The aiin of this research is to investigate the process by which techniques for the remedial treatment of contaminated land evolve. This is accomplished through the study of the relationships between: environmental policy and law; industrial practice; and research and development. Previous studies of the barriers and driving forces of such development have tended to be from a technical point of view. However, this research aims to explore the dynamics of technological innovation. Structured interviews, questionnaires and case studies were used to collect qualitative data from a cross section of the contaminated land industry. Interview transcripts were produced and subsequently, after consultation with the interviewees, presented as synoptic summaries including contextual information. Ile interview, questionnairea nd case study information is critically assessedw ith referencet o relevant contextual information. The following areas are discussed: public opinion; regulation and policy; uncertainty and development; political lobbying; measures intended to stimulate the use of treatment techniques; and, the relationship between vendors, consultants and property developers. It is concluded that: the development of treatment techniques is dependent upon the incremental accumulation of knowledge by politicians, scientists and the developers of remedial treatment techniques and that differences in these phenomena can result in "friction" in relation to the development of remedial treatment techniques. Particular emphasis is placed upon the adoption of pragmatic, deregulatory approachest o the regulation of contaminatedl and and the adoption of risk managementa pproachesI.t is emphasised that uncertainty relating to the performance of treatment techniques remains, particularly in relation to treatment time and cost. It is concluded that the commercial success of a treatment technique depends as much upon its ability to comply with the managerial constraints on the redevelopment process as upon its technical proficiency.

Landfill leachate with its variable and complex characteristics poses a well established threat to the environment. Enhancement of the environmental quality through the minimization of the leachate problem should therefore be the major objective of good landfill management. The need to control and manage landfill leachate has resulted in various treatment alternatives which include both biological and physical-chemical processes. The research described in this thesis discusses the feasibility of biological and physical-chemical treatment of leachate based on laboratoryscale reactors. After a short introduction, a review of the relevant literature on solid waste disposal including landfilling, leachate generation and the treatment alternatives was presented. Comparative experimental studies were then carried out using an aerobic rotating biological contactor (RBC), an upflow anaerobic filter (UAF) and an activated carbon (AC) adsorption column for treating landfill leachate. The effect of a range of parameters on the performance and operation of the RBC, the UAF and the AC column has been evaluated in the study From the experimental results, an RBC was found to achieve a better performance when treating a low strength (LS) leachate, whereas a high strength (HS) leachate would be much better treated by a UAF. For the LS leachate treatment, a COD removal of 80% at a loading rate of 6 kg COD/m3.d was achieved by the RBC as compared to only 60% by the UAF. Whereas for the HS leachate the RBC achieved a COD removal of only 50% at the loading rate of 14 kg COD/m3.d as compared to 60% by the UAF. Direct physical-chemical treatment process in treating leachate using an AC adsorption was also investigated. The results obtained showed that the adsorption process was not capable of achieving the desired effluent requirement, with 20% residual organic fractions still remaining in the effluent. The need to remove this biodegradable organic matter by biological processes was found to be necessary. It is suggested that to achieve satisfactory treatment, anaerobic UAF treatment of leachate followed by aerobic RBC and a final polishing with AC column should be used.

The remediation of two contaminated soils in the Tyne and Wear Metropolitan district was examined. These were a sediment dredged from the river bed at Dunston Coal Staiths on the River Tyne (downstream from Derwenthaugh coke work site) and coke work-contaminated soil from the Derwenthaugh site, Blaydon, Nr. Newcastle-upon-Tyne. The river Tyne dredgings were of a very fine material (70% silt; 24% clay) with high water retention capacity. Levels of (EDTA available) Zn (490mg/kg), total Cd (7.5mg/kg) and total Pb (510mg/kg) were above the Department of Environment's (1987) threshold values for soil contaminants. Barley (Hordeuin vulgare L. cv Kym) sown in the drcdgings in ten outdoor plots (Irn x 0.5m), grew very poorly (yield = 2.4g dry wt. /plant, compared with that on an uncontaminatedc. ontrol soil (7.4g dry wt./ plant). The barley exhibited all the classic signs of metal phytotoxicity despite the addition of fcrtiliscr and organic waste (straw and spent mushroom compost). When lime was added to raise the pH of the dredgings in the plots to over pH 7.1, the growth rate and the yield of barley improved significantly (yield = 6.8g dry wt. /plant). Levels of available Zn, Cd, Pb and Cu in the limed dredgings were now lower than in the unlimed dredgings. Copper and zinc levels in leaves of barley raised on the limed material were lower than levels in barley grown on unlimed dredgings. There was no significant difference in yield or growth rate between the different plots of dredgings in which organic supplementation parameters were varied. In conclusion, pH was the dominant factor in the remediation of the heavy metal phytotoxicity in the dredged material. Gas chromatography/mass spectrophotometry analysis showed the principal contaminants of the coke works soil to be organic. The soil was heavily contaminated with coal tars (19.0%) consisting of a complex mixture of aliphatic, polycyclic and aromatic compounds including phenols (160mg/kg). Viable counts of the soil microflora, on selective media, showed the presence of bacteria capable of degrading phenol and several of its alkylated homologues and thiocyanate, which was converted to ammonia and used as aN source. The coke works soil was treated on a laboratory scale using microbially based clean-up methods. Soil was incubated in glass jars under laboratory conditions. Nu trients (yeast extract) and microbial biomass (a mixed culture, previously isolated and enriched by growth on cresol and thiocyanate, but capable of oxidising a wide range of alkylated phenols), were inoculated into the contaminated soil. The addition of such biomass (106 organisms /g soil) led to a marked improvement in the rate of phenolic degradation in the soil (26% loss in'22 weeks, compared with 9% in the untreated control. ). Degradation rates decreased after 14 days but a repeated application of biomass (106 organisms/g soil) caused further phenolic loss (47% total loss). Cresol (100mg/kg) subsequently added to the bacterial ly-amended soil disappeared within 7 days, showing that the biomass amendment was still biochemically very active. These findings demonstrate the importance and the effectiveness of two different treatment methods in the rcmediation of contaminated soil.

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<p>  </p> <p>The proper classification and disposal of waste are crucial in reducing environmental impacts and promoting sustainability. Several solid waste classification systems have been developed over the years, ranging from manual sorting to mechanical and automated sorting. Manual sorting is the oldest and most commonly used method, but it is time-consuming and labor-intensive. Mechanical sorting is a more efficient and cost-effective method, but it is not always accurate, and it requires constant maintenance. Automated sorting systems use different types of sensors and algorithms to classify waste, making them more accurate and efficient than manual and mechanical sorting systems. In this thesis, we propose the development of an intelligent solid waste detection, classification and tracking system using artificial deep learning techniques. To address the limited samples in the TrashNetV2 dataset and enhance model performance, a data augmentation process was implemented. This process aimed to prevent overfitting and mitigate data scarcity issues while improving the model's robustness. Various augmentation techniques were employed, including random rotation within a range of -20° to 20° to account for different orientations of the recycled materials. A random blur effect of up to 1.5 pixels was used to simulate slight variations in image quality that can arise during image acquisition. Horizontal and vertical flipping of images were applied randomly to accommodate potential variations in the appearance of recycled materials based on their orientation within the image. Additionally, the images were randomly scaled to 416 by 416 pixels, maintaining a consistent image size while increasing the dataset's overall size. Further variability was introduced through random cropping, with a minimum zoom level of 0% and a maximum zoom level of 25%. Lastly, hue variations within a range of -20° to 20° were randomly introduced to replicate lighting condition variations that may occur during image acquisition. These augmentation techniques collectively aimed to improve the dataset's diversity and the model's performance. In this study, YOLOv8, EfficientNet-B0 and VGG16 architectures were evaluated, and stochastic gradient descent (SGD) and Adam were used as the optimizer. Although, SGD provided better test accuracies compared to Adam. </p> <p>Among the three models, YOLOv8 showed the best performance, with the highest average precision mAP of 96.5%. YOLOv8 emerges as the top performer, with ROC values varying from 92.70% (Metal) to 98.40% (Cardboard). Therefore, the YOLOv8 model outperforms both VGG16 and EfficientNet in terms of ROC values and mAP. The findings demonstrate that our novel classifier tracker system made of YOLOv8, and supervision algorithms surpass conventional deep learning methods in terms of precision, resilience, and generalization ability. Our contribution to waste management is in the development and implementation of an intelligent solid waste detection, classification, and tracking system using computer vision and deep learning techniques. By utilizing computer vision and deep learning algorithms, our system can accurately detect, classify, and localize various types of solid waste on a moving conveyor, including cardboard, glass, metal, paper, and plastic. This can significantly improve the efficiency and accuracy of waste sorting processes.</p> <p>This research provides a promising solution for detection, classification, localization, and tracking of solid waste materials in real time system, which can be further integrated into existing waste management systems. Through comprehensive experimentation and analysis, we demonstrate the superiority of our approach over traditional methods, with higher accuracy and faster processing times. Our findings provide a compelling case for the implementation of intelligent solid waste sorting.</p>