4.5 Article

Identification of soil contamination hotspots with veterinary antibiotics using heavy metal concentrations and leaching data-a field study in China

Journal

ENVIRONMENTAL MONITORING AND ASSESSMENT
Volume 186, Issue 11, Pages 7693-7707

Publisher

SPRINGER
DOI: 10.1007/s10661-014-3960-x

Keywords

Veterinary antibiotics; Heavy metals; Manure; Top- and subsoil; Leaching

Funding

  1. German Federal Ministry of Education and Research (BMBF) [FKZ: 0330847D]
  2. Chinese Ministry of Science and Technology (MOST) [2009DFA32710]

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In regions with high livestock densities, the usage of antibiotics and metals for veterinary purposes or as growth promoters poses a risk in manured soils. We investigated to which degree the concentrations and depth distributions of Cu, Zn, Cr and As could be used as a tracer to discover contaminations with sulfonamides, tetracyclines and fluoroquinolones. Besides, we estimated the potential vertical translocation of antibiotics and compared the results to measured data. In the peri-urban region of Beijing, China, soil was sampled from agricultural fields and a dry riverbed contaminated by organic waste disposal. The antibiotic concentrations reached 110 mu g kg(-1) sulfamethazine, 111 mu g kg(-1) chlortetracycline and 62 mu g kg(-1) enrofloxacin in the topsoil of agricultural fields. Intriguingly, total concentrations of Cu, Zn, Cr and As were smaller than 65, 130, 36 and 10 mg kg(-1) in surface soil, respectively, therewith fulfilling Chinese quality standards. Correlations between sulfamethazine concentrations and Cu or Zn suggest that in regions with high manure applications, one might use the frequently existing monitoring data for metals to identify potential pollution hotspots for antibiotics in topsoils. In the subsoils, we found sulfamethazine down to a parts per thousand yen2 m depth on agricultural sites and down to a parts per thousand yen4 m depth in the riverbed. As no translocation of metals was observed, subsoil antibiotic contamination could not be predicted from metal data. Nevertheless, sulfonamide stocks in the subsoil could be estimated with an accuracy of 35-200 % from fertilisation data and potential leaching rates. While this may not be sufficient for precise prediction of antibiotic exposure, it may very well be useful for the pre-identification of risk hotspots for subsequent in-depth assessment studies.

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