No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Ongoing development of modern society, based on consumption of goods and services, leads to the increase of compulsoriness of economic agents to face market requirements by increasing the degree of local and regional industrialization. Establishment of new economic activities generates negative pressures on the environment and surface waters, generating increased pollution, manifested by vulnerability of aquatic ecosystems to stressors. Preliminary studies carried out within the doctoral thesis entitled 'Research on the evolution, magnitude and complexity of the impact of economic activities on the East Jiu' include information on characteristic elements of the East Jiu River basin, in accordance with the Water Framework Directive 2000/60/CE. The objectives of the field research aimed to identify economic activities in the eastern Jiu Valley generating an impact on the environment (especially the mining industry, but also timber exploitation and processing, local agriculture, animal husbandry and waste storage), establishing a quarterly monitoring program of the river basin, identification of flora and fauna species and identification of areas vulnerable to potential pollution. Based on observations made in situ and on information obtained from the evolution process of the monitoring program, the appropriate methodologies for assessing physical-chemical and ecological quality of the water were selected. Study of the evolution of the impact generated by economic activities on the East Jiu was carried out by mathematical modelling, with finite volumes, of the East Jiu River basin and plotting of pollutant dispersion maps. The magnitude and complexity of impact generated by economic activities was studied by using a complex system based on fuzzy logic, designed based on interactions between natural and artificial systems, between physical-chemical indicators of water and ecosystem. The research carried out substantiates in development of necessary technical measures to reduce the impact generated by economic activities located in eastern Jiu Valley, without significantly changing the hydrodynamics of the river basin. Following research, during different research stages, methods, techniques and tools were designed and accomplished with the help of which, water and aquatic ecosystems’ quality can be assessed, as well as the impact generated by human activity on the Jiu River, at a given moment and/or continuously.:CONTENT ACKNOWLEDGEMENTS SUMMARY LIST OF FIGURES LIST OF TABLES ABBREVIATIONS INTRODUCTION PURPOSE OF THE THESIS AND RESEARCH METHODOLOGY CHAPTER 1 THE EAST JIUL RIVER HYDROGRAPHIC BASIN 1.1. Soil and subsoil of the Eastern part of Jiu Valley 1.2. Climate description of the Eastern part of Jiu Valley 1.3. Geology particularities of the Eastern part of Jiu Valley 1.4. Groundwater features of the Eastern part of Jiu Valley 1.5. Flora and fauna of the Eastern part of Jiu Valley CHAPTER 2 SOURCES OF IMPACT ON THE QUALITY OF WATER, RIPARIAN, TERRESTRIAL AND AQUATIC ECOSYSTEMS 2.1. Mining industry 2.2. Wood processing industry in the Eastern part of Jiu Valley 2.3. Urban agriculture and local animal husbandry 2.4. Inappropriate urban household waste storage CHAPTER 3 MONITORING PROGRAM AND METHODS OF EVALUATION OF THE QUALITY OF THE EAST JIUL RIVER 3.1. Establishment of monitoring (control) sections 3.2. Monitoring program of the East Jiu River basin 3.3. Sampling, transport and analysis of water samples 3.4. Methodology used to establish the water quality CHAPTER 4 QUALITY ASSESSMENT OF WATER IN THE EASTERN JIU HYDROGRAPHIC BASIN 4.1. Section 1 - Jieț River - upstream of household settlements (blank assay) 4.2. Section 2 - East Jiu River - in the area of Tirici village 4.3. Section 3 - Răscoala brook - before the confluence with East Jiu River 4.4. Section 4 - East Jiu River - after the confluence with the Răscoala brook 4.5. Section 5 - Taia River - upstream of the confluence with East Jiu River 4.6. Section 6 - East Jiu River - before the confluence with the Taia River 4.7. Section 7 - East Jiu River - after the confluence with the Taia River 4.8. Section 8 - Jiet River downstream of household settlements 4.9. Section 9 - East Jiu River - after the confluence with the Jieț River 4.10. Section 10 - East Jiu River - before the confluence with Banița River 4.11. Section 11 - Roşia River - upstream of household settlements 4.12. Section 12 - Bănița River - after the confluence with the Roșia River 4.13. Section 13 - East Jiu River - after the confluence with the Banița River 4.14. Section 14 - Maleia River - before the confluence with East Jiu River 4.15. Section 15 - Slătioara River - before the confluence with East Jiu River 4.16. Section 16 – East Jiu River - before the confluence with West Jiu River CHAPTER 5 INFLUENCES OF PHYSICAL-CHEMICAL FACTORS ON AQUATIC ICHTHYOFAUNA IN THE EAST JIU RIVER BASIN 5.1. Total suspended solids and aquatic ecosystems 5.2. Acidity or basicity reaction of surface watercourses 5.3. Aquatic ecosystem requirements for gas oversaturation 5.4. Nitrogenous compounds in watercourse 5.5. Phenols, aquatic ecosystems and water quality CHAPTER 6 ANALYSIS OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN PART OF JIU VALLEY 6.1. Impact analysis of mining industry in the Eastern Part of Jiu Valley 6.2. The general impact of Eastern Jiu Valley dumps to water quality 6.3. Research on effective infiltration in the Eastern part of Jiu Valley 6.4. Research on groundwater quality in the Eastern part of Jiu Valley 6.5. Analysis of the impact generated by local micro-agriculture 6.6. Analysis of the impact generated by deforestation and wood processing 6.7. Analysis of the impact generated by non-compliant landfilling of waste CHAPTER 7 EVOLUTION OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN JIU VALLEY 7.1. Analysis of the dynamic elements of the watercourse - RMA2 mode 7.2. Analysis of pollutants concentration evolution in the water course - RMA4 module 7.3. Computational field and composition of the energy model of the East Jiu River 7.4. Extension and evolution of the impact generated by economic activities on the East Jiu River 7.5. Extension and evolution of the impact caused by organic pollution of the East Jiu River CHAPTER 8 MAGNITUDE AND COMPLEXITY OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN JIU VALLEY 8.1. Definition of input linguistic variables 8.2. Linguistic outputs of the fuzzy interference system 8.3. Defining the Black Box set of rules 8.4. Proficiency testing of complex systems based on fuzzy logic 8.5. While it is all about the wheel do not forget about the cube CONCLUSIONS AND PERSONAL CONTRIBUTIONS REFERENCES

No description available.

Für die Luftqualitätsbewertung in Städten sind Informationen zur raumzeitlichen Variabilität luftgetragener Feinstäube auf kleiner Skala von wichtiger Bedeutung. Standardisierte Messverfahren, zur Bestimmung von Partikelkonzentrationen, sind mit hohem Aufwand verbunden, weshalb dichte Messnetze fehlen. Partikelausbreitungsmodelle sind kompliziert in der Anwendung und/oder benötigen hohe Computerrechenleistung. Infolgedessen gibt es bezüglich örtlicher Partikelkonzentrationen große Informationslücken. Diese Arbeit untersucht die mikroskalige Variabilität von Aerosolen in Raum und Zeit mit unterschiedlichen Methoden. Es wurden Erhebungen mit mobilen Sensoren und eine Passantenbefragung durchgeführt. Weiterhin wurden in dieser Arbeit die physikalischen Partikeltransportmodelle ENVI-met und Austal2000 in ihrer Leistung bewertet und in angewandten Studien eingesetzt. Weiterhin wurde ein neuronales Netzwerk zur Vorhersage von Partikelkonzentrationen entwickelt. Die Untersuchungen erfolgten in den Städten Aachen und Münster. Es konnten unerwartete Verteilungsmuster hinsichtlich der Massekonzentration von Partikeln beobachtet werden. In einem innerstädtischen Park wurden diffuse Partikelquellen identifiziert, mit einem deutlichen Hinweis darauf, dass feuchtgelagerte Wegedecken einen maßgeblichen Anteil an lokalen Partikelimmissionen hatten. Weiterhin wurde Straßenverkehr als wichtiger Beitrag zum städtischen Aerosol identifiziert. Passanten, die verschiedenen Partikelkonzentrationen ausgesetzt waren, konnten diese perzeptiv nicht unterscheiden. Simulationsergebnisse von Austal2000 und ENVI-met wiesen Unterschätzungen im Vergleich zu Messwerten auf. Das entwickelte neuronale Netzwerk prognostizierte Partikelkonzentrationen teilweise mit hoher Genauigkeit. Das große Potenzial von neuronalen Netzen für die Vorhersage von Partikelkonzentrationen in räumlicher und zeitlicher Ausdehnung, auch für den Bereich der Luftqualitätsüberwachung, wurde aufgezeigt. / Knowledge about the micro-scale variability of airborne particles is a crucial criterion for air quality assessment within complex terrains such as urban areas. Due to the significant costs and time consumption related to the work required for standardized measurements of particle concentrations, dense monitoring networks are regularly missing. Models that simulate the transmission of particles are often difficult to use and/or computationally expensive. As a result, information regarding on-site particle concentrations at small scales is still limited. This thesis explores the micro-scale variability of aerosol concentrations in space and time using different methods. Experimental fieldwork, including measurements with mobile sensor equipment alongside a survey, and modeling approaches were conducted. Applied simulation studies, a performance assessment of two popular particle dispersion models, namely Austal2000 and ENVI-met, as well as the development of an ANN model are presented. The cities of Aachen and Münster were chosen as case studies for this research. Unexpected patterns of particle mass concentrations could be observed, including the identification of diffuse particle sources inside a park area with strong evidence that unpaved surfaces contributed to local aerosol concentration. In addition, vehicle traffic was proved to be a major contributor of particles, particularly close to traffic lanes. Results of the survey reveal that people were not able to distinguish between different aerosol concentration levels. Austal2000 and ENVI-met turned out to have room for improvement in terms of the reproduction of observed particle concentration levels, with both models having a tendency toward underestimation. The newly developed ANN model was confirmed to be a fairly accurate tool for predicting aerosol concentrations in both space and time, and demonstrates the principal ability of the approach also in the domain of air quality monitoring.