No abstract

A total of 168 PM 10 samples were collected during the year of 2005 at eight sites in the city of Wuxi in China. Fifteen chemical elements, three water-soluble ions, total carbon and organic carbon were analyzed. Six source categories were identified and their contributions to ambient PM 10 in Wuxi were estimated using a nested chemical mass balance method that reduces the effects of colinearity on the chemical mass balance model. In addition, the concentrations of secondary aerosols, such as secondary organic carbon, sulfate and nitrate, were quantified. The spatially averaged PM 10 was high in the spring and winter (123 μg$m -3 and low in the summer-fall (90 μg$m -3 ). According to the result of source apportionment, resuspended dust was the largest contributor to ambient PM 10 , accounting for more than 50% of the PM 10 mass. Coal combustion (14.6%) and vehicle exhaust (9.4%) were also significant source categories of ambient PM 10 . Construction and cement dust, sulfates, secondary organic carbon, and nitrates made contributions ranging between 4.1% and 4.9%. Other source categories such as steel manufacturing dust and soil dust made low contributions to ambient PM 10 .

Although environmental contaminants are usually encountered as nonequitoxic mixtures, most studies have investigated the toxicity of equitoxic mixtures. In the present study, a method for prediction of the toxicity of nonequitoxic mixtures was developed using the similarity parameter (λ). The joint effect of multiple contaminants at the median inhibition concentration in equitoxic ([Formula: see text]) and nonequitoxic ([Formula: see text]) binary, ternary, and quaternary mixtures was investigated using Vibrio fischeri. The observed results indicate that the concentration ratios of individual chemicals in the mixtures influenced the joint effects, and that λ could be employed to evaluate the relation between [Formula: see text] and [Formula: see text]. Prediction models for the joint effects of nonequitoxic ([Formula: see text]) mixtures were derived from a combination of [Formula: see text] and λ. The predictive capabilities of these models were validated by comparing the predicted data with the observed data for binary, ternary, and quaternary mixtures. The prediction models have promising applications in controlling environmental pollution, evaluating drug interactions, and optimizing combinations of pesticides used in agriculture.