Storm water pollution source identification in Washington, DC, using bayesian chemical mass balance modeling

Research output: Contribution to journalArticlepeer-review


  • Mohammad Masoud Haghshenas
  • Tolessa Deksissa
  • Peter Green
  • William Hare
  • Arash Massoudieh

Colleges, School and Institutes

External organisations

  • Catholic Univ. of America
  • Univ. of District of Columbia
  • University of California, Davis


A Bayesian chemical mass balance (CMB) model was used to identify the sources of heavy metals in a highly urbanized area at the vicinity of the Anacostia River in Washington, DC. This method uses the elemental profiles of potential sources and the storm water runoff samples at two outfalls into the Anacostia River to infer the contribution of each source by providing the joint probability densities of the contribution of each source and the credible intervals of the inference. For this purpose, the potential sources of heavy metals in the urban catchment were identified and multiple samples of each were collected and analyzed by using an inductively coupled plasma mass spectrometry technique to determine their elemental profiles. Next, a Bayesian CMB method was employed to infer the contribution of various sources to the storm water runoff. The results of the analysis revealed that paved surfaces that accommodate traffic (i.e., street, bridge, and parking lot) are the major contributors to both dissolved and particulate metals in storm water. It was also found that for both dissolved fraction and total pollutants, the wet deposition source has a small contribution to all elements and that the runoff originating from roofs can be responsible for up to 50% of the Pb in the storm water.


Original languageEnglish
Article number04013015
JournalJournal of Environmental Engineering
Issue number3
Early online date18 Dec 2013
Publication statusPublished - 1 Mar 2014


  • Bayesian inference, Chemical mass balance, Markov chain Monte Carlo sampling, Source apportionment, Urban storm water