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The present study deals with the development and application of Artificial Neural Network (ANN) models as a tool for the evaluation of human thermal comfort conditions in the urban environment. ANNs are applied to forecast for three consecutive days during the hot period of the year (May-September) the human thermal comfort conditions as well as the daily number of consecutive hours with high levels of thermal discomfort in the great area of Athens (Greece). Modeling was based on bioclimatic data calculated by two widely used biometereorogical indices (the Discomfort Index and the Cooling Power Index) and microclimatic data (air temperature, relative humidity and wind speed) from 7 different meteorological stations for the period 2001-2005. Model performance showed that the risk of human discomfort conditions exceeding certain thresholds can be successfully forecasted by the ANN models. In addition, despite the limitations of the models, the results of the study demonstrated that ANNs, when adequately trained, could have a high applicability in the area of prevention human thermal discomfort levels in urban areas, based on a series of relatively limited number of bioclimatic data values calculated prior to the period of interest.

In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located in southern Greece. Four meteorological stations were established in the canyon; the models were then applied to estimate air temperature values as a function of the corresponding values of one or more reference stations. The evaluation of the ANN model results showed that fair to very good air temperature estimations may be achieved depending on the number of the meteorological stations used as reference stations. In addition, the ANN model was found to have better performance than the MLR model: mean absolute error values were found to be in the range 0.82-1.72 degrees C and 0.90-1.81 degrees C, for the ANN and the MLR models, respectively. These results indicate that ANN models may provide advantages over more traditional models or methods for temperature and other data estimations in areas where meteorological stations are sparse; they may be adopted, therefore, as an important component in various environmental modeling and management studies.

Soil temperature simulation is an important component in environmental modeling since it is involved in several aspects of pollutant transport and fate. This paper deals with the performance of the soil temperature simulation algorithms of the well-known environmental model PRZM. Model results are compared and evaluated based on the basis of its ability to predict in situ measured soil temperature profiles in an experimental plot during a 3-year monitoring study. The evaluation of the performance is based on linear regression statistics and typical model statistical errors such as the root mean square error (RMSE) and the normalized objective function (NOF). Results show that the model required minimal calibration to match the observed response of the system. Values of the determination coefficient R(2) were found to be in all cases around the value of 0.98 indicating a very good agreement between measured and simulated data. Values of the RMSE were found to be in the range of 1.2 to 1.4 degrees C, 1.1 to 1.4 degrees C, 0.9 to 1.1 degrees C, and 0.8 to 1.1 degrees C, for the examined 2, 5, 10 and 20 cm soil depths, respectively. Sensitivity analyses were also performed to investigate the influence of various factors involved in the energy balance equation at the ground surface on the soil temperature profiles. The results showed that the model was able to represent important processes affecting the soil temperature regime such as the combined effect of the heat transfer by convection between the ground surface and the atmosphere and the latent heat flux due to soil water evaporation.

This paper deals with an experimental study of various techniques that have been applied for soil disinfestation purposes under greenhouse conditions. Various meteorological parameters and soil temperatures were measured for four different experimental soil segments (three associated with different disinfestation techniques and one as a reference) at depths varying between 0-1 m and with a time interval of 5 min in a greenhouse located in the Agricultural University of Athens Campus, Greece. Results showed that plastic polyethylene films such as covers, metallic conductors or a combination of both were able to enhance heat transfer and temperature increase in greenhouse soil. For typical disinfestation conditions, the depth-averaged temperature values for plastic covers, metallic conductors, and the combination of both were found to be higher than those for the reference of about 5 degrees C, 12 degrees C and 15 micro C, respectively. Moreover, the remained population percentages 50 days after the initiation of the experiment were found to be 19.3%, 25.3%, 37.3% Kcat 94% of the initial population, for the combination of metallic conductors and plastic covers, metallic conductors, plastic cover, and for the reference, respectively.

Predictive modeling techniques are applied to investigate their potential usefulness in providing first order estimates on atmospheric emission flux of gaseous soil mercury and in identifying those parameters most critical in controlling such emissions. Predicted data by simulation and statistical techniques are compared to previously published observational data. Results showed that simulation techniques using air/soil coupling may provide a plausible description of mercury flux trends with a RMSE of 24.4ngm(-2)h(-1) and a mean absolute error of 10.2ngm(-2)h(-1) or 11.9%. From the statistical models, two linear models showed the lowest predictive abilities (R2=0.76 and 0.84, respectively) while the Generalized Additive model showed the closest agreement between estimated and observational data (R2=0.93). Predicted values from a Neural Network model and the Locally Weighted Smoother model showed also very good agreement to measured values of mercury flux (R2=0.92). A Regression Tree model demonstrated also a satisfactory predictability with a value of R2=0.90. Sensitivities and statistical analyses showed that surface soil mercury concentrations, solar radiation and, to a lesser degree, temperature are important parameters in predicting airborne Hg flux from terrestrial soils. These findings are compatible with results from recent experimental studies. Considering the uncertainties associated with mercury cycling and natural emissions, it is concluded, that predictions based on simple modeling techniques seem quite appropriate at present; they can be useful tools in evaluating the role of terrestrial emission sources as part of mercury modeling in local and regional airsheds.

A modeling analysis of factors influencing deposited airborne mercury mass balance in terrestrial landscapes is presented. Computer simulations are performed with a recently developed model for various sets of conditions. Preliminary estimates based on variability and sensitivity analysis of simulations are compatible with the currently ‐available knowledge of mercury terrestrial cycling. Atmospheric deposition of divalent mercury (Hg(II)) is the main source for mercury in uncontaminated terrestrial systems. Mercury mass balance is characterized by the intensive exchange of mercury between atmosphere and ground surface. Atmospheric deposition, weather conditions, hydrologic transport, Hg(H) reduction and landscape scale characteristics are the most important factors regulating conditions that have impacts on mercury mass balance components. Deposited mercury displays a strong tendency to be remobilized into the atmosphere as elemental mercury (Hg°) formed by the reduction of divalent mercury in t...

Data from a multimedia survey on fluoride contamination in the vicinity of an aluminum plant are presented and applied along with multimedia‐based simulation modeling techniques in order to investigate the long term response of fluoride in soils and seasonal trends of accumulation by olive trees. Results from the modeling analysis are in fair agreement with observed data. Soils show elevated fluoride concentrations that decrease with distance from the source and depth below the surface. Fluoride is largely retained in the upper 5 cm of soil. Sensitivity analyses showed that accumulation of fluoride is dominated by atmospheric loading. The strong retention of fluoride in the calcareous soils of the area with high organic matter and clay content, is also an important factor for controlling its accumulation in these soils. In the case of olive tree leaves, direct deposition is the main source for fluoride accumulation during the year; washoff is the dominant sink during the wet months; during dry mo...

This screening modeling analysis examines mercury sources and fate in the upper canals of the South Florida Everglades. Mass balance modeling techniques are applied along with available data to examine the relative importance of external sources and internal cycling of mercury and the relative influence of several transport and transformation processes. WASP5, a general dynamic mass balance model for aquatic systems, was modified to represent elemental mercury, divalent mercury, and methyl mercury. A new soil mercury mass balance model was developed to track the transport of these mercury components from soil surfaces to the water via surface runoff. Subsurface flow (drainage) loads were estimated on a seasonal basis using information from a modified simulation model for muck soil hydraulics and transport. Predicted mercury concentrations in the Everglades Agricultural Area (EAA) canals were compared to observed data provided by a region‐wide statistical sampling program. The modeling system repr...