Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

EVALUATION AND QUANTIFICATION OF HEAT HOTSPOTS IN THE CHAPADA DOS GUIMARÃES-MT NATIONAL PARK

The study aimed to analyze the spatial distribution of heat sources inside and outside the Chapada dos Guimarães National Park (PNCG) in the State of Mato Grosso. The analyzes were performed through the estimate of kernel density (KDE) and Ripley's K function from 2005 to 2014. The data related to the number of hot spots were obtained from the National Institute for Space Research (INPE) from 2005 to 2014, and the vector files were acquired from the cartographic base of the Brazilian Institute of Geography and Statistics (IBGE). In the 10 years of analysis, 579 hot spots were detected in the PNCG, where it was found that the months of August and September had the highest incidence of hot spots in the park. The kernel maps demonstrated that most hotspots were observed in the years 2007, 2010 and 2012. The years 2005 and 2013 presented average densities and the years 2006, 2008, 2009, 2011 and 2014 indicated low density of the hot spots. Ripley's K function, calculated to observe the spatial distribution of the hot spots, rejected the hypothesis of complete spatial randomness (CSR), indicating that they showed a tendency to cluster during the study time series at the PNCG.

Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

With climate change, significant fluctuations in wildfires have been observed on the Mongolian Plateau. The ability to predict the distribution of wildfires in the context of climate change plays a critical role in wildfire management and ecosystem maintenance. In this paper, Ripley’s K function and a Random Forest (RF) model were applied to analyse the spatial patterns and main influencing factors affecting the occurrence of wildfire on the Mongolian Plateau. The results showed that the wildfires were mainly clustered in space due to the combination of influencing factors. The distance scale is less than 1/2 of the length of the Mongolian Plateau; that is, it does not experience boundary effects in the study area and it meets the requirements of Ripley’s K function. Among the driving factors, the fraction of vegetation coverage (FVC), land use degree (La), elevation, precipitation (pre), wet day frequency (wet), and maximum temperature (tmx) had the greatest influences, while the aspect had the lowest influence. The likelihood of fire was mainly concentrated in the northern, eastern, and southern parts of the Mongolian Plateau and in the border area between the Inner Mongolia Autonomous Region (Inner Mongolia) and Mongolian People’s Republic (Mongolia), and wildfires did not occur or occurred less frequently in the hinterland area. The fitting results of the RF model showed a prediction accuracy exceeding 90%, which indicates that the model has a high ability to predict wildfire occurrences on the Mongolian Plateau. This study can provide a reference for predictions and decision-making related to wildfires on the Mongolian Plateau.