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In this research, we propose a novel technique for visualizing nonstationarity in geostatistics, particularly when confronted with a single realization of data at irregularly spaced locations. Our method hinges on formulating a statistic that tracks a stable microergodic parameter of the exponential covariance function, allowing us to address the intricate challenges of nonstationary processes that lack repeated measurements. We implement the fused lasso technique to elucidate nonstationary patterns at various resolutions. For prediction purposes, we segment the spatial domain into stationary sub-regions via Voronoi tessellations. Additionally, we devise a robust test for stationarity based on contrasting the sample means of our proposed statistics between two selected Voronoi subregions. The effectiveness of our method is demonstrated through simulation studies and its application to a precipitation dataset in Colorado. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

This is the second of three papers dealing with metal-bearing circumneutral mine drainage from the inactive Rico-Argentine mine site located at an elevation of ≈ 2740 m (9000 feet) in the San Juan mountain range in southwestern Colorado. This paper evaluates two years of mine drainage treatment using a passive system that included a vertical-flow engineered biotreatment cell. The collapsed St. Louis Tunnel (SLT) discharges circumneutral mine water from several sources that contains elevated concentrations of Cd, Cu, Fe, Mn, Zn. A demonstration-scale 114 L/min (30 gpm) gravity-flow passive treatment system was installed, consisting of a settling basin (utilizing coagulant addition to improve suspended solids settling efficiency), an anaerobic sulfate-reducing bioreactor, and an aeration cascade for effluent polishing. The treatment system generally met target treatment goals for Cd, Cu, Fe, and Pb. Nanophase ZnS in system effluent decreased the frequency of meeting total Zn project treatment goals. Unexpectedly high levels of Mn removal were observed in both the anaerobic bioreactor and the aeration cascade. Large seasonal variations in influent metals concentrations and pH present the greatest challenge in managing system performance. [ABSTRACT FROM AUTHOR]

The principle of reciprocity, called Maxwell–Betti theorem, initially used in mechanics in an elastic structure, establishes a relation of equality between two distinct strains under different loads. In this paper, we extend and apply this principle to flow and solute transport equations in porous media, in order to perform the pollution sources identification in aquifers. We developed general 2D expressions of the reciprocity principle for transient transport problems. This model leads to a linear equations set, with point sources coordinates, concentrations and associated water fluxes as unknowns The proposed model is then applied to the Rocky Mountain Arsenal aquifer (Konikow in Modeling Chloride Movement in the Alluvial Aquifer at the Rocky Mountain Arsenal, Colorado. Technical Report Water-Supply Paper 2044, USGS, 1979), where polluted water is injected into a well in the domain. The used inverse technique successfully recovered the position and the pollutant concentration in addition to the associated water flux. In addition, we developed and implemented the inverse method for different knowledge levels of the degrees of the aquifer contamination, i.e. more or less data available in the field. Multiple pollution point sources and noisy data situations are also developed and tested with high efficiency. The proposed method would be easy and useful to be implemented in the modeling software now widely used by researchers and groundwater managers. It can thus be applied in real case studies, to help authorities and regulators to efficiently identify the polluters and the contamination process, i.e. its location, onset, duration and the associated mass and water fluxes. [ABSTRACT FROM AUTHOR]

The Aria cryogenic distillation plant, located in Sardinia, Italy, is a key component of the DarkSide-20k experimental program for WIMP dark matter searches at the INFN Laboratori Nazionali del Gran Sasso, Italy. Aria is designed to purify the argon, extracted from underground wells in Colorado, USA, and used as the DarkSide-20k target material, to detector-grade quality. In this paper, we report the first measurement of argon isotopic separation by distillation with the 26 m tall Aria prototype. We discuss the measurement of the operating parameters of the column and the observation of the simultaneous separation of the three stable argon isotopes: 36 Ar , 38 Ar , and 40 Ar . We also provide a detailed comparison of the experimental results with commercial process simulation software. This measurement of isotopic separation of argon is a significant achievement for the project, building on the success of the initial demonstration of isotopic separation of nitrogen using the same equipment in 2019. [ABSTRACT FROM AUTHOR]

Wildfires are increasing in scale, frequency and longevity, and are affecting new locations as environmental conditions change. This paper presents a dataset collected during a community evacuation drill performed in Roxborough Park, Colorado (USA) in 2019. This is a wildland–urban interface community including approximately 900 homes. Data concerning several aspects of community response were collected through observations and surveys: initial population location, pre-evacuation times, route use, and arrival times at the evacuation assembly point. Data were used as inputs to benchmark two evacuation models that adopt different modelling approaches. The WUI-NITY platform and the Evacuation Management System model were applied across a range of scenarios where assumptions regarding pre-evacuation delays and the routes used were varied according to original data collection methods (and interpretation of the data generated). Results are mostly driven by the assumptions adopted for pre-evacuation time inputs. This is expected in communities with a low number of vehicles present on the road and relatively limited traffic congestion. The analysis enabled the sensitivity of the modelling approaches to different datasets to be explored, given the different modelling approaches adopted. The performance of the models were sensitive to the data employed (derived from either observations or self-reporting) and the evacuation phases addressed in them. This indicates the importance of monitoring the impact of including data in a model rather than simply on the data itself, as data affects models in different ways given the modelling methods employed. The dataset is released in open access and is deemed to be useful for future wildfire evacuation modelling calibration and validation efforts. [ABSTRACT FROM AUTHOR]

This publication is a case study of the seasonal variability of mine water drainage from the Saint Louis Tunnel (SLT) at the inactive Rico-Argentine mine site located in southwestern Colorado. It is an introductory paper for the two passive water treatment system technology evaluations contained in this issue. Mine water chemistry changes from baseflow to a snowmelt runoff event (SMRE) where snowmelt runoff follows preferential migration pathways to flush acidic weathering products from the upper mine workings to the SLT. Baseflow mine drainage is characterized as circumneutral, with Zn, Cd, Mn, and Ni concentrations primarily in the dissolved form. Dissolved Zn, Mn, Fe, and potentially Cd illustrate equilibrium with carbonate minerals. Total concentrations of Fe, Cu, Pb, and As are primarily in the suspended form and suggest sorption to Fe oxides. Mine water chemistry during the SMRE reflects mixing of circumneutral baseflow waters with more acidic waters flushing the upper mine workings. Geothermal activity provides for a consistently warm mine water discharge from the SLT. The two seasons that provide the most challenge to passive water treatment of SLT mine drainage are the SMRE period and the low flow stage of the Dolores River. Mine water flow and chemistry during SMRE are highly correlated with Dolores River flow and this site conceptual model was and will be used to assist in pilot project evaluation, water treatment system design, monitoring system design, a seasonal compliance approach, and water management. [ABSTRACT FROM AUTHOR]

Marginalized individuals are less likely to participate or have their interests represented in political processes than historically privileged individuals. Interest groups are considered the best means to address this gap, but there is little research on the role of interest groups in mobilizing people to participate in political processes and none on marginalized communities in particular. This paper is the first to test hypotheses about organizational strategies used to mobilize vulnerable communities for political participation around unconventional oil and gas policies in California and Colorado. Based on a survey of interest groups in both states, the results show that interest groups working in vulnerable communities do more outside advocacy (i.e., connecting residents to representatives) and use more personal communication methods (i.e., door-to-door canvassing) than interest groups working in historically privileged communities. However, organizational strategies in general are not well predicted by the target community's composition, suggesting that decisions around mobilization strategies are driven by other factors. [ABSTRACT FROM AUTHOR]

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets. [ABSTRACT FROM AUTHOR]

Since early 2020, non-pharmaceutical interventions (NPIs)—implemented at varying levels of severity and based on widely-divergent perspectives of risk tolerance—have been the primary means to control SARS-CoV-2 transmission. This paper aims to identify how risk tolerance and vaccination rates impact the rate at which a population can return to pre-pandemic contact behavior. To this end, we developed a novel mathematical model and we used techniques from feedback control to inform data-driven decision-making. We use this model to identify optimal levels of NPIs across geographical regions in order to guarantee that hospitalizations will not exceed given risk tolerance thresholds. Results are shown for the state of Colorado, United States, and they suggest that: coordination in decision-making across regions is essential to maintain the daily number of hospitalizations below the desired limits; increasing risk tolerance can decrease the number of days required to discontinue NPIs, at the cost of an increased number of deaths; and if vaccination uptake is less than 70%, at most levels of risk tolerance, return to pre-pandemic contact behaviors before the early months of 2022 may newly jeopardize the healthcare system. The sooner we can acquire population-level vaccination of greater than 70%, the sooner we can safely return to pre-pandemic behaviors. [ABSTRACT FROM AUTHOR]

Point sources in spatially referenced data can impact outcomes in surrounding locations (e.g., a factory that emits air pollution). Previous statistical methods have sought to describe the non-stationary correlation induced by the presence of a point source, with fewer attempting to quantify its overall impact. We introduce directionally varying change points (DVCP), a model that aims to estimate the magnitude of the impact of a point source as well as its range of influence across the spatial domain. DVCP allows for a unique change point parameter, describing the range of influence of a point source, at every angle extending from the source and uses a Gaussian process with directionally defined correlation function to facilitate estimation of the parameters. The Gaussian predictive process approximation is used for fitting the model to large datasets. Through simulation, we show that DVCP can easily accommodate a wide range of shapes defining the range of influence. We apply DVCP to better understand spatial patterns of ambient PM 2.5 concentrations and issues related to environmental inequity in California and Colorado. The method is available in the R package DVCP. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Extreme changes in weather across the globe are substantially responsible to incite calamities like drought. While we cannot control weather, it is a harsh reality that we cannot stop droughts. However, the consequent detriments can discerningly be reduced to a tolerable limit by developing long term rational planning. To attain this objective, probability models are imperative as they measure and analyze the variation in the random phenomena in an orderly way and help to collect useful information leading toward meaningful predictions. In this paper, an explicit distribution based on the convolution of stochastic variables is derived from the Bivariate Affine-Linear Exponential (BALE) distribution to model the interarrival time of drought. The proposed model follows the trend of the observed drought data of the Colorado Drainage Basin in the USA that provides the rationale for its reliable forecasting. The return periods are also estimated for the interarrival time of drought to obtain important inferences for future planning. Finally, some quantiles associated with this model are provided, which are useful to predict changes in the interarrival times of droughts. [ABSTRACT FROM AUTHOR]

This paper introduces a method to identify the bending modes of vibration of railway tracks by using a laser Doppler vibrometer (LDV) mounted on a moving platform. Two sets of experiments were conducted at Transportation Technology Center Inc. (TTCI) in Pueblo Colorado, in order to validate the proposed method. First, the bending vibration modes were identified using the signals collected from a rail span (rail section between two consecutive sleepers) by accelerometers under moving car excitation. Then, vibration measurements from rail spans were obtained by using an LDV mounted on the moving railcar. All tests were carried out at four different rail car speeds: 8 km/h (5 mph), 16 km/h (10 mph), 35 km/h (22 mph), and 45 km/h (28 mph). To find LDV signal segments corresponding to rail spans, a novel approach based on the sleeper passing frequency is introduced. Comparison of the results from both sets of tests demonstrated good agreement for all speeds. [ABSTRACT FROM AUTHOR]

Two common tasks when processing point cloud data sets are surface estimation and point cloud registration. In this paper, a statistical approach is developed to solve both of these problems simultaneously. In particular, a surface is estimated from a pair of unregistered three-dimensional scans of the same spatial region. In this method, one point cloud defines the fixed coordinate system, and a rigid transformation is applied to the second cloud. Observations from both scans are considered a single realization of a Gaussian process. The registration problem is solved by jointly optimizing the likelihood over the parameters specifying the domain transformation and the mean and covariance functions. Given parameter estimates, surface estimation follows using the spatial stochastic model. While other existent approaches do not account for registration uncertainty, the likelihood-based approach to solving the registration and surface estimation problems jointly allows uncertainty in registration to be propagated to the surface prediction variance. The new method is motivated and illustrated using a digital elevation model estimation problem near the Chalk Cliffs in Colorado. The method developed is compared against the popular iterative closest point method. The results of a simulation study show significant improvement in transformation parameter estimates using the statistical approach. In a cross-validation experiment with the Chalk Cliffs data, there is an 18 % reduction in predictive mean squared error using the likelihood method over iterative closest point. [ABSTRACT FROM AUTHOR]

Though a small fraction of the US citizenry, agricultural producers are directly responsible for the stewardship of almost half of the country's land. This group is therefore an especially important one to understand from the standpoint of how they process and respond to science as it relates to agroecological phenomena. Data from a sample (n = 111) of farmers and ranchers located in the US state of Colorado are used to expand our understanding of how food producers process scientific claims. These insights, I argue, help us think through public understandings of science more generally. Using semi-structured interviews, the paper unpacks an identified asymmetry in how respondents perceive climate science and the science associated with genetically modified food and seed. These tensions are interrogated with the help of a novel methodological design that generated data converted to shading matrices—also known as heat maps. The heat maps illuminate certain cultural values among respondents, which were reinforced by motivated reasoning. This allows for an interrogation of tensions and inconsistencies in respondents' remarks about a variety of scientific claims. The heat maps, coupled with the qualitative data, allow for an exploration into how respondents perceive certain salient socio-technical issues. [ABSTRACT FROM AUTHOR]

Uncertainty in return level estimates for rare events, like the intensity of large rainfall events, makes it difficult to develop strategies to mitigate related hazards, like flooding. Latent spatial extremes models reduce the uncertainty by exploiting spatial dependence in statistical characteristics of extreme events to borrow strength across locations. However, these estimates can have poor properties due to model misspecification: Many latent spatial extremes models do not account for extremal dependence, which is spatial dependence in the extreme events themselves. We improve estimates from latent spatial extremes models that make conditional independence assumptions by proposing a weighted likelihood that uses the extremal coefficient to incorporate information about extremal dependence during estimation. This approach differs from, and is simpler than, directly modeling the spatial extremal dependence; for example, by fitting a max-stable process, which is challenging to fit to real, large datasets. We adopt a hierarchical Bayesian framework for inference, use simulation to show the weighted model provides improved estimates of high quantiles, and apply our model to improve return level estimates for Colorado rainfall events with 1% annual exceedance probability. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

River flow data are usually subject to several sources of discontinuity and inhomogeneity. An example is seasonality, because climatic oscillations occurring on inter-annual timescale have an obvious impact on the river flow. Further sources of alteration can be caused by changes in reservoir management, instrumentation or even unexpected shifts in climatic conditions. When such changes are ignored the results of a statistical analysis can be strongly misleading, so flexible procedures are needed for building the appropriate models, which may be very complex. This paper develops an automatic procedure to estimate the number and locations of changepoints in Periodic AutoRegressive (PAR) models, which have been extensively used to account for seasonality in hydrology. We aim at filling the literature gap on multiple changepoint detection by allowing several time segments to be detected, inside of which a different PAR structure is specified, with the resulting model being employed to successfully capture the discontinuities of river flow data. The model estimation is performed by optimization of an objective function based on an information criterion using genetic algorithms. The proposed methodology is evaluated by means of simulation studies and it is then employed in the analysis of two river flows: the South Saskatchewan, measured at Saskatoon, Canada, and the Colorado, measured at Lees Ferry, Arizona. For these river flows we build changepoint models, discussing the possible events that caused discontinuity, and evaluate their forecasting accuracy. Comparisons with the literature on river flow analysis and on existing methods for changepoint detection confirm the efficiency of our proposal. [ABSTRACT FROM AUTHOR]

Objectives The uptake and actual use of the current guidelines from the American College of Obstetrics and Gynecology (ACOG) is unknown. Methods Family planning providers across Colorado and Iowa were surveyed as part of statewide initiatives to reduce unintended pregnancy in 2010 and 2012, both before and after the release of the guidelines. These initiatives focused on the promotion of intrauterine devices (IUDs) and implants. These surveys included questions on providers' views regarding the suitability and safety of the copper T IUD, hormonal IUD, and single rod implant for various subgroups of clients. The results are contrasted with guidelines provided in July of 2011 by ACOG. This strategy provides both baseline and follow-up models about the methods promoted in these guidelines. Results Findings show that there is some improvement in beliefs that IUDs are suitable and safe for women who are post-partum, post-abortion, have had an ectopic pregnancy, are nulliparous, teenagers, or have a history of STIs. However, these clinicians' views are not entirely in alignment with ACOG recommendations in their beliefs that these methods should not be used immediately post-partum or post-abortion. Notable percentages of these clinicians were hesitant to recommend these effective methods for other groups of patients, approved for use by ACOG. Conclusions While the cost of these methods is a barrier to adoption, these data suggest that there are continuing provider barriers to their use as well. The paper concludes with suggestions for further training for family planning providers. [ABSTRACT FROM AUTHOR]

This paper uses the results from a community food security assessment survey of 684 residents and three focus groups in Pueblo County, Colorado to examine the question: what community and individual factors contribute to or alleviate food insecurity, and are these factors consistent throughout a sub-county population. Importantly, we use a technique called cluster analysis to endogenously determine the key factors pertinent to food access and fruit and vegetable consumption. Our results show significant heterogeneity among sub-population clusters in terms of the community and individual factors that would make it easier to get access to fruits and vegetables. We find two distinct clusters of food insecure populations: the first was significantly less likely to identify increased access to fruits and vegetables proximate to where they live or work as a way to improve their household's healthy food consumption despite being significantly less likely to utilize a personal vehicle to get to the store; the second group did not report significant challenges with access, rather with affordability. We conclude that though interventions focused on improving the local food retail environment may be important for some subsamples of the food insecure population, it is unclear that proximity to a store with healthy food will support enhanced food security for all. We recommend that future research recognizes that determinants of food insecurity may vary within county or zip code level regions, and that multiple interventions that target sub-population clusters may elicit better improvements in access to and consumption of fruits and vegetables. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine factors that are associated with tooth loss in older adults living in the San Luis Valley (SLV), Colorado, which is a rural and large geographical area (roughly the size of Connecticut) that has a large population age 60 years or older. Data used in this manuscript were collected as a part of the SLV Community Health Survey. The analyzed sample included 308 adults over the age of 65 years who completed the survey. Basic descriptive statistics and a series of step-wise binary logistic regression analyses were conducted; the dependent variable was the number of permanent teeth removed because of tooth decay or gum disease. Fifty-two percent of the participants were male, Hispanic participants made up 40 % of the sample and 76 % of the participants had at least a high school education. Tooth loss was significantly associated with older age ( OR = 1.09; p = 0.02), lower income ( OR = 0.01; p = 0.00), less than high school education ( OR = 0.32; p = 0.01), being Hispanic ( OR = 2.15; p = 0.05), self-reported fair-poor health status (OR 2.94; p = 0.02), consumption of one or more than one sweet beverage per day ( OR = 4.52 ; p = 0.00), no dental insurance ( OR = 4.70; p = 0.01) and length of time since last dental visit ( OR = 0.21; p = 0.01). The findings of the present study suggest possible causes for tooth loss in rural adults and underscore the need for in-depth research to study the overall oral health of rural older adults living in SLV. [ABSTRACT FROM AUTHOR]

The Mountain Pine Beetle (MPB) infestation in Colorado and Wyoming impacts federal and state forest managers, the recreation and tourism industry, ecosystem services, and the general sustainability of livelihoods in proximity to forested areas. Using qualitative analyses, this paper investigates whether a linkage exists between individuals' value orientations and their attitudes toward forest management in relation to MPB infestations. Data were obtained in 2011 from three open-ended questions in a mail survey of residents in Colorado and Wyoming. Our qualitative analysis revealed four value orientation types: 1) biocentric, 2) anthropocentric, 3) spiritual/therapeutic, and 4) pluralist. Additionally, respondents were generally supportive of forest management practices and the majority had a 'do what you need to save the forest' attitude. A linkage between value orientations toward the national forest and management practices was identified. Research and management implications are discussed. [ABSTRACT FROM AUTHOR]

Understanding policy change is among the most enduring pursuits of public policy studies. Despite the wealth of policy change literature, a central challenge in the study of policy change remains measuring and assessing change. A useful starting point in measuring and assessing policy change is a thorough analysis of policy composition. The textual composition of policy change is analyzed by drawing on the Institutional Analysis and Development framework for conceptual definitions and data collection methods. Lessons from the policy implementation, design, and tools' literatures guide comparative data analysis and evaluation. The approach is illustrated through a study of smoking ban change in Colorado, USA. The conclusion discusses the methodological contribution offered in this paper and its potential for facilitating theoretical development. [ABSTRACT FROM AUTHOR]

In this paper we advance a dual-prong method for evaluating whether data from a natural experiment can be leveraged to draw convincing implications about causal effects of neighborhood context on health. In particular, we probe a natural experiment's ability to surmount potentially confounding measurement issues arising from geographic selection bias. We offer methods for assessing the degree of this potential bias both immediately after households are assigned to neighborhoods and over longer durations. We then illustrate these methods using data from a natural experiment involving scattered-site public housing in Denver and test whether neighborhood context affects the hazard of children's asthma diagnoses. We find that, conditioned on ethnicity, the tenant allocation process used by the Denver Housing Authority produced a quasi-random initial assignment across neighborhood characteristics and that the range of bias arising subsequently through selective out-migration can be narrowly delineated by replicating analyses for alternative samples defined by period of residence in public housing. We further find support for the proposition that observing no correlation between neighborhood and observed household characteristics is tantamount to observing no correlation between neighborhood and (typically) unobserved household characteristics. There appears to be little bias reintroduced through selective out-migration and staying in initially assigned neighborhood. These findings suggest that, after employing the evaluation strategy suggested here, natural experiments can indeed be a powerful tool for assessing neighborhood effects on health. [ABSTRACT FROM AUTHOR]

The Study to Explore Early Development (SEED) is a multi-site case-control study designed to explore the relationship between autism spectrum disorder (ASD) phenotypes and etiologies. The goals of this paper are to (1) describe the SEED algorithm that uses the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) to classify children with ASD, (2) examine psychometric properties of different ASD classification methods, including the SEED method that incorporates rules for resolving ADI-R and ADOS discordance, and (3) determine whether restricted interests and repetitive behaviors were noted for children who had instrument discordance resolved using ADI-R social and communication scores. Results support the utility of SEED criteria when well-defined groups of children are an important clinical or research outcome. [ABSTRACT FROM AUTHOR]