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Electric Arc Furnace Dust (EAFD) generated through the production of carbon and alloy steel products
is classified as environmentally hazardous waste mainly because of its relatively high levels of Pb, Cd
and Cr. The aim of the present work was to investigate the effect of the EAFD addition on the properties
of concrete building products (e.g concrete bricks) and to stabilize the toxic elements contained in
EAFD in order to get environmentally accepted products. Concrete specimens were prepared by vibration
and press-forming of mixtures containing cement, sand, dolomite, calcium stearate with water and the
addition of different percentages of EAFD. For the immobilization of the toxic elements of EAFD within the
matrix of the specimens, different percentages of each one of NaH2PO4, H3PO4 and of a mixture of H3PO4
- FeSO4.7H2O were added successively to each one of the mixtures as stabilizing materials. Modulus of
rupture (MOR) and water absorption (WA) tests showed that the specimens produced had specifications
within accepted limits for relevant products. Leaching tests applied on the specimens proved the compliance
with imposed leachate Maximum Acceptable Limits (MAL), since the practiced stabilizing materials
effectively immobilize toxic elements within the prepared specimens.

An artificial neural network (ANN) model-based approach was developed and applied to estimate
values of air temperature and relative humidity in remote mountainous areas. The application site
was the mountainous area of the Samaria National Forest canyon (Greece). Seven meteorological
stations were established in the area and ANNs were developed to predict air temperature and
relative humidity for the five most remote stations of the area using data only from two stations
located in the two more easily accessed sites. Measured and model-estimated data were compared
in terms of the determination coefficient (R2), the mean absolute error (MAE) and residuals
normality. Results showed that R2 values range from 0.7 to 0.9 for air temperature and from 0.7 to
0.8 for relative humidity whereas MAE values range from 0.9 to 1.8 oC and 5 to 9%, for air
temperature and relative humidity, respectively. In conclusion, the study demonstrated that ANNs,
when adequately trained, could have a high applicability in estimating meteorological data values in
remote mountainous areas with sparse network of meteorological stations, based on a series of
relatively limited number of data values from nearby and easily accessed meteorological stations.

Human thermal comfort conditions were determined in two mountainous regions of Greece, Gerania
mountains (MG) in east continental Greece, and mountainous Nafpaktia (MN) in west continental
Greece. Both regions are unexploited with considerable tourist potential. Four sites in each study
region were selected on the basis of different altitude. Air temperature and humidity, 1.5 m above
ground surface, were recorded simultaneously every 15 minutes by sensors with dataloggers in
selected sites between 23 June and 28 August 2007. Data of the above parameters were used for
the calculation of the thermohygrometric index from which thermal comfort conditions were
evaluated. Also, an artificial neural network model, was applied for the THI values evaluation at the
highest examined altitudes based on the respective values of the lowest examined altitudes in both
MG and MN. Results showed that from 09:00 to 20:00 h, MN was found to be more suitable, in
relation to MG, for tourist and recreation activities at altitudes of 1338 m. At lower altitudes, both
study regions could be proposed in an equal basis for the above activities during summer. Also, for
the same period, thermal comfort conditions at the highest examined altitudes of MN and MG can
accurately be predicted using artificial neural network models on the basis of those at lowest
examined altitudes. From 21:00 to 08:00 h, MN can be considered clearly as a better tourist
destination than MG.

Although solar salterns worldwide use seawater of identical chemical composition as the raw
material for salt production, the size and quality of the halite crystals that precipitate in the
crystallizer ponds is highly variable. Biological processes have been implicated to be responsible for
the differences observed, but the “missing link” between saltworks biology and solar salt quality has
never unequivocally been identified. This paper presents an overview of the different organic
chemicals that are formed by the members of the microbial communities in saltern evaporation and
crystallizer ponds as osmotic stabilizers as well as different compounds formed during further
microbial metabolism of those osmotic solutes. Examination of the in situ concentrations and the
possible role of glycerol, glycine betaine, ectoine, dihydroxyacetone, acetate, lactate, and other
organic compounds failed to identify one or more compounds that may accumulate at concentrations
high enough to significantly modify the formation of sodium chloride crystals in the salterns and to
negatively influence the quality of the salt produced.