Benefits and limitations of field-based monitoring approaches for respirable dust and crystalline silica applied in a sandstone quarry.

With the advent of new sensing technologies and robust field-deployable analyzers, monitoring approaches can now generate valuable hazard information directly in the workplace. This is the case for monitoring respirable dust and respirable crystalline silica concentration levels. Estimating the quartz amount of a respirable dust sample by nondestructive analysis can be carried out using portable Fourier transform infrared spectroscopy (FTIR) units. Real-time respirable dust monitors, combined with small video cameras, allow advanced assessments using the Helmet-CAM methodology. These two field-based monitoring approaches, developed by the National Institute for Occupational Safety and Health (NIOSH), have been trialed in a sandstone quarry. Twenty-six Helmet-CAM sessions were conducted, and forty-one dust samples were collected around the quarry and analyzed on-site during two events. The generated data generated were used to characterize concentration levels for the monitored areas and workers, to identify good practices, and to illustrate activities that could be improved with additional engineered control technologies. Laboratory analysis of the collected samples complemented the field finding and provided an assessment of the performance of the field-based techniques. Only a fraction of the real-time respirable dust monitoring sessions data could be corrected with laboratory analysis. The average correction factor ratio was 5.0. Nevertheless, Helmet-CAM results provided valuable information for each session. The field-based quartz monitoring approach overestimated the concentration by a factor of 1.8, but it successfully assessed the quartz concentration trends in the quarry. The data collected could be used for the determination of a quarry calibration factor for future events. The quartz content in the dust was found to vary from 14% to 100%, and this indicates the need for multiple techniques in the characterization of respirable dust and quartz concentration and exposure. Overall, this study reports the importance of the adoption of field-based monitoring techniques when combined with a proper understanding and knowledge of the capabilities and limitations of each technique.

Monitoring Worker Exposure to Respirable Crystalline Silica: Application for Data-driven Predictive Modeling for End-of-Shift Exposure Assessment.

In the ever-expanding complexities of the modern-day mining workplace, the continual monitoring of a safe and healthy work environment is a growing challenge. One specific workplace exposure concern is the inhalation of dust containing respirable crystalline silica (RCS) which can lead to silicosis, a potentially fatal lung disease. This is a recognized and regulated health hazard, commonly found in mining. The current methodologies to monitor this type of exposure involve distributed sample collection followed by costly and relatively lengthy follow-up laboratory analysis. To address this concern, we have investigated a data-driven predictive modeling pipeline to predict the amount of silica deposition quickly and accurately on a filter within minutes of sample collection completion. This field-based silica monitoring technique involves the use of small, and easily deployable, Fourier transform infrared (FTIR) spectrometers used for data collection followed by multivariate regression methodologies including Principal Component Analysis (PCA) and Partial Least Squares (PLS). Given the complex nature of respirable dust mixtures, there is an increasing need to account for multiple variables quickly and efficiently during analysis. This analysis consists of several quality control steps including data normalization, PCA and PLS outlier detection, as well as applying correction factors based on the sampler and cassette used for sample collection. While outside the scope of this article to test, these quality control steps will allow for the acceptance of data from many different FTIR instruments and sampling types, thus increasing the overall useability of this method. Additionally, any sample analyzed through the model and validated using a secondary method can be incorporated into the training dataset creating an ever-growing, more robust predictive model. Multivariant predictive modeling has far-reaching implications given its speed, cost, and scalability compared to conventional approaches. This contribution presents the application of PCA and PLS as part of a computational pipeline approach to predict the amount of a deposited mineral of interest using FTIR data. For this specific application, we have developed the model to analyze RCS, although this process can be implemented in the analysis of any IR-active mineral, and this pipeline applied to any FTIR data.

A novel sampling cassette for field-based analysis of respirable crystalline silica.

Field-based methods for the analysis of respirable crystalline silica are now possible with the availability of portable instrumentation. Such methods also require the use of cassettes that facilitate direct-on-filter analysis of field samples. Conventional sampling cassettes can be modified such that they are amenable to direct-on-filter analysis while remaining compatible with common respirable dust samplers. The required modifications are described herein, and one version of such an analysis-ready cassette is described and evaluated in comparison to more traditional cassette designs. The novel cassette was found to result in a slightly higher mass of collected respirable material (for the same sampling duration), though this is likely due to the conductive material of the cassettes, which prevents particle wall losses in comparison to the more commonly used styrene cassette material. Both types of cassettes demonstrated comparable predictability in terms of respirable crystalline silica in a sample.

Performance Comparison of Four Portable FTIR Instruments for Direct-on-Filter Measurement of Respirable Crystalline Silica.

Exposure to dusts containing respirable crystalline silica is a recognized hazard affecting various occupational groups such as miners. Inhalation of respirable crystalline silica can lead to silicosis, which is a potentially fatal lung disease. Currently, miners' exposure to respirable crystalline silica is assessed by collecting filter samples that are sent for laboratory analysis. A more timely field-based silica monitoring method using direct-on-filter (DoF) analysis is being developed by researchers at the National Institute for Occupational Safety and Health (NIOSH) to provide mine operators with the option to evaluate miners' exposure at the mine. This field-based silica monitoring technique involves the use of portable Fourier transform infrared (FTIR) instruments. As a step in the development of this new analytical technique, four commercially available portable FTIR instruments were evaluated for their ability to provide reproducible measurements from filter samples containing respirable crystalline silica. Reported testing indicates that measurements varied within ±4.1% between instruments for filter samples that contained high-purity respirable crystalline silica. Measurements varied within ±3.0% between instruments for filter samples that contained varying mineral composition. Filter samples were repeatedly analyzed by the same instrument over short and extended periods of time, and mean coefficients of variation did not exceed ±1.6 and ±2.4%, respectively. Mixed model analysis revealed that there was no statistically significant (P < 0.05) change in average measurements made over an extended period of time for all instruments. Results suggest that each of the four FTIR instruments evaluated in this study were able to generate precise and reproducible DoF analysis results of respirable dust samples.

Use of the Field-Based Silica Monitoring Technique in a Coal Mine: A Case Study.

Exposure to respirable crystalline silica (RCS) can cause serious and irreparable negative health effects, including silicosis and lung cancer. Workers in coal mines have the potential of being exposed to RCS found in dust generated by various mining processes. The silica content of respirable dust in one single mine can vary substantially over both time and location. The current monitoring approach for RCS relies on the use of traditional air sampling followed by laboratory analysis. Results generated using this approach are generally not available for several days to several weeks after sampling, and this delay prevents timely and effective intervention if needed. An alternate analytical method is needed to reduce the time required to quantify the RCS exposure of mine workers. The National Institute for Occupational Safety and Health (NIOSH) has developed a new method using commercially available portable infrared spectrometers for measuring RCS at the end of the sampling shift. This paper will describe the application of the new field-based RCS analytical process for coal mines, including the use of the new method with the existing Coal Mine Dust Personal Sampler Unit. In a case study conducted by NIOSH with a coal mine operator in West Virginia, field-based RCS analysis was completed at a mine site to evaluate the new technique. The RCS analysis results obtained by the field-based method in this case study showed sufficiently strong correlation with results obtained by the MSHA standard laboratory analysis method to allow the mine operator to use the field-based method for evaluating process improvements.

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).

Separating spatial patterns in pollution attributable to woodsmoke and other sources, during daytime and nighttime hours, in Christchurch, New Zealand.

During winter nights, woodsmoke may be a predominant source of air pollution, even in cities with many sources. Since two major earthquakes resulted in major structural damage in 2010 and 2011, reliance on woodburning for home heating has increased substantially in Christchurch, New Zealand (NZ), along with intensive construction/demolition activities. Further, because NZ is a relatively isolated western country, it offers the unique opportunity to disentangle multiple source impacts in the absence of long-range transport pollution. Finally, although many spatial saturation studies have been published, and levoglucosan is an established tracer for woodburning emissions, few studies have monitored multiple sites simultaneously for this or other organic constituents, with the ability to distinguish spatial patterns for daytime vs. nighttime hours, in complex urban settings. We captured seven-day integrated samples of PM2.5, and elemental and organic tracers of woodsmoke and diesel emissions, during "daytime" (7 a.m. - 5:30 p.m.) and "nighttime" (7 p.m. - 5:30 a.m.) hours, at nine sites across commercial and residential areas, over three weeks in early winter (May 2014). At a subset of six sites, we also sampled during hypothesized "peak" woodburning hours (7 p.m. - 12 a.m.), to differentiate emissions during "active" residential woodburning, vs. overnight smouldering. Concentrations of PM2.5 were, on average, were twice as high during nighttime than daytime [µâ€¯= 18.4 (SD = 6.13) vs. 9.21 (SD = 6.13) µg/m3], with much greater differences in woodsmoke tracers (i.e., levoglucosan [µâ€¯= 1.83 (SD = 0.82) vs. 0.34 (SD = 0.17) µg/m3], potassium) and indicators of treated- or painted-wood burning (e.g., arsenic, lead). Only nitrogen dioxide, calcium, iron, and manganese (tracers of vehicular emissions) were higher during daytime. Levoglucosan and most PAHs were higher during "active" woodburning, vs. overnight smouldering. Our time-stratified spatial saturation detected strong spatial variability throughout the study area, which distinctly differed during daytime vs. night time hours, and quantified the substantial contribution of woodsmoke to overnight spatial variation in PM2.5 across Christchurch. Daytime vs. nighttime differences were greater than those observed across sites. Traffic, especially diesel, contributed substantially to daytime NO2 and spatial gradients in non-woodsmoke constituents.

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.

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.

A comparison of respirable crystalline silica concentration measurements using a direct-on-filter Fourier transform infrared (FT-IR) transmission method vs. a traditional laboratory X-ray diffraction method.

Evaluation and control of respirable crystalline silica (RCS) exposures are critical components of an effective mine industrial hygiene program. To provide more timely exposure data in the field, an end-of-shift Fourier transform infrared (FT-IR) spectrometry method has been developed for evaluation of direct-on-filter RCS. The present study aimed to apply this FT-IR method using field samples collected in three Northwestern U.S. metal/nonmetal mines and compare the results to traditional laboratory X-ray diffraction analysis (XRD). Seventy-five dust samples were analyzed using both methods. Samples for each mine were split in half by random assignment, with half used to create a calibration factor for the FT-IR analysis and half used to apply the calibration. Nonparametric correlational and two-sample comparative tests were used to assess the strength of association and the level of agreement between the two methods. Strong, positive correlations were observed between FT-IR and XRD RCS concentrations, with Spearman rank correlation coefficients ranging between 0.84 and 0.97. The mean RCS concentrations determined through FT-IR analysis were lower than through XRD analysis, with mean differences ranging from -4 to -133 ug/m3 and mean percent errors ranging from 12% to 28%. There was a statistically significant improvement in the level of agreement between log FT-IR and log XRD RCS concentrations following calibration at two of the three mines, with mean differences of -0.03 (p = 0.002) and -0.02 (p = 0.044) in the log scale. The reduction in mean difference following calibration at the other mine was not statistically significant (mean log scale difference = -0.05, p = 0.215), but the differences between FT-IR and XRD were not significantly different without calibration (mean log scale difference = -0.07, p = 0.534). The results indicate that mine-specific calibration factors can improve the level of agreement between RCS concentrations determined via a field-based, end-of-shift FT-IR method in metal/non-metal mines as compared to traditional XRD analysis.

Evaluating the use of a field-based silica monitoring approach with dust from copper mines.

Monitoring worker exposure to respirable crystalline silica in dusty environments is an important part of a proactive health and safety program. This is the case for surface copper mines in Arizona and New Mexico. The spatial and temporal variability of respirable dust and crystalline silica concentrations in those mines, coupled with the time lapse in obtaining crystalline silica analysis results from accredited laboratories, present a challenge for an effective exposure monitoring approach and the resulting intervention strategies. The National Institute for Occupational Safety and Health (NIOSH) is developing a novel approach to be used at a mine site for the quantification of crystalline silica in respirable dust samples collected with traditional sampling techniques. The non-destructive analysis is carried out using a portable Fourier transform infrared spectroscopy (FTIR) unit. In this study, respirable dust samples were collected over two visits to each of five copper mines, for a total of 10 datasets. The silica in each respirable dust sample was estimated by analyzing the sample with the portable FTIR unit. The quality of the estimation was assessed using the results of the NIOSH 7500 method on the same samples. The confounding effect of other minerals present in the respirable dust in the mines was also assessed, and two quantification approaches were investigated to address it: a sector-specific and a mine-specific approach. The results showed that the sector-specific approach is not effective due to the high variability of relative composition of the minerals among mines. For this approach the combined average relative difference was -13% (-17.6%, -8.9% CI). When using the mine-specific quantification approach, the average relative difference was as low as 2.8% (-3.7%, 9.3% CI); however, this approach was still affected by the variable relative composition of the minerals in the dust in each mine. The use of a multivariate approach on the analysis of each sample was proposed as the next step to achieve consistent low relative differences. This study demonstrates the potential of using a portable FTIR for estimation of crystalline silica in respirable dust samples for in-field exposure monitoring.

Association of IL-6 with PM2.5 Components: Importance of Characterizing Filter-Based PM2.5 Following Extraction.

Filter-based toxicology studies are conducted to establish the biological plausibility of the well-established health impacts associated with fine particulate matter (PM2.5) exposure. Ambient PM2.5 collected on filters is extracted into solution for toxicology applications, but frequently, characterization is nonexistent or only performed on filter-based PM2.5, without consideration of compositional differences that occur during the extraction processes. To date, the impact of making associations to measured components in ambient instead of extracted PM2.5 has not been investigated. Filter-based PM2.5 was collected at locations (n = 5) and detailed characterization of both ambient and extracted PM2.5 was performed. Alveolar macrophages (AMJ2-C11) were exposed (3, 24, and 48 h) to PM2.5 and the pro-inflammatory cytokine interleukin (IL)-6 was measured. IL-6 release differed significantly between PM2.5 collected from different locations; surprisingly, IL-6 release was highest following treatment with PM2.5 from the lowest ambient concentration location. IL-6 was negatively correlated with the sum of ambient metals analyzed, as well as with concentrations of specific constituents which have been previously associated with respiratory health effects. However, positive correlations of IL-6 with extracted concentrations indicated that the negative associations between IL-6 and ambient concentrations do not accurately represent the relationship between inflammation and PM2.5 exposure. Additionally, seven organic compounds had significant associations with IL-6 release when considering ambient concentrations, but they were not detected in the extracted solution. Basing inflammatory associations on ambient concentrations that are not necessarily representative of in vitro exposures creates misleading results; this study highlights the importance of characterizing extraction solutions to conduct accurate health impact research.

Characterizing Particle Size Distributions of Crystalline Silica in Gold Mine Dust.

Dust containing crystalline silica is common in mining environments in the U.S. and around the world. The exposure to respirable crystalline silica remains an important occupational issue and it can lead to the development of silicosis and other respiratory diseases. Little has been done with regard to the characterization of the crystalline silica content of specific particle sizes of mine-generated dust. Such characterization could improve monitoring techniques and control technologies for crystalline silica, decreasing worker exposure to silica and preventing future incidence of silicosis. Three gold mine dust samples were aerosolized in a laboratory chamber. Particle size-specific samples were collected for gravimetric analysis and for quantification of silica using the Microorifice Uniform Deposit Impactor (MOUDI). Dust size distributions were characterized via aerodynamic and scanning mobility particle sizers (APS, SMPS) and gravimetrically via the MOUDI. Silica size distributions were constructed using gravimetric data from the MOUDI and proportional silica content corresponding to each size range of particles collected by the MOUDI, as determined via X-ray diffraction and infrared spectroscopic quantification of silica. Results indicate that silica does not comprise a uniform proportion of total dust across all particle sizes and that the size distributions of a given dust and its silica component are similar but not equivalent. Additional research characterizing the silica content of dusts from a variety of mine types and other occupational environments is necessary in order to ascertain trends that could be beneficial in developing better monitoring and control strategies.

Spatial variation in diesel-related elemental and organic PM2.5 components during workweek hours across a downtown core.

Capturing intra-urban variation in diesel-related pollution exposures remains a challenge, given its complex chemical mix, and relatively few well-characterized ambient-air tracers for the multiple diesel sources in densely-populated urban areas. To capture fine-scale spatial resolution (50×50m grid cells) in diesel-related pollution, we used geographic information systems (GIS) to systematically allocate 36 sampling sites across downtown Pittsburgh, PA, USA (2.8km2), cross-stratifying to disentangle source impacts (i.e., truck density, bus route frequency, total traffic density). For buses, outbound and inbound trips per week were summed by route and a kernel density was calculated across sites. Programmable monitors collected fine particulate matter (PM2.5) samples specific to workweek hours (Monday-Friday, 7 am-7 pm), summer and winter 2013. Integrated filters were analyzed for black carbon (BC), elemental carbon (EC), organic carbon (OC), elemental constituents, and diesel-related organic compounds [i.e., polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes]. To our knowledge, no studies have collected this suite of pollutants with such high sampling density, with the ability to capture spatial patterns during specific hours of interest. We hypothesized that we would find substantial spatial variation for each pollutant and significant associations with key sources (e.g. diesel and gasoline vehicles), with higher concentrations near the center of this small downtown core. Using a forward stepwise approach, we developed seasonal land use regression (LUR) models for PM2.5, BC, total EC, OC, PAHs, hopanes, steranes, aluminum (Al), calcium (Ca), and iron (Fe). Within this small domain, greater concentration differences were observed in most pollutants across sites, on average, than between seasons. Higher PM2.5 and BC concentrations were found in the downtown core compared to the boundaries. PAHs, hopanes, and steranes displayed different spatial patterning across the study area by constituent. Most LUR models suggested a strong influence of bus-related emissions on pollution gradients. Buses were more dominant predictors compared to truck and vehicular traffic for several pollutants. Overall, we found substantial variation in diesel-related concentrations in a very small downtown area, which varied across elemental and organic components.

Spatial variation in inversion-focused vs 24-h integrated samples of PM2.5 and black carbon across Pittsburgh, PA.

A growing literature explores intra-urban variation in pollution concentrations. Few studies, however, have examined spatial variation during "peak" hours of the day (e.g., rush hours, inversion conditions), which may have strong bearing for source identification and epidemiological analyses. We aimed to capture "peak" spatial variation across a region of complex terrain, legacy industry, and frequent atmospheric inversions. We hypothesized stronger spatial contrast in concentrations during hours prone to atmospheric inversions and heavy traffic, and designed a 2-year monitoring campaign to capture spatial variation in fine particles (PM2.5) and black carbon (BC). Inversion-focused integrated monitoring (0600-1100 hours) was performed during year 1 (2011-2012) and compared with 1-week 24-h integrated results from year 2 (2012-2013). To allocate sampling sites, we explored spatial distributions in key sources (i.e., traffic, industry) and potential modifiers (i.e., elevation) in geographic information systems (GIS), and allocated 37 sites for spatial and source variability across the metropolitan domain (~388 km(2)). Land use regression (LUR) models were developed and compared by pollutant, season, and sampling method. As expected, we found stronger spatial contrasts in PM2.5 and BC using inversion-focused sampling, suggesting greater differences in peak exposures across urban areas than is captured by most integrated saturation campaigns. Temporal variability, commercial and industrial land use, PM2.5 emissions, and elevation were significant predictors, but did not more strongly predict concentrations during peak hours.

Characterization of ambient and extracted PM2.5 collected on filters for toxicology applications.

Research on the health effects of fine particulate matter (PM2.5) frequently disregards the differences in particle composition between that measured on an ambient filter versus that measured in the corresponding extraction solution used for toxicological testing. This study presents a novel method for characterizing the differences, in metallic and organic species, between the ambient samples and the corresponding extracted solutions through characterization of extracted PM2.5 suspended on filters. Removal efficiency was found to be 98.0 ± 1.4% when measured using pre- and post-removal filter weights, however, this efficiency was significantly reduced to 80.2 ± 0.8% when measured based on particle mass in the extraction solution. Furthermore, only 47.2 ± 22.3% of metals and 24.8 ± 14.5% of organics measured on the ambient filter were found in the extraction solution. Individual metallic and organic components were extracted with varying efficiency, with many organics being lost entirely during extraction. Finally, extraction efficiencies of specific PM2.5 components were inversely correlated with total mass. This study details a method to assess compositional alterations resulting from extraction of PM2.5 from filters, emphasizing the need for standardized procedures that maintain compositional integrity of ambient samples for use in toxicology studies of PM2.5.

Identifying Perceived Neighborhood Stressors Across Diverse Communities in New York City.

There is growing interest in the role of psychosocial stress in health disparities. Identifying which social stressors are most important to community residents is critical for accurately incorporating stressor exposures into health research. Using a community-academic partnered approach, we designed a multi-community study across the five boroughs of New York City to characterize resident perceptions of key neighborhood stressors. We conducted 14 community focus groups; two to three in each borough, with one adolescent group and one Spanish-speaking group per borough. We then used systematic content analysis and participant ranking data to describe prominent neighborhood stressors and identify dominant themes. Three inter-related themes regarding the social and structural sources of stressful experiences were most commonly identified across neighborhoods: (1) physical disorder and perceived neglect, (2) harassment by police and perceived safety and (3) gentrification and racial discrimination. Our findings suggest that multiple sources of distress, including social, political, physical and economic factors, should be considered when investigating health effects of community stressor exposures and psychological distress. Community expertise is essential for comprehensively characterizing the range of neighborhood stressors that may be implicated in psychosocial exposure pathways.

Saturation sampling for spatial variation in multiple air pollutants across an inversion-prone metropolitan area of complex terrain.

BACKGROUND: Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients. METHODS: We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season. RESULTS: During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants. CONCLUSIONS: Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling.

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.