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Heatwaves—excessively hot ambient conditions that are considered a serious threat to human health—are often associated with poor air quality. The aim of this study was to examine the impact of an early heatwave episode in an industrialized plain in the eastern Mediterranean region (Thriasio, Greece) on human thermal discomfort and urban air quality. The heatwave occurred in mid (15–20) May 2020, shortly after some of the restrictions that were improsed to halt the spread of coronavirus disease 2019 (COVID-19) in Greece were lifted (on 4 May). The discomfort index (DI) and the daily air quality index (DAQI) were calculated on an hourly basis throughout spring 2020 (March, April, May) using data from two stations that measure meteorological parameters and air pollutant concentrations in the Thriasio Plain. The analysis showed that the air temperature increased during 7–17 May to levels that were more than 10 °C above the monthly average value (25.8 °C). The maximum measured air temperature was 38 °C (on 17 May). The results showed a high level of thermal discomfort. The DI exceeded the threshold of 24 °C for several hours during 13–20 May. Increased air pollution levels were also identified. The average DAQI was estimated as 0.83 ± 0.1 and 1.14 ± 0.2 at two monitoring stations in the region of interest during the heatwave. Particulate matter (diameter < 10 μm) appeared to contribute significantly to the poor air quality. Significant correlations between the air temperature, DI, and AQSI were also identified.

The influence of physiological acclimatization and psychological adaptation on thermal perception is well documented and has revealed the importance of thermal experience and expectation in the evaluation of environmental stimuli. Seasonal patterns of thermal perception have been studied, and calibrated thermal indices' scales have been proposed to obtain meaningful interpretations of thermal sensation indices in different climate regions. The current work attempts to quantify the contribution of climate to the long-term thermal adaptation by examining the relationship between climate normal annual air temperature (1971-2000) and such climate-calibrated thermal indices' assessment scales. The thermal sensation ranges of two thermal indices, the Universal Thermal Climate Index (UTCI) and the Physiological Equivalent Temperature Index (PET), were calibrated for three warm temperate climate contexts (Cfa, Cfb, Csa), against the subjective evaluation of the thermal environment indicated by interviewees during field surveys conducted at seven European cities: Athens (GR), Thessaloniki (GR), Milan (IT), Fribourg (CH), Kassel (DE), Cambridge (UK), and Sheffield (UK), under the same research protocol. Then, calibrated scales for other climate contexts were added from the literature, and the relationship between the respective scales' thresholds and climate normal annual air temperature was examined. To maintain the maximum possible comparability, three methods were applied for the calibration, namely linear, ordinal, and probit regression. The results indicated that the calibrated UTCI and PET thresholds increase with the climate normal annual air temperature of the survey city. To investigate further climates, we also included in the analysis results of previous studies presenting only thresholds for neutral thermal sensation. The average increase of the respective thresholds in the case of neutral thermal sensation was about 0.6 °C for each 1 °C increase of the normal annual air temperature for both indices, statistically significant only for PET though.

Photosynthetic production in forest ecosystems occurs through the absorption of light and CO 2 . The present work deals with CO 2 and carbon flux densities above a deciduous forest in Greece, the southernmost monitoring site in Europe. Results show annual net carbon absorption of 7.6 t C ha –1 y –1 , indicating that this forest is a strong carbon sink compared to other European sites. However, absorbance may be reduced by 1 μmol m –2 s –1 for every 1.5 oC above-canopy air temperature increase or 0.015 cm3 cm–3 active root-zone moisture depletion, beyond estimated optimum values. The forest acts as a carbon sink from late April to early November and as a source of carbon in the remaining period of the year. The highest CO 2 net absorption flux occurs during June-July (approximately 200 g C m –2 month –1 ).

This data article presents the data collected through an extensive research work conducted in urban areas in the city of Athens (Greece) during the period 2010–2012. Data concerns 2287 questionnaires and microclimatic data collected through interviews to the visitors of the examined areas with parallel monitoring of the urban microclimatic characteristics. The field surveys carried out occasionally throughout the year covering as much as possible the different seasons under Mediterranean climate conditions. These data are related to the research articles with the titles: Seasonal differences in thermal sensation in the outdoor urban environment of Mediterranean climates–the example of Athens, Greece (Tseliou et al., 2017) and Outdoor thermal sensation in a Mediterranean climate (Athens): The effect of selected microclimatic parameters (Tseliou et al., 2016).

There are several cases where a circular variable is associated with a linear one. A typical example is wind direction that is often associated with linear quantities such as air temperature and air humidity. The analysis of a statistical relationship of this kind can be tested by the use of parametric and non-parametric methods, each of which has its own advantages and drawbacks. This work deals with correlation analysis using both the parametric and the non-parametric procedure on a small set of meteorological data of air temperature and wind direction during a summer period in a Mediterranean climate. Correlations were examined between hourly, daily and maximum-prevailing values, under typical and non-typical meteorological conditions. Both tests indicated a strong correlation between mean hourly wind directions and mean hourly air temperature, whereas mean daily wind direction and mean daily air temperature do not seem to be correlated. In some cases, however, the two procedures were found to give quite dissimilar levels of significance on the rejection or not of the null hypothesis of no correlation. The simple statistical analysis presented in this study, appropriately extended in large sets of meteorological data, may be a useful tool for estimating effects of wind on local climate studies.

In the Mediterranean area, rural abandonment and the rapid urbanisation after the 1950's led to complex environmental problems, such as the exploitation of natural resources, the environmental pollution, whilst the unstrained urban growth caused excessive pressure on the existing infrastructure, which affects buildings, public transportation, water quality and public health. Various studies demonstrate that the implementation of green roof technology can moderate environmental problems, through the reduction of heat flux and solar reflectivity, the minimisation of buildings’ energy consumption, the air pollution removal, the storm water runoff reduction, the air cooling and the effective management of the urban heat island effect. In this framework, the aim of this study is to assess the green roof potential and the quantification of its benefits over Thessaloniki, Greece's second largest city. In order to do so, very high spatial resolution satellite image and natural colour orthoimagery were used along with a geographical object-based image analysis approach. Carbon sequestration potential, rainwater retention and energy conservation were estimated based on coefficients adopted after a comprehensive literature review and actual dynamic energy simulations.

In the Mediterranean area, rural abandonment and the rapid urbanisation after the 1950׳s have led to complex environmental problems, such as the exploitation of natural resources, the environmental pollution and the rapid increase of greenhouse gas emissions. Especially the unrestrained urban growth caused excessive pressure on the existing infrastructure, which affects buildings, public transportation, road networks, water quality, waste collection and public health. Green roofs are emerging building technologies that can help communities mitigate some of the above mentioned problems. Various studies have demonstrated that the implementation of green roof technology can moderate some of these environmental problems, through the reduction of heat flux and solar reflectivity, the minimisation of heat losses through the building׳s envelope, the air pollution removal, the storm water runoff reduction, the air cooling as well as the effective management of the urban heat island effect. In this line of thought, the aim of this study is to assess the green roof potential and the quantification of its benefits over Thessaloniki, Greece׳s second largest city. To delineate the available green roof areas, very high spatial resolution satellite image and natural colour orthoimagery were used along with a geographical object-based image analysis approach. Potential green roof extent was extracted with a mean error of 8.78 m 2 , and discriminated from staircases, penthouse setbacks and terracotta tiles. Subsequently GIS were used to extrapolate the results from the building to the city scale. Carbon sequestration potential, rainwater retention and energy conservation were estimated based on coefficients adopted after a comprehensive literature review and actual dynamic energy simulations. The green roof potential in Thessaloniki approximated 17% of the gross built areas of the city, which means that there is a strong potential for green roof retrofit. Depending on the vegetation type installed in an extensive green roof, C stocks in Thessaloniki can increase by 2.5 times, while the dynamic energy simulations suggested that in certain city blocks reduced heating and cooling consumptions up to 5% and 16% respectively could be achieved. In regard to rainwater retention, implementation of green roofs in Thessaloniki is estimated to contribute in rainwater retention by almost 45%, allowing more than half of the rainwater precipitation to be reserved at the 50% of the city blocks.

Previous research that conducted in three typical public areas in Athens showed that the acceptable air temperature range that determines thermal comfort varies among 17.0–21.0 °C and 26.0–32.0 °C for the cool and the warm period of the year, respectively. The present research goes further and determines the acceptable environmental conditions via two wide acknowledged thermal indices, which are PET and UTCI, in order to evaluate, whether or not, thermal comfort conditions are presented in three typical outdoor urban areas of a Mediterranean city, like Athens. Thus, environmental model ENVI-met 3.1 was applied to calculate the daily microclimatic variation of the three examined areas for the same dates, one typical for the warm period and one typical for the cool period. Thermal sensation conditions were then assessed by the indices. Results showed that the examined areas approach the acceptable environmental conditions only for the cool day, whereas an assumed urban design scenario was able only to ameliorate thermal discomfort but not to reach thermal comfort during a hot day.

The present study deals with the estimation of human thermal sensation zones in a Mediterranean climate (Athens). Field monitoring along with field surveys leading to a database of 2313 questionnaires were carried out in three open urban areas. Thermal sensation conditions were analysed under the influence of air temperature, relative humidity, solar radiation and wind speed. Thermal sensation zones were first defined using air temperature as a single parameter affecting thermal sensation. Logistic regression was appropriately used to define each thermal sensation zone. The thermal sensation zones were then determined using a combination of the microclimatic parameters to investigate the extent to which these parameters affect thermal sensation. Results showed that the thermal comfort zone is characterized by a quite wide air temperature range, on the order of more than 10°C. In addition, this zone presents always statistical insignificance in the P-values of residuals. These results imply that thermal co...

The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature.

The ability of various semi-analytical models to predict soil temperature profiles in an experimental plot during a 16-year monitoring study for soil depths up to 120 cm is evaluated. The models are developed from an analytical model by replacing the steady-state soil temperature with easily obtained hourly and daily average temperature values. Such values include the hourly air temperature, the daily average air temperature, the hourly soil temperature of selected soil depths from three daily observations, the daily average of the soil temperature profile and the hourly soil temperature for the bottom depth. The performance evaluation results show that, in principle, all models exhibit high correlation (R2 values in the range 0.85–0.97), indicating a very good agreement between measured and predicted values. In addition, error statistics reveal that the best performance in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) is the model based on the daily average of the soil temp...

The cooling effect in a courtyard's 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 stron...

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.

Rainfall is very influential on the alteration of atmospheric conditions. The existence of precipitated water in the atmosphere leads to a reduction of both air and surface temperature values and also to a simultaneous increase of relative humidity. The feeling of increased human discomfort conditions is, however, very common after a storm during the warm period. The present study deals with a preliminary assessment on the effect of rainfall events on human thermal comfort under hot weather conditions. For the purpose of this study, meteorological data along with the thermal comfort indices THI and PET were used. More than 6 meteorological parameters were analyzed before and after a number of selected rainfall events. Results revealed that a rapid alteration of the PET index values after rainfall events is occurred as a consequence of global radiation, air temperature and relative humidity variations.

This work investigates how to reduce, by appropriate urban design, air temperature at the street level and to improve pedestrian thermal conditions in summer. The case study is a street in Athens with low aspect ratio H/W (H: building height; W: street width), low trees canopy coverage level, and high traffic. Urban variables for design are trees canopy coverage area ratio, traffic load, walls surfaces albedo, and H/W. Their thermal effect is separately and altogether estimated by applying the microclimatic Green-CTTC model whereas thermal stress is assessed using the Physiological Equivalent Temperature (PET) index. The studied day is relatively hot (36.5 °C at midday) with eight hours of heat stress (two and six hours of extreme and strong heat stress, respectively) at street level. Acceptable near-to-discomfort limits for local pedestrians are considered whereas PET classification heat stress levels are adjusted to local conditions by applying a correction equation to obtained PET values. Results show that the examined scenarios are associated with air temperature decrease and improvement of thermal comfort in the shade, especially during the day's hottest hours. The trees thermal effect is the dominant factor followed by the increase of H/W and of the walls surfaces albedo. All the studied design scenarios are found able to reduce the number of heat stress hours but the trees canopy coverage area ratio increase scenario was found by itself able to be associated only with moderate heat stress for local pedestrians. Alternative local smart controls for thermal comfort (SCAT) options are also discussed.

The present study deals with human thermal comfort, as it is quantified by two well-known human biometeorological indices, Physiologically Equivalent Temperature (PET) and Humidex, in selected urban areas with different tree and building structures. The study took place during July 2003, at Agricultural University of Athens, Greece. Meteorological and environmental measurements were carried out at six sites. The selected sites differ in regard to sky view factor (SVF) values, environmental configuration and green (vegetation) coverage. The results of this study indicate a striking influence of site config- uration on human thermal comfort. For example, in an outdoor lawn area surrounded by trees (green atrium) the PET was greater than 41 °C (human perception of "very hot") for 13 % of the measurement time, while in an open building atrium (courtyard) PET was greater than 41 °C for 28 % of the time. In addition, 'comfortable' conditions as PET quantifies formed during 26 % of the measurement time in green atrium but less than 15 % of the time in the building atrium. Especially during daytime the difference between these two sites reached 8.7 °C according to the PETand 4.3 °C according to the Humidex. At sites with low SVF values and dense green coverage the human biometeorological conditions were improved compared to sites with high SVF values and those with buildings nearby. Significant relationships between SVF and biometeorological indices classes were indicated. The PET index better represented human comfort than Humidex.

The present work investigates the causes of the variability in air temperature patterns among urban streets with trees in Athens, as obtained from in situ measurements, in a suburb area and in the city center. Urban variables studied are: the extent of tree canopy coverage, traffic load, surfaces albedo modification, street deepening aspect and the street’s ventilation. The thermal effect of each variable is estimated by simulations using the Green CTTC analytical microclimate model. The model was first validated with measurements data and consequently used to study the effect of the various urban variables in attenuating the high values of air temperature within the studied streets considering two feasible scenarios for each of the examined streets. Model results revealed that the total attenuation effect in reducing the air temperatures inside the streets may reach as much as 5 K at noontime with a daily average cooling of 3 K. Further, the study demonstrates the potential use of microclimate modeling in assessing the merits of proposed passive cooling strategies in urban streets.

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.

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.

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 microg/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.

This study proposes an innovative approach for investigating the accumulated meteorological effects on cotton during the growing season. The quantification of the weather effects is based on the application of the Bhalme and Mooley drought index (BMDI) methodology on the vegetation condition index (VCI) extracted by NOAA/AVHRR data. The resulted Bhalme and Mooley vegetation condition index (BMVCI) is in the same scale as the Z-index, as proposed in the Palmer drought severity index (PDSI) for drought monitoring. The study area comprises the region of Thessaly, which is the most cotton productive area in Greece. Eighteen years of NOAA/AVHRR data are examined and processed with the BMVCI to examine the unfavorable conditions for cotton production. For the validation of BMVCI an empirical relationship between the cotton production of Thessaly and the BMVCI values is derived for the period 1982–1997. The methodology is validated for the two following years (1998 and 1999). The estimated cotton production approximates 5% and 2%, respectively, the real values for the validated years. The results confirm the usefulness of this integrated methodological approach as an effective tool to assess cotton production in Thessaly. � 2004 Elsevier Ltd. All rights reserved.

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.

A new methodological approach is presented for quantifying the meteorological effects on cotton production during the growing season in Greece. The proposed Bhalme and Mooley Vegetation Condition Index (BMVCI) is based on the Vegetation Condition Index (VCI) extracted by National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. In this approach the VCI data is processed with the Bhalme and Mooley methodology to assess the accumulated meteorological effects on cotton from April to August. The resulting index is at the same scale as the Z-Index, which is the classification of the Palmer Drought Severity Index (PDSI) extensively used for drought monitoring. For this study 16 years of data are examined to illustrate that the weather development as identified from satellite data with the use of BMVCI confirm unfavourable conditions for cotton production. For the validation of BMVCI an empirical relationship between the cotton production and the BMVCI values is derived. The resultant high correlation coefficient refers to very encouraging results and confirms the usefulness of the proposed integrated methodological approach as an effective tool for early assessment of the cotton production in Greece.

Satellite data can significantly contribute to agricultural monitoring. The reflected radiation, as recorded by satellite sensors, provides an indication of the type, density and condition of canopy. A widely used index for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI) derived from the National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data provided in high temporal resolution. An extension of the NDVI is the Vegetation Condition Index (VCI). VCI is a tool for monitoring agrometeorological conditions, providing a quantitative estimation of weather impact to vegetation. The primary objective of this paper is the quantitative assessment of the cotton yield before the end of the growing season by examining the weather effects as they are depicted by the VCI. The study area comprises several cotton producing areas in Greece. Ten-day NDVI maximum value composites (MVC) are initially utilized for the period 1982–1999. The correlatio...

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.

Various statistical models were developed for assessing airborne fluoride (F) levels in natural vegetation near an aluminum reduction plant using as predictor variables the distance from the emission source, the predominating wind, and characteristic topography-geomorphology parameters. Results revealed that F concentrations in vegetation showed a predictable response to both wind conditions and landscape features. The linear model was found to give good estimations, taking advantage of the relatively strong linear correlation between concentration and distance. A nonlinear relationship between the F concentration in vegetation and the other variables was also found, while interactions between the variables were found to be non-first-order. The nonlinear relationship hypothesis was supported by the improved results of various nonlinear models that also indicated the importance of the area's topography-geomorphology and meteorology in model predictions. The application of an artificial neural network (ANN) model showed the closest agreement between predicted and observed values with a correlation coefficient of 0.92. The improved reliability of the ANN and a regression tree model (RTM) also were indicated by the normal distribution of their residuals. The RTM and the ANN were further investigated and found to be capable of identifying the importance of the variables in vegetation exposure to air emissions.

This study investigates the potential for passive cooling patterns inside the urban fabric in the Mediterranean climate city of Athens (Greece), especially with regard to quantify air temperature reduction and thermal comfort amelioration at the neighbourhood scale. Using both field measurements and an urban microclimate simulation model, we assessed cooling and warming patterns in various sites of an Athens residential neighbourhood. Results show that, under Mediterranean climate conditions, urban design elements such as wooded courtyards and appropriately oriented urban design elements such as galleries have a considerable cooling effect and can be used as cool places inside the neighbourhood for occupants' comfort amelioration and also as passive cooling tools for buildings to reduce summer energy consumption. They may then function as passive design strategies to adapt the urban site form to different climate change scenarios.

The abilities of a screening-level model to predict variations in elemental mercury (Hg0) air emissions from soils in terrestrial landscapes are examined by comparing simulation results to published observational data and by performing sensitivity analyses. Despite uncertainties and simplifications, the model results obtained offer some degree of confidence in the model's joint ability to relate readily available environmental parameters to airborne emissions of Hg predicted by coupling simple atmospheric and soil parameters with Hg cycling and transport algorithms. The model reasonably predicted the observational data in the considered data sets except for one site for which significant uncertainty was associated with model input data. Predictions are consistent with many trends observed in the field studies; better predictions were obtained for nonvegetated systems (relative errors between 0.4 and 9.7%) than for shaded-soil landscapes (relative errors between 2.3 and 27%). The model reflected field data showing that daily average emission rates of Hg0, formed by the reduction of Hg(II), are primarily controlled by changes in solar radiation, soil moisture, temperature, and, to a lesser extent, wind conditions. The model may have potential use in several preliminary studies to characterize trends of airborne Hg emitted from terrestrial sources to the atmosphere.

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.

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...

The development of an extensively modified version of the environmental model GLEAMS to simulate fate and transport of mercury in small catchments is presented. Methods for parameter estimation are proposed and in some cases simple relationships for mercury processes are derived. An application is presented for the agricultural organic soils in the South Florida Everglades (USA). Model results compare well with the limited available field observations. Mass balance and sensitivity analyses indicate that atmospheric deposition and reduction leading to evasion are the most important parameters controlling concentrations of total mercury in the upper soil. For methyl mercury, the methylation and demethylation rates are also important. Because of the special characteristics of the soils examined here, results should be interpreted in a relative sense. Limitations and potential uses of the current status of model are discussed.

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...

Two methods widely used in agrometeorogical simulation models for describing field soil water regimes are compared and evaluated based on their ability to predict experimental data from a single field study. The first method is based on a numerical solution to the Richards' equation for describing water movement in the soil profile, while the second method is based on soil-water capacity terms. The evaluation of the performance of the two methods is based on goodness-of-fit statistics and graphical analysis. Results indicate that the first method is more accurate for both the upper and the lower soil zones; the second method, despite its low performance in the upper soil zone can adequately describe soil moisture conditions in the lower soil zones. Recommendations are made for model use based on comparison results, the intended model use, and the difficulty in the acquisition of required model inputs.

The present study deals with the application of various semi-analytical models to estimate ground temperature profiles. The models are based on the general formula of an analytical model which, considering a quasi steady state system, implements the superposition of annual and daily sinusoidal fluctuations. The models are then developed from the analytical model by replacing the steady state soil temperature with easily obtained daily average temperatures such as the average surface temperature, the average temperature of selected soil depths, the average soil temperature of the considered soil profile and, in the context of minimal data requirements, the average air temperature. Field measurements of soil temperatures at the surface and at various soil depths of the National Observatory of Athens station are used to evaluate the performance of the various models whereas the models’ applicability is also discussed.

Three biometeorological indices were examined in terms of their potential to describe the actual thermal sensation as this is experienced by humans at areas with different climatological characteristics. The thermal comfort scales, as derived from using Physiological Equivalent Temperature (PET), the Temperature-Humidity Index (THI) and the wind chill index (K), were compared to Actual thermal Sensation Votes (ASV) data as expressed on a 5-point scale. These data were collected by surveys that covered all four seasons of the year and were conducted in seven European cities (Athens, Thessaloniki, Milan, Fribourg, Cambridge, Sheffield and Kassel). Results show that, for any given ASV class, the corresponding classes calculated according to PET, THI and K, present a strong correlation with the climatic mean temperature of the survey site, which in turn leads to misclassification of the thermal sensation. Accordingly, an effort was made to apply an adjustment to the indices based on climatic mean temperature. Only small improvements were observed on the performance of the indices.

In this study an innovative approach for investigating the accumulated meteorological effects on cotton production during the growing season is presented. The quantification of the meteorological effects is based on the incorporation of the Bhalme and Mooley Drought Index (BMDI) methodology into the Vegetation Condition Index (VCI) extracted by NOAA/AVHRR data. The resulted Bhalme and Mooley Vegetation Condition Index (BMVCI) uses the same scale as the Z-Index of the Palmer Drought Severity Index (PDSI) for drought monitoring. The study area consists of the country of Greece. Eighteen years of NOAA/AVHRR data are examined and processed with the BMVCI to examine the unfavourable conditions for cotton production. For the validation of BMVCI an empirical relationship between the cotton production and the BMVCI values is derived. The method is developed based on the first sixteen years time series data and validated utilizing the following two years. The resultant high correlation coefficient and the approximation of the production for the validated years refer to very favourable results and confirms the usefulness of this integrated methodological approach as an effective tool to assess cotton production in Greece.

Dynamics of airborne mercury deposited onto catchment areas is investigated within the framework of a simulation model. Model results show that, for a particular atmospheric deposition rate, significant interannual variability in mercury transport flux in catchments is caused by climatology and corresponding differences in catchment soil loss rates; in comparison to the normal year, runoff flux increased by a factor of 2–3 for the wet year (rainfall 35% above normal) while for the dry year (rainfall 18% below normal) runoff flux decreased by factors of 5–7. The interaction of parameters describing soil type, topography and vegetation cover causes variability in both transport and emission fluxes among catchments; as soil loss rate increases by a factor of 5 due to variations in these parameters among the examined catchments, annual average transport flux increases by a factor of 3; and annual average emission flux of mercury (as Hg 0 ) from soil to the atmosphere decreases by a factor of 2 due to the decreased levels of soil mercury associated with catchment soil loss increases. Seasonal variability of transport flux is associated with seasonal changes in precipitation and soil loss rates while seasonal changes of emission flux are primarily due to changes in soil moisture regime and temperature. Although modeled results are consistent with observational data from previous studies, they must be interpreted in a relative sense due to the screening-level character of this study.