RESUMO
Variations in the carbonaceous aerosol contents, organic carbon (OC) and elemental carbon (EC), in particulate matter less than 10 µm in size (PM10), were analyzed at sites influenced by coal mining in an open-pit mine located in northern Colombia. Samples were collected during different seasonal periods throughout 2015. Meteorological variables for each site were examined during the different seasons. Aerosols were detected using a thermal-optical reflectance protocol method. The highest PM10 concentrations, between the ranges of 28.2 ± 8.2 µg m-3 and 75.0 ± 36.5 µg m-3, were recorded during the dry season. However, the highest concentrations of OC (4.8-14.2 µg m-3) and EC (2.9-13.9 µg m-3) in PM10 were observed during the transition period between the dry and wet seasons. The strong correlation between OC and EC in PM10 (r = 0.6-1.0) during the transition season indicates a common primary combustion source. High OC (> 8.3 µg m-3) and EC (> 6.9 µg m-3) concentrations were associated with low wind speeds (< 2.1 m s-1) moving in different directions. Analyses of the sources of atmospheric aerosol pollutants in the mining area in northern Colombia showed that the daily maximum total carbon concentrations were mainly associated with regional atmospheric transport of particulate matter from industrial areas and biomass burning sites located in the territory of Venezuela.
Assuntos
Poluentes Atmosféricos , Aerossóis/análise , Poluentes Atmosféricos/análise , Carbono/análise , China , Carvão Mineral/análise , Monitoramento Ambiental , Tamanho da Partícula , Material Particulado/análise , Estações do Ano , VentoRESUMO
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.
RESUMO
This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance. To validate the proposal, this framework is applied to solve three different [Formula: see text]-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.