Michael K. Miyittah

Radon is a source of ionizing radiation that shows a carcinogenic potential. Thus, tracking radon exposure levels in the environment and managing exposure conditions is necessary for reducing the hazards such as lung cancer, which is known to be the second largest impact of radon on humans worldwide. A LR‐115 Type II strippable detector for monitoring radon radiation was installed in 30 mud and 26 block houses in the periurban (Duakor) areas in the Cape Coast Metropolis, Central Region, for 3 months to determine the exposure levels. The results showed that block houses (n = 26) had a radon ionization concentration in a range of 125–356 Bq m⁻³ and mean of 221 ± 61.4 Bq m⁻³, which was significantly higher than that of mud houses (n = 30), which ranged from 50.8 to 349 Bq m⁻³, with a mean of 186 ± 13.3 Bq m⁻³. About 80% and 10% of both types of houses showed levels that were above WHO’s lower (100 Bq m⁻³) and upper (300 Bq m⁻³) limits of the annual residential guidelines. Moreover, over 70% of both building types showed annual effective doses of indoor radon levels that were within the action levels (3–10 mSv year⁻¹). The data shows that the occupants have lived in their apartments for more than 10 years, while only about 3.0% have ever received some education on radon. Additional steps to manage indoor radon exposure in the community to avoid lung cancer are necessary.

This study examined the potential for green microalgae to produce lipid that could be used for biodiesel production from wastewater substrate. Green microalgae has the potential to be used in the production of biodiesel because it accumulates 5.3211 g of lipid in 39.2246 g of centrifuged and oven-dried algae biomass, accounting for 13.57% of its dry cellular weight. This was obtained with wastewater substrate under open-culture growth conditions. The wastewater substrate contained phosphate, nitrate, nitrite, ammonia, and sulfate at relatively optimal concentrations for culturing algae. The algae assimilated 95% of these nutrients on average for growth until the stationary phase was reached in 13 days. The most rapid cell doubling time observed was 192 h, and the maximum biomass production rate observed was 0.00057 mg/mg/day, reaching a peak algae weight fraction of 0.00926 mg/mg in 13 days. This study also examined and presented the scale-up of growing green microalgae in an open raceway culture system for obtaining maximum lipid yield from a wastewater substrate. The cost-benefit analysis of designing and building such a system was also presented in this study. The open raceway system described and modeled in this study measured 8 m * 3 m * 0.6 m and had a capacity of 10,000 liters, yielding 0.45 kg of biocrude lipid for each cycle of green microalgae culture harvested.

Rapid and onsite determination of the soil status and quality parameters holds a brighter potential for improving food security, and minimizing waste of the excessive application of soil amendments hence reducing environmental pollution. In this study, a pocket‐sized shortwave NIR spectroscopy (740–1070 nm) and multivariate statistics were used to classify soil from different land‐use types and simultaneously predict nitrogen (N), phosphorus (P), potassium (K), calcium (Ca²⁺), magnesium (Mg²⁺), and pH in Ghana. Different Algorithms. Linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares algorithms (full‐range partial least square, FrPLS; interval partial least squares, IPLS; synergy interval partial least squares, Si‐PLS) were attempted for building a suitable classification and quantification model. The models were assessed by the classification rate, coefficient of determination (R_p²), and root mean square error of prediction (RMSEP) in the prediction set. A total of 110 soil samples from 0 to 15 cm, 15 to 30 cm, and 30 to 45 cm layers were collected from the field of different land‐use cropping systems. The results obtained showed that SVM had a 98.61% classification rate of the soil from the cropping system. While Si‐PLS was superior in predicting N, P, K, Mg²⁺, Ca²⁺, and pH. The performance of the Si‐PLS model for N, P, K, Mg²⁺, Ca²⁺, and pH were 0.571, 0.779, 0.910, 0.778, 0.826, and 0.904 for R_p² and 0.033%, 0.738 mg·kg⁻¹, 0.117 cmol·kg⁻¹, 0.654 cmol·kg⁻¹, 3.0219 cmol·kg⁻¹, and 0.4760 pH unit for RMSEP, respectively. The results revealed that the portable NIR spectroscopic technique could be used to measure the soil status and some quality parameters. However, further studies are needed to proof its application. This could lead to improving the yield and saving the cost of fertilizer application.

The classification of mangrove forests in tropical coastal zones, based only on passive remote sensing methods, is hampered by mangrove complexities, topographical considerations, and cloud cover effects, among others. This paper reports on a study that combines optical and radar data to address the challenges of distinguishing mangrove stands in cloud-prone regions. The Google Earth Engine geospatial processing platform was used to extract multiple scenes of Landsat surface reflectance Tier 1 and synthetic aperture radar (C-band and L-band). The images were enhanced by creating a feature that removes clouds from the optical data and using speckle filters to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Classification was evaluated using three scenarios: classification of optical data only, classification of radar data only, and combination of optical and radar data. Our results revealed that the scenario that combines optical and radar data performed better. Further analysis showed that about 16.9% and 21% of mangrove and other vegetation/wetland cover were lost between 2009 and 2019. Whereas water body and bare land/built-up areas increased by 7% and 45%, respectively. Accuracy was evaluated based on the three scenarios. The overall accuracy of the 2019 classification was 98.9% (kappa coefficient = 0.979), 84.6% (kappa coefficient = 0.718), and 99.1% (kappa coefficient = 0.984), for classification of optical data only, classification of radar data only, and combination of optical and radar data, respectively. This study has revealed the potential to map mangroves correctly, enabling on-site conservation practices in the climate change environment.