Prioritized regional management for antibiotics and heavy metals in animal manure across China

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch

Antibiotics are widely used in humans and animals, but there is a big concern about their negative impacts on ecosystem and human health after use. So far there is a lack of information on emission inventory and environmental fate of antibiotics in China. We studied national consumption, emissions, and multimedia fate of 36 frequently detected antibiotics in China by market survey, data analysis, and level III fugacity modeling tools. Based on our survey, the total usage for the 36 chemicals was 92700 tons in 2013, an estimated 54000 tons of the antibiotics was excreted by human and animals, and eventually 53800 tons of them entered into the receiving environment following various wastewater treatments. The fugacity model successfully predicted environmental concentrations (PECs) in all 58 river basins of China, which are comparable to the reported measured environmental concentrations (MECs) available in some basins. The bacterial resistance rates in the hospitals and aquatic environments were found to be related to the PECs and antibiotic usages, especially for those antibiotics used in the most recent period. This is the first comprehensive study which demonstrates an alarming usage and emission of various antibiotics in China.