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The study assessed 18 Polycyclic Aromatic Hydrocarbons (PAHs) in O. niloticus (Nile tilapia) sampled from an aquaculture cage (farm) and a wild catch. The PAHs in fish samples were analysed using Gas Chromatography-Mass Spectrometry. Four PAHs (in order of levels: Indeno [1,2,3-cd] pyrene > Anthracene > Perylene > Pyrene; 100-0.8 µg/kg) and only one PAH (Pyrene: 4 µg/kg) were detected in raw samples from the cage and wild catch respectively. Chargrilling significantly increased Pyrene levels after cooking (wild: 4-11 µg/kg; cage: 5-23 µg/kg, p

Artisanal and Small-Scale Mining (ASM) landscapes form integral part of the Land use land cover (LULC) in the developing worlds. However, the spatial, spectral, and temporal footprints of ASM present some challenges for using most of the freely available optical satellite sensors for change analysis. The challenge is even profound in tropical West African countries like Ghana where there is prolonged cloud cover. Whiles very few studies have used Sentinel-2 data to map change analysis in ASM landscape, none examined the contribution of individual S2 bands to the ASM classifications. Also, despite the capabilities of Machine Learning (ML) and Deep Learning (DL) models for LULC classifications, few studies have compared the performances of different classifiers in mapping ASM landscape. This study utilized Sentinel-2 data, four ML and DL models (Artificial Neural Network –ANN, Random Forest – RF, Support Vector Machines –SVM, a pixel-based Convolutional Neural Network-CNN) and image segmentation to examine the performance of S2 bands and ML and DL algorithms for change analysis in ASM landscape, with the Birim Basin in Ghana as a study area. The result of the change analysis was used to assess changes in LULC during the recent ban on the expansion of ASM in the country. It was found out that ANN is a better classifier of ASM achieving the highest overall accuracy (OA) of 99.80% on the segmented Sentinel-2 bands. The study also found out that the Band 5 Vegetation Red Edge (VRE) 1 contributed most to classifying ASM, with the segmented VRE 1 being superlative over the other predictors. In terms of expansion, ASM increased by 59.17 km2 within the period of the study (January 2017 to December 2018), suggesting that ASM still took place under the watch of the ban. The classification results showed that most of the peripheral of forest and farmland have been converted to ASM with little disturbance within the interior of the forest reserves. The study revealed that, the ban was yielding very little or no results due to a number of policy deficiencies including low staff strength, lack of logistics and low remuneration. Enforcement of legal instruments against ASM and farming activities within the forest reserves, improvement in the monitoring systems and intensification of public education on the value of forest and the need to protect it are some of the major recommendations that could control encroachment on the forest reserves.

Unsustainable anthropogenic activities such as indiscriminate logging of trees, mineral exploitation, conversion of forest into agricultural lands are known to cause major environmental changes, thereby triggering a chain of irreversible forest depletion. This has called an urgent need by government and private agencies to institute policies and programs to curtail the destruction of the ecosystem due to the pressure on the available land. In this study, the Land use/land cover changes between the period of 1986 and 2020 in the tropical rainforest of Ghana was considered. A combination of machine learning and Markov chain approach was adopted to project future LULC for 2040 and 2060.The results showed that area covered by Open Forest declined from 21,531.87 km2 to 14,518.82 km2 and Dense Forest also declined from 14,313 km2 to 8202.98 km2 over a period of 34 years. The CA-Markov model was used to predict the future land use land cover, and it was observed that the total forest cover could decline to 15,551.79 km2 in 2040 and further decrease to 13,401.79 km2 in 2060. It was also found that settlement, mining and agricultural land, which is be driven by rapid population increase, has contributed significantly to the rapid declining forest cover. The results of this study have demonstrated the impact of unsustainable use of natural resources in these three regions. It also highlights the need for concerted effort to develop comprehensive environmental policies to encapsulate sustainable conversion and utilisation of natural resources by focusing on water-energy-food nexus.