A comparative study of the grain-size distribution of surface dust and stormwater runoff quality on typical urban roads and roofs in Beijing, China

Tire dust is a significant pollutant, especially as a source of zinc in the urban environment. This study characterizes the morphology and chemical composition of heavy metal particles embedded in tire dust and traffic-related materials (brake dust, yellow paint, and tire tread) as measured by a field emission scanning electron microscope equipped with an energy dispersive X-ray spectrometer (FESEM/EDX). In 60 samples of tire dust, we detected 2288 heavy metal particles, which we classified into four groups using cluster analysis according to the following typical elements: cluster 1: Fe, cluster 2: Cr/Pb, cluster 3: multiple elements (Ti, Cr, Fe, Cu, Zn, Sr, Y, Zr, Sn, Sb, Ba, La, Ce, Pb), cluster 4: ZnO. According to their morphologies and chemical compositions, the possible sources of each cluster were as follows: (1) brake dust (particles rich in Fe and with trace Cu, Sb, and Ba), (2) yellow paint (CrPbO(4) particles), (3) brake dust (particulate Ti, Fe, Cu, Sb, Zr, and Ba) and heavy minerals (Y, Zr, La, and Ce), (4) tire tread (zinc oxide). When the chemical composition of tire dust was compared to that of tire tread, the tire dust was found to have greater concentrations of heavy metal elements as well as mineral or asphalt pavement material characterized by Al, Si, and Ca. We conclude that tire dust consists not only of the debris from tire wear but also of assimilated heavy metal particles emitted from road traffic materials such as brake lining and road paint.

Since ancient times, it is understood that stormwater from constructed areas should be managed somehow. Waste and pollution transported by stormwater poses quantity and quality problems, affecting public health and the quality of the environment. Sanitation infrastructures in urbanized regions have different development levels and the perception of stormwater changed considerably during the centuries and especially in recent years. Still, there is an evident worldwide heterogeneity when analyzing the lack of studies on urban stormwater conducted in some Asian or African countries. Strategies for sustainable stormwater management are needed at different decision levels (political, regional or local scale, for instance) but all of them need information and a clear understanding of the possibilities that are at stake as well as the main consequences of each decision. A sound approach to stormwater management should be flexible, based on local characteristics, and should take into consideration temporal, spatial and administrative factors and law, among other issues. Economic or technical constraints define different decision scenarios. Best Management Practices should be seen as an opportunity for development and improvement of social, educational and environmental conditions in urbanized and surrounding areas. Therefore they require an ample perspective and the participation of different stakeholders. High-quality decision needs time and a fair overview of the problem: the purpose of this document is to contribute to sustainable stormwater management, informing on the most relevant factors that should be assessed and their interaction. A flowchart has been produced and is presented, indicating the most relevant steps, processes and information that should be taken into account in urban development. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Kruskal-Wallis (KW) nonparametric analysis of variance is often used instead of a standard one-way ANOVA when data are from a suspected non-normal population. The KW omnibus procedure tests for some differences between groups, but provides no specific post hoc pair wise comparisons. This paper provides a SAS(®) macro implementation of a multiple comparison test based on significant Kruskal-Wallis results from the SAS NPAR1WAY procedure. The implementation is designed for up to 20 groups at a user-specified alpha significance level. A Monte-Carlo simulation compared this nonparametric procedure to commonly used parametric multiple comparison tests. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.