The objective of this work is to examine the spatial distribution of Continental Surface Temperature (CST) of the urban area of Belem / PA and the influence of the change of use and soil cover from remote sensing techniques. Products from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) sensors coupled, respectively, to Landsat 5 and 8 satellites were used. The images acquired from the years 1994, 2008 and 2017 were processed, resampled (spatial resolution of 120 meters) and, finally, centroids were extracted with a total of 1252 points, using the Quantum GIS software. Subsequently, spectral indices, NDVI, NDBI and albedo were calculated, which represent, respectively, the presence of vegetation, exposed soil or built area and reflectivity rate. The results showed that CST showed an increase in all sectors of the study area, mainly between the years 2008 and 2017. The sector with the highest elevation of the CST was the urban center, as it presented values below 25.0 ºC in the image of 1994 and above 35.0 ºC in the 2017 image. In contrast, the ecological park sector showed the lowest increase in CST, from 20.0 ºC (1994) to 25.0 ºC (2017). According to the analysis of the spectral indices, the intensification of CST is directly associated with the strong territorial expansion, since from the NDVI values the degradation of the vegetation cover was noted. This degradation is observed in the comparisons of the images, in which it is possible to verify the decrease in the NDVI values in the entire study area, whose values represent the decrease in the vegetation cover. The sector with the greatest withdrawal of green areas was the northern zone, as it showed a drop in NDVI values, from 0.7 in 1994 to 0.3 in the 2017 image. It was also observed that the density of the constructed area was intensified, presenting increasing values of NDBI. Added to these NDVI and NDBI values, higher reflectivity rate values were noted, whose values in the urban center of Belem in 1994 were 0.1% and which exceeded 0.5% in the image for the year 2017, ratifying the impact of changes in land cover and the direct association between changes in the environment and CST. In general, the results indicate that the uncontrolled expansion of the urban process and the change in land cover cause the intensification of CST.
Esta obra está licenciada com uma Licença Creative Commons Atribuição 4.0 Internacional (CC BY 4.0).O conteúdo desta obra e seus dados em sua forma, correç ão e confiabilidade são de responsabilidade exclusiva dos autores. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.
Neste trabalho, a qualidade do ar na região de Interlagos e Ponte dos remédios é definida em termos da concentração do material particulado com diâmetro menor que 10µm (MP10). A concentração do MP10 no ar é resultado final de processos complexos, oriundos de vários fatores, que compreendem não só a emissão pelas fontes, bem como suas interações físicas (dispersão) e químicas (reações). A variabilidade da dispersão está diretamente ligada com a topografia e as condições meteorológicas. Diante desse contexto, o objetivo deste trabalho foi analisar a relação entre variáveis meteorológicas (VV, UR, T e P) e a concentração de MP10 nos meses extremos de 2014 (maior e menor concentração) utilizando o modelo de regressão linear múltipla. Os resultados mostraram que a concentração de MP10 está correlacionada com a VV e UR nas duas localidades em análise. Ademais, a maior parte dos dias desfavoráveis à dispersão do MP10 em 2014 ocorreu nos meses de junho e agosto. Meses do qual incidiram as maiores concentrações do MP10 nas duas localidades. Palavras-chave: MP10, variáveis meteorológicas, regressão linear múltipla. Temporal analysis of the meteorological conditions and concentration of Particulate Matter (PM10) in the Metropolitan Region of São Paulo-SP A B S T R A C T In this work, the air quality in the Interlagos and Ponte dos Remédios region is defined in terms of particulate matter concentration with a diameter smaller than 10μm (PM10). The PM10 concentration in the air is the final result of complex processes, which are due to several factors, that include not only the emission by the sources, but also their physical (dispersion) and chemical interactions (reactions). The variability of dispersion is directly connected to topography and weather conditions. In this context, the objective of this work was to analyze the relationship between meteorological variables (wind speed, relative humidity, temperature and precipitation.) and PM10 concentration in the extreme months of 2014 (highest and lowest concentrations) using the multiple linear regression model. The results showed that the MP10 concentration is correlated with the wind speed and relative humidity in the two localities under analysis. In addition, most of the unfavorable days to the PM10 dispersion in 2014 occurred in June and August. Months that focused the highest concentrations of PM10 in the two localities.
This case study analyzes water vapor flux that is vertically integrated into the atmosphere in episodes of the South Atlantic Convergence Zone (SACZ). The scope of this study is two cases that occurred between January and February 2018. We use the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF) to build the maps of vertically integrated water vapor flux and its divergence. We use two 5º by 5º sub-areas, centralized over Belo Horizonte and São Paulo, as control for water vapor balance. The results point to the existence of water vapor transport from the Amazon region to Southeastern Brazil in association to the SACZ. Convergence areas of vertically integrated water vapor flux predominate along the Northwest-Southeast line. The two cases over the Belo Horizonte area presented an average of water vapor balance of -1.8 and -12.9 mm/day. The average at the São Paulo area was -3.6 and 2.0 mm/day. The negative values indicate that precipitation exceeded evapotranspiration, that is, the area served as a water vapor sink.
The type of land use and land cover plays a decisive role in land surface temperature (LST). As cities are composed of varied covers, including vegetation, built-up areas, buildings, roads and areas without vegetation, understanding LST patterns in complex urban spaces is becoming increasingly important. The present study investigated the relationship between LST and albedo, NDVI, NDWI, NDBI and NDBaI in the period between 1994 and 2017. Images from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) onboard the Landsat 5 and 8 satellites, respectively, were used in the study. The images were processed, resampled (spatial resolution of 120 m) in the environment of the QGIS 3.0 software and, finally, centroids were extracted resulting in a total of 1252 points. A classical regression (CR) model was applied to the variables, followed by spatial autoregressive (SARM) and spatial error (SEM) models, and the results were compared using accuracy indices. The results showed that the highest correlation coefficient was found between albedo and NDBaI (r = 0.88). The relationship between albedo and LST (r = 0.7) was also positive and significant at р < 0.05. The global Moran's I index showed spatial dependence and non-stationarity of the LST (I = 0.44). The SEM presented the best accuracy metrics (AIC = 3307.15 and R2 = 0.65) for the metropolitan region of Belém, explaining considerably more variations in the relationship between explanatory factors and LST when compared to conventional CR models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.