Metals in urban dust : potentials of spider web biomonitoring and statistical approaches to identify anthropogenic sources of urban particulate matter

Laaten, Neele Birte van GND

Elevated levels of particulate matter can be observed in the atmosphere of many urban areas today. To lower effectively those levels, actual sources of particulate matter have to be identified. For this purpose, four different, rather novel methods to characterize dust were tested. Contents of up to 52 elements were determined in the samples and subjected to a multivariate statistical evaluation, aiming at an identification of sources. Sampling materials that were investigated are spider webs, moss bags, dust from windows and long-term deposition samples. Sampling of all of them is comparatively cost-effective and space-saving. Most of those samples were taken in the city of Jena (Central Germany), which serves as a model environment for medium-sized cities located in valleys. An adaption and comparison of the methods as well as the identification of dust sources have been the main aims of this work. The focus has been on spider webs, for which the biggest data set was recorded. By means of a combination of correlation coefficients, cluster analysis and factor analysis of the element contents it could be demonstrated that sampling and evaluation of spider webs is an effective method for the identification of dust sources. For the city of Jena, three sources could be identified: resuspended natural/geogenic dust (high contents of Al, Ca, Co, Cs, La, Li, Mg, Sr, Th, Ti, Y, Zr), brake wear from car traffic (Cu, Sb, Sn, Zn) and abrasion of tram/train tracks (Ba, Cr, Fe, Mn, Ni). Furthermore, it could be demonstrated that resuspension of natural dust is mainly caused by driving cars. A comparable identification of sources could not be performed with data from the other materials. Especially moss bags and long-term deposit samples are rather suited for the display of seasonal/annual differences or a general burden of particulate matter as well as for the identification of highly polluted locations


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