Organophosphate esters (OPEs), used as flame retardants and plasticizers, are widely present in environmental waters. Development of accurate determination methods for trace OPEs in water is urgent for understanding the fate and risk of this class of emerging pollutants. However, the wide use of OPEs in experimental materials results in blank interference, which influences the accuracy of analytical results. In the present work, blank contamination and recovery of pretreatment procedures for analysis of OPEs in water samples were systematically examined for the first time. Blank contaminations were observed in filtration membranes, glass bottles, solid phase extraction cartridges, and nitrogen blowing instruments. These contaminations could be as high as 6.4–64 ng/L per treatment. Different kinds of membranes were compared in terms of contamination levels left after common glassware cleaning, and a special wash procedure was proposed to eliminate the contamination from membranes. Meanwhile, adsorption of highly hydrophobic OPEs on the inside wall of glass bottles was found to be 42.4%–86.1%, which was the primary cause of low recoveries and was significantly reduced by an additional washing step with acetonitrile. This work is expected to provide guidelines for the establishment of analysis methods for OPEs in aqueous samples. 相似文献
In this paper, we present a three-step methodological framework, including location identification, bias modification, and out-of-sample validation, so as to promote human mobility analysis with social media data. More specifically, we propose ways of identifying personal activity-specific places and commuting patterns in Beijing, China, based on Weibo (China’s Twitter) check-in records, as well as modifying sample bias of check-in data with population synthesis technique. An independent citywide travel logistic survey is used as the benchmark for validating the results. Obvious differences are discerned from Weibo users’ and survey respondents’ activity-mobility patterns, while there is a large variation of population representativeness between data from the two sources. After bias modification, the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%. Synthetic data proves to be a satisfactory cost-effective alternative source of mobility information. The proposed framework can inform many applications related to human mobility, ranging from transportation, through urban planning to transport emission modeling.
