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This study investigated the levels and spatial distributions of four selected heavy metals in the soil and drainage components emanating from informal E-waste recycling activities at Ashaiman scrapyard, in the Greater Accra Region, Ghana. The metals are Cadmium (Cd), Chromium (Cr), Copper (Cu) and Lead (Pb). Five sampling sites were randomly selected, with top and sub-soil sampled from the two open burning areas (hereafter H and F). Three sites in the drainage that runs through the scrapyard were similarly selected for sample collection; a control upstream (soil-sediment-water; WSC) and two experimental units downstream (soil-sediment-water; WS1 and surface water only; WS2). Four control topsoil samples were taken at distances of 25, 50, 75 and 100 m away from the scrapyard. Composite sample of three sampling units per site, including pH analysis, with two replications per treatment, were investigated using standards methods. Spatial distribution of the metals in the scrapyard were analyzed using Inverse Distance Weighted (IDW) interpolation method. Coefficient of variation (CV) was used to investigate the source of pollution. The pollution levels were investigated using three criteria, namely Geoaccumulation Index (Igeo), Contamination Factor (CF) and Pollution Load Index (PLI). Correlation analysis was used evaluate the relationships between the metals. Mean CV of 88.5% suggests that the scrapyard pollution is anthropogenically-driven. Igeo of soil samples from the scrapyard revealed the following: (i) Cd and Pb (unpolluted to strongly polluted), (ii) Cu (unpolluted to moderately polluted), and (iii) Cr (unpolluted). CF revealed the following: (i) Cd (moderate to strong pollution), (ii) Cu (moderate pollution), (iii) Cr (low pollution), and (iv) Pb (high pollution), but the metals exhibited moderate PLIs. Spatial distribution maps revealed heavy metal pollution decline with distance away from the scrapyard, which was inversely related to pH levels. WSC showed lower heavy metal concentrations than WS 1, while the lowest levels were detected in WS 2. Generally, moderate to very strong correlations existed among the metals in the scrapyard. In conclusion, the scrapyard was the epicenter of E-waste pollution primarily driven by human activities.

Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall~variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall. These relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño–Southern Oscillation (ENSO) and putative links, such as North Atlantic Oscillation, that invite further research.

The Nigerian agricultural production system is predominantly rain-fed. Over reliance of the agricultural production system on rainfall is an indication of vulnerability to climate change and variability of rainfall. On the other hand, climate resilience agriculture could ensure sustainable agricultural production and food security (including, availability, access, and stability) for Nigeria, being the most populous and largest economy in Africa. We therefore, investigated the impact of both rainfall variability and irrigation on agricultural production with a view to informing appropriate agricultural policy for adapting to climate change in Nigeria. Time series data spanning 43 years were used for the analyses on degree of variability and impact. The generalized methods of moment (GMM) econometric analytical technique was employed to quantify the impact of rainfall (in millilitres per annual) and irrigation (in proportion of arable land) on aggregate agricultural production index. We found evidence for the impact of irrigation as a tool for adapting to climate change, and for promoting climate-resilient agriculture in Nigeria. Irrigation had positive and significant impact on aggregate agricultural production. The findings suggest the need for the minimization of the impact of climate-induced agricultural production risks through climate- resilient agriculture which would involve expansion of arable land area under irrigation.

The paper presents an evaluation of post-project impacts of the Barekese Dam on three riparian communities downstream about 30 years after its construction. A network diagram, which incorporated a mathematical model, was used for impact identification and analysis. The expected environmental impacts, expressed as quantitative weighted impact scores, showed that the dam appeared to have exerted adverse impacts on the environmental quality of the communities. The impacts in the communities, however, increased with relative distances away from the dam and the river suggestive of cumulative impacts transmitted downstream. Strategic measures for improving environmental quality of the communities have been given.

The complex dynamic climate system often exhibits hierarchical modularity of its organization and function. Scientists have spent decades trying to discover and understand the driving mechanisms behind western African Sahel summer rainfall variability, mostly via hypothesis-driven and/or first-principles based research. Their work has furthered theory regarding the connections between various climate patterns, but the key relationships are still not fully understood. We present Coupled Heterogeneous Association Rule Mining (CHARM), a computationally efficient methodology that mines higher-order relationships between these subsystems' anomalous temporal phases with respect to their effect on the system's response. We apply this to climate science data, aiming to infer putative pathways/cascades of modulating events and the modulating signs that collectively define the network of pathways for the rainfall anomaly in the Sahel. Experimental results are consistent with fundamental theories of phenomena in climate science, especially physical processes that best describe sub-regional climate.

A dynamic physical system often undergoes phase transitions in response to fluctuations induced on system parameters. For example, hurricane activity is the climate system's response initiated by a liquid-vapor phase transition associated with non-linearly coupled fluctuations in the ocean and the atmosphere. Because our quantitative knowledge about highly non-linear dynamic systems is very meager, scientists often resort to linear regression techniques such as Least Absolute Deviation (LAD) to learn the non-linear system's response (e.g., hurricane activity) from observed or simulated system's parameters (e.g., temperature, precipitable water, pressure). While insightful, such models still offer limited predictability, and alternatives intended to capture non-linear behaviors such as Stepwise Regression are often controversial in nature. In this paper, we hypothesize that one of the primary reasons for lack of predictability is the treatment of an inherently multi-phase system as being phase less. To bridge this gap, we propose a hybrid approach that first predicts the phase the system is in, and then estimates the magnitude of the system's response using the regression model optimized for this phase. Our approach is designed for systems that could be characterized by multi-variate spatio-temporal data from observations, simulations, or both.

This paper analysed policy correlates of agricultural production and agricultural production sustainability outcomes in Ghana and Nigeria. It underscores the influence of political systems and international development agendas as correlates of agricultural production and agricultural production sustainability outcomes. This is to the extent of providing evidence policy on agricultural production and agricultural production sustainability outcomes. Ghana and Nigeria have comparable farming/agricultural system and policy environment. Data used for the analyses spanned five decades. Trends analysis and inferential statistics were employed. The results revealed that policy correlates can contribute to the current discourse in sustainable development agenda and to resolving the dilemma of agricultural policy implementation for sustainable agricultural development, especially in Ghana and Nigeria. The findings reinforce the need for appropriate policies in transforming the agricultural sector while ensuring sustainable development outcomes.

Although dams have beneficial effects, they are also acknowledged as having serious environmental repercussions if they are not properly managed. The objective of this work was to examine the impact of the Barekese Dam in Ghana on the health status of three riparian communities downstream against a control. The environmental health status of the communities was analysed with reference to traditional endemic communicable water-related diseases in the catchment area, which were identified as malaria, urinary schistosomiasis, infectious hepatitis, diarrhoeal diseases and scabies. Case-control study was then conducted in the three phases of the dam (pre-construction, at the end of the construction and in the late operational phases) to analyse the health status of the communities as a function of the phases of the dam. The results showed that the control community consistently had a much better health status than two of the riparian communities, which were closer to the dam in all the three phases. However, it had a better health status than the third riparian community, which was farthest downstream, only in the first two phases. This community maintained a fairly constant health status retrospectively and did not appear to have been affected by the presence of the dam. On contrary, the health status of the two communities in close proximity to the dam deteriorated in the late operational phase. The study therefore showed that there was a strong association between the presence of the dam and poorer health status of the downstream communities in close proximity to it.

The latent behavior of a physical system that can exhibit extreme events such as hurricanes or rainfalls, is complex. Recently, a very promising means for studying complex systems has emerged through the concept of complex networks. Networks representing relationships between individual objects usually exhibit community dynamics. Conventional community detection methods mainly focus on either mining frequent subgraphs in a network or detecting stable communities in time-varying networks. In this paper, we formulate a novel problem--detection of predictive and phase-biased communities in contrasting groups of networks, and propose an efficient and effective machine learning solution for finding such anomalous communities. We build different groups of networks corresponding to different system's phases, such as higher or low hurricane activity, discover phase-related system components as seeds to help bound the search space of community generation in each network, and use the proposed contrast-based technique to identify the changing communities across different groups. The detected anomalous communities are hypothesized (1) to play an important role in defining the target system's state(s) and (2) to improve the predictive skill of the system's states when used collectively in the ensemble of predictive models. When tested on the two important extreme event problems--identification of tropical cyclone-related and of African Sahel rainfall-related climate indices--our algorithm demonstrated the superior performance in terms of various skill and robustness metrics, including 8---16 % accuracy increase, as well as physical interpretability of detected communities. The experimental results also show the efficiency of our algorithm on synthetic datasets.