Issue 1

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.      

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.

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.

The composite media, ZeliacTM was developed with the initial aim to provide low cost adsorbent with promising adsorption capacity. This study was conducted to investigate the removal of UV absorbance at 254 nm (UV254) in Kerian river water using ZeliacTM as the media. Batch experiments study was carried out to determine the optimum removal of UV254 by ZeliacTM. The experimental data were fitted to Langmuir and Freundlich isotherms to investigate the adsorption mechanism. The results from batch study exhibit that ZeliacTM is capable to remove 74.4% UV254 at the dosage of 7g/100 ml. Linear isotherm analysis suggests that the best fitting linear line is Freundlich isotherm with R2 values of 0.9294 indicating multilayer adsorption. Similarly, non-linear regression analysis reveals that the adsorption of UV254 by ZeliacTM is attributed by physisorption. The non-linear Freundlich isotherm gives a better fit to the adsorption of UV254 than Langmuir isotherm with R2 values of 0.9488. The results are supported with low values of X2, ARE, HYBRID and MPSED from the error function analysis.  Additionally, it is noted that the linear analysis overestimates the constant parameters’ values for Freundlich isotherm, which cause larger errors as estimated by the error function analysis. Hence, non-linear analysis is more appropriate in explaining the batch experiment data.