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Earth Observation (EO) data can become an essential tool in the transformation of a raw materials sector that aims to reconfigure its model of operation. The high demand for the mineral resources necessary for the transition to a carbon neutral and circular economy conflicts with the increasing difficulties of finding new deposits. As the sector heads towards embracing circularity and reducing the environmental impacts, a clear focus has been set on developing appropriate tools to boost the efficiency of mineral resource management, both technologically and economically. In this scenario, the Sentinel satellites of the European Copernicus program come into play. Despite being satellites considered medium resolution, they provide great temporal and spatial coverage in a continuous record, which makes them tools with great potential for the raw materials sector. However, the lack of applications in the raw materials sector suggests that these technological advances have remained underrated by sectoral actors. The RawMatCop program was designed to bridge this gap. This program, co-funded by the European Commission and EIT RawMaterials, aims to develop applications and promote the use of Copernicus data in the raw materials sector to contribute to a safe and sustainable supply of mineral resources. The presented applications can be grouped into three categories covering the whole mining cycle from exploration to exploitation and post-mining. Two of the presented case studies cover the study of primary sources including exploration of Iron Oxide Copper Gold mineralisations to identify high-potential mining areas and mapping of informal gold mining and its environmental impacts. Another project focused on secondary sources tackled data applications for grade mapping and sample optimisation in mining residues. And the forth project focused on monitoring ground stability related to mining activity. The results demonstrate the high cost-effectiveness of Sentinel 1 and 2 in extending ground-based measurements to larger areas, especially when these are hard-to-reach areas. Finally, the presented projects examine the industrial and social impacts of technological innovations, as well as contribute to the achievement of prominent European Union policy objectives and the United Nations Sustainable Development Goals., This paper includes four research projects related to the RawMatCop Programme (2018-2019), funded by the European Commission and EIT RawMaterials. Special thanks to Serge Monturet, the LifeLong learning manager for his support. The authors would like to thank Dr. Rima Dapous, Wesley Crock, Dr. Patrick Nadoll, Ms. Eliane Furuyama and Sven Kreigenfeld from EIT RawMaterials and supervisors: Professor Roberto Bruno and Professor Emanuele Mandanici from University of Bologna, Dr. Efthymios Balomenos from Mytilineous S.A, Professor Thorkild Maack Rasmussen from Lulea University of Technology, Professor Eric Pirard from University of Liege, Dr. Pierre Lacroix from the University of Geneva and UNEP/GRID-Geneva, Mr. David Jensen, Ms. Inga Peterson, Mr. Juan Bello, and Ms. Luisa Lema from UNEP, Mr. Rafael Sanchez the secretary of mines at El Bagre, Ms. Janneth Zoraida Duque Quintero and Ms. Ana Castana from Corantioquia, Professor Jose Fern ' andez and Mr. Joaquin Escayo from IGEO-CSIC, Professor Ramon Carbonell and Dr. Martin Schimmel, for their important feedback and suggestions; and the Atalaya Mining company for their support. Finally we would like to thank Dr.Francesco Tinti for the encouragement behind the paper creation.

A mesophilic anaerobic digester, followed by a psychrophilic aerobic post-treatment, was used to treat food waste (FW) with different proportions of fruit and vegetable waste (FVW). Two types of FW were used: low fruit and vegetable mix (LFV, with 56.5% of FVW) and high fruit and vegetable mix (HFV, with 78.3% of FVW). The anaerobic digester fed with LFV failed at an organic loading rate of 1.6 g VS.L-1.d-1 (volatile fatty acid (VFA) = 6000 mg.L-1) due to high ammonia (reaching 3000 mg.L-1). It was shown that, in an unstable anaerobic environment, ammonia is highly correlated ( r2 = 0.77) with VFA and is negatively correlated with volatile solids, total solids, and chemical oxygen demand (COD) removal rates ( r2 = 0.88, r2 = 0.71, and r2 = 0.91, respectively). In contrast, the anaerobic digester fed with HFV exhibited a stable performance (VFA = 1243 mg.L-1), with limited ammonia accumulation (940 mg.L-1). Methane generation was affected by the FVW content and reached 531 ml CH4.g VS-1 (CH4 = 52%) with LFV feed and 478 ml CH4.g VS-1 (CH4 = 57.4%) with HFV. The overall TS, VS and COD removal rates (all ranging between 94% and 97%), were closely similar for LFV and HFV. Accordingly, the aerobic post-treatment seems to compensate for the reduced performance of the disturbed anaerobic system fed with LFV.

The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R2 = 0.84) was achieved with TRMM forcing, which indicates its potent...

Over the decades, anthropogenic air emissions have resulted in the degradation of ambient air quality and posed major health risks. In this context, efforts have increased to understand the dispersion of pollutants in the earth's atmosphere, their spatiotemporal patterns, and long-term effect on climate. Of interest in this domain is the relationship between atmospheric aerosols and trace gases, and their influence on pollutant dispersion. As such, this paper addressed the relationship between tropospheric Nitrogen Dioxide (NO 2 ) and atmospheric aerosols; reflected by the Aerosol Optical Thickness (AOT) parameter over Lebanon. NO 2 column densities at low spatial resolution of about 7km were obtained from the Copernicus Sentinel 5p recently launched satellite, while AOT was retrieved at 10m resolution from the twin Sentinel-2 satellites. Results reveal a significant correlation between ln(NO 2 ) and AOT on several days in different seasons, with correlation coefficient r2 up to 0.775. This allows the determination of hotspots at 10m resolution. These hotspots were mainly on the Lebanese coastline, the most urbanized area in the country.

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used.

Thermal infrared remote sensing was used to investigate the land surface temperature variation between the Naameh landfill and its surroundings during the winter and summer seasons. Landsat 8 Operational Land Imager and Thermal Infrared Sensor were used to retrieve at-sensor temperature, which was corrected for atmospheric effects using three different split-widow algorithms. Land surface average temperature was calculated for six land-cover classes, and it was determined that the variation between the land surface temperature and the at-sensor temperature was ranging from 1 to 5 oK, yet this difference depends on the type of land cover and on the ambient temperature. Thus, this shows the importance of obtaining surface temperature from at-sensor temperature for this study area. In addition, the Naameh landfill's surface temperature was consistently higher than the surrounding areas during summer seasons, reaching a difference of 6 oK with vegetated areas and 2 oK with bare soil.

To investigate bio-chemical processes of intertidal sediments, variations in sediment properties such as moisture content, mud content, and chlorophyll a content need to be understood. Remote sensing has been an efficient alternative to traditional data collection methods for such properties. Yet, with the availability of various types of useful sensors, choosing a suitable spatial resolution is challenging, especially that each type has its own cost, availability, and data specifications. This paper investigates the losses in spatial information of sediment properties on the Molenplaat, an intertidal flat on the Western-Scheldt estuary, upon the use of various resolutions. This was carried out using a synergy between remote sensing and geostatistics. The results showed that for the Molenplaat, chlorophyll a content can be well represented by low to medium resolutions. Yet, for moisture and mud content, spatial structures would be lost upon any decrease of resolution from a 4 m×4 m pixel size.

Anaerobic digestion under psychrophilic conditions (ambient temperature) was used to treat a mixture of food and landscape waste. A pilot scale anaerobic digester was built to test the efficiency of the process as the variation of methane (CH4) generation with respect to variation in daily ambient temperature. At the end of the 13 week experiment, the CH4 content in the produced biogas was 65 % and the CO2 content was 34%. As for the volume of produced CH4, it was observed to be about 23 L/week for a weekly loading rate of 4,000 g of food and landscape waste. These results were used to estimate the impact of an up-scaled system to treat the waste generated in Okaibeh town. It was found that the large scale design could produce around 27.4 MWh per month of electrical energy which is more than 2 times the electricity needed to satisfy the consumption of the municipality building and the street lights during times of power outage.

Naameh landfill is a vital sanitary landfill for Lebanon that started in 1997 and was closed in 2015 and the reutilized for an emergency phase in 2016. It served Lebanon's capital, Beirut and the Caza of Mount Lebanon. Ever since the landfill was active, it faced various controversies as it was originally meant to be an emergency plan for a short period of time. The locals of Naameh areas and its surrounding were defiant of the continuous prolongation of the landfill. This study aimed at addressing Naameh landfill using a remote sensing approach to assess its evolution in time. Due to the absence of field data for public or academic use, remote sensing offers a great alternative to understand the changes in the landfill and its vegetated surrounding in time. The study has shown that the vegetated areas were in place of the landfill with the possible presence of a small quarry in the 80s. The landfill area increased about 25% in surface area between 1998 and 2003 (about 6 hectares), and about 27% between 2003 and 2014 (about 8 hectares). Furthermore, the study shows that there was a major shift in the vegetation cover when comparing September 1984 (pre-landfill conditions) and September 2003. A less dense vegetation cover existed in 2003 where a total decrease in dense and sparse trees reaches about 100 hectares, from which 25% were lost to the landfill, 50% to urban areas including roads to the landfill, and the remaining 25%, i.e. about 25 hectares were due to indirect impacts and were replaced by low density sparse short vegetation or shrubs. Such information is important for proper management of the site and its rehabilitation, and represents a first step of in-depth analysis of the landfill using remote sensing technology.

Earth dams in Lebanon are typically used in remote agricultural areas where irrigation channels and large-scale reservoirs are not available. The use of non-invasive techniques for earth dam applications can be a significant step in the efficient monitoring of such structures, especially in Lebanon where there is substantial risk of earth dam failure. This work investigated the potential use of electrical resistivity tomographies (ERTs) in detecting dam seepage and failure using physical models at controlled laboratory conditions. It was shown that ERTs can indicate failure in such dams. Furthermore, a geostatistical investigation of these tomographies showed semivariogram analysis was successful in detecting failure and quantifying its spatial scale.

The aim of this study is to set a foundation for mapping Beirut soils using available soil reports obtained from excavations. With the availability of a certain number of excavations and soil reports, one could attempt to gather soil data in order to understand the subbase and map the stratigraphy of the city. However, only a limited number of boreholes and soil data are available. Consequently, the use of innovative methods to analyze and assess limited numbers of heterogeneous data is necessary for developing countries where data is generally scarce. The paper investigates the available soil report data, specifically soil cohesion (c), soil friction angle (φ), and soil type classification. The results show that soil cohesion and the soil friction angle of Beirut have a high negative correlation r2 = -0.71. Then, a mixture of Gaussians (MG) clustering approach is utilized to find meaningful patterns in the data, and thus resulting in three categories according to which the soil is to be mapped in future work. The categories are related to ranges in values of c and φ and their corresponding soil types.

The aim of this study is to assess the suitability of four spaceborne multispectral sensors (Spot 5 HRG, Landsat 5 TM, Landsat 7 ETM+, and IKONOS) for inter-tidal sediment characterization, in comparison to a hyperspectral image of 4 m × 4 m spatial resolution and 116 spectral bands. Four sediment properties were considered: organic matter content, moisture content, chlorophyll a content, and mud content. The utilized data were a hyperspectral image and its accompanying field data. The methodology included spectral and spatial resampling of this image to the properties of the spaceborne multispectral sensors. Then, these resampled data were analyzed by means of unsupervised classification. The results showed that spaceborne multispectral data have the potential for sediment characterization. Yet, compared to the hyperspectral image, the characterization of the different properties generally decreased. The results showed the spectral suitability of Landsat sensors to characterize all properties and the spectral and spatial suitability of all sensors to characterize chlorophyll a content.

The quantification and monitoring of sediment dynamics in estuaries has received plenty of attention in recent years due to both economic and environmental interests. Numerical models have been widely applied for predicting the morphodynamic evolution of these areas. However, the degree of uncertainty of these models is often very high due to the lack of measurements necessary for a complete model calibration and validation. In this paper, different methods have been integrated to overcome these limitations. The hydrodynamics and suspended sediment transport in the IJzer estuary were estimated by means of field data and with the use of a two-dimensional hydrodynamic model coupled to a morphodynamic model. Tidal pumping was recognized as the main driving mechanism of sediments inside the estuary. Erosion-deposition areas calculated with the numerical model were compared to the locations and volumes of dredged material. Model results and measurements were generally in good agreement. Additional support for the morphodynamic model results came from airborne hyperspectral images acquired in 2001 and 2005.