Temporal and spatial variation in C, N, S and trace element contents in the leaves of Quercus ilex within the urban area of Naples

2000 ◽  
Vol 109 (1) ◽  
pp. 119-129 ◽  
Author(s):  
A Alfani ◽  
D Baldantoni ◽  
G Maisto ◽  
G Bartoli ◽  
A Virzo De Santo
2021 ◽  
Vol 10 (3) ◽  
pp. 160
Author(s):  
Ting Peng ◽  
Caige Sun ◽  
Shanshan Feng ◽  
Yongdong Zhang ◽  
Fenglei Fan

The urban heat island effect caused by the rapid increase in urban anthropogenic heat has gradually become an important factor affecting the living environment of urban residents. Studying the temporal and spatial variation characteristics of urban anthropogenic heat is of great significance for urban planning and urban ecological service systems. In this study, the urban anthropogenic heat flux (AHF) in 2004, 2009, 2014, and 2020 in the central urban area of Guangzhou was retrieved based on Landsat data and the surface energy balance equation, and the temporal and spatial characteristics of different types of anthropogenic heat were explored by combining the transfer matrix and the migration of the gravity center. The results showed that: (1) The overall change trend of anthropogenic heat in the central urban area of Guangzhou was enhanced, and the degree of enhancement was related to the type of urban functional land. (2) Different types of anthropogenic heat had different characteristics in terms of area expansion and spatial changes. Low-value anthropogenic heat (zero-AHF zone, low-AHF zone, medium-AHF zone) changed drastically in terms of area expansion. High-value anthropogenic heat (medium-AHF zone, high-AHF zone) changed more drastically in space. The increase in urban population, rapid economic development, and increased industrial production activities have stimulated the emission of anthropogenic heat, which has a positive impact on the intensity of anthropogenic heat.


2018 ◽  
Vol 1 (1) ◽  
pp. 1-13

Background: Thyroid cancer is an internationally important health problem. The aim of this exploratory study was to evaluate whether significant changes in the thyroid tissue levels of Ag, Co, Cr, Fe, Hg, Rb, Sb, Sc, Se, and Zn exist in the malignantly transformed thyroid. Methods: Thyroid tissue levels of ten trace elements were prospectively evaluated in 41 patients with thyroid malignant tumors and 105 healthy inhabitants. Measurements were performed using non-destructive instrumental neutron activation analysis with high resolution spectrometry of long-lived radionuclides. Tissue samples were divided into two portions. One was used for morphological study while the other was intended for trace element analysis. Results: It was found that contents of Ag, Co, Cr, Hg, and Rb were significantly higher (approximately 12.8, 1.4, 1.6, 19.6, and 1.7 times, respectively) in cancerous tissues than in normal tissues. Conclusions: There are considerable changes in trace element contents in the malignantly transformed tissue of thyroid.


Paleobiology ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 171-177
Author(s):  
James C. Lamsdell ◽  
Curtis R. Congreve

The burgeoning field of phylogenetic paleoecology (Lamsdell et al. 2017) represents a synthesis of the related but differently focused fields of macroecology (Brown 1995) and macroevolution (Stanley 1975). Through a combination of the data and methods of both disciplines, phylogenetic paleoecology leverages phylogenetic theory and quantitative paleoecology to explain the temporal and spatial variation in species diversity, distribution, and disparity. Phylogenetic paleoecology is ideally situated to elucidate many fundamental issues in evolutionary biology, including the generation of new phenotypes and occupation of previously unexploited environments; the nature of relationships among character change, ecology, and evolutionary rates; determinants of the geographic distribution of species and clades; and the underlying phylogenetic signal of ecological selectivity in extinctions and radiations. This is because phylogenetic paleoecology explicitly recognizes and incorporates the quasi-independent nature of evolutionary and ecological data as expressed in the dual biological hierarchies (Eldredge and Salthe 1984; Congreve et al. 2018; Fig. 1), incorporating both as covarying factors rather than focusing on one and treating the other as error within the dataset.


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