Observations from 1980 to 2013 of 20 aerosol constituents, ozone and mercury at Alert, Canada (82.50°N, 62.35°W), were analyzed for trends and dominant factors of the Arctic haze during winter and spring. Trends reflect changing emissions in Eurasia, the main source region for surface pollution in the high Arctic. SO₄²⁻, H⁺, NH₄⁺_, K⁺, Cu, Ni, Pb, Zn, nonsoil V, nonsoil Mn, and equivalent black carbon decreased between 23% and 80% as emissions declined rapidly in northern Eurasia during the early 1990s. NO₃⁻ increased by 20% as aerosol acidity declined. Metals were linked to emissions from smelting and fossil fuel combustion. In winter, ozone increased by 5% over 23 years, consistent with other observations and global modeling. Twelve PMF factors emerged for the dark period (November to February) and 13 for the light period (March to May). Eleven PMF factors are common to both dark and light, a twelfth factor was associated with sulfate in the dark and nitrate in the light, and the thirteenth (light period) was related to ozone and gaseous mercury depletion near Alert. IODINE and NITRATE factors, important for Arctic chemistry, changed with sunlight. In the light, 50% of all NO₃⁻ was on the NITRATE factor, while in the dark, most was associated with MODIFIED SEA SALT and equivalent black carbon. In the dark (light), 90% (28%) of iodine were found on the factor IODINE and 58% associated with SEA‐SALT and MODIFIED SEA‐SALT. These results help in understanding the role of atmospheric chemistry in weather and climate processes.

Both routine radiation surveys and incident response--as currently performed by human workers--are time-consuming and involve significant, potentially unanticipated radiation dose (especially for accident response where levels prohibit human presence entirely). Previous efforts addressed routine surveys using robotic systems, but they must also localize and characterize discrete sources when anomalies occur. To be effective, characterization must account for real-world complications, including multiple sources, environmental attenuation, spatial localization, and isotopic identification. Recursive Bayesian estimation (RBE) using grid-based estimators and particle filters has been previously investigated, but we eliminate previous open-space assumptions by including attenuation models and sensor position height in the sequential sampling strategy. The method also incorporates autonomous isotopic identification via gamma spectroscopy, which supports the attenuation modeling and improves the computational efficiency in multisource cases. In addition, survey points are autonomously optimized using the Fisher information. A radioactive decay model is implemented to generalize the method by addressing short-lived nuclides. The developed hardware is evaluated in multiple scenarios where no operator intervention is required, effectively eliminating operator dose uptake from localization/characterization in real-world scenarios. Two different mobile robots are used, demonstrating the portability of the data collection and analysis software. Also, the generalized complexity is documented to support planning in future scenarios.