Microbial Activities and Dissolved Organic Matter Dynamics in Oil-Contaminated Surface Seawater from the Deepwater Horizon Oil Spill Site

The Deepwater Horizon oil spill triggered a complex cascade of microbial responses that reshaped the dynamics of heterotrophic carbon degradation and the turnover of dissolved organic carbon (DOC) in oil contaminated waters. Our results from 21-day laboratory incubations in rotating glass bottles (roller bottles) demonstrate that microbial dynamics and carbon flux in oil-contaminated surface water sampled near the spill site two weeks after the onset of the blowout were greatly affected by activities of microbes associated with macroscopic oil aggregates. Roller bottles with oil-amended water showed rapid formation of oil aggregates that were similar in size and appearance compared to oil aggregates observed in surface waters near the spill site. Oil aggregates that formed in roller bottles were densely colonized by heterotrophic bacteria, exhibiting high rates of enzymatic activity (lipase hydrolysis) indicative of oil degradation. Ambient waters surrounding aggregates also showed enhanced microbial activities not directly associated with primary oil-degradation (β-glucosidase; peptidase), as well as a twofold increase in DOC. Concurrent changes in fluorescence properties of colored dissolved organic matter (CDOM) suggest an increase in oil-derived, aromatic hydrocarbons in the DOC pool. Thus our data indicate that oil aggregates mediate, by two distinct mechanisms, the transfer of hydrocarbons to the deep sea: a microbially-derived flux of oil-derived DOC from sinking oil aggregates into the ambient water column, and rapid sedimentation of the oil aggregates themselves, serving as vehicles for oily particulate matter as well as oil aggregate-associated microbial communities

Experimental Study of the Loosening Phenomenon of an Assembly with Multi Fixations Under Vibration in the Transversal Direction

International audienc

Optimum planting dates for four maturity groups of maize varieties grown in the Guinea savanna zone

Five maize varieties, comprising NAES EE W SR (extra-early, normal maize (NM)), Dorke SR (early, NM), Abeleehi (intermediate, NM), Obatanpa (intermediate, quality protein maize) and Okomasa (late, NM), were sown at 2-week intervals from mid-May to last week of July in 1993 and 1994 at Nyankpala in the Guinea savanna zone. The varieties were assigned to main-plots and planting dates to sub-plots in the randomized complete block with four replications per year. Effects due to variety and planting date were highly significant (P< 0.01) for grain yield. The variety W planting date interaction was not significant for yield. Grain yields averaged over planting dates were 3890, 5252, 5798, 5830, and 5883 kg/ha for the varieties NAES EE W SR, Dorke SR, Abeleehi, Obatanpa and Okomasa, respectively. Grain yields for the six sowing dates averaged over varieties were 5919, 5900, 6232, 4895, 4537 and 4502 kg/ha. Grain yields for the first three planting dates did not differ significantly from each other. Similar results were obtained for the last three planting dates. Yields on the average were 30 per cent higher for the first three planting dates than for the last three. Plant dry matter yield, number of ears per plant and thousand grain weight were the parameters which showed significant positive correlations with grain yield among planting dates. The data showed that (1) for all maturity groups, maize sown from mid-May to mid-June significantly out-yielded the later plantings, (2) the extra-early and early maize varieties were lower yielding than the later varieties, and (3) there was no yield advantage in the late varieties over the intermediate types in the Guinea savanna zone. (Ghana Journal of Agricultural Science, 1997, 30(1): 63-70

Towards understanding the environmental and climatic changes and its contribution to the spread of wildfires in Ghana using remote sensing tools and machine learning (Google Earth Engine)

Data processing and climate characterisation to study its impact is becoming difficult due to insufficient and unavailable data, especially in developing countries. Understanding climate's impact on burnt areas in Ghana (Guinea-savannah (GSZ) and Forest-savannah Mosaic zones (FSZ)) leads us to opt for machine learning. Through Google Earth Engine (GEE), rainfall (PR), maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tmean), Palmer Drought Severity Index (PDSI), relative humidity (RH), wind speed (WS), soil moisture (SM), actual evapotranspiration (ETA) and reference evapotranspiration (ETR) have been acquired through CHIRPS (Climate Hazards group Infrared Precipitation with Stations), FLDAS dataset (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System) and TerraClimate platform from 1991 to 2021. The objective is to analyse the link and the contribution of climatic and environmental parameters on wildfire spread in GSZ and FSZ in Ghana. Variables were analysed (area burnt and the number of active fires) through Spearman correlation and the cross-correlation function (CCF) (2001 to 2021). The tests (Mann-Kendall and Sens's slope trend test, Pettitt test and the Lee and Heghinian test) showed the overall decrease in rainfall and increase in temperature respectively (−0.1 mm; + 0.8°C) in GSZ and (−0.9 mm; + 0.3°C) in FSZ. In terms of impact, PR, ETR, FDI, Tmean, Tmax, Tmin, RH, ETA and SM contribute to fire spread. Through the codes developed, researchers and decision-makers could update them at different times easily to monitor climate variability and its impact on fires