Forest Structure and Tree Species Diversity of the Abasumba Globally Significant Biodiversity Area, Ghana

We studied the forest structure and tree species diversity with diameter-at-breast-height (dbh) ≥ 10 cm in the Abasumba Globally Significant Biodiversity Area Ghana. Sixteen 25 m * 25 m plots were demarcated and trees with dbh ≥ 10 cm were inventoried following International Plant Nomenclature Index. The characteristic three–storey structure of tropical forests was shown, 68.7% of trees were in the lower 4.5–18 m and middle 18–30 m storeys. A majority 91.4% of 342 trees was in the dbh of 10–30 cm and a least 8.6% of 32 trees in 31–60 cm had dbh ˃ 60 cm. Total of 46 species, 38 genera and 17 families, with mean Alpha, Shannon and Simpson’s Diversity indices of 13.9, 1.44 and 0.07 and importance value index of 300.0 for 374 trees ha-1 was recorded. Plant families Sterculiaceae, Meliaceae, Leguminosae, Ulmaceae and Bombacaceae was the majority encountered while Triplochiton scleroxylon, Cola millenii, Trichilia monadelpha, Hymenostegia afzelii, Celtis mildbraedii, Ceiba pentandra and Ficus sur was the most occurring species in 54.0% of the plots accounting for 52.0% of the IVI for all trees. Blighia sapida, Bridelia grandis, Dialium guineense, Draceana arborea, Ficus sur, Holarrhena floribunda, Holoptelea grandis, Margaritaria discoidea, Rauvolfia vomitoria, Trilepisium madagascariense, Vitex ferruginea, Ximenia americana and Xylia evansii had one individual in the 10,000 m2 area indicated that they are rare and should be given conservation priority in the forest reserve.

Utilization of artificial recharged effluent for irrigation: pollutants' removal and risk assessment

The reclaimed water from soil aquifer treatment (SAT) column was reused for irrigation as the source water, pollutants' removal and health risk assessment was analyzed via the comparison with secondary and tertiary effluents. The effect of the SAT pre-treatment on the qualities and growth of different crops (Lachca sativa – lettuce, Brasica rapa var chinensis – pak choi, Cucumis sativus – cucumber, Brassica oleracea – cabbage, and Zea mays – maize) were evaluated. Experimental results demonstrated that the tertiary and SAT treatments had no significant effect on the crop qualities, and could efficiently decrease the accumulation of heavy metals (especially for SAT pre-treatment). Moreover, the carcinogenic risk of the chemical carcinogens for the 1.5 m SAT effluent irrigation declined roughly an order of magnitude as compared with the secondary effluent, and three to four orders of magnitude decreasing of the virus risk. These findings are significant for the safe and cheap reuse of secondary effluent for irrigation purposes

Modeling the performance of Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process with backpropagation neural network and response surface methodology

Two novel feedforward backpropagation Artificial Neural Networks (ANN)-based-models (8:N:1 and 7:N:1) combined with Box-Behnken design of experiments methodology was proposed and developed to model NH and Total Nitrogen (TN) removal within an upflow-sludge-bed (USB) reactor treating nitrogen-rich wastewater via Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process. ANN were developed by optimizing network architecture parameters via response surface methodology. Based on the goodness-of-fit standards, the proposed three-layered NH and TN removal ANN-based-models trained with Levenberg-Marquardt-algorithm demonstrated high-performance as computations exhibited smaller deviations-(±2.1%) as well as satisfactory coefficient of determination (R), fractional variance-(FV), and index of agreement-(IA) ranging 0.989–0.997, 0.003–0.031 and 0.993–0.998, respectively. The computational results affirmed that the ANN architecture which was optimized with response surface methodology enhanced the efficiency of the ANN-based-models. Furthermore, the overall performance of the developed ANN-based models revealed that modeling intricate biological systems (such as SNAP) using ANN-based models with the view to improve removal efficiencies, establish process control strategies and optimize performance is highly feasible. Microbial community analysis conducted with 16S rRNA high-throughput approach revealed that Candidatus Kuenenia was the most pronounced genera which accounted for 13.11% followed by Nitrosomonas-(6.23%) and Proteocatella-(3.1%), an indication that nitrogen removal pathway within the USB was mainly via partial-nitritation/anammox process