Spatial Targeting of Soil Loss Using RUSLE in GIS: the case of Asokore Mampong Municipality, Ghana

Soil erosion is a serious environmental problem that is associated with societal impacts including flooding, poor water quality, and loss of plant nutrient leading to low agricultural productivity. Soil erosion wears away the top soil and is controlled by the interaction between several factors including rainfall, steepness of slope, length of slope, vegetation cover, and land management practices. This study developed Geographic Information System (GIS) graphical model based on the Revised Universal Soil Loss Equation (RUSLE), to calculate soil loss in the Asokore Mampong Municipality of the Ashanti region, Ghana. The estimated soil loss was examined the spatial patterns of soil loss and intensity per areas, as an important method for proper planning of management measures. The graphical model was developed using the popular open source GIS software, QGIS, ensuring the availability of the model, automation for any specific area, and its execution to the general public. Data sources used include Digital Elevation Model (DEM) derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), soil properties data obtained from the Global Soil Grids, land cover data from the Global Land Cover by National Mapping Organization (GLCNMO), NDVI (normalized difference vegetation index) data from MODIS (MOD13Q1, 16 Day), and rainfall data from GPCC version 7 (Global Precipitation Climatology Centre). Our results show high levels of soil loss (in tons per hectare per year) in the Municipality, with the capability to spatially target mitigation measures leading to cost effective environmental management

Transport related air pollution and its implications on public health along selected road corridors in lagos metropolis, nigeria

The study investigated the ambient air quality caused by vehicular emission and its implications on the public health around major roadways in Lagos metropolis Nigeria. Field data on vehicular volumes and mix were collected for three months in the morning, afternoon, and evening peak periods for the five (5) selected major routes. Concurrently, air pollutants from vehicles were measured by portable gas detectors on the routes. Questionnaires were administered to the respondent near the routes to investigate the implications of exposure on their health. The concentration level of the air pollutants is highest between 8-9 am morning peak periods and lowest between 12-1 pm afternoon periods. The ambient air quality is polluted on all the studied routes and revealed a strong correlation (p<0.05) between pollutants concentration and traffic flow. The questionnaire results also showed that 74% of the sampled respondents around the corridor suffered from chest pain, frequent cough, nose running and sneezing, sore throat, difficulty in breathing, body weakness, fatigue, eye irritation, loss of appetite, headache, and fast breathing of which 6% of children and 54% of women were the most susceptible. The study recommended measures for the reduction of the negative impacts on ambient air quality and public health in developing African citiesPapers presented at the 40th International Southern African Transport Conference on 04 -08 July 202

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Exploring the characterization of uncertainty in census and borehole data using rough sets

This research introduces rough sets to better characterizing spatial relationships and uncertainty in two examples. First, scale issues in census data are addressed. Census data provide demographic and socio-economic information at specific area units. Hence, derived spatial information are scale-dependent leading to uncertainty when analyzing results at different scales. Rough sets mitigate scale distortions and provide scale-sensitivity measure during scale transition. It employs the metaphor of topology to illustrate the ability of rough sets to retain spatial relationships of adjacency and contiguity. Second, rough sets and transition probability are used to characterize sediment distribution. The study simulates sediment state and transitions for low and high quality borehole data by providing better geological understanding. It also assesses Geological Survey of Canada standardization scheme for classifying borehole data. The utility of rough sets is demonstrated as a knowledge base tool for characterizing uncertainty irrespective of the data under study

Minimizing the effects of inaccurate sediment description in borehole data using rough sets and transition probability

Borehole data, Standardization, Rough sets, Transition probability, C00,