The pollution of antibiotic resistance genes (ARGs) in livestock farms is a problem which need to be paid more attention to, due to the severe resistance dissemination and the further human health risk. In this study, all the relevant exposure matrices (manure, soil and water) of sixteen animal farms in Southeastern China were sampled to determine twenty-two ARGs conferring resistance to five major classes of antibiotics including tetracyclines, sulfonamides, quinolones, aminoglycosides, and macrolides. The results showed that the spread property of sul genes was most extensive and strong, followed by tet and erm genes. The abundance of tet genes expressing ribosomal protection proteins (tetM, tetO, tetQ, tetT and tetW) was higher than that expressing efflux pump proteins (tetA, tetC, tetE and tetG) in each type of samples. The high abundance and frequency of ermB gene in the matrices should be paid more attention, because macrolides is a major medicine for human use. For manures, it was found that the similar ARGs distribution rules were existing in poultry manure or porcine manure samples, despite of the different origins of these two types of livestock farms. Meanwhile, it was interesting that the distribution rule of tet genes in animal manure was nearly the same as all the ARGs. For soils, the result of nonmetric multi-dimensional scaling (NMDS) analysis showed that the pollution of ARGs in the soils fertilized by poultry and cattle manures were more substantial in northern Jiangsu, but no significant ARGs diversity was observed among porcine manured soils of five different regions. Furthermore, most ARGs showed significant positive relationships with environmental variables such as concentration of sulfonamides, tetracyclines, Cu, Zn and total organic carbon (TOC). The pollution profile and characteristics of so many ARGs in livestock farms can provide significative foundation for the regulation and legislation of antibiotics in China.

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When estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the plug-in estimator particularly when the data contain noise. Krätschmer et al. (2014) propose a new framework to examine the qualitative robustness of such estimators for the tail-dependent law invariant risk measures on Orlicz spaces, which is a step further from an earlier work by Cont et al. (2010) for studying the robustness of risk measurement procedures. In this paper, we follow the stream of the research to propose a quantitative approach for verifying the statistical robustness of tail-dependent law invariant risk measures. A distinct feature of our approach is that we use the Fortet-Mourier metric to quantify variation of the true underlying probability measure in the analysis of the discrepancy between the law of the plug-in estimator of the risk measure based on true data and the one based on perturbed data, this approach enables us to derive an explicit error bound for the discrepancy when the risk functional is Lipschitz continuous over a class of admissible sets. Moreover, the newly introduced notion of Lipschitz continuity allows us to examine the degree of robustness for taildependent risk measures. Finally, we apply our quantitative approach to some well-known risk measures to illustrate our results and give an illustrative example about the tightness of the proposed error bound.