Search Results (4)

In various engineering applications, remote sensing images such as digital elevation models (DEMs) and orthomosaics provide a convenient means of generating 3D representations of physical assets, enabling the discovery of new insights and analyses. However, the presence of noise and artefacts, particularly unwanted natural features, poses significant challenges, and their removal requires the application of filtering techniques prior to conducting analysis. Unmanned aerial vehicle-based photogrammetry is used at Melbourne Water’s Western Treatment Plant as a cost-effective and efficient method of inspecting the floating covers on the anaerobic lagoons. The focus of interest is the elevation profile of the floating covers for these sewage-processing lagoons and its implications for sub-surface scum accumulation, which can compromise the structural integrity of the engineered assets. However, unwanted artefacts due to trapped rainwater, debris, dirt, and other irrelevant structures can significantly distort the elevation profile. In this study, a machine learning algorithm is utilised to group distinct features on the floating cover based on an image segmentation process. An unsupervised k-means clustering algorithm is employed, which operates on a stacked 4D array composed of the elevation of the DEM and the RGB channels of the associated orthomosaic. In the cluster validation process, seven cluster groups were considered optimal based on the Calinski–Harabasz criterion. Furthermore, by utilising the k-means method as a filtering technique, three clusters contain features related to the elevations associated with the floating cover membrane, collectively representing 84% of the asset, with each cluster contributing at least 19% of the asset. The artefact groups constitute less than 6% of the asset and exhibit significantly different features, colour characteristics, and statistical measurements from those of the membrane groups. The study found notable improvements using the k-means filtering method, including a 59.4% average reduction in outliers and a 36.3% decrease in standard deviation compared to raw data. Additionally, employing the proposed method in the scum hardness analysis improved correlation strength by 13.1%, removing approximately 16% of the artefacts in total assets, in contrast to a 3.6% improvement with the median filtering method. This improved imaging will lead to significant benefits when integrating imagery into deep learning models for structural health monitoring and asset performance. Full article

Twelve beaches located in Ceuta (Spain) were studied from February to April 2019 to assess litter amounts (expressed as number of items), categories and temporal distribution. At each beach, three surveys were conducted, i.e., one per month (i.e., 36 in total). Selected beaches covered urban (7), rural (2) and remote (3) bathing areas. Plastic represented the dominant material, i.e., 35.2% of all debris, followed by glass (18.2%), pottery/ceramics (14.6%), wood (11.4%), metal (11.4%), paper/cardboard (4.8%), cloth (3.5%), rubber (0.7%), organic (0.3%) and other materials (0.1%). The Clean Coast Index was calculated to classify beaches in five categories for evaluating the cleanliness level of the coast observed at each survey: “Very Clean” (7 surveys), “Clean” (10), “Moderately Dirty” (8), “Dirty” (2) and “Extremely Dirty” (9). Litter occurrence was assessed by the Litter Grade methodology, which allowed to classify beaches in four grades: “A”: very good (0); “B”: good (4); “C”: fair (7); and “D”: poor (25). In a few surveys, some beaches were considered “good”, but their management should not be ignored because in other surveys those beaches reached fair and poor scores. Several potentially harmful litter items were related to beach users. Severe eastern storms removed litter at many of the beaches investigated and favored accumulation at others. Data analysis shows significant differences in litter abundance with respect to site, beach typology and the presence of cleaning operations but no important differences between the studied months. Rural beaches recorded the most litter, followed by urban and remote beaches. All beaches require immediate and more appropriate management actions to improve their environmental status. Full article

The organic wastes of plant origin and, in particular, those coming from sources related to tourism activities, such as those generated from golf courses and touristic coasts, constitute an increasing concern due to the rise in their production and their unsuitable management. Thus, this work aimed to assess the use of different composting strategies to manage these specific green wastes, such as grass clippings and pruning waste from a golf course and marine plant debris, mainly from posidonia (Posidonia oceanica L.). To this end, two composting scenarios were established: the first only considered green wastes in the composition of the composting mixtures, and the second used sewage sludge as a co-composting agent. The temperature of the piles was monitored, and physicochemical and chemical parameters were also studied throughout the process. The results obtained showed that composting is a feasible method to manage and recycle this type of green waste, obtaining end products with suitable physicochemical and chemical characteristics. However, proportions of sea plant wastes in the composting mixture higher than 30% can compromise the fertilizing value of the final compost. Moreover, the use of an additional co-composting agent (sewage sludge) improved the characteristics of the end products obtained, provided that this co-composting agent had suitable initial characteristics. Full article

Rivers are major pathways for litter to enter the ocean, especially plastic debris. Yet, further research is needed to improve knowledge on rivers contribution, increase data availability, refine litter origins, and develop relevant solutions to limit riverine litter inputs. This study presents the results of three years of aquatic litter monitoring on the Adour river catchment (southwest of France). Litter monitoring consisted of collecting all litter stranded on river banks or stuck in the riparian vegetation in defined areas identified from cartographic and hydromorphological analyses, and with the support of local stakeholders. Litter samples were then sorted and counted according to a list of items containing 130 categories. Since 2014, 278 litter samplings were carried out, and 120,632 litter items were collected, sorted, and counted. 41% of litter could not be identified due to high degradation. Food and beverage packaging, smoking-related items, sewage related debris, fishery and mariculture gear, and common household items represented around 70% of identifiable items. Overall, the present study contributes to our knowledge of litter sources and pathways, with the target of reducing the amounts entering the ocean. The long-term application of this monitoring is a way forward to measure societal changes as well as assess effectiveness of measures. Full article