Self-reported exposure to dust and diesel exhaust, respiratory symptoms, and use of respiratory protective equipment among Arctic miners

Arctic miners face significant risks from diesel exhaust and dust exposure, potentially leading to adverse respiratory health. Employers must limit harmful exposures, using personal protective equipment (PPE) as a last line of defense. This study explored the association between reported respiratory exposure and symptoms, and PPE training and usage. Data from the MineHealth study (2012-2014) included a total of 453 Arctic open pit miners in Norway, Sweden, and Finland. Participants answered questions on exposure to dust and diesel exhaust, respiratory symptoms, and PPE use, in addition to age, gender, BMI, smoking, and self-rated health. Estimated exposure to dust was common, reported by 91%, 80%, and 82% and that of diesel exhaust by 84%, 43%, and 47% of workers in Sweden, Finland, and Norway, respectively. Reported dust exposure was significantly related to respiratory symptoms (OR 2.2, 95% CI 1.3-3.7), diesel exposure increased the occurrence of wheezing (OR 2.6, 95% CI 1.3-5.4). PPE use varied between the studied mines. Non-use was common and related to reduced visibility, wetness, skin irritation and fogging of the respiratory PPE. Future research should employ more precise exposure assessment, respiratory function as well as explore the reasons behind the non-compliance of PPE use.

A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.

Habitat Loss Does Not Always Entail Negative Genetic Consequences

Although habitat loss has large, consistently negative effects on biodiversity, its genetic consequences are not yet fully understood. This is because measuring the genetic consequences of habitat loss requires accounting for major methodological limitations like the confounding effect of habitat fragmentation, historical processes underpinning genetic differentiation, time-lags between the onset of disturbances and genetic outcomes, and the need for large numbers of samples, genetic markers, and replicated landscapes to ensure sufficient statistical power. In this paper we overcame all these challenges to assess the genetic consequences of extreme habitat loss driven by mining in two herbs endemic to Amazonian savannas. Relying on genotyping-by-sequencing of hundreds of individuals collected across two mining landscapes, we identified thousands of neutral and independent single-nucleotide polymorphisms (SNPs) in each species and used these to evaluate population structure, genetic diversity, and gene flow. Since open-pit mining in our study region rarely involves habitat fragmentation, we were able to assess the independent effect of habitat loss. We also accounted for the underlying population structure when assessing landscape effects on genetic diversity and gene flow, examined the sensitivity of our analyses to the resolution of spatial data, and used annual species and cross-year analyses to minimize and quantify possible time-lag effects. We found that both species are remarkably resilient, as genetic diversity and gene flow patterns were unaffected by habitat loss. Whereas historical habitat amount was found to influence inbreeding; heterozygosity and inbreeding were not affected by habitat loss in either species, and gene flow was mainly influenced by geographic distance, pre-mining land cover, and local climate. Our study demonstrates that it is not possible to generalize about the genetic consequences of habitat loss, and implies that future conservation efforts need to consider species-specific genetic information.