Ioannis X. Tsiros

Modeling Mercury Fluxes and Concentrations in a Georgia Watershed Receiving Atmospheric Deposition Load from Direct and Indirect Sources

Abstract This paper presents a modeling analysis of airborne mercury (Hg) deposited on the Ochlockonee River watershed located in Georgia. Atmospheric deposition monitoring and source attribution data were used along with simulation models to calculate Hg buildup in the subwatershed soils, its subsequent runoff loading and delivery through the tributaries, and its ultimate fate in the mainstem river. The terrestrial model calculated annual watershed yields for total Hg ranging from 0.7 to 1.1 μg/m2. Results suggest that approximately two-thirds of the atmospherically deposited Hg to the watershed is returned to the atmosphere, 10% is delivered to the river, and the rest is retained in the watershed. A check of the aquatic model results against survey data showed a reasonable agreement. Comparing observed and simulated total and methylmercury concentrations gave root mean square error values of 0.26 and 0.10 ng/L, respectively, in the water column, and 5.9 and 1 ng/g, respectively, in the upper sediment layer. Sensitivity analysis results imply that mercury in the Ochlockonee River is dominated by watershed runoff inputs and not by direct atmospheric deposition, and that methylmercury concentrations in the river are determined mainly by net methylation rates in the watershed, presumably in wetted soils and in the wetlands feeding the river.

An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations

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°C and 0.90–1.81°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.

Thermal and comfort conditions in a semi-closed rear wooded garden and its adjacent semi-open spaces in a Mediterranean climate (Athens) during summer

The cooling effect in a courtyards garden and in the adjoining ground- and first floor verandas, attached to the NNE side of a two-storey building is evaluated with measurements performed during a hot weather summer period in Athens. Results revealed a well defined and strong daytime cool island between the buildings rear garden (with about 85% canopy covering) and an air temperature reduction for the ground floor veranda, as compared with an urban square with low canopy coverage (about 15%), reaching a maximum air temperature reduction of 6.5 K during daytime. Compared with a nearby densely wooded park, the garden and the veranda were found 1–1.5 K cooler during 4 and 8 hours during daytime, respectively. Using the physiologically equivalent temperature thermal index with appropriate adjustments to local conditions, it was found that those two sites, compared with the urban square, were able to mitigate the extreme thermal stress conditions and to decrease the daily number of hours associated with strong thermal stress conditions. It is concluded that appropriately designed semi-open spaces in residential buildings, well known from vernacular architecture for their qualitative benefits, may be considered as positive bioclimatic pedestrian transitional elements in sustainable urban design for Mediterranean climates.

Artificial neural network models as a useful tool to forecast human thermal comfort using microclimatic and bioclimatic data in the great Athens area (Greece)

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.

A Preliminary Study of the Application of Some Predictive Modeling Techniques to Assess Atmospheric Mercury Emissions from Terrestrial Surfaces

Abstract 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 24.4 ng m−2 h−1 and a mean absolute error of 10.2 ng m−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.

Seasonal differences in thermal sensation in the outdoor urban environment of Mediterranean climates – the example of Athens, Greece

Outdoor urban areas are very important for cities and microclimate is a critical parameter in the design process, contributing to thermal comfort which is important for urban developments. The research presented in this paper is part of extensive field surveys conducted in Athens aimed at investigating people’s thermal sensation in a Mediterranean city. Based on 2313 questionnaires and microclimatic data the current work focuses on the relative frequencies of people’s evaluation of the thermal along with the sun and wind sensations between two seasons trying to identify the seasonal differences in thermal sensation. The impact of basic meteorological factors on thermal discomfort with respect to season are also examined, as well as the use of the outdoor environment. Results show that psychological adaptation is an important contributing factor influencing perception of the thermal environment between seasons. In addition, the thermal sensation votes during the cool months show that individuals are satisfied to a great extend with the thermal environment whereas the combination of high air temperature, strong solar radiation and weak wind lead to thermal discomfort during summertime. As far as the appropriate urban design in the Mediterranean climate is concerned, priority should be given to the warm months of the year.

A note on the evolution of the daily pattern of thermal comfort-related micrometeorological parameters in small urban sites in Athens

Studies on human thermal comfort in urban areas typically quantify and assess the influence of the atmospheric parameters studying the values and their patterns of the selected index or parameter. In this paper, the interpretation tools are the first derivative of the selected parameters (∆Parameter/∆t) and the violin plots. Using these tools, the effect of sites’ configuration on thermal conditions was investigated. Both derivatives and violin plots indicated the ability of vegetation to act as a buffer to the rapid changes of air temperature, mean radiant temperature, and the physiologically equivalent temperature (PET). The study is focused on the “thermal extreme” seasons of winter (December, January, and February) and summer (June, July, and August) during a 3-year period of measurements in five selected sites under calm wind and sunny conditions. According to the results, the absence of vegetation leads to high derivative values whereas the existence of dense vegetation tends to keep the parameters’ values relatively low, especially under hot weather conditions.

Combining Neural Network Models to Predict Spatial Patterns of Airborne Pollutant Accumulation in Soils around an Industrial Point Emission Source

Abstract Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.

Estimating airborne pollutant concentrations in vegetated urban sites using statistical models with microclimate and urban geometry parameters as predictor variables: A case study in the city of Athens Greece

The present study demonstrates the efficiency of applying statistical models to estimate airborne pollutant concentrations in urban vegetation by using as predictor variables readily available or easily accessible data. Results revealed that airborne cadmium concentrations in vegetation showed a predictable response to wind conditions and to various urban landscape features such as the distance between the vegetation and the adjacent street, the mean height of the adjacent buildings, the mean density of vegetation between vegetation and the adjacent street and the mean height of vegetation. An artificial neural network (ANN) model was found to have superiority in terms of accuracy with an R2 value on the order of 0.9. The lowest R2 value (on the order of 0.7) was associated with the linear model (SMLR model). The linear model with interactions (SMLRI model) and the tree regression (RTM) model gave similar results in terms of accuracy with R2 values on the order of 0.8. The improvement of the results with the use of the non-linear models (RTM and ANN) and the inclusion of interaction terms in the SMLRI model implied the nonlinear relationships of pollutant concentrations to the selected predictors and showed the importance of the interactions between the various predictor variables. Despite the limitations of the models, some of them appear to be promising alternatives to multimedia-based simulation modeling approaches in urban areas with vegetation, where the application of typical deposition models is sometimes limited.

An evaluation of the performance of the soil temperature simulation algorithms used in the PRZM model.

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 R2 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°C, 1.1 to 1.4°C, 0.9 to 1.1°C, and 0.8 to 1.1°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.