Furthermore, because FIB survival in the surfzone determines the duration of transport, factors regulating FIB growth and mortality in coastal waters are also central to our understanding of bacterial pollution
(Anderson et al., 2005, Boehm, 2003 and Boehm et al., 2005). Beach pollution events are often poorly predicted, and about 40% of contamination postings are erroneous (Kim and Grant, 2004). With over 550 million annual person-visits to California beaches, this inaccuracy impacts both selleck chemical individual beach goers and California’s multi-billion dollar coastal tourism industry (Grant et al., 2001). Predictive modeling of bacterial pollution using readily measured (or modeled) physical parameters (wave height/direction, river flow, rainfall, etc.) could be a cost-effective way to improve the accuracy of beach contamination postings. However, to be effective in a range of settings, these models require
mechanistic understanding of bacterial sources, transports, and extra-enteric growth or decay. Mechanistic understanding moves beyond correlations, and examines the effects of individual processes structuring beach pollution. Currently, mechanistic FIB models range in complexity from simple mass balance equations (Boehm, 2003, Boehm et al., 2005 and Kim et al., 2004) to 3D hydrodynamic Sirolimus datasheet simulations (Sanders et al., 2005, Liu et al., 2006, Thupaki et al., 2010, de Brauwere et al., 2011 and Zhu et al., 2011). In conjunction with field observations and laboratory studies, these models have been used to identify processes structuring nearshore FIB contamination such as alongshore currents (Kim et al., 2004, Liu et al., 2006 and Thupaki et al., 2010), tides (de Brauwere et al., 2011), internal waves (Wong et al., 2012), rip cells (Boehm, 2003 and Boehm et al., 2005), cross-shore diffusion (Thupaki et al., 2010 and Zhu et al., 2011), sediment resuspension buy Etoposide (Sanders
et al., 2005), solar insolation (Boehm et al., 2009, Liu et al., 2006 and Thupaki et al., 2010), and temperature (de Brauwere et al., 2011). To date, however, only a handful of studies have used models to look at the relative importance of these processes in the nearshore. Thupaki et al. (2010) used a 3D hydrodynamic model to show that FIB loss in Lake Michigan due to alongshore current reversals and diffusion was over an order of magnitude greater than loss due to mortality. Zhu et al. (2011), however, revealed the opposite pattern in a quiescent Florida embayment. Furthermore, simple mass budget models for California’s Huntington State Beach suggest that multiple processes can interchangeably dominate FIB dynamics (Boehm, 2003, Kim et al., 2004, Boehm et al., 2005 and Grant et al., 2005). Taken together, these studies imply that the processes controlling surfzone FIB are likely to vary both in time (at a given beach), and space (beach to beach).