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). Ecol Chem Eng S. 2017;24(2):285-298. DOI: 10.1515/eces-2017-0020. [4] Karbassi A, Nouri J, Mehrdadi N, Ayaz G. Flocculation of heavy metals during mixing of freshwater with Caspian Sea water. Environ Geol. 2008;53(8):1811-1816. DOI: 10.1007/s00254-007-0786-7. [5] Najafpour S, Alkarkhi A, Kadir M, Najafpour GD. Evaluation of spatial and temporal variation in river water quality. Int J Environ Res. 2009;2(4):349-358. DOI: 10.1016/j.jenvman.2009.11.001. [6] Wahaab RA, Badawy MI. Water quality assessment of the River Nile system: an overview. Biomed Environ Sci. 2004


The aim of this study was to evaluate the temporal variations of selected heavy metals level in anaerobic fermented and dewatered sewage sludge. Sewage sludge samples were collected in different seasons and years from three municipal wastewater treatment plants (WWTPs) located in Northern Greece, in Kavala (Kavala and Palio localities) and Drama (Drama locality) Prefectures. An investigation of the potential of sludge utilization in agriculture was performed, based on the comparison of average total heavy metal concentrations and of chromium species (hexavalent, trivalent) concentrations with the allowed values according to the Council Directive 86/278/EEC and Greek national legislation (Joint Cabinet Decision 80568/4225/91) guidelines. In this regard, all the investigated heavy metals (Cd, Cr, Cu, Ni, Pb, Zn, Hg) and chromium species Cr(VI) and Cr(III) have average concentrations (dry matter weight) well below the legislated thresholds for soil application, as following: 2.12 mg kg−1 Cd; 103.7 mg kg−1 Cr; 136.4 mg kg−1 Cu; < 0.2 mg kg−1 Hg; 29.1 mg kg−1 Ni; 62.0 mg kg−1 Pb; 1253.2 mg kg−1 Zn; 1.56 mg kg−1 Cr(VI) and 115.7 mg kg−1 Cr(III). Values of relative standard deviation (RSD) indicate a low or moderate temporal variability for domestic-related metals Zn (10.3-14.7%), Pb (27.9-44.5%) and Cu (33.5-34.2%), and high variability for the metals of mixed origin or predominantly resulted from commercial activities, such as Ni (42.4-50.7%), Cd (44.3-85.5%) and Cr (58.2-102.0%). For some elements the seasonal occurrence pattern is the same for Kavala and Palio sludge, as following: a) Cd and Cr: spring>summer>winter; b) Cu, Ni and Pb: winter>spring>summer. On average, in summer months (dry season) metal concentrations are lower than in spring and winter (wet seasons), with the exception of Zn. For Kavala and Palio the results demonstrate that the increased number of inhabitants (almost doubled) in summer time due to tourism does not influence the metal levels in sludge. Comparing the results obtained for similar spring-summer-winter sequences in 2007 and 2010/11 and for the spring season in 2007, 2008 and 2010, it can be noticed that, in general, the average heavy metal contents show an increasing tendency towards the last year. In all the measurement periods, the Palio sludge had the highest metal contents and Kavala sludge the lowest, leading to the conclusion that the WWTP operating process rather than population has a significant effect upon the heavy metal content of sludge. Cr(VI)/Cr(total) concentration ratios are higher for Kavala sludge in the majority of sampling campaigns, followed by Drama and Palio sludge. The metals which present moderate to strong positive correlation have common origin, which could be a domestic-commercial mixed source.

References [1] Steinnes E, Uggerud HTh, Pfaffhuber KA, Berg T. Atmospheric deposition of heavy metals in Norway, National moss survey 2015. Norwegian Environmental Agency. 2016; M-594:57. ISBN 9788242528599, . [2] European Atlas: Spatial and Temporal Trends in Heavy Metal Accumulation in Mosses in Europe (1990-2005). United Kingdom: UNECE ICP Vegetation; 2008. ISBN 978185531239-5. [3] Harmens H, Norris D, Mills G, and

, Abdel-Aziz NE, Khidr AAA. Evaluation of spatial and temporal variations of surface water quality in the Nile River. Ecol Chem Eng S. 2018;25:569-80. DOI: 10.1515/eces-2018-0038. [4] Taylor SD, He Y, Hiscock KM. Modelling the impacts of agricultural management practices on river water quality in Eastern England. J Environ Manage. 2016;180:147-63. DOI: 10.1016/j.jenvman.2016.05.002. [5] Pirani M, Panton A, Purdie DA, Sahu SK. Modelling macronutrient dynamics in the Hampshire Avon River: A Bayasian approach to estimate seasonal variability and total flux. Sci Total

.1016/j.chemosphere.2004.04.013. [4] Munir S, Habeebullah TM, Serowi AR, Gabr SS, Mohammed AMF, Morsy EA. Quantifying temporal trends of atmospheric pollutants in Makkah (1997-2012). Atmos Environ. 2013;77:647-655. DOI: 10.1016/j.atmosenv.2013.05.075. [5] Olszowski T. Seasonal values of the gaseous concentrations of air quality ratings in a rural area. Ecol Chem Eng S. 2013;20(4):719-732. DOI: 10.2478/eces-2013-0050. [6] An DD, Co HX, Oanh NTK. Photochemical smog introduction and episode selection for the ground-level ozone in Hanoi, Vietnam. VNU Journal of Science

Database. , 2009. [22] Kyle AD, Wright CC, Caldwell JC, Buffler PA, Woodruff TJ. Evaluating the health significance of hazardous air pollutants using monitoring data. Public Health Rep. 2001;16:32-44. DOI: 10.1093/phr/116.1.32. [23] Tecer LH, Tagil S. Spatial and Temporal Variations of Nitrogen Dioxide and Ozone Concentrations Assessment Using a GIS Based Geostatistical Approach in Balikesir, Turkey. 12th International Multidisciplinary Scientific GeoConference, , SGEM2012 Conference Proceedings/ ISSN 1314


This work refers to the modelling of heat transfer in horizontal ground heat exchangers. For different conditions of collecting heat from the ground and different boundary condition profiles of temperature in the ground were found, and temporal variations of heat flux transferred between the ground surface and its interior were determined. It was taken into account that this flux results from several different mechanisms of heat transfer: convective, radiative, and that connected with moisture evaporation. It was calculated that ground temperature at great depths is greater than the average annual ambient temperature.


According to global inventories the agricultural field production contributes in a significant measure to increase of concentration of greenhouse gases (CO2, N2O, CH4) in the atmosphere, however their estimated data of emissions of soil origin differ significantly. Particularly estimates on nitrogen-oxides emissions show a great temporal and spatial variability while their formations in microbial processes are strongly influenced by biogeochemical and physical properties of the soil (eg microbial species, soil texture, soil water, pH, redox-potential and nutrient status) and land use management through the impact of the application of natural and synthetic fertilisers, tillage, irrigation, compaction, planting and harvesting. The different monitoring systems and inventory models were developed mostly from atmospheric chemistry point of view and little comprehensive data exist on the processes related to GHG emissions and their productions in agricultural soils under ecological conditions of Central Europe. This paper presents the new results of a project aimed elaboration of an experimental system suitable for studying relationships between the production and emission of greenhouse gases and plant nutrition supply in agricultural soils under Hungarian ecological conditions. The system was based on a long-term fertilisation field experiment. Mesocosm size pot experiments were conducted with soils originating from differently treated plots. The production of CO2 and N2O was followed during the vegetation period in gas traps built in 20 cm depth. Undisturbed soil columns were prepared from the untreated side parcels of the field experiment and the production of CO2 and N2O was studied at 20, 40 and 60 cm depth. A series of laboratory microcosm experiments were performed to clarify the microbial and environmental effects influencing the gas production in soils. The CO2 and N2O were determined by gas chromatography. The NOx was detected by chemiluminescence method in headspace of microcosms. In the mesocosm and soil columns experiments influence of plant nutrition methods and environmental factors was successfully clarified on seasonal dynamics and depth profile on CO2 and N2O productions. The database developed is suitable for estimating CO2 and N2O emissions from agricultural soils.


This study aimed to demonstrate efficiency of documented index method “universal water quality index-UWQI” to evaluate surface water quality and investigate seasonal and temporal changes, in the case of Gediz River Basin Turkey. UWQI expressed results relative to levels according to criteria specified in European legislation (75-440 EEC). The method produced a unitless number ranging from 1 to 100 and a higher number was indicator of better water quality. Water quality is classified into five classes and index scores between 95-100 represent excellent and lower than 24 represent poor quality. In the study, dissolved oxygen-DO, pH, mercury-Hg, cadmium-Cd, total phosphorus-TP, biochemical oxygen demand- BOD and nitrate nitrogen-NO3-N have been chosen as index determinants. Samples analyzed for these variables were collected from five stations on monthly basis along two years. Based on UWQI classification scheme, water quality at sampling stations had scores below 40 and assigned to “marginal” which is between fair and poor quality class. On the other hand sub-indices of water quality determinants showed seasonal differences for some parameters. Cd concentrations were higher in “high flow” and lower values were observed in “low flow” periods. This was explained by negative impact of urban runoff on water quality. On the other hand DO concentrations were higher in “high flow” period. Under “low flow” conditions water quality at upstream stations (where the industrial density is low) was comparably better than downstream part. The study showed that index approach can be efficient tool to: a) evaluate water quality, b) investigate spatial and seasonal variations and finally, c) extract required information from complex data sets that is understandable by non-technical people.

15.11.2013. [6] Harmens H, Norris D, Mills G. Heavy metals and nitrogen in mosses: spatial patterns in 2010/2011 and long-term temporal trends in Europe. ICP Vegetation Programme Coordination Centre, Bangor, UK: Centre for Ecology and Hydrology; 2013:63. [7] Harmens H, Norris DA, Koerber GR, Buse A, Steinnes E, Rühling A. Temporal trends (1990-2000) in the concentration of cadmium, lead and mercury in mosses across Europe. Environ Pollut. 2008;151:368-376. DOI: 10.1016/j.envpol.2007.06.043. [8] Harmens H, Ilyin I, Mills G, Aboal JR, Alber R, Blum O, et al. Country