Characterization of silica content in gold mine dust with respect to particle size

Globally, silicosis is responsible for thousands of deaths each year and is a major public health concern in industries like mining. Silicosis is caused by exposure to respirable crystalline silica, and while incidence of silicosis has declined in recent decades, its continued occurrence in young workers indicates that high crystalline silica exposures in the contemporary workforce persist despite monitoring efforts and regulatory enforcement. Crystalline silica exposure is monitored in the mining industry via collection of respirable dust samples, from which both dust and crystalline silica concentrations are determined. Accurate quantification of crystalline silica is vital to assessing workers’ exposure, and to limiting exposure through selection of appropriate engineering controls and personal protective equipment. To quantify crystalline silica in a sample, one of two analytic methods is used: X-ray diffraction and infrared spectroscopy. Previously, confounding effects of mineral composition and size distribution of dust were assumed to have only minor impact on the accuracy of both methods; however, as mining technologies evolve, so do the characteristics of the dust generated in mines, and such effects may no longer be negligible. Evaluating the characteristics of mine dust with respect to particle size and crystalline silica content is imperative to understanding how crystalline silica analysis may be affected by these characteristics. To date, few studies have investigated particle size-related crystalline silica content in occupational dusts, and while some efforts have been made to characterize coal mine dusts, there has been no such effort to characterize metal/non-metal mine dusts. This study undertakes detailed characterization of dusts from three gold mine operations, via analysis of size distribution using particle sizers and a cascade impactor; crystalline silica content by infrared and X-ray diffraction methods; and single-particle composition via scanning electron microscopy. Results indicate that the size distribution of crystalline silica within a particular dust is not equivalent to the dust’s size distribution; the abundance of crystalline silica in a dust varies with particle size; the two methods of quantifying crystalline silica yield variable results depending on particle size; and, like crystalline silica, particle types of different elemental composition vary in abundance with respect to particle size

Interdisciplinary Design Studio: Programming Document Visioning for a Robotic Demonstration, Research, and Engagement Dairy

The 2022 COLLABORATE Design Studio brought together students from various disciplines to address a complex, real-world project which required collaborative input from different perspectives. The studio worked to advance the co-creation of knowledge between external stakeholders, students, and instructors. The course was co-taught by faculty from different disciplines, and areas of expertise. During the semester, Nate Bicak and Steven Hardy worked with students from Architecture and Interior Design in collaboration with students in Dr. Tami Brown-Brandl’s students in Biological Systems Engineering and Animal Science to explore the values, spatial qualities, and area requirements of a Robotic Demonstration, Research, and Engagement Dairy. Students organized a series of meetings and participatory activities to gather information from a range of project stakeholders including: Heather Akin (Agricultural Leadership, Education & Communication), Kris Bousquet (NE Dairy Association), Paul Kononoff (Animal Science), Eric Markvicka (Mechanical and Material Engineering), Julia McQuillan (Sociology), Santosh Pitla (BioSystems and Agricultural Engineering), Ling Ling Sun (NE Public Media), and Rosanna Villa Rojas (Food Science & Technology). The information gathered helped to frame the overall problem - both quantitative and qualitative - to be addressed during the design visioning stage (not included in this document). Student contributors included: Sarah Alduaylij, Noor Al-Maamari, Devyn Beekman, Kelsey Belgum, Lauren Chubb, Nicholas Forte, Mitchell Hill, Joshua Holstein, Dylan Lambe, Phuong Le, Mia LeRiger, Elizabeth Loftus, Josh Lorenzen , Megan Lovci, Alex Martino, Zade Miller, Hannah Morgan , Annabelle Nichols , Collin Shearman, Rebecca Sowl, Nalin Theplikhith, Angela Vu, Shaylee Wagner, Ethan Watermeier, Trever Zelenk

Standardization in digital pathology: Supplement 145 of the DICOM standards

As digital slides need a lot of storage space, lack of a singular method to acquire and store these large, two-dimensional images has been a major stumbling block in the universal acceptance of this technology. The DICOMS Standard Committee Working Group 26 has put in a tremendous effort to standardize storage methods so that they are more in line with currently available PACS in most hospitals for storage of radiology images. A recent press release (Supplement 145) of these standards was hailed by one and all involved in the field of digital pathology as it will make it easier for hospitals to integrate digital pathology into their already established systems without adding too much overhead costs. Besides, it will enable different vendors developing the scanners to upgrade their products to storage systems that are common across all systems

Complexity of Respirable Dust Found in Mining Operations as Characterized by X-ray Diffraction and FTIR Analysis

The mineralogical complexity of mine dust complicates exposure monitoring methods for occupational, respirable hazards. Improved understanding of the variability in respirable dust characteristics, e.g., mineral phase occurrence and composition, is required to advance on-site monitoring techniques that can be applied across diverse mining sectors. Principal components analysis (PCA) models were applied separately to XRD and FTIR datasets collected on 130 respirable dust samples from seven mining commodities to explore similarities and differences among the samples. Findings from both PCA models classified limestone, iron, and granite mine samples via their analytical responses. However, the results also cautioned that respirable samples from these commodities may not always fit patterns observed within the model. For example, one unique sample collected in a limestone mine contained no carbonate minerals. Future predictive quantification models should account for unique samples. Differences between gold and copper mine dust samples were difficult to observe. Further investigation suggested that the key to their differentiation by FTIR may lie in the characterization of clays. The results presented in this study provide foundational information for guiding the development of quantification models for respirable mineral hazards in the mining industry

Tewatenweiest: Owira’neha Tsi Kanonsote: Nurturing and language learning in a Mohawk language nest

In this paper we discuss our Mohawk language nest and how it runs. We also discuss a study we conducted to assess children’s acquisition in the language nest. We also provide an overview of the Mohawk language situation in the community

Spatial Patterns in Rush-Hour vs. Work-Week Diesel-Related Pollution across a Downtown Core

Despite advances in monitoring and modelling of intra-urban variation in multiple pollutants, few studies have attempted to separate spatial patterns by time of day, or incorporated organic tracers into spatial monitoring studies. Due to varying emissions sources from diesel and gasoline vehicular traffic, as well as within-day temporal variation in source mix and intensity (e.g., rush-hours vs. full-day measures), accurately assessing diesel-related air pollution within an urban core can be challenging. We allocated 24 sampling sites across downtown Pittsburgh, Pennsylvania (2.8 km2) to capture fine-scale variation in diesel-related pollutants, and to compare these patterns by sampling interval (i.e., “rush-hours” vs. “work-week” concentrations), and by season. Using geographic information system (GIS)-based methods, we allocated sampling sites to capture spatial variation in key traffic-related pollution sources (i.e., truck, bus, overall traffic densities). Programmable monitors were used to collect integrated work-week and rush-hour samples of fine particulate matter (PM2.5), black carbon (BC), trace elements, and diesel-related organics (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), in summer and winter 2014. Land use regression (LUR) models were created for PM2.5, BC, total elemental carbon (EC), total organic carbon (OC), elemental (Al, Ca, Fe), and organic constituents (total PAHs, total hopanes), and compared by sampling interval and season. We hypothesized higher pollution concentrations and greater spatial contrast in rush-hour, compared to full work-week samples, with variation by season and pollutant. Rush-hour sampling produced slightly higher total PM2.5 and BC concentrations in both seasons, compared to work-week sampling, but no evident difference in spatial patterns. We also found substantial spatial variability in most trace elements and organic compounds, with comparable spatial patterns using both sampling paradigms. Overall, we found higher concentrations of traffic-related trace elements and organic compounds in rush-hour samples, and higher concentrations of coal-related elements (e.g., As, Se) in work-week samples. Mean bus density was the strongest LUR predictor in most models, in both seasons, under each sampling paradigm. Within each season and constituent, the bus-related terms explained similar proportions of variance in the rush-hour and work-week samples. Rush-hour and work-week LUR models explained similar proportions of spatial variation in pollutants, suggesting that the majority of emissions may be produced during rush-hour traffic across downtown. Results suggest that rush-hour emissions may predominantly shape overall spatial variance in diesel-related pollutants

Fine-Scale Source Apportionment Including Diesel-Related Elemental and Organic Constituents of PM2.5 across Downtown Pittsburgh

Health effects of fine particulate matter (PM2.5) may vary by composition, and the characterization of constituents may help to identify key PM2.5 sources, such as diesel, distributed across an urban area. The composition of diesel particulate matter (DPM) is complicated, and elemental and organic carbon are often used as surrogates. Examining multiple elemental and organic constituents across urban sites, however, may better capture variation in diesel-related impacts, and help to more clearly separate diesel from other sources. We designed a “super-saturation” monitoring campaign of 36 sites to capture spatial variance in PM2.5 and elemental and organic constituents across the downtown Pittsburgh core (~2.8 km2). Elemental composition was assessed via inductively-coupled plasma mass spectrometry (ICP-MS), organic and elemental carbon via thermal-optical reflectance, and organic compounds via thermal desorption gas-chromatography mass-spectrometry (TD-GCMS). Factor analysis was performed including all constituents—both stratified by, and merged across, seasons. Spatial patterning in the resultant factors was examined using land use regression (LUR) modelling to corroborate factor interpretations. We identified diesel-related factors in both seasons; for winter, we identified a five-factor solution, describing a bus and truck-related factor [black carbon (BC), fluoranthene, nitrogen dioxide (NO2), pyrene, total carbon] and a fuel oil combustion factor (nickel, vanadium). For summer, we identified a nine-factor solution, which included a bus-related factor (benzo[ghi]fluoranthene, chromium, chrysene, fluoranthene, manganese, pyrene, total carbon, total elemental carbon, zinc) and a truck-related factor (benz[a]anthracene, BC, hopanes, NO2, total PAHs, total steranes). Geographic information system (GIS)-based emissions source covariates identified via LUR modelling roughly corroborated factor interpretations

Assessment of Spatial Variability across Multiple Pollutants in Auckland, New Zealand

Spatial saturation studies using source-specific chemical tracers are commonly used to examine intra-urban variation in exposures and source impacts, for epidemiology and policy purposes. Most such studies, however, has been performed in North America and Europe, with substantial regional combustion-source contributions. In contrast, Auckland, New Zealand, a large western city, is relatively isolated in the south Pacific, with minimal impact from long-range combustion sources. However, fluctuating wind patterns, complex terrain, and an adjacent major port complicate pollution patterns within the central business district (CBD). We monitored multiple pollutants (fine particulate matter (PM2.5), black carbon (BC), elemental composition, organic diesel tracers (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), and nitrogen dioxide (NO2)) at 12 sites across the ~5 km2 CBD during autumn 2014, to capture spatial variation in traffic, diesel, and proximity to the port. PM2.5 concentrations varied 2.5-fold and NO2 concentrations 2.9-fold across the CBD, though constituents varied more dramatically. The highest-concentration constituent was sodium (Na), a distinct non-combustion-related tracer for sea salt (µ = 197.8 ng/m3 (SD = 163.1 ng/m3)). BC, often used as a diesel-emissions tracer, varied more than five-fold across sites. Vanadium (V), higher near the ports, varied more than 40-fold across sites. Concentrations of most combustion-related constituents were higher near heavy traffic, truck, or bus activity, and near the port. Wind speed modified absolute concentrations, and wind direction modified spatial patterns in concentrations (i.e., ports impacts were more notable with winds from the northeast)