International Journal of Big Data Mining for Global Warming

The study aims at assessing the agrobiodiversity and carbon stocks by the pine agroforests in the Sudano-Guinean zone of Cameroon. Five $100\times 20$m sampling transects were established in each chronosequence, it was undertaken to assess the growth characteristics and biomass. Estimates of stocks of carbon in aboveground biomass, belowground biomass (BGB), total biomass (TB) and CO₂ equivalent stock were incorporated in allometric equation based on nondestructive method. A total of 24 species from 23 genera and 17 families were inventoried. Annona senegalensis, Syzygium guineensis and Hymenocardia acida contributed the most to the importance value index (IVI). Density ranged between $569\pm 1.2$–$652\pm 2.4$ stems/ha; basal area between $5.2\pm 0.02$–$14.5\pm 0.04$m²/ha; Shannon index between $2.68\pm 1.57$–$6.4\pm 2.5$ with the highest value for 8-year-old stands; Pielou’s evenness between $0.48\pm 0.01$–$1\pm 0.02$ with the lowest value in 24-year-old stands. Aboveground biomass ranged between $5.41\pm 0.01$–$172.14\pm 8.43$ Mg C/ha with the highest value in 16-year-old stands; belowground carbon from $1.40\pm 0.78$ Mg C/ha to $44.75\pm 1.73$ Mg C/ha and total carbon from $6.81\pm 0.04$ Mg C/ha to $216.90\pm 8.56$ Mg C/ha. The sequestration potential ranged from $25.03\pm 1.25$ Mg CO${}_{2\text{eq}}$/ha to $795.30\pm 34.24$ Mg CO${}_{2\text{eq}}$/ha. The sequestration rates were 84.77, 49.7 and 28.6 Mg CO${}_{2\text{eq}}$.ha${}^{-1}$yr${}^{-1}$ in 8-, 16- and 24-year-old stands, respectively. Although our data reported that pine stands hosted a few number of species; they are true carbon sinks and useful to the REED$+$ community.

The survival of human population is adversely affected by the global warming due to increasing temperature of earth surface caused by emissions of various gases (such as carbon dioxide, methane, etc.) and particulate matters from vehicles in the traffic. These gases, called greenhouse gases, present in the atmosphere above a certain threshold result to elevate the average temperature of earth surface affecting the human population. To comprehend this problem, in this paper, a nonlinear mathematical model is proposed to study the detrimental effects on human population of the increased earth surface temperature caused by traffic emissions. In the model formulation, five nonlinearly interacting variables namely; the cumulative densities of human population and vehicles in traffic, cumulative concentration of greenhouse gases and particulate matters due to traffic emissions and average global warming temperature of earth surface have been used. Using stability theory, certain inferences have been drawn regarding the local and nonlinear stability behaviour of the equilibrium points. It is shown that as the cumulative concentration of greenhouse gases emitted from vehicles in the traffic reaches beyond its threshold level, the average temperature of earth surface rises above its threshold value leading to global warming with diminishing effects on the growth of human population density. The growth of human population is further declined with increase in the cumulative concentration of particulate matters emitted from vehicles in traffic. Numerical simulations have also been performed to validate the analytical findings.

As economies integrate financially and both investment and output increase, the environment may be affected depending on the nature of international financial resources attracted into the country. Hence, this study examines the effect of financial integration, output growth, and foreign direct investment (FDI) on the environment in selected African countries involving Nigeria, South Africa, Egypt, Algeria, and Angola between 1980 and 2017. The study uses carbon emissions and particulate emissions (PM) to proxy pollution and analyze the data through the fully modified ordinary least squares (FMOLS) technique. Empirical results show that financial integration worsens pollution in Egypt, Nigeria, Algeria, and in Africa; output growth deteriorates pollution in South Africa, Algeria, Angola, and in Africa; while FDI fuels environmental degradation in Egypt and South Africa. We recommend that African countries should strive to establish specific targets for lowering emissions even though the Kyoto Protocol did not set specific emissions reduction targets for them.

Based on temperature and rainfall recorded at 34 meteorological stations in Bangladesh during 1989–2018, the trends of yearly average maximum and minimum temperatures have been found to be increasing at the rates of 0.025^∘C and 0.018^∘C per year. Analysis of seasonal average maximum temperature showed increasing trend for all seasons except the late autumn season. The increasing trend was particularly significant for summer, rainy and autumn seasons. Seasonal average minimum temperature data also showed increasing trends for all seasons. The trend of yearly average rainfall has been found to be decreasing at a rate of 0.014mm per year in the same period; especially, for most of the meteorological stations the rainfall demonstrates an increasing trend for rainy season and a decreasing trend in the winter season. It means that in Bangladesh dry periods became drier and wet periods became wetter.

Global climate change has already had observable effects on the environment. Glaciers have shrunk, ice on rivers and lakes is breaking up earlier, plant and animal ranges have shifted and trees are flowering sooner. Under these conditions, air pollution is likely to reach levels that create undesirable living conditions. Anthropogenic activities, such as industry, release large amounts of greenhouse gases into the atmosphere, increasing the atmospheric concentrations of these gases, thus significantly enhancing the greenhouse effect, which has the effect of increasing air heat and thus the speedup of climate change. The use of sophisticated data analysis methods to identify the causes of extreme pollutant values, the correlation of these values with the general climatic conditions and the general malfunctions that can be caused by prolonged air pollution can give a clear picture of current and future climate change. This paper presents a thorough study of preprocessing steps of data analytics and the appropriate big data architectures that are appropriate for the research study of Climate Change and Atmospheric Science.