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The indoor air quality of a residential home during winter in Fairbanks, Alaska, was investigated and contrasted with outdoor levels. Twenty-four-hour average indoor and outdoor filter samples were collected from January 17 to February 25, 2022, in a residential area with high outdoor PM2.5 concentrations. The oxidative potential of PM2.5 was determined using the dithiothreitol-depletion assay (OPDTT). For the unoccupied house, the background indoor-to-outdoor (I/O) ratio of mass-normalized OP (OPmDTT), a measure of the intrinsic health- relevant properties of the aerosol, was less than 1 (0.53 ± 0.37), implying a loss of aerosol toxicity as air was transported indoors. This may result from transport and volatility losses driven by the large gradients in temperature (average outdoor temperature of −19°C/average indoor temperature of 21 °C) or relative humidity (average outdoor RH of 78%/average indoor RH of 11%), or both. Various indoor activities, including pellet stove use, simple cooking experiments, incense burning, and mixtures of these activities, were conducted. The experiments produced PM2.5 with a highly variable OPmDTT. PM2.5 from cooking emissions had the lowest OP values, while pellet stove PM2.5 had the highest. Correlations between volume-normalized OPDTT (OPvDTT), relevant to exposure, and indoor PM2.5 mass concentration during experiments were much lower compared to those in outdoor environments. This suggests that mass concentration alone can be a poor indicator of possible adverse effects of various indoor emissions. These findings highlight the importance of considering both the quantity of particles and sources (chemical composition), as health metrics for indoor air quality. 

The oxidative potential (OP) of outdoor PM2.5 in wintertime Fairbanks, Alaska, is investigated and compared to those in wintertime Atlanta and Los Angeles. Approximately 40 filter samples collected in January−February 2022 at a Fairbanks residential site were analyzed for OP utilizing dithiothreitol-depletion (OPDTT) and hydroxylgeneration (OPOH) assays. The study-average PM2.5 mass concentration was 12.8 μg/m3, with a 1 h average maximum of 89.0 μg/m3. Regression analysis, correlations with source tracers, and contrast between cold and warmer events indicated that OPDTT was mainly sensitive to copper, elemental carbon, and organic aerosol from residential wood burning, and OPOH to iron and organic aerosol from vehicles. Despite low photochemically-driven oxidation rates, the water-soluble fraction of OPDTT was unusually high at 77%, mainly from wood burning emissions. In contrast to other locations, the Fairbanks average PM2.5 mass concentration was higher than Atlanta and Los Angeles, whereas OPDTT in Fairbanks and Atlanta were similar, and Los Angeles had the highest OPDTT and OPOH. Site differences were observed in OP when normalized by both the volume of air sampled and the particle mass concentration, corresponding to exposure and the intrinsic health-related properties of PM2.5, respectively. The sensitivity of OP assays to specific aerosol components and sources can provide insights beyond the PM2.5 mass concentration when assessing air quality. 

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.