Air quality and meteorological patterns of an early spring heatwave event in an industrialized area of Attica, Greece.

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.

Data on verbal expressions for thermal sensation and comfort in the Greek language.

This article presents data collected during a web-based survey on expressions used to describe thermal sensation and comfort in the Greek language. The survey used a structured questionnaire and delivered through Google Forms. The survey was promoted through social networks and conducted in spring 2019. The data presented herein comprise of the participants' responses to the questionnaire. A total of 359 questionnaires were completed. The participants were Greek speakers, older than 12, with at least a basic knowledge of the English language. The participants were asked to: (a) select the most appropriate translation, from English to Greek, of the nine-point ISO 10551 scale of perceptual judgment on personal thermal state, (b) formulate five, seven and nine-point thermal sensation scales, (c) report the category of the thermal sensation scale that signifies thermal comfort and (d) to assess the relative distances between the thermal sensation categories of the five, seven and nine-point thermal sensation scales. For the translation of the ISO 10551, the respondents were allowed to choose from a list of 30 Greek wordings. The data have been analysed in the research article entitled "Native influences on the construction of thermal sensation scales" [1].

Native influences on the construction of thermal sensation scales.

Thermal scales assess thermal environments in terms of thermal sensation and comfort. The number of scale's categories and their verbal realization/labels, especially when translated for local applications, are subjects of research. This study examined variations from the ISO 10551 thermal scale when translated into Greek language. We conducted an online survey asking participants to translate the English ISO 10551 scale, develop their own scales (five, seven and nine-point), report a wording for thermal comfort, and assign discrete values to scales' categories proportional to their perceived distances. Overall, 357 participants enrolled in the survey while data from 321 questionnaires included in the analysis. The interpretations of ISO 10551 categories varied (6-18) although the majority consisted of the exact translation. The wordings of the formulated scales differed from ISO 10551 scale indicating a more intense expression mode. The labels overlapped in the extreme categories of the nine-point scale supporting the use of the seven-point scale. Most participants (~ 65%) reported thermal comfort equivalent to neutrality. About half of the participants reported equal distances between the categories of the scales. The results can be applied on thermal sensation studies having a possible impact on the use of outdoor spaces under various contexts, i.e., public health, urban design, and energy conservation.

Correction to: Thermal sensation and climate: a comparison of UTCI and PET thresholds in different climates.

The article was published without special issue designation resulting in regular issue compilation. The author group and publisher regret the error and ask the article be considered for Special Issue: Subjective approaches to thermal perception.

Data on thermal sensation, perception and microclimatic parameters in a city with Mediterranean climate.

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).

Thermal sensation and climate: a comparison of UTCI and PET thresholds in different climates.

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.

Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station.

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.

Testing and application of simple semi-analytical models for soil temperature estimation and prediction in environmental assessments.

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 temperature profile with MAE values in the range of 0-0.4°C and RMSE values in the range of 0.1-1.5°C.

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.

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.

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 R(2) value on the order of 0.9. The lowest R(2) 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 R(2) 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 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 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.

Modeling mercury fluxes and concentrations in a Georgia watershed receiving atmospheric deposition load from direct and indirect sources.

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.

Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source.

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 preliminary study of the application of some predictive modeling techniques to assess atmospheric mercury emissions from terrestrial surfaces.

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.

An application of parametric and nonparametric models to the assessment of fluoride levels in vegetation exposed to stack emissions of an aluminum reduction plant in Greece.

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.