The Spatial Variation of Dust Particulate Matter Concentrations during Two Icelandic Dust Storms in 2015

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20 μm) mixing ratio, vertically integrated mass of dust aerosol (9-20 μm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods

Glacier surface facies are valuable indicators of changes experienced by a glacial system. The interplay of accumulation and ablation facies, followed by intermixing with dust and debris, as well as the local climate, all induce observable and mappable changes on the supraglacial terrain. In the absence or lag of continuous field monitoring, remote sensing observations become vital for maintaining a constant supply of measurable data. However, remote satellite observations suffer from atmospheric effects, resolution disparity, and use of a multitude of mapping methods. Efficient image-processing routines are, hence, necessary to prepare and test the derivable data for mapping applications. The existing literature provides an application-centric view for selection of image processing schemes. This can create confusion, as it is not clear which method of atmospheric correction would be ideal for retrieving facies spectral reflectance, nor are the effects of pansharpening examined on facies. Moreover, with a variety of supervised classifiers and target detection methods now available, it is prudent to test the impact of variations in processing schemes on the resultant thematic classifications. In this context, the current study set its experimental goals. Using very-high-resolution (VHR) WorldView-2 data, we aimed to test the effects of three common atmospheric correction methods, viz. Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH); and two pansharpening methods, viz. Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on thematic classification of facies using 12 supervised classifiers. The conventional classifiers included: Mahalanobis Distance (MHD), Maximum Likelihood (MXL), Minimum Distance to Mean (MD), Spectral Angle Mapper (SAM), and Winner Takes All (WTA). The advanced/target detection classifiers consisted of: Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), Matched Filtering (MF), Mixture-Tuned Matched Filtering (MTMF), Mixture-Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF), Orthogonal Space Projection (OSP), and Target-Constrained Interference-Minimized Filter (TCIMF). This experiment was performed on glaciers at two test sites, Ny-Ålesund, Svalbard, Norway; and Chandra–Bhaga basin, Himalaya, India. The overall performance suggested that the FLAASH correction delivered realistic reflectance spectra, while DOS delivered the least realistic. Spectra derived from HCS sharpened subsets seemed to match the average reflectance trends, whereas GS reduced the overall reflectance. WTA classification of the DOS subsets achieved the highest overall accuracy (0.81). MTTCIMF classification of the FLAASH subsets yielded the lowest overall accuracy of 0.01. However, FLAASH consistently provided better performance (less variable and generally accurate) than DOS and QUAC, making it the more reliable and hence recommended algorithm. While HCS-pansharpened classification achieved a lower error rate (0.71) in comparison to GS pansharpening (0.76), neither significantly improved accuracy nor efficiency. The Ny-Ålesund glacier facies were best classified using MXL (error rate = 0.49) and WTA classifiers (error rate = 0.53), whereas the Himalayan glacier facies were best classified using MD (error rate = 0.61) and WTA (error rate = 0.45). The final comparative analysis of classifiers based on the total error rate across all atmospheric corrections and pansharpening methods yielded the following reliability order: MXL > WTA > MHD > ACE > MD > CEM = MF > SAM > MTMF = TCIMF > OSP > MTTCIMF. The findings of the current study suggested that for VHR visible near-infrared (VNIR) mapping of facies, FLAASH was the best atmospheric correction, while MXL may deliver reliable thematic classification. Moreover, an extensive account of the varying exertions of each processing scheme is discussed, and could be transferable when compared against other VHR VNIR mapping methods.

Potential Source Contribution Function Analysis of High Latitude Dust Sources over the Arctic: Preliminary Results and Prospects

The results of a preliminary investigation of the dust sources in the Arctic based on their geochemical properties by potential source contribution function (PSCF) analysis are presented in this paper. For this purpose, we considered one year of aerosol geochemical data from Ny-Ålesund, Svalbard, and a short list of chemical elements (i.e., Al, Fe, Mn, Ti, Cr, V, Ni, Cu, and Zn) variably related to the dust fraction. Based on PSCF analysis: (i) four different dust source areas (i.e., Eurasia, Greenland, Arctic-Alaska, and Iceland) were characterized by distinguishing geochemical ranges and seasonal occurrence; and (ii) a series of typical dust days from the distinct source areas were identified based on the corresponding back trajectory patterns. Icelandic dust samples revealed peculiar but very variable characteristics in relation to their geographical source regions marked by air mass back trajectories. The comparison between pure and mixed Icelandic dust samples (i.e., aerosols containing Icelandic dust along with natural and/or anthropogenic components) revealed the occurrence of different mixing situations. Comparison with Icelandic soils proved the existence of dilution effects related to the emission and the transport processes.

Vertical distribution of aerosols in dust storms during the Arctic winter

High Latitude Dust (HLD) contributes 5% to the global dust budget, but HLD measurements are sparse. Dust observations from Iceland provide dust aerosol distributions during the Arctic winter for the first time, profiling dust storms as well as clean air conditions. Five winter dust storms were captured during harsh conditions. Mean number concentrations during the non-dust flights were <5 particles cm⁻³ for the particles 0.2–100 µm in diameter and >40 particles cm⁻³ during dust storms. A moderate dust storm with >250 particles cm⁻³ (2 km altitude) was captured on 10^th January 2016 as a result of sediments suspended from glacial outburst flood Skaftahlaup in 2015. Similar concentrations were reported previously in the Saharan air layer. Detected particle sizes were up to 20 µm close to the surface, up to 10 µm at 900 m altitude, up to 5 µm at 5 km altitude, and submicron at altitudes >6 km. Dust sources in the Arctic are active during the winter and produce large amounts of particulate matter dispersed over long distances and high altitudes. HLD contributes to Arctic air pollution and has the potential to influence ice nucleation in mixed-phase clouds and Arctic amplification.

Responding to Climate and Environmental Change Impacts on Human Health via Integrated Surveillance in the Circumpolar North: A Systematic Realist Review

Environments are shifting rapidly in the Circumpolar Arctic and Subarctic regions as a result of climate change and other external stressors, and this has a substantial impact on the health of northern populations. Thus, there is a need for integrated surveillance systems designed to monitor the impacts of climate change on human health outcomes as part of broader adaptation strategies in these regions. This review aimed to identify, describe, and synthesize literature on integrated surveillance systems in Circumpolar Arctic and Subarctic regions, that are used for research or practice. Following a systematic realist review approach, relevant articles were identified using search strings developed for MEDLINE® and Web of Science™ databases, and screened by two independent reviewers. Articles that met the inclusion criteria were retained for descriptive quantitative analysis, as well as thematic qualitative analysis, using a realist lens. Of the 3431 articles retrieved in the database searches, 85 met the inclusion criteria and were analyzed. Thematic analysis identified components of integrated surveillance systems that were categorized into three main groups: structural, processual, and relational components. These components were linked to surveillance attributes and activities that supported the operations and management of integrated surveillance. This review advances understandings of the distinct contributions of integrated surveillance systems and data to discerning the nature of changes in climate and environmental conditions that affect population health outcomes and determinants in the Circumpolar North. Findings from this review can be used to inform the planning, design, and evaluation of integrated surveillance systems that support evidence-based public health research and practice in the context of increasing climate change and the need for adaptation.