title: Using event trees to quantify pathogen levels on root crops from land application of treated sewage sludge.
abstract: OBJECTIVE
  To quantify the incremental exposure of root crops, at point of harvest, to enteric pathogens from sewage sludge applied to agricultural land according to current regulations and guidance (Safe Sludge Matrix).
  
  
  RESULTS
  A quantitative risk assessment based on the Source-Pathway-Receptor approach is developed for Cryptosporidium and salmonellas. Event trees are constructed to model the partitioning of pathogens present in raw sewage into sludge at the sewage treatment works and to model to the pathways by which root crops may be exposed to those pathogens after treatment and land application of the sludge. The main barriers are sewage sludge treatment, and decay and dilution of the pathogens in the soil. The exposures are expressed in terms of the arithmetic mean. This represents the total loading and accommodates fluctuations not only in the levels of pathogens present in sewage but also in the removal efficiencies by the various barriers. One source of uncertainty is the degree of by-pass of sludge treatment at operational scale.
  
  
  CONCLUSIONS
  The models predict that land application of sewage sludge treated by conventional processes (achieving 2-log removal) increases the exposures of root crops to salmonellas and Cryptosporidium oocysts by counts of 0.070 and 0.033 kg(-1), respectively. These predictions are based on decay periods in the soil of 5 and 12 weeks, respectively, and are therefore worst case in not allowing for the full extent of no harvesting periods. A Monte Carlo simulation predicts that 0.01% of 1-kg batches contained > 50 salmonellas and demonstrates that, for risk assessment, it is acceptable to use the arithmetic mean exposure directly in the dose-response curve.
  
  
  CONCLUSIONS
  The predicted numbers of pathogens on root crops at point of harvest provide a basis for modelling the excess risks to humans consuming such crops. The approach underpins scientifically the Safe Sludge Matrix.
authors.affiliation: gale_p@wrcpl.co.uk.
authors.forename: P
authors.initials: P
authors.lastname: Gale
mesh_terms: D000383:Agriculture; D000818:Animals; D003458:Cryptosporidium / Q000302:isolation & purification*; D004781:Environmental Exposure; D008954:Models, Biological; D009010:Monte Carlo Method; D033661:Oocysts; D018570:Risk Assessment / Q000379:methods; D012475:Salmonella / Q000302:isolation & purification*; D012722:Sewage / Q000382:microbiology*; D013997:Time Factors
chemical_list: D012722:Sewage