In the study, environmetric methods were successfully performed a) to explore natural and anthropogenic controls on reservoir water quality, b) to investigate spatial and temporal differences in quality, and c) to determine quality variables discriminating three reservoirs in Izmir, Turkey. Results showed that overall water quality was mainly governed by “natural factors” in the whole region. A parameter that was the most important in contributing to water quality variation for one reservoir was not important for another. Between summer and winter periods, difference in arsenic concentrations were statistically significant in the Tahtalı, Ürkmez and iron concentrations were in the Balçova reservoirs. Observation of high/low levels in two seasons was explained by different processes as for instance, dilution from runoff at times of high flow seeped through soil and entered the river along with the rainwater run-off and adsorption. Three variables “boron, arsenic and sulphate” discriminated quality among Balçova & Tahtalı, Balçova & Ürkmez and two variables “zinc and arsenic” among the Tahtalı & Ürkmez reservoirs. The results illustrated the usefulness of multivariate statistical techniques to fingerprint pollution sources and investigate temporal/spatial variations in water quality.
The aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.
In the study suitability of water quality index approach and environmetric methods in fi ngerprinting heavy metal pollution as well as comparison of spatial variability of multiple contaminants in surface water were assessed in the case of The Gediz River Basin, Turkey. Water quality variables were categorized into two classes using factor and cluster analysis. Furthermore, soil contamination index was adapted to water pollution index and used to fi nd out the relative relationship between the reference standards and the current situation of heavy metal contamination in water. Results revealed that surface water heavy metal content was mainly governed by metal processing, textile and tannery industries in the region. On the other hand, metal processing industry discharges mainly degraded quality of water in Kemalpasa and Menemen. Furthermore, Kemalpasa region has been heavily affected from tannery and textile industries effl uents. Moreover, pollution parameters have not been infl uenced by changes in physical factors (discharge and temperature). This study indicated the effectiveness of water quality index approach and statistical tools in fi ngerprinting of pollution and comparative assessment of water quality. Both methods can assist decision makers to determine priorities in management practices.
This paper presents active inductor based VCO design for wireless applications based on analysis of active inductor models (Weng-Kuo Cascode active inductor & Liang Regular Cascode active inductor) with feedback resistor technique. Embedment of feedback resistor results in the increment of inductance as well as the quality factor whereas the values are firstname.lastname@example.orgGHz (Liang) and email@example.comGHz (Weng-Kuo). The Weng-Kuo active inductor based VCO shows a tuning frequency of 1.765GHz ~2.430GHz (31.7%), while consuming a power of 2.60 mW and phase noise of -84.15 dBc/Hz@1MHz offset. On the other hand, Liang active inductor based VCO shows a frequency range of 1.897GHz ~2.522GHz (28.28%), while consuming a power of 1.40 mW and phase noise of -80.79 dBc/Hz@1MHz offset. Comparing Figure-of-Merit (FoM), power consumption, output power and stability in performance, designed active inductor based VCOs outperform with the stateof- the-art.