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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]