Federal College of Education, Kano

Since 1950 to 2018, about 6.3 billion tonnes of plastics have been produced worldwide, 9% and 12% of which have been recycled and incinerated, respectively. Human population increase and consistent demand for plastics and plastic products are responsible for continuous increase in the production of plastics, generation of plastic waste and its accompanied environmental pollution. We have reviewed in this paper, the most relevant literatures on the different types of plastics in production, the hazardous chemical constituents, prevailing disposal methods and the detrimental effects of these constituents to air, water, soil, organisms and human health viz-a-viz the different disposal methods. Papers that reported environmental and public health effects of plastic constituents but not plastics directly were also reviewed. Varieties of plastics used in the production of many consumable products including medical devices, food packaging and water bottles contain toxic chemicals like phthalates, heavy metals, bisphenol A. brominated flame retardants, nonylphenol, polychlorinated biphenylethers, dichlorodiphenyldichloroethylene, phenanthrene etc.

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.

The global economic growth has triggered the continual increase in oil demand for transportation and petrochemical sectors which offshoots environmental pollution. Though there exists literature on environmental Kuznets curve, however, very few examine the scope in the light of fossil fuel energy production. This paper investigated the dynamic effect of oil production on carbon emissions in 15 oil-producing countries by accounting for the role of electricity production, economic growth, democracy, and trade over the period 1980–2010. Using the novel Method of Moments Quantile Regression (MMQR) with fixed effects, the results found an inverted U-shape relationship between economic growth and CO2 emissions only at median and higher emission countries, thus, validating the environmental Kuznets curve hypothesis. Oil production increases CO2 emissions significantly from the first to sixth quantiles with greater effect at the lowest quantile and weaker effect at the highest quantile. Electricity production was found to increase CO2 emissions while trade condenses CO2 emissions across all the quantiles thereby confirming the pollution halo hypothesis for oil-producing countries. The effect of democracy was positive across all the quantiles but only significant in countries with average CO2 emissions. The findings provide insight for policymakers to mitigate CO2 emissions in oil-producing countries through diversification and clean energy technologies such as carbon capture and storage.

Renewable energy technologies are promising, yet, very little is known about its role as a limiting factor in fossil fuel-attributable environmental degradation — especially in high-income countries. This study investigated the dynamic effect of renewable energy consumption, economic growth, biocapacity and trade policy on environmental degradation in the United States from 1985Q1 to 2014Q4. To achieve this objective, the study applied an autoregressive distributed lag (ARDL) model to obtain the long-run and short-run dynamic coefficients. Toda-Yamamoto causality test was used to examine the direction of causality while Cholesky decomposition test was for innovative accounting to validate the estimated models. The empirical results divulged that a decline in environmental degradation can be attributed to an increase in renewable energy consumption through its negative effects on ecological footprint. Economic growth and biocapacity were found to exert upward pressure on ecological footprint; however, trade policy exerts downward pressure on ecological footprint. A two-sided causal relationship was established between economic growth and ecological footprint as well as economic growth and biocapacity. In contrast, a one-way causality was confirmed running from trade policy to renewable energy consumption and from renewable energy consumption to biocapacity. The innovative accounting revealed that 14.79% and 8.41% of renewable energy consumption and trade policy caused 0.60% and 9.88% deterioration in the environment. Hence, country-specific energy policies that increase the share of renewable energy in the energy portfolio are recommended.

Powdered adsorbent prepared from Albizia lebbeck pods as agricultural waste has been used for the adsorption of Pb(II), Cd(II), Zn(II) and Cu(II) ions from aqueous solutions. The powdered adsorbent was characterized by X-ray diffraction, Fourier transform infrared spectroscopy and Brunauer–Emmett–Teller. Effects of various parameters like contact time, solution pH, initial concentration dosage and temperature were investigated on a batch adsorption system. Equilibrium and kinetic experiments were carried out at the optimum pH of 6, 8 and 10 at 29 °C using particle size of 250 μm for Cd(II), Pb(II), Zn(II) and Cu(II) ions. Changes in free energy, enthalpy and entropy were also evaluated. The adsorption data fitted well with the Langmuir isotherm model with correlation coefficient ( $$R^{2} > 0.94$$ ), whereas the adsorption kinetics followed the pseudo-second-order kinetics. The thermodynamic parameters proved that adsorption of metal ions is endothermic and non-spontaneous at low temperatures, while spontaneity occurred at higher temperatures. This study shows that powdered Albizia lebbeck pods prove to be a promising inexpensive adsorbent for metal ion removal from aqueous solutions.

Renewable energy plays a vital role in achieving environmental sustainability, however, the mitigating effect varies across countries depending on the share of renewables in the energy mix. Herein, we analyze the effect of renewable energy consumption, energy prices, and trade on emissions in G-7 countries. The results demonstrate that renewable energy and energy prices exert negative pressure on CO2 emissions while trade volume exerts a robust positive pressure on CO2 emissions. The country-specific estimation results provide evidence of a negative effect of energy prices on CO2 emissions. While the environmental Kuznets curve hypothesis is validated at the panel and country-specific levels, the effect of renewable energy consumption and trade, are disparate across countries. The panel Granger causality shows a mono-directional causality flowing from energy prices, GDP, the quadratic term of GDP and trade to CO2 emissions. Renewable energy consumption, however, has no causal relationship with CO2 emissions but indirectly affects CO2 emissions through its direct effect on energy prices. Joint action on trade, energy prices, and country-specific renewable energy policies have implications for environmental sustainability and the attainment of the Sustainable Development Goals (SDGs).

Bioaccumulation of heavy metals (Zn, Pb, Cd, and Cu) was determined in the liver, gills, and flesh from benthic and pelagic fish species collected from Lake Geriyo covering two seasons. The levels of the heavy metals varied significantly among fish species and organs. Flesh possessed the lowest concentration of all the metals. Liver was the target organ for Zn, Cu, and Pb accumulations. Cd however exhibited higher concentration in the gills. Fish species showed interspecific variation of metals. These differences were discussed for the contribution of potential factors that affected metals uptake like age, geographical distribution, and species-specific factors. The concentration of metals in fish flesh was accepted by the international legislation limits for Cu, Zn, and Cd; however, Pb transcend in Clarias and Tilapia during wet season and Heterotis in both seasons, hence unsafe for human consumption and a threat to public health. These levels might be due to anthropogenic inputs as there is no industrial activity around the lake.

Human activities including industrialization and agricultural practices contributed immensely in no small measure to the degradation and pollution of the environment which adversely has an effect on the water bodies (rivers and ocean) that is a necessity for life. This paper tries to discuss basically what water pollution is and equally to address the source, effect control and water pollution management as a whole. Some recommendations such as introduction of environmental education were mentioned. DOI: 10.5901/mjss.2013.v4n8p65

Deep fat fried foods are very popular food because of their unique quality characteristics. The process is based on the immersing food at high temperatures, depending on the raw materials, thereby leading to physical and chemical changes such as starch gelatinization, protein denaturation browning, and crust formation. In order to obtain a product with a low fat content, it is essential to understand the mechanisms involved during the frying process so that oil migration into a food product can be minimized. The purpose of this study is to review literature findings on frying of food. The review also aims to draw the attention of stakeholders, including decision makers, on the need to assess the health risks associated with consumption of fried food product and, consequently, the necessary measures and steps to reduce such risks in order to have safer food in the world.

Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.

The aim of this paper is to review machine learning (ML) algorithms and techniques for hate speech detection in social media (SM). Hate speech problem is normally model as a text classification task. In this study, we examined the basic baseline components of hate speech classification using ML algorithms. There are five basic baseline components - data collection and exploration, feature extraction, dimensionality reduction, classifier selection and training, and model evaluation, were reviewed. There have been improvements in ML algorithms that were employed for hate speech detection over time. New datasets and different performance metrics have been proposed in the literature. To keep the researchers informed regarding these trends in the automatic detection of hate speech, it calls for a comprehensive and an updated state-of-the-art. The contributions of this study are three-fold. First to equip the readers with the necessary information on the critical steps involved in hate speech detection using ML algorithms. Secondly, the weaknesses and strengths of each method is critically evaluated to guide researchers in the algorithm choice dilemma. Lastly, some research gaps and open challenges were identified. The different variants of ML techniques were reviewed which include classical ML, ensemble approach and deep learning methods. Researchers and professionals alike will benefit immensely from this study.

Despite the high commitments of the European Union (EU) member countries toward achieving the sustainable development goals (SDGs), on average, the region has reportedly under performed in the area of ensuring sustainable production and consumption. This paper uses the Generalized Method of Moments (GMM) estimation of panel vector autoregressive (PVAR) with impulse response functions (IMFs) to assess the effects of domestic material consumption, renewable energy, financial development, and greenhouse gas emissions on environmental quality in the EU-28 countries based on the panel data for the period 2000:Q1–2017:Q4. The empirical results reveal that the shocks to domestic material consumption, renewable energy, economic growth, financial development, and greenhouse gas emissions affect the drives towards a sustainable environment. Particularly, the shocks to renewable energy and financial development improve environmental quality, while the shocks to domestic material consumption and greenhouse gas emission deteriorate environment quality. The shock to economic growth improves environmental quality up to the 4th horizon after which it begins to deteriorate environment quality. Furthermore, the panel causality results indicate bidirectional causality between greenhouse gas emissions and the rest of the variables except renewable energy, which is unidirectional. The causality between economic growth and renewable energy, economic growth and financial development, and financial development and renewable energy has a feedback effect while a unidirectional causality flows from economic growth to domestic material consumption. These findings have implications for sustainable production and consumption.

The distributions of naturally occurring radionuclides 226Ra, 232Th and 40K in sediments of Oguta Lake, Nigeria were determined using gamma ray spectrometry in order to assess the radiological health hazards and excess lifetime cancer risks associated with the use of the sediments. The mean activity concentrations of 226Ra, 232Th and 40K were found to be 47.89 ± 18.67 Bq kg−1, 55.37 ± 32.74 Bq kg−1 and 1023 ± 474 Bq kg−1, respectively. The results of the radiological indices and dose rates obtained in this study were all higher than their worldwide mean values but lower than their maximum recommended limits indicating that the use of the sediments as building materials do not constitute any excessive radiological hazards. The area is known to be subjected to environmental degradations due to oil exploration. Therefore, the results of this study could serve as an important radiometric baseline data upon which future epidemiological studies and environmental monitoring initiatives could be based.

This study investigated the dynamic linkage between fiscal policy, energy and CO2 emissions from heterogeneous fossil fuel sources in the context of the environmental Kuznets curve (EKC) framework for Thailand. With annual data from 1972 to 2014 while incorporating structural breaks, the study employed a Maki cointegration test and the dynamic ordinary least squares estimation approach. The results found that a 1% increase in fiscal policy brought about a 6.5% (p < 0.05) increase in the low CO2 emitting gaseous fuel sources (natural gas), a 0.2% (p < 0.01) reduction in the intermediate CO2 emitting liquid fuel sources (crude oil derivatives), and an insignificant increase 0.2% (p > 0.05) in the high CO2 emitting solid fuel sources (coal derivatives). While a 1% increase in fiscal policy abates aggregated CO2 emissions by 0.2% (p < 0.05), the existence of the EKC hypothesis was validated in all models. The causality test revealed a bi-directional causal relationship between fiscal policy and CO2 emissions and unidirectional flow from fiscal policy to energy consumption. This confirms that fiscal policy initiatives towards energy consumption have long-run implications for environmental quality. Our findings support the energy-led growth hypothesis for the Thai economy. The implication of the finding is that increasing the share of clean and renewable energy sources should be encouraged—rather than energy conservation policies, which obstruct energy supply and utilization. This highlights a more efficient way of harnessing energy sources through the instrumentality of fiscal policy.

As a result of increasing environmental concerns/legislative pressure for dumping of non-biodegradable plastics in landfills and rapid increases in the cost of petroleum, the development of “environmental friendly” materials has attracted extensive interest. Recently, bioplastics are one of the most innovative environmental friendly materials developed. This review paper is intended to provide information about alternative to conventional plastics for the betterment of earth environment. They have some advantages such as lower carbon footprint, independence, energy efficiency, and eco-safety. For the sustainability, recycling systems and production technology may be developed for bioplastics and by-product should be used for their production. It is concluded that the use of bioplastics will help in sustainability and national development thus, making the environment less overwhelmed with greenhouse gases and reduction of waste biomas. And finally recommended by the reviewers that use of biomas for plastics production should be embraced especially those found to be biodegradable and use of petroplastics be incapacitated.

This study was aim to determined the levels of some heavy metals in the gills, liver, stomach, kidney, bones and flesh of four fish species (Tilapia zilli, Clarias anguillaris, Synodentis budgetti and Oreochronmis niloticus) collected at River Benue in Vinikilang, Adamawa State, Nigeria for analysis of Cu, Zn, Co, Mn, Fe, Cr, Cd, Ni and Pb. These metals were chosen because at higher concentrations there might be toxic to the fish and by extension humans that depends on such fish as food. The concentrations of the metals were carried out using Flame Atomic Absorption Spectrophotometer (AAS, Unicam 969). Large differences in trace metal concentrations were observed between different tissues within each fish. The highest concentration of Fe (12.65 μg/g) was recorded in gill of Synodentis budgetti, while the lowest value of 0.68 μg/g was recorded in the flesh of Oreochronmis niloticus. The liver of Synodentis budgetti accumulates significant higher levels of Mn and Cd than other species; Fe and Zn was highest in the stomach of Tilapia zilli, while Clarias angullaris shows more of Cr, Pb, Cd and Co. The stomach of Synodentis budgetti accumulate significant higher levels of Fe than other species; Zn was highest in the stomach of Tilapia zilli, while Clarias angullaris shows more of Mn, Cr, Cu, Cd and Pb. Similarly, the bone of Synodentis budgettiaccumulates significant higher levels of Mn and Cd than other species; Zn and Fe were highest in the bone of Tilapia zilli, while Clarias angullaris shows more of Cr, Pb, Ni, and Co. The highest levels of Fe (12.65 μg/g) observed in this study was recorded in the gill of Synodentis budgetti and it was below the high residue concentrations of Fe (34 - 107 ppm) in fish samples. Based on the above results, it can therefore be concluded that metals bioaccumulation in the entire fish species study did not exceeds the permissible limits set for heavy metals by FAO, FEPA and WHO.

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement.

The prime conditions for the removal of Cu (II) and Pb (II) onto defatted papaya seeds (DPS) from aqueous solution were studied. The effects of three adsorption variables (adsorbent dosage, shaking speed as well as initial concentrations) were investigated using central composite design (CCD) which is a subset of response surface methodology (RSM). Quadratic models were developed for both Cu (II) and Pb (II) percentage removals. The optimum adsorption conditions obtained were adsorbent dosage of 0.30 g, shaking speed of 180 rpm as well as initial concentration of 150 mg/L with desirability of 0.987. The predicted and experimental values obtained were 96.65% and 97.55% for Cu (II) as well as 98.07 and 99.96% for Pb (II), showing good agreement between the experimental values and those predicted from the models with relatively small errors which were only 0.89 and 1.89, respectively. Langmuir isotherm model was found to be the best fit for the equilibrium adsorption data of both Cu (II) and Pb (II) on DPS giving rise to monolayer adsorption capacities of 17.29 and 53.02 mg/g respectively.

= 0.73). The study concludes that constraints are major obstacles to the compliance and prospects of e-learning in the Private Tertiary Institutions in Nigeria.

Approximately 15–18% of crops losses occur as a result of animal pests, while weeds and microbial diseases cause 34 and 16% losses, respectively. Fungal pathogens cause about 70–80% losses in yield. The present strategies for plant disease control depend transcendently on agrochemicals that cause negative effects on the environment and humans. Nanotechnology can help by reducing the negative impact of the fungicides, such as enhancing the solubility of low water-soluble fungicides, increasing the shelf-life, and reducing toxicity, in a sustainable and eco-friendly manner. Despite many advantages of the utilization of nanoparticles, very few nanoparticle-based products have so far been produced in commercial quantities for agricultural purposes. The shortage of commercial uses may be associated with many factors, for example, a lack of pest crop host systems usage and the insufficient number of field trials. In some areas, nanotechnology has been advanced, and the best way to be in touch with the advances in nanotechnology in agriculture is to understand the major aspect of the research and to address the scientific gaps in order to facilitate the development which can provide a rationale of different nanoproducts in commercial quantity. In this review, we, therefore, described the properties and synthesis of nanoparticles, their utilization for plant pathogenic fungal disease control (either in the form of (a) nanoparticles alone, that act as a protectant or (b) in the form of a nanocarrier for different fungicides), nano-formulations of agro-nanofungicides, Zataria multiflora, and ginger essential oils to control plant pathogenic fungi, as well as the biosafety and limitations of the nanoparticles applications.