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EN
In this study, traffic noise in Seferihisar, which is a holiday resort by the Aegean Sea in the West of Turkey, was investigated. The noise occurring in summer and winter, on weekdays, and at weekends was mapped separately. Besides, land uses exposed to traffic noise were specified. In the carried out method, the land uses were primarily mapped by using the satellite images of the study area. Then, the noise was measured at 46 points during ten days in summer and winter, on weekdays and at weekends, and it was mapped by using Inverse Distance Weighting (IDW) method and by means of Geographic Information System (GIS) techniques. The measurements were taken in the daytime (07:00-19:00), in the evening (19:00-23:00), and at night (23:00-07:00) as stated in the Turkish Regulation of Assessment and Management of Environmental Noise. Since the time interval of the daytime measurements was large, the noise was measured in three different periods for the daytime measurements. In the formation of noise maps, logarithmic averages of all measurements taken at each point were used. The noise maps were overlapped with the land use maps by the help of the GIS techniques; the land use affected by the noise was analyzed. The results showed that the road traffic noise varied from summer to winter seasons in the study area depending on heavy or light traffic. The noise occurring in summer showed an increase both on weekdays and at weekends. While the limit value was exceeded in 49 of the measurement averages taken in winter, the limit value was exceeded in 96 measurements taken in summer. The noise maps formed according to the IDW method displayed that the areas in which the limit value was exceeded in the daytime in summer took up 14.27% of the study area while this rate went down to 4.06% in winter. The study area is one of the most important destinations for summer holiday. 20.12% of the area that exceeded the limit value in summer was builtup and 10.69% of them was tourism area. The noise together with population and traffic that increase in summer season is an important environmental problem against which some precautions should be taken. The findings of the study present significant results which might guide local governments in preventing and managing the noise.
EN
There have been many studies on rainfall erosivity and erosivity density (ED). However, it was not widely developed in Indonesia as a tropical country and has unique precipitation patterns. They are indicators for assessing the potential risk of soil erosion. The Air Bengkulu Watershed is undergoing severe land degradation due to soil erosion. This study aimed to analyze spatial-temporal in rainfall erosivity and ED based on monthly rainfall data (mm). The data used consisted of 19 weather stations during the period 2006–2020 and which are sparsely distributed over the watershed. The analysis was done by using Arnold’s equation. Then, the trend was tested using parametric and non-parametric statistics, and analysed with linear regression equation, and Spearman’s Rho and Mann Kendall’s tests. The spatial distribution of both algorithms was analysed using the inverse distance weighted (IDW) method based on the geographic information system (GIS). Unlike previous research findings, The long-term average monthly rainfall erosivity and ED revealed a general increase and decreasing trend, whereas it was found to be non-signifi- cant when both indices were observed. However, these results indicate a range from 840.94 MJ · mm−1 · ha−1 · h−1 · a−1,552.42 MJ · mm−1 · ha−1 · h−1 · a−1 to 472.09 MJ · mm−1 · ha−1 · h−1 · a−1  in that November month followed by December and April are the most susceptible months for soil erosion. Therefore, The upstream area of the region shows that various anthropogenic activities must be managed properly by taking into account the rainfall erosivity on the environment and that more stringent measures should be followed in soil and water conservation activities.
EN
The objective of this research is to determine the atmospheric concentrations and spatial distribution of benzene (B), toluene (T), ethylbenzene (E) and xylenes (X) (BTEX) and inorganic air pollutants (O3, NO2 and SO2) in the Yalova atmosphere during summer 2015. In this study, a combination of passive sampling and Geographical Information System-based geo-statistics are used with spatial statistics of autocorrelation to characterise the spatial pattern of the quality of air based on concentrations of these pollutants in Yalova. The spatial temporal variations of pollutants in the air with five types of land-use, residence, rural, highway, side road and industrial areas were investigated at 40 stations in Yalova between 7th August 2015 and 26th August 2015 using passive sampling. An inverse distance weighting interpolation technique was used to estimate variables at an unmeasured location from observed values at nearby locations. The spatial autocorrelation of air pollutants in the city was investigated using the statistical methods of Moran’s I in addition to the Getis Ord Gi. During the summer, highway and industrial sites had higher levels of BTEX then rural areas. The average concentration of toluene was measured to be 5.83 μg/m3 and this is the highest pollutant concentration. Average concentrations of NO2, O3 and SO2 are 35.64, 84.23 and 3.95 μg/m3, respectively. According to the global results of Moran’s I; NO2 and BTEX had positive correlations on a global space at a significant rate. Moreover, the autocorrelation analysis on the local space demonstrated significant hot spots on industrial sites and along the main roads.
EN
Road traffic noise visualization is vital in three-dimensional (3D) space. Designing noise observation points (NOPs) and the developments of spatial interpolations are key elements for the visualization of traffic noise in 3D. Moreover, calculating road traffic noise levels by means of a standard noise model is vital. This study elaborates on the developments of data and spatial interpolations in 3D noise visualization. In 3D spatial interpolation, the value is interpolated in both horizontal and vertical directions. Eliminating flat triangles is vital in the vertical direction. Inverse distance weighted (IDW), kriging, and triangular irregular network (TIN) are widely used to interpolate noise levels. Because these interpolations directly support the interpolation of three parameters, the developments of spatial interpolations should be applied to interpolate noise levels in 3D. The TIN noise contours are primed to visualize traffic noise levels while IDW and kriging provide irregular contours. Further, this study has identified that the TIN noise contours fit exactly with NOPs in 3D. Moreover, advanced kriging interpolation such as empirical Bayesian kriging (EBK) also provides irregular shape contours and this study develops a comparison for such contours. The 3D kriging in EBK provides a significant approach to interpolate noise in 3D. The 3D kriging voxels show a higher accurate visualization than TIN noise contours.
EN
This paper is a part of an ongoing research to examine the capability of geostatistical analysis for mobile networks coverage prediction, simulation and tuning. Mobile network coverage predictions are used to find network coverage gaps and areas with poor serviceability. They are essential data for engineering and management in order to make better decision regarding rollout, planning and optimisation of mobile networks.The objective of this research is to evaluate different interpolation techniques in coverage prediction. In method presented here, raw data collected from drive testing a sample of roads in study area is analysed and various continuous surfaces are created using different interpolation methods. Two general interpolation methods are used in this paper with different variables; first, Inverse Distance Weighting (IDW) with various powers and number of neighbours and second, ordinary kriging with Gaussian, spherical, circular and exponential semivariogram models with different number of neighbours. For the result comparison, we have used check points coming from the same drive test data. Prediction values for check points are extracted from each surface and the differences with actual value are computed.The output of this research helps finding an optimised and accurate model for coverage prediction.
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EN
In this paper, the hardness water parameter has been used to presentation spatial interpolation algorithm. Those analyses were carried out based on the results of water quality database from the Chief Sanitary Inspection for the Silesian Province (2012–2014). Only the points which have been assigned the value of water hardness below the 60 [mg/l] and above 400 [mg/l] were selected. These points can be sensitive to loss health safety of drinking water in supply system. Making above mentioned analysis allows to limit the number of water quality control points from 1500 to 150 for Silesia Province. The analysis showed clearly that the use of GIS in procedures of selecting monitoring points allow to reduce the cost of controls on both the official control and internal control carried out by water company.
PL
W artykule, do prezentacji algorytmu interpolacji przestrzennej wykorzystano wartości parametru twardość wody. Przeprowadzone analizy oparto na wynikach jakości wody pochodzących z baz danych Głównego Inspektora Sanitarnego dla województwa śląskiego (2012–2014). W analizach wyselekcjonowano tylko te punkty, o przypisanych wartościach granicznych, dla których stężenie parametru wskaźnikowego przyjmowało wartość poniżej 60 [mg/l] i wyższą niż 400 [mg/l]. Tak wyselekcjonowane punkty mogą być wrażliwe na utratę bezpieczeństwa wody. Przeprowadzenie tych analiz dla województwa śląskiego umożliwia znaczne ograniczenie ilości punktów kontroli jakości wody z ponad 1500 do ok 150. Analiza wykazała jednoznacznie, iż wykorzystanie GIS w typowaniu punktów monitoringu umożliwia obniżenie kosztów kontroli zarówno po stronie urzędowej jak i przedsiębiorstwa wodociągowego.
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