Predictive Modelling of Heavy Metals in Urban Lakes
Abstract: Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes. Sediment and water data for this study were collected from ten small lakes in the Stockholm area, the Eastern parts of Lake Mälaren, the innermost areas of the Stockholm archipelago and from literature studies. By correlating calculated metal loads to the land use of the catchment areas (describing urban and natural land use), the influences of the local urban status on the metal load could be evaluated. Copper was most influenced by the urban status and less by the regional background. The opposite pattern was shown for cadmium, nickel and zinc (and mercury). Lead and chromium were in-between these groups. It was shown that the metal load from the City of Stockholm is considerable. There is a 5-fold increase in sediment deposition of cadmium, copper, mercury and lead in the central areas of Stockholm compared to surrounding areas. The results also include a model for the lake characteristic concentration of suspended particulate matter (SPM), and new methods for empirical model testing. The results indicate that the traditional distribution (or partition) coefficient Kd (L kg-1) is unsuitable to use in modelling of the particle association of metals. Instead the particulate fraction, PF (-), defined as the ratio of the particulate associated concentration to the total concentration, is recommended. Kd is affected by spurious correlations due to the definition of Kd as a ratio including SPM and also secondary spurious correlations with many variables correlated to SPM. It was also shown that Kd has a larger inherent within-system variability than PF. This is important in modelling.
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