Focus on the removal of lead and cadmium ions from aqueous solutions using starch derivatives: A review.

Lead and cadmium are typical heavy metals that poses series of environmental and health risks when discharged indiscriminately. Starch is an abundant, renewable, physicochemically versatile and environment friendly biopolymer. Consequently, application of starch derivatives adsorbents (SDAs) in the removal/adsorption (as used interchangeably hereafter) of aqueous heavy metal ions (AHMIs) from water constitutes economical and sustainable water treatment techniques. Starch derivatives are developed to offset the intrinsic limitations of native starch, and impact requisite functional and structural properties. SDAs have been extensively studied for the removal of AHMIs, and this review is focused on the removal of Pb2+ and Cd2+, using SDAs categorized as single modified starch (SMS), dual modified starch (DMS), multiple modified starch (MMS), and graft copolymer/composite starch (GCS). This review also discuss the methods used in the synthesis of the SDAs, their affinity for the removal of Pb2+ and Cd2+, and the mechanisms involved in the processes.

High Spatial Resolution Simulation of Sunshine Duration over the Complex Terrain of Ghana.

In this paper, we propose a remote sensing model based on a 1 × 1 km spatial resolution to estimate the spatio-temporal distribution of sunshine percentage (SSP) and sunshine duration (SD), taking into account terrain features and atmospheric factors. To account for the influence of topography and atmospheric conditions in the model, a digital elevation model (DEM) and cloud products from the moderate-resolution imaging spectroradiometer (MODIS) for 2010 were incorporated into the model and subsequently validated against in situ observation data. The annual and monthly average daily total SSP and SD have been estimated based on the proposed model. The error analysis results indicate that the proposed modelled SD is in good agreement with ground-based observations. The model performance is evaluated against two classical interpolation techniques (kriging and inverse distance weighting (IDW)) based on the mean absolute error (MAE), the mean relative error (MRE) and the root-mean-square error (RMSE). The results reveal that the SD obtained from the proposed model performs better than those obtained from the two classical interpolators. This results indicate that the proposed model can reliably reflect the contribution of terrain and cloud cover in SD estimation in Ghana, and the model performance is expected to perform well in similar environmental conditions.