0000000000472136
AUTHOR
Secundino Del Valle-tascón
Long-term ozone exposure of potato: Free radical content and leaf injury analysed by Q-band ESR spectroscopy and image analysis
This paper presents Q-band electron spin resonance (ESR) studies on free radicals (FR) generated in potato leaves exposed to different O(3) levels in open-top chambers (OTC), together with a quantitative study of the relationship between FR signal intensity and area of potato leaf damage. The advantages of Q-band when compared to X-band ESR spectroscopy are analysed, the main advantage being an absence of overlapping between Mn(II) and FR signals, allowing a quantitative study of FR signal intensity. This study also reports on a graphical method developed to quantitatively measure the damaged area on leaves caused by ozone exposure. Results indicate a direct relationship between FR signal i…
The intracellular Arabidopsis COPT5 transport protein is required for photosynthetic electron transport under severe copper deficiency
Copper is an essential micronutrient that functions as a redox cofactor in multiple plant processes, including photosynthesis. Arabidopsis thaliana possesses a conserved family of CTR-like high-affinity copper transport proteins denoted as COPT1-5. COPT1, the only family member that is functionally characterized, participates in plant copper acquisition. However, little is known about the function of the other Arabidopsis COPT proteins in the transport and distribution of copper. Here, we show that a functional fusion of COPT5 to the green fluorescent protein localizes in Arabidopsis cells to the prevacuolar compartment. Plants defective in COPT5 do not exhibit any significant phenotype und…
Application of Machine Learning Techniques in the Study of the Relevance of Environmental Factors in Prediction of Tropospheric Ozone
This work presents a new approach for one of the main problems in the analysis of atmospheric phenomena, the prediction of atmospheric concentrations of different elements. The proposed methodology is more efficient than other classical approaches and is used in this work to predict tropospheric ozone concentration. The relevance of this problem stems from the fact that excessive ozone concentrations may cause several problems related to public health. Previous research by the authors of this work has shown that the classical approach to this problem (linear models) does not achieve satisfactory results in tropospheric ozone concentration prediction. The authors’ approach is based on Machin…
Impact of elevated ozone on chlorophyll a fluorescence in field-grown oat (Avena sativa).
Oat (Avena sativa) plants were grown in the field near the urban area of Valencia, Eastern Spain. The data on air quality showed that ozone was the main phytotoxic pollutant present in ambient air reaching a 7-h mean of 46 nl l(-1) and a maximum hourly peak of 322 nl l(-1). The effect of ambient ozone on PSII activity was examined by measurements of chlorophyll (Chl) a fluorescence. In leaves with visible symptoms, the function of PSII was changed at high actinic irradiances. Nonphotochemical quenching (NPQ) was higher and quantum efficiency of PSII (Phi(PSII)), photochemical quenching (q(p)), quantum efficiency of excitation capture and PSII electron flow (F(v)'/F(m)') were lower. An enhan…
Study of the diurnal cycle of stressed vegetation for the improvement of fluorescence remote sensing
Chlorophyll fluorescence (Chf) emission allows estimating the photosynthetic activity of vegetation - a key parameter for the carbon cycle models - in a quite direct way. However, measuring Chf is difficult because it represents a small fraction of the radiance to be measured by the sensor. This paper analyzes the relationship between the solar induced Chf emission and the photosynthetically active radiation (PAR) in plants under water stress condition. The solar induced fluorescence emission is measured at leaf level by means of three different methodologies. Firstly, an active modulated light fluorometer gives the relative fluorescence yield. Secondly, a quantitative measurement of the Ch…
Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration
Abstract This paper deals with tropospheric ozone modelling by using Artificial Neural Networks (ANNs). In this study, ambient ozone concentrations are estimated using surface meteorological variables and vehicle emission variables as predictors. The work is especially focused on analysing the importance of the input variables used by these models. This analysis is carried out in different time windows: all the time of study (April of 1997, 1999 and 2000), one month (April 1999), and finally, an hourly analysis. All the information extracted from these analyses can determine the most important factors in tropospheric ozone formation, thus achieving a qualitative model from the quantitative …