic and carcinogenic constituents (e.g., polycyclic aromatic compounds (PAC), nitro-PAC) have been studied in populations in Europe, America, Asia, and increasingly in third-world counties. Bioassay-directed fractionation studies of particulate organic air pollution have identified mutagenic and carcinogenic polycyclic aromatic hydrocarbons (PAH), nitrated PAH, nitro-lactones, and lower molecular weight compounds from cooking. A number of these components are significant sources of human exposure to mutagenic and carcinogenic chemicals that may also cause oxidative and DNA damage that can lead to reproductive and cardiovascular effects. Chemical and physical tracers have been used to apportion outdoor and indoor and personal exposures to airborne particles between various combustion emissions and other sources. These sources include vehicles (e.g., diesel and gasoline vehicles), heating and power sources (e.g., including coal, oil, and biomass), indoor sources (e.g., cooking, heating, and tobacco smoke), as well as secondary organic aerosols and pollutants derived from long-range transport. Biomarkers of exposure, dose and susceptibility have been measured in populations exposed to air pollution combustion emissions. Biomarkers have included metabolic genotype, DNA adducts, PAH metabolites, and urinary mutagenic activity. A number of studies have shown a significant correlation of exposure to PM(2.5) with these biomarkers. In addition, stratification by genotype increased this correlation. New multivariate receptor models, recently used to determine the sources of ambient particles, are now being explored in the analysis of human exposure and biomarker data. Human studies of both short- and long-term exposures to combustion emissions and ambient fine particulate air pollution have been associated with measures of genetic damage. Long-term epidemiologic studies have reported an increased risk of all causes of mortality, cardiopulmonary mortality, and lung cancer mortality associated with increasing exposures to air pollution. Adverse reproductive effects (e.g., risk for low birth weight) have also recently been reported in Eastern Europe and North America. Although there is substantial evidence that PAH or substituted PAH may be causative agents in cancer and reproductive effects, an increasing number of studies investigating cardiopulmonary and cardiovascular effects are investigating these and other potential causative agents from air pollution combustion sources.     

Fine airborne urban particles (PM2.5) sequester lung surfactant and amino acids from human lung lavage

Components of surfactant act as opsonins and enhance phagocytosis of bacteria; whether this process occurs with atmospheric fine particles has not been shown. We have studied the interactions of fine particles (urban PM(2.5)) and surfactant removed from normal human lungs by lavage, using a surface analysis technique. The aim was to identify which of the chemical components of brochoalveolar lavage (BAL) deposit on the surfaces of urban PM(2.5). Deposition of surfactant components on urban PM(2.5) surfaces was reported in previous studies, but molecular identification and relative quantification was not possible using simple data analysis. In this study, we were able to identify adsorbed components by applying an appropriate statistical technique, factor analysis. In this study, the most strongly associated mass fragment on PM(2.5) surfaces exposed to BAL (and undetected on both untreated samples and saline controls) was di-palmitoyl-phosphatidylcholine, a component of lung surfactant. Amino acids were also strongly associated with BAL-exposed PM(2.5) surfaces and not other sample types. Thirteen mass fragments were identified, diagnostic of the amino acids alanine, arginine, asparagine, aspartic acid, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine, serine, and valine. This study provides evidence that lung surfactant and amino acids related to opsonin proteins adsorb to nonbiological particle surfaces exposed to human lung lining fluid. Disruption of normal surfactant function, both physical and immunological, is possible but unproven. Further work on this PM-opsonin interaction is recommended.     

An informative Bayesian structural equation model to assess source-specific health effects of air pollution

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.     

Benefits of hyperspectral remote sensing for tracking plant invasions

Aim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade‐off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long‐term constructive conservation plans for protecting and maintaining natural ecosystems.     

Mutant allele frequencies in domestic cat populations in Arkansas and Tennessee

We conducted surveys of mutant allele frequencies of four cat populations in Arkansas and Tennessee during 2002. Our calculations and analyses support that Southwestern cat populations were relatively more genetically similar to each other than compared to cat populations in other areas of North America. However, the cat population of Fort Smith is slightly different from the other cat populations studied in the Southwestern United States. Although there is a clear significant spatial geographic pattern for many mutant coat allele frequencies in the United States and Canada cat populations (d, l, S, and W), our results revealed that there is not a significant isolation-by-distance model affecting these cat populations. Our data also support the historical migration hypothesis because our calculated allele frequencies were genetically similar to cat populations located in ancestral areas of Europe. Different phenograms, including new European cat genetic profiles, showed that the Southwestern cat populations studied are of a clear British origin. Therefore, migration routes of early Arkansas and Tennessee settlers help explain the similarities of allele frequencies among domestic cat populations.     

Mapping of the Asian longhorned beetle’s time to maturity and risk to invasion at contiguous United States extent

Anoplophora glabripennis, the Asian Longhorned Beetle (ALB), is an invasive species of high economic and ecological relevance given the potential it has to cause tree damage, and sometimes mortality, in the United States. Because this pest is introduced by transport in wood-packing products from Asia, ongoing trade activities pose continuous risk of transport and opportunities for introduction. Therefore, a geographic understanding of the spatial distribution of risk factors associated with ALB invasions is needed. Chief among the multiple risk factors are (a) the potential for infestation based on host tree species presence/absence, and (b) the temperature regime as a determinant of ALB’s growth time to maturity. This study uses an empirical model of ALB’s time to maturity as a function of temperature, along with a model of heat transfer in the wood of the host and spatial data describing host species presence/absence data, to produce a map of risk factors across the conterminous United States to define potential for ALB infestation and relative threat of impact. Results show that the region with greatest risk of ALB infestation is the eastern half of the country, with lower risk across most of the western half due to low abundance of host species, less urban area, and prevalence of cold, high elevations. Risk is high in southeastern states primarily because of temperature, while risk is high in northeastern and northern central states because of high abundance of host species.     

Determination of urban pollution islands by using remote sensing technology in Moscow,Russia

Pollution of the atmosphere with harmful substances is currently the most dangerous form of degradation of the natural environment in Russia. The peculiarities of the environmental situation and the emerging environmental problems in some areas of the Russian Federation are caused by local natural conditions and the nature of the impacts from industries, transport, utilities, and agriculture (the specifics of enterprises, their capacity, location, technologies used). As a rule, the magnitude of air pollution depends on the degree of urbanization and anthropogenic transformation of the territory and climatic conditions that determine the potential for atmospheric pollution. During high-temperature technological processes, the smallest aerosol particles (0.5..0.10 μm) formed, poorly captured by gas purification plants, and can migrate in the atmosphere for considerable distances. Larger particles (2.5 μm and above) are formed due to the mechanical decomposition of solid particles and enter the atmosphere due to wind erosion, the dusting of dirt roads, the erasure of vehicle tires. The particles suspended with a diameter of not more than 2.5 μm (PMX) are the most destructive to health since they penetrate and get deposited deep into human lungs. These microns, present in a suspended state in the air, consist of a complex mixture of large and small, solid and liquid particles, of both inorganic and organic substances. The boundary between the two fractions is usually particles with a diameter of 2.5 μm (PM2.5). This study sought to build a model for determining fine dust PM2.5 in the Moscow air environment using Landsat 8 OLI satellite image channels and data on the concentrations of fine dust PM2.5 obtained by weather stations in the city. In addition, a correlation analysis was carried out to determine a regression model for studying the dispersion of fine dust in the city. The results obtained are presented on a map of the concentration of fine dust PM2.5 in Moscow, supporting management decisions and decision-making on environmental policy in urban planning.     

Influenza A M2 recruits M1 to the plasma membrane: A fluorescence fluctuation microscopy study

Influenza A virus (IAV) is a respiratory pathogen that causes seasonal epidemics with significant mortality. One of the most abundant proteins in IAV particles is the matrix protein 1 (M1), which is essential for the virus structural stability. M1 organizes virion assembly and budding at the plasma membrane (PM), where it interacts with other viral components. The recruitment of M1 to the PM as well as its interaction with the other viral envelope proteins (hemagglutinin [HA], neuraminidase, matrix protein 2 [M2]) is controversially discussed in previous studies. Therefore, we used fluorescence fluctuation microscopy techniques (i.e., scanning fluorescence cross-correlation spectroscopy and number and brightness) to quantify the oligomeric state of M1 and its interactions with other viral proteins in co-transfected as well as infected cells. Our results indicate that M1 is recruited to the PM by M2, as a consequence of the strong interaction between the two proteins. In contrast, only a weak interaction between M1 and HA was observed. M1-HA interaction occurred only in the event that M1 was already bound to the PM. We therefore conclude that M2 initiates the assembly of IAV by recruiting M1 to the PM, possibly allowing its further interaction with other viral proteins.     

Population structure and spatial influence of agricultural variables on Hessian fly populations in the southeastern United States

Population structure dictates the evolution of each population, and thus, the species as a whole. Incorporating spatial variables with population genetic statistics allows for greater discovery beyond traditional population genetics alone and can inform management decisions. The understanding of population structure in Hessian fly, Mayetiola destructor (Say), a pest of wheat, has been limited in the past. We scored 14 microsatellite loci from 12 collections of Hessian fly in the southeastern United States. Through Bayesian clustering analysis, we found two major populations of Hessian fly covering the entire southeastern United States. We evaluated correlations between agriculturally significant spatial variables and population genetic differentiation to test if genetic structure has an ecological component in a wheat agro-ecosystem. Our results suggest the total amount of alternative host plants in the county may be driving some genetic differentiation. Although planting date may also be influential, geographic distance, mean annual temperature, and harvested wheat for grain do not seem to be contributing factors. The ecological or spatial component to population structure, however, may be minimal compared to factors such as genetic drift.     

Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models

Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information for dry deposition levels in the United States: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is the output of the regional scale air quality models, called Models-3. A related problem is the evaluation of these numerical models for air quality applications, which is crucial for control strategy selection. We develop formal methods for combining sources of information with different spatial resolutions and for the evaluation of numerical models. We specify a simple model for both the Models-3 output and the CASTNet observations in terms of the unobserved ground truth, and we estimate the model in a Bayesian way. This provides improved spatial prediction via the posterior distribution of the ground truth, allows us to validate Models-3 via the posterior predictive distribution of the CASTNet observations, and enables us to remove the bias in the Models-3 output. We apply our methods to data on SO2 concentrations, and we obtain high-resolution SO2 distributions by combining observed data with model output. We also conclude that the numerical models perform worse in areas closer to power plants, where the SO2 values are overestimated by the models.