Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention

Monitoring nearshore sea water pollution using connected smart devices could be nowadays impracticable due to the aggressive saline environment, the network availability and the maintain and calibration costs. Accurate forecast of marine pollution is most needed to evaluate the adverse effects on coastal inhabitants’ health when fishes and mussels farming economically characterizes the local social background. In an operational context, numerical simulations are performed routinely on a dedicated computational infrastructure producing space and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples. In this paper we present our results in developing a community open source Lagrangian pollutant transport and dispersion model, leveraging on hierarchical parallelism implying distributed memory, shared memory and GPGPUs. Some numerical details are also discussed. This system has been used to develop an alarm system to help local authorities in making decisions regarding the collection of mussels. The model setup and the simulation results will be improved using FairWind, an under development system dedicated to coastal marine crowdsourced data gathering and sharing, based on smart devices and Internet of Things afloat.