Georgios Sylaios

Water quantity and quality assessment of lower Nestos river, Greece

Monitoring quantity and quality data of a 3-yr (2006–2009) survey in the lower reach of Nestos river are presented. For the purposes of this study, monitoring was taking place at six sites along the river course. Discharge measurements were made at four stations. Physicochemical parameters (i.e., electrical conductivity, pH, dissolved oxygen and water temperature) were measured in situ while water quality samples were collected on a bi-weekly basis at all six stations along the river. Water samples for chemical analyses were collected for the determination of total phosphorus, orthophosphate, nitrite and nitrate nitrogen, ammonia, biochemical oxygen demand (BOD), chemical oxygen demand (COD), chlorophyll-a, TSS, alkalinity, sulfuric and chloride anions, major cations (Na+, K+, Mg+, Ca+) and metals. Two autographic telemetric stations also provided continuous stage and water quality data at two of the sites. ANOVA and t-test statistical analysis were used for the interpretation of the collected data. Nutrient concentrations were found within acceptable limits, while bicarbonates were the dominant ions. No significant seasonal and spatial variations were observed, suggesting little impact from human activities on the part of river basin under investigation.

Assessment of Trace Metals Contamination in the Suspended Matter and Sediments of a Semi-enclosed Mediterranean Gulf

This study investigates the distribution of heavy metals in the suspended particulate matter and the sediments of Kavala Gulf, located in Northern Greece. Particulate trace metals were determined in accordance to water column physicochemical parameters. Metals in sediments were related to the textural characteristics, the organic matter, and carbonate contents of surficial sediments. Results illustrated that the diverse human coastal activities influence the distribution of particulate and sediment metal concentrations, since lower trace metal levels were detected along the western (tourism) and eastern (aquaculture) coast, while increased values were observed at the central (urban and industrial). Overall, particulate metals show moderately low levels compared to other similar sites, with the exception of Ni and Cd depicting local peaks along the industrial coastal zone. Significantly increased levels for particulate lead (123.1 μg/g) and chromium (113.9 μg/g) were detected at the sites near the industrial zone. Direct effluents from a fertilizer plant appeared responsible for the increased Pb, Cr, and Cd levels, while the increased values in Zn, Cu, and Hg appear related to phosphogypsum leaching. Sediment contamination assessment indicated that the Kavala Gulf is characterized by clean to marginally polluted sediments, with moderately severe pollution levels along the urban coastline.

Fuzzy Modeling for Nitrogen and Phosphorus Removal Estimation in Free-Water Surface Constructed Wetlands

Modeling nutrient behavior and estimating nutrient removal in constructed wetland systems appears essential for their effective design and operation. In this work, a fuzzy logic model has been developed and applied to predict nitrogen and phosphorus removal efficiency in free-water surface (FWS) constructed wetlands (CWs). Fuzzy sets and fuzzy logic models have been widely used to model uncertainty when applied to complex systems and phenomena, not easily described by traditional mathematics. In order to develop and validate a fuzzy-logic model, experimental data regarding nitrogen and phosphorus removal obtained from a two-year experiment were used. Predicted values (model) fitted well the experimental data, and this also applied for independent data from several other CWs with different design and operating conditions. Therefore, it can be concluded that this methodology constitutes a satisfactory modeling tool for nitrogen and phosphorus removal estimation in free-water surface constructed wetlands.

CHLfuzzy: a spreadsheet tool for the fuzzy modeling of chlorophyll concentrations in coastal lagoons

CHLFuzzy is a user-friendly, flexible, multiple-input single-output Takagi-Sugeno fuzzy rule based model developed in a MS-Excel® spreadsheet environment. The model receives a raw dataset consisting of four predictor variables, e.g., water temperature, dissolved oxygen content, dissolved inorganic nitrogen concentration, and solar radiation levels. It then defines fuzzy sets according to a collection of fuzzy membership functions, allowing for the establishment of fuzzy ‘if–then’ rules, and predicts chlorophyll-a concentrations, which highly compare to the measured ones. The performance of the model was tested against the Adaptive Neural Fuzzy Inference System (ANFIS), showing satisfactory results. An extensive dataset of environmental observations in Vassova Lagoon (Northern Greece), during the years 2001–2002, was used to train the model and an independent dataset collected during 2004 was used to validate CHLFuzzy and ANFIS models. Although both models showed a similar performance on the training dataset, with quite satisfactory agreement between observed and modeled chlorophyll-a values, the best results were obtained using the CHLfuzzy model. Similarly, the CHLfuzzy model depicted a fairly good ability to hindcast chlorophyll-a concentrations for the verification dataset, thus improving ANFIS model forecasts. Overall results suggest that CHLfuzzy can potentially be used as a lagoon water quality forecasting tool requiring limited computational cost.

Assessment of Urban Pollution Footprint in a Typical Mediterranean Semi-Enclosed Gulf

This work assesses the changes, the causes and the impact of pollutant loads from treated wastewater in a typical Mediterranean coastal water body. Focusing on wastewater treatment and industrial plants, assessment is based on the analysis of systematic pollution indicators, such as the carbonaceous material, suspended solids and nutrients for the period 2009–2012. It occurs that Kavala Gulf receives annually approximately 5 million m3 from point sources. Organic matter, ammonia, nitrate and nitrite, and phosphorus annual loads increased sharply in 2012 to 130, 13, 8 and 12 tons, respectively, due to wastewater treatment technical inefficiencies. Additional nitrogen (390 tons) and phosphorus (53 tons) annual discharges from a phosphoric fertilizer plant raise significantly the inorganic pollution levels in the study area, causing frequent eutrophication incidents. Reduction of discharged volumes could be achieved through wastewater reclamation and reuse, either covering internal wastewater treatment plant requirements and/or watering urban and suburban green areas and groves and forests, cleaning streets, supplying water to firefighting systems, and irrigating agricultural and peri-urban landscape.

Sustainable Solutions to Wastewater Management: Maximizing the Impact of Territorial Co-operation

This issue of Environmental Processes presents a collection of 7 papers initially presented at the WASTEnet Program International Conference BSustainable Solutions to Wastewater Management: Maximizing the Impact of Territorial Co-operation^. The conference was held from June 19–21, 2015, in Kavala, East Macedonia, Greece (http://wastenet2015.org/). The conference was the final event of the WASTEnet Program funded by the Joint Managing Authority for the Joint Operational Program BBlack Sea Basin 2007–2013^. The scope of the international conference was to explore the potentials of low cost and energy solutions to wastewater management, and to present through the activities carried out by the WASTEnet partnership the state-of-the-art in the field of Sustainable Wastewater Treatment Systems, with emphasis on Natural Treatment Systems, Constructed Wetlands and other small or on-site treatment systems. The ultimate aim was the implementation potential of such wastewater treatment systems in the wider Black Sea Basin area, and generally, in developing countries. The conference also emphasized on the diffusion of the best available knowledge in this field from international experts and the exchange of experiences with an audience composed of key decision makers from the local, the regional and the national level, the EU representation in these Black Sea countries, NGOs, professional (Suppl 1):S1–S3 DOI 10.1007/s40710-016-0186-7

An integrated coastal zone observatory at municipal level: the case of Kavala Municipality, NE Greece

The Integrated Coastal Zone Management (ICZM) Observatory of Kavala Municipality has been developed during the Mare Nostrum Project following the well-known DPSIR concept (Drivers, Pressures, States, Impacts and Responses). The Observatory aims to aid the day-to-day operations of the Municipality, collecting, analyzing and processing historic, present and forecasted environmental, socio-economic and legal-institutional ICZM datasets collected from a variety of sources. Data are presented in a series of well-structured layers imported in a Geographic Information System (GIS) web-environment, to aid decision-makers at Municipal, Regional and National level to formulate policies on ICZM matters; to provide reliable datasets to scientists and engineers; to engage active citizens and the broader public into coastal zone issues; to promote the distribution of information among coastal managers, stakeholders and the general public. Static datasets as topographic, bathymetric, geologic and other base maps, urban and rural planning maps, local municipal acts, public networks and locations of public interest were digitized and embedded in the system. Historic environmental data together with dynamic data produced daily by a series of coupled operational models (meteorological, hydrologic, hydrodynamic, wave and eutrophication) and climate change datasets were also included. The legal-institutional framework of the coastline and the interactive tools to enhance public participation represent novel elements of the herein presented ICZM Observatory. The Kavala Gulf mussel farmers, the local Port Authority and the decision-makers on siting hydroplane landing-zones are the main user groups of the ICZM Observatory.

Sub-optimal model-based deficit irrigation scheduling with realistic weather forecasts

This paper analyses the performance of sub-optimal irrigation schedules obtained daily by solving a multi-objective optimization problem with updated weather measurements and forecasts. The approach was tested using five crops at four European locations with contrasting weather conditions. Four- and 6-day Global Forecast System (GFS) forecasts were used at all locations, and comparison with a down-scaled locally tuned model was conducted at one location. Accurate GFS temperature forecasts were observed at all four locations, but the accuracy of the potential evapotranspiration calculated from the GFS forecasts was not as consistent. Precipitations forecasts were very poor at all locations. In Greece, the down-scaled locally tuned forecasts were only marginally better than the GFS ones. In most cases, recomputing the sub-optimal irrigation schedule daily greatly reduced the impact of the imperfect weather forecasts on the final results. Using 4- or 6-day actual forecasts did not yield results appreciably better than those obtained using only historical averages as surrogate forecasts. The main consequence of the imperfect forecasts was that the final yield differed from the target one, but the (yield, irrigation) combination remained close to optimal, unless the target yield was set too high and water availability was not the main factor limiting crop development.

Impact of Irrigation Technologies and Strategies on Cotton Water Footprint Using AquaCrop and CROPWAT Models

Different irrigation technologies and strategies have been proposed as means to improve the overall irrigation management and thus the rational use of the available water resources. The water footprint has been proposed as an index of the rational use of the water needed to produce goods and services. In agriculture, the water footprint is defined as the ratio of the sum of crop’s cumulative evapotranspiration plus the amount of freshwater polluted during the cultivation process against final yield, and is further discerned to green, blue and gray components. In this study, we used two FAO’s agronomic models named AquaCrop and CROPWAT, as well as two water footprint methodological frameworks to assess the impact of irrigation technology and strategy on the reduction of cotton water footprint in Northern Greece. Results showed that the impact of both irrigation technology and irrigation strategy in the green, blue and total water footprints was better estimated by AquaCrop model while CROPWAT model seems to be able to evaluate only the changes in the irrigation strategy. The drip technology could reduce the total water footprint by 5%, when compared to sprinkler, while deficit irrigation by roughly 12%, when compared to full. Lastly, in all cases the green water footprint was approximately 55% of the total.

Seagrass detection in the mediterranean: A supervised learning approach

Abstract We deal with the problem of detecting seagrass presence/absence and distinguishing seagrass families in the Mediterranean via supervised learning methods. By merging datasets about seagrass presence and other external environmental variables, we develop suitable training data, enhanced by seagrass absence data algorithmically produced based on certain hypotheses. Experiments comparing several popular classification algorithms yield up to 93.4% accuracy in detecting seagrass presence. In a feature strength analysis, the most important variables determining presence–absence are found to be Chlorophyll-α levels and Distance-to-Coast. For determining family, variables cannot be easily singled out; several different variables seem to be of importance, with Chlorophyll-α surpassing all others. In both problems, tree-based classification algorithms perform better than others, with Random Forest being the most effective. Hidden preferences reveal that Cymodocea and Posidonia favor the low, limited-range chlorophyll-α levels ( 39), while Ruppia prefers euryhaline conditions (37.5–39).