Predicting ozone-induced reduction of carbon sequestration of forests under elevated tropospheric ozone concentrations requires robust mechanistic leaf-level models, scaled up to whole tree and stand level. As ozone effects depend on genotype, the ability to predict these effects on forest carbon cycling via competitive response between genotypes will also be required. This study tests a proces...

Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...

the objective of this study was to analyze spatial patterns of ground-level ozone concentrationin the dallas-fort worth, texas metropolitan area. average daily maximum eight-hour ozone concentration,number of days with concentrations exceeding 75 ppb, average outside air temperature, and resultant winddirection at 20 monitoring stations were compiled for january-december, 2013. recent estimates...

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing reduce impact pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied forecasts day-ahead maximum value ozone several geographical locations southern Switzerland. Due low density measurem...