Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA.

Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R2 (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R2 values show that local regression models (mean adj. R2 = 0.80) outperformed the global regression models (mean adj. R2 = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.

Phytoremediation potentials of Eichhornia crassipes for nutrients and organic pollutants from textile wastewater.

We used live water hyacinth (WH, Eichornnia crassipes) to purify effluents from textile factories and monitored changes in the physicochemical properties, organic pollutants, and WH biomass. Although the water plant could not thrive in the highly polluted effluents after eight weeks, it achieved 55, 91, 53, 84, 96, 53, and 55% removal efficiency for total Kjeldahl-N (tK-N), NH3-N, organic-N, PO43-, SO42-, Cl-, and hardness, respectively. Likewise, the biomass growth showed a positive and strong correlation with NH3-N (0.998), tK-N (0.956), organic-N (0.923), pH (0.853), and EC (0.712). In contrast, chemical oxygen demand and total oil and grease (TOG) evinced negative and strong correlations of -0.994 and -0.807, respectively. Further, Cl- correlated mildly (-0.38), while alkalinity (0.154) and water hardness (-0.296) were less influential on the biomass growth. From the removal models, an average of 312 ± 7.7 g of WH would ensure 100% remediation of the nutrients in 29.2 ± 2.5 days. Except for organic-N, the removal kinetics generally favors pseudo-first-order, suggesting the sorbates' concentration and contact time as the limiting factors. Conclusively, WH is a phytoremediator of high potentials for industrial textile effluents, provided the effluents are conditioned at optimum concentration before contact with mature WH of sufficient biomass weight. Novelty statement Eichhornia crassipes was used for simultaneous removal of nutrients and organics from textile effluents. The influence of the macrophte's biomass weight and maturity on the remediation process were examined. Also, the limiting parameters that govern the remediation process were investigated via statistical correlation and kinetic study.

Geospatial assessment of oil spill pollution in the Niger Delta of Nigeria: An evidence-based evaluation of causes and potential remedies.

Based on the archival data on oil facilities, oil spill incidents, and environmental conditions, we researched the plausible causes of oil spill disasters in the Niger Delta of Nigeria between 2006 and 2019. The data were analyzed for geospatial and statistical patterns, using ArcGIS and R programming platforms, respectively. A fuzzy logic algorithm was employed to generate three oil spill disaster models (hazard, vulnerability, and risk). Ordinary Least Square algorithm was adopted to model the relationships between oil spill and two sets of predictor variables: oil facilities (oil well, flow station, and pipeline) and disaster models. We found that, during the 23 years, the Niger Delta experienced 7940 oil spill incidents, of which 67% occurred onshore. A total of 4,950, 501, 855 episodes were attributed to sabotage, corrosion, and equipment failure, with 87%, 62%, and 45% occurring onshore, respectively. Besides, 81% of the 5320 onshore oil spill cases were attributed to sabotage, while corrosion and equipment failure accounted for mere 6% and 7% of the incidents, respectively. The estimated average risk index (R = 0.20) shows that the risk of an oil spill disaster in the Niger Delta is low. Whereas, 5% of the region is characterized by a high risk of oil spill disaster. Furthermore, the regression model infers that the oil spillages exhibit a positive relationship with disaster models and oil facilities at α = 0.10. However, only 16% of the incidents were explained by disaster models, while the oil facilities account for 23% of the total cases, indicating the influence of other factors. To avert further socio-environmental damage in the Niger-Delta, oil theft and sabotage should be curbed, polluted areas are remediated, and an all-inclusive socio-economic development is prioritized.

Agro-Climato-Edaphic Zonation of Nigeria for a Cassava Cultivar using GIS-Based Analysis of Data from 1961 to 2017.

To investigate the optimal cultivation conditions for cassava cultivar (TMS98/0505) in Nigeria, we employed agro-ecological zoning to delineate the cultivated lands. Using GIS-based multi-criteria analysis, we researched the influence of some meteorological and soil parameters on the clone cultivation. From the multiple-parameter climato-edaphic zoning map, an average yield of 26 t ha-1 was estimated. The dry Rainforest and southern Guinea Savanna account for 80% of the favorable zones. However, with irrigation, the cultivar would yield optimally in the northern marginal zones. Further, the significant climatic parameters are sunshine hour (t = 3.292, α = 0.0064) and rainfall (t = 2.100, α = 0.0575). Thus, the potentials of a location for cassava cultivation in Nigeria largely depend on the soil conditions, sunshine hour, and rainfall. Generally, the cassava yield correlates strongly (+0.88) with the suitability map. Considering future climate variability based on the annual rainfall data, we projected an average annual rainfall range of 565-3,193 mm between 2070 and 2099. Likewise, the projected range of daily temperature for 2046-2100 is 24.57-31.94 °C. Consequently, with currently allotted farmlands, Nigeria can double her current cassava production through soil fertility enhancement and irrigation.