The aim of this study is to achieve a greater insight regarding quiet areas in agglomerations and contribute to their identification. The small urban setting of Mytilene located in the island of Lesvos (North Aegean, Greece), was the case study of this research. The need to control and manage environmental noise has led to the implementation of legislation that in many cases overlooks the acoustic perception of individuals. Due to the fact that noise management efforts along with the promotion of quietness in agglomerations, concerns primarily the residents of the city under consideration, it was essential to practically involve them in the decision making process. Based on citizen science contribution, a number of “places” were highlighted. The “places” mentioned from this procedure were checked by means of acoustic measurements, concerning the noise levels that occurred within the 24h period. A novel method regarding the duration, repetition, check spot and the positioning of measurement was used, in order to calculate the day, evening and night period’s noise levels (Lden). A performance matrix was then created in order to compare the results, in relation to acoustical, functional and visual criteria. Furthermore, in order to evaluate all the potential Quiet Areas in pairwise comparisons, an Analytical Hierarchy Process (AHP) was implemented. The provision of quietness, as a direct ecosystem service, is a major indicator of environmental quality. Additionally, the way that city inhabitants perceive their acoustic surroundings could determine the character of the landscape along with the quality of the soundscape and define the meaning of quietness that still remains vague.
In this study, the post-fire regeneration of three coniferous species (Pinus brutia, Cupressus sempervirens and Cupressus arizonica) was examined in the peri-urban forest of Thessaloniki, Northern Greece. The wildfire took place in July 1997 and burned almost 60% of the forest vegetation. During the autumn of 2010, 34 experimental plots were established in all aspects within the burned area. In each experimental plot the following measurements were carried out: height, diameter at breast height and crown projection in two perpendicular diameters. The results show that the Pinus brutia individuals, most of which came from natural regeneration, presented the best growth, in relation to the two other species in all aspects. As for Cupressus sempervirens, equal parts of which came from natural and artificial regeneration was characterized by remarkable growth especially in the Northeastern aspect. Finally, Cupressus arizonica existed in all aspects except the Northeastern. It also presented a satisfactory development, especially on the Southern aspect. Fourteen years after the fire pure or mixed stands of the above mentioned species show vigorous growth and good stem quality. Finally, the rates of participation of individual forest species indicate that the restoration has been achieved mainly by natural regeneration.
Air Quality Modeling is a method used to manage urban air quality. Various pollutant dispersion models are available, and each of these models is characterized by its own advantages and disadvantages. Thus, we aimed to evaluate the advantages and disadvantages of the models and to determine their performance by applying them to a specific district. This study also enabled the determination of the contribution of pollution sources to the total pollution and the current air quality of the study area according to the selected pollutants. In this study, both steady-state models (the American Meteorological Society/Environmental Protection Agency Regulatory Model-AERMOD and the Industrial Source Complex Short Term Model-ISCST-3) and the Lagrangian model (the California Puff Model-CALPUFF) were used as the dispersion models. The Körfez district of Kocaeli was selected as the study area. SO2 and PM10 emissions were observed as pollutants. The statistical methods of mean squared error (MSE) and fractional bias (FB) were employed to evaluate the performance of these models.
The results of the study revealed that the highest concentration varied according to the models and time options. However, when the modeling results for all of the sources were examined, the highest concentration was calculated by ISCST-3. The effect of the line source was less than the other sources (point and area). The contributions of the pollution sources differed according to each modeling program. The results of the statistical methods, which were used for evaluating the performance of the models, varied according to both the pollutant type and the time option. An overall ranking regarding modeling performance is as follows: CALPUFF > AERMOD > ISCST-3 for PM10 and ISCST-3 > CALPUFF > AERMOD for SO2. The MSE/FB results demonstrated that the predicted values were lower than the measured outcomes. Similarly, a comparison of the predicted and measured values with national and international limits revealed that various measures are necessary to reduce SO2 and PM10.
The scientific community has recognized the necessity for more efficiently selected inputs in artificial neural network models (ANNs) in river flows and has worked on this despite some shortcomings. Moreover, there is none or limited inclusion of ANN inputs coupled with atmospheric circulation under various patterns arising from the need of data downscaling for climate change predictions in hydrology domain. This paper presents the results of a novel multi-stage methodology for selecting input variables used in artificial neural network (ANN) models for river flow forecasting. The proposed methodology makes use of data correlations together with a set of crucial statistical indices for optimizing model performance, both in terms of ANN structure (e.g. neurons, momentum rate, learning rate, activation functions, etc), but also in terms of inputs selection. The latter include various previous time steps of daily areal precipitation and temperature data coupled with atmospheric circulation in the form of circulation patterns, observed river flow data and time expressed via functions of sine and cosine. Additionally, the no-linear behavior between river flow and the respective inputs is investigated by the ANN configuration itself and not only by correlation indices (or other equivalent contingency tools). The proposed methodology revealed the river flow of past four days, the precipitation of past three days and the seasonality as robust input variables. However, the temperature of three past days should be considered as an alternative against the seasonality. The produced models forecasting ability was validated by comparing its one-step ahead flow prediction ability to two other approaches (an auto regressive model and a genetic algorithm (GA)-optimized single input ANN).
Alternatively, to other studies that used parametric distributions (e.g. Gamma) in the estimation of the Standardized Precipitation Index (SPI), this study aims to apply a nonparametric method based on Kernel Density Estimator (KDE) for calculating the SPI. Results of the proposed method were compared with the ones from the most widely used parametric distribution, using a long dataset of monthly precipitation of four meteorological stations in Iran (including Bushehr, Mashhad, Tehran and Esfahan) over a period of 107 water years (1895-2002). The capability of KDE-based SPI was compared with the Gamma-based SPI at four-time scales of 3, 6, 9 and 12 months. The frequencies of the drought classes of SPI were calculated and compared with corresponding expected frequencies. The results revealed that the KDE is more consistent with the expected values of the SPI drought/wet classes frequencies (especially in the extreme classes) at all stations as well as at the four-time scales, compared to the Gamma distribution. The greatest deviation from the expected frequencies for KDE and Gamma distribution were about 10% and 150%, respectively. This study proposes a new analytical approach in modeling SPI that provides more accurate results pertaining frequency of occurrences of extreme drought events. The output of the study can be used in many fields (e.g. tourism, agriculture, insurance, etc.) that are influenced by severe droughts.
Tabriz is a large and industrial city in the north west of Iran which suffers from severe air pollution due to being surrounded by mountains. Considering lack of official reports about the levels of heavy metals in Tabriz air, two zones of the city were selected for measurement of heavy metals in the air of this city. One of the two zones was located in a very heavy traffic zone of the city center, while the other was situated in a zone with relatively low levels of pollution. Three metals of lead, cadmium, and mercury were measured in these two zones. Among the three measured metals, lead with a concentration of 662 ng m-3 had the highest concentration, followed by cadmium and mercury with concentrations of 92 and 8 ng m-3 respectively. The concentrations of lead and cadmium in cold seasons were 30-50% greater than in warm seasons, whereas no significant correlation was observed between the concentration of mercury and the ambient temperature. No significant difference was observed between the concentrations of these metals in the two sampling sites; the levels of lead in winter in the city center were 20% greater than that in the countryside.