With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand – an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a modelbased analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by winddominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport “pathways”. General temporal trends which were also part of our model – weekly, seasonal and linear changes – showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.

Human activities such as urbanisation, sewage treatment, industrialisation and agriculture represent major human interference in water resources. The water resources are affected both quantitatively as well as qualitatively by these activities. The impact of human interference in the Mukuvisi River catchment hydrology was studied by determining the concentration values of eight selected physico - chemical and biological parameters. These are pH, temperature, total nitrates, total phosphates, Dissolved Oxygen, Biological Oxygen Demand, lead, copper and Total Faecal Coliform Count. Seven sites were sampled along the river, from up the river in Mukuvisi woodlands up to the point where the river discharges into Lake Chivero. Analysis of the results obtained was undertaken using SPSS (paired sample T test) and descriptive graphs were drawn using Microsoft Excel 2010. Nitrates, phosphates, copper, lead and Total Faecal Coliform Counts were found to be higher than the Zimbabwe National Water Authority (ZINWA) maximum and World Health Organisation, 2011 (WHO) permissible standards from site 3 to site 7. Site 6 recorded the highest concentrations of all the measured parameters, except for pH and Dissolved Oxygen. Mean DO and BOD concentrations were 2.53 mg/l and 40 mg/l respectively at site 6. Mean total nitrates were 17.5 mg/l at site 6 above the ZINWA and WHO threshold of 10 mg/l. Site 6 also recorded a mean total phosphate of 5.9 mg/l which was above the ZINWA and WHO threshold of 0.5 mg/l. Mean TFCC was 992.6 mpn100ml-1 higher than the threshold of nil according to ZINWA and WHO threshold. Site 3 recorded the mean DO and TFCC of 2.4 mg/l and 2.80 mpn100ml-1 respectively. Site 2 had the lowest mean TFCC concentrations of 2.80 mpn100ml-1, which did not differ significantly from the WHO and ZINWA threshold of nil at p<0.05. The quality of water in the river varied from site to site in direct relation to the intensity and type of human activities along the river course. Levels of all the water quality indicators increased after discharge from the Firle sewage treatment plant at site 6 with the exception of temperature and pH. Sewage effluents, agricultural runoff and industrial effluents were found to be responsible for the high nutrient levels and high metal concentrations in the river which in turn reduced DO levels and increased BOD / College of Agriculture and Environmental Sciences / M. Sc. (Environmental Science)