Transport of pollutants through the water and subsurface soil using simulated acid rain

The mobility of heavy metals (Zn, Cd, Pb and Ni) was studied in the laboratory acidic leaching two different soils around Ibadan with simulated acid rain. The sampling was carried out from two different sites viz: Orogun and Ilupeju respectively. For Orogun site a depth of 128cm was reached (consisting of four horizons). Different length of polyvinyl chloride (PVC) pipes were cut for different soil horizon depth as observed on the field. The PVC pipes were packed with requires masses of soil. This is then leached using simulated acid rain of different pH of 2.0, 4.0, 6.0 and 8.0 after spiking with known volume of standard solution of metals of interest. It was found that simulated acid rain enhanced the mobility of metals in solution. The pH, Cation Exchange capacity, % clay and organic matter were found to contributed majority to the mobility of metals. Generally as observed, the mobility of metal was to follow the order Zn>Ni>Pb>Cd as the soil is becoming more acidic

Wind-driven desertification: process modeling, remote monitoring, and forecasting

Arid and semiarid landscapes comprise nearly a third of the Earth's total land surface. These areas are coming under increasing land use pressures. Despite their low productivity these lands are not barren. Rather, they consist of fragile ecosystems vulnerable to anthropogenic disturbance.

The purpose of this thesis is threefold: (I) to develop and test a process model of wind-driven desertification, (II) to evaluate next-generation process-relevant remote monitoring strategies for use in arid and semiarid regions, and (III) to identify elements for effective management of the world's drylands.

In developing the process model of wind-driven desertification in arid and semiarid lands, field, remote sensing, and modeling observations from a degraded Mojave Desert shrubland are used. This model focuses on aeolian removal and transport of dust, sand, and litter as the primary mechanisms of degradation: killing plants by burial and abrasion, interrupting natural processes of nutrient accumulation, and allowing the loss of soil resources by abiotic transport. This model is tested in field sampling experiments at two sites and is extended by Fourier Transform and geostatistical analysis of high-resolution imagery from one site.

Next, the use of hyperspectral remote sensing data is evaluated as a substantive input to dryland remote monitoring strategies. In particular, the efficacy of spectral mixture analysis (SMA) in discriminating vegetation and soil types and detennining vegetation cover is investigated. The results indicate that hyperspectral data may be less useful than often thought in determining vegetation parameters. Its usefulness in determining soil parameters, however, may be leveraged by developing simple multispectral classification tools that can be used to monitor desertification.

Finally, the elements required for effective monitoring and management of arid and semiarid lands are discussed. Several large-scale multi-site field experiments are proposed to clarify the role of wind as a landscape and degradation process in dry lands. The role of remote sensing in monitoring the world's drylands is discussed in terms of optimal remote sensing platform characteristics and surface phenomena which may be monitored in order to identify areas at risk of desertification. A desertification indicator is proposed that unifies consideration of environmental and human variables.

Exploitation of soil biota ecosystem services in agriculture: a bioeconomic approach

24 p.

A GIS-based model of soil erosion and transport

Soil erosion is a natural process that occurs when the force of wind, raindrops or running water on the soil surface exceeds the cohesive forces that bind the soil together. In general, vegetation cover protects the soil from the effects of these erosive forces. However, land management activities such as ploughing, burning or heavy grazing may disturb this protective layer, exposing the underlying soil. The decision making process in rural catchment management is often supported by the predictive modelling of soil erosion and sediment transport processes within the catchment, using established techniques such as the Universal Soil Loss Equation [USLE] and the Agricultural Nonpoint Source pollution model [AGNPS]. In this article, the authors examine the range of erosion models currently available and describe the application of one of these to the Burrishoole catchment on the north-west coast of Ireland, which has suffered heavy erosion of blanket peat in recent years.

Modelling soil erosion and transport in the Burrishoole catchment, Newport, Co. Mayo, Ireland

The Burrishoole catchment is situated in County Mayo, on the northwest coast of the Republic of Ireland. Much of the catchment is covered by blanket peat that, in many areas, has become heavily eroded in recent years. This is thought to be due, primarily, to the adverse effects of forestry and agricultural activities in the area. Such activities include ploughing, drainage, the planting and harvesting of trees, and sheep farming, all of which are potentially damaging to such a sensitive landscape if not managed carefully. This article examines the sediment yield and hydrology of the Burrishoole catchment. Flow and sediment concentrations were measured at 8-hourly intervals from 5 February 2001 to 8 November 2001 with an automatic sampler and separate flow gauge, and hourly averages were recorded between 4 July 2002 and 6 September 2002 using an automatic river monitoring system [ARMS]. The authors describe the GIS-based model of soil erosion and transport that was applied to the Burrishoole catchment during this study. The results of these analyses were compared, in a qualitative manner, with the aerial photography available for the Burrishoole catchment to see whether areas that were predicted to contribute large proportions of eroded material to the drainage network corresponded with areas where peat erosion could be identified through photo-interpretation.