Numerical and Implementation Issues in Food Quality Modeling for
Human Diseases Prevention
A. Galletti
1
, R. Montella
1,2
, L. Marcellino
1
, A. Riccio
1
, D. Di Luccio
1
, A. Brizius
2
and I. Foster
2
1
Department of Science and Technology, University of Naples “Parthenope”, Centro Direzionale,
Isola C4, 80143, Napoli, Italy
2
Computation Institute, University of Chicago, 5735 South Ellis Avenue, 60637, Chicago, IL, U.S.A.
Keywords:
Lagrangian Methods, Numerical Interpolation, Food Quality, Human Diseases, Cloud Computing, High Per-
formance Computing, Scientific Workflow, Smart Devices, Internet of Things, Marine Data Crowdsourcing.
Abstract:
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.
1 INTRODUCTION
Human health can be adversely affected by pollutants
emission into sea water, specially from seafood con-
tamination caused by inshore discharges or offshore
spills in areas close to aquaculture farms.
Fish and mussel farms are critically sensitive to
coastal water quality, and thus require continuous
monitoring to enforce food security and quality and to
prevent any possible disease affecting human health.
The potentially toxic substances emitted from
point sources can, in more or less short time, reach the
mussel farms and promote, in relation to the mussel-
pollutant contact time, the bioaccumulation in filter
feeders organisms. Several studies have shown that
pathogens, such as bacteria and enteric viruses, can
be transmitted by mussels and the widespread habit
of consuming raw or slightly cooked shellfish con-
tributes to maintaining the incidence of hepatitis A
cases in the southern Italy at high level (Croci et al.,
2003).
On the other hand, nevertheless the availability of
technologies for remote water quality monitoring sys-
tem using wireless sensors (Haron et al., 2009), the
livestock sampling and the microbiological spottily
analysis fails if the goal is a consistent data time series
needed by any process aimed to make inference with
human health. The use of connected smart devices
is fully feasible in a context where the farms are in a
limited environment, while the challenges rise for fish
and mussel farms in marine nearshore, but open wa-
ters: the extreme weather events, the aggressive saline
environment, the network and energy availability and,
last but not least, the need for continuous maintenance
and sensors calibration could have a negative impact
on the use of a technical solution fully based on the
Internet of Things afloat approach.
To face the above depicted scenario, we de-
signed and implemented WaComM (Water Commu-
nity Model), a three dimensional Lagrangian model
enforcing the decision support system and enabling
the simulation and prediction of pollutant spills, trans-
port and dispersion in both inshore and offshore envi-
ronments (Giunta et al., 2005).
Here, we provide some details about the way in
which the input data of our Lagrangian model are ob-
Galletti A., Montella R., Marcellino L., Riccio A., Di Luccio D., Brizius A. and Foster I.
Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention.
DOI: 10.5220/0006297905260534
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
tained. Moreover, we give some insights about the
related parallel implementation.
In order to support the numerical model solu-
tion, we leverage on crowdsourced data acquired by
the system called "FairWind" a smart, cloud-enabled,
multifunctional navigation system for leisure and pro-
fessional vessels. This system has been developed as
an open technologies developing platform based on
the Android operating system for smart devices.
In this context, the system is used to collect data
from on board marine instruments such as, but not
limited to, GPS, heading, pitch, roll, yaw and speed
sensors, water temperature, depth and weather sen-
sors. Collected data are sent on the land using grid
file transfer technologies, and then processed in order
to improve data quality and consistency.
Data, made available to the community as open
data, are used to improve the model setup (i.e. high
resolution coastal digital depth model) and will be
used for model evaluation and improvement (surface
temperature, surface current).
The rest of the paper is organized as follows: sec-
tion 2 is about related work; section 3 focuses on the
numerical issues driving the design of our Lagrangian
model; section 4 is on details about the WaComM im-
plementation with focus on parallel cores; section 5 is
about the novel contribution of the crowdsourcing ap-
proach in this application research field; section 6 de-
scribes some preliminary computational evaluations;
finally section 7 presents conclusions and future de-
velopments.
2 RELATED WORK
The Regional Ocean Modeling System (ROMS)
is a free-surface, terrain-following, primitive equa-
tions ocean model widely used by the scientific
community to characterize and simulate the meso-
and sub-mesoscale ocean and coastal water dynam-
ics (Haidvogel et al., 2000; Wilkin et al., 2005;
Shchepetkin and McWilliams, 2003; Shchepetkin and
McWilliams, 2005). In this application ROMS is
forced by a high-resolution atmospheric forecast and
provides the flow velocity on a curvilinear boundary-
fitted grid.
ROMS was used to deploy a real-time forecast for
the transport and deposition of water pollutants us-
ing the particle-tracking WaComM model, which im-
plements a Lagrangian technique consistent with the
advection-diffusion equation (Rodean, 1996).
In this kind of model, the dispersion phenomenon
is reproduced by imaginary numerical particles; at
each of these particles different characteristics are as-
signed, e.g. pollutant concentration and settling ve-
locity.
At each time step, the particle position is calcu-
lated on the basis of the flow velocity, computed by
the hydrodynamic model ROMS, and a random jump
representing the turbulence diffusion.
GVirtuS (Montella et al., 2011) is a general-
purpose virtualization service for high performance
computing applications on cloud environments, fo-
cusing on NVidia CUDA GPGPU virtualization and
MPI based virtual clusters.
The RAPID GVirtuS incarnation (Montella et al.,
2016b) was used as GPGPU remoting provider for hi-
erarchical parallelism, sending the instruction set ker-
nel to the accelerating hardware, processing data on
the device and then sending back results to the gen-
eral purpose CPU.
The FACE-IT (Framework to Advance Climate,
Economic, and Impact Investigations with IT) project
(Pham et al., 2012) has been developed, and contin-
ues to be developed, to provide a cloud-based sci-
ence gateway for the web-based access to a range of
data projects, simulation models and analysis tools
(Montella et al., 2015). FACE-IT builds on the
Globus Galaxies platform, which has been devel-
oped over the past several years at the University of
Chicago, initially in support of the Globus Genomics
project (Madduri et al., 2015). FACE-IT is used as
main computational playground for the implementa-
tion of the WaComM Lagrangian model running as an
on-demand and routinely workflow (Montella et al.,
2016a).
3 DESIGN
WaComM is the evolution of the LAMP3D model.
We optimized the algorithms in order to improve
its performance on high performance computing en-
vironment adding features as restarting and shared
memory parallelization. The description of the un-
derlying mathematical model is as follows.
Pollutants are considered as inert Lagrangian par-
ticles, tracing the marine circulation without feedback
interactions with sea current fields and other particles.
Each particle is assumed to have:
position r(t) = (x(t),y(t),z(t)) at time t;
initial position r
0
= r(0) = (x
0
,y
0
,z
0
) at the initial
time t = t
0
= 0;
velocity v(r(t),t) = U(r(t),t) + η(r(t),t) at time
t, where U(r(t),t) denotes the deterministic ve-
locity, and η(r(t),t) is the stochastic fluctuation
arising from the Langevin equation model in or-
der to describe the Brownian motion of particles
(Rodean, 1996).
Given U(r(t),t), or an estimate of it, for each posi-
tion r(t) and at each time t, the final particle posi-
tion r(t
k+1
) = r(t
k
+t), at time t
k+1
= t
k
+t, can be
computed by means of the equation:
r(t
k
+t) = r(t
k
)+
Z
t
k
+t
t
k
v(r(t),t) dt, (k = 0,1, ...),
(1)
where r(t
k
) is a starting position, at the starting time
t
k
, and t > 0 denotes a time interval length (in Wa-
ComM we set t = 1 h).
Numerical integration of (1) could be made in sev-
eral ways; in our approach we use the Eulero method
that considers a discretization of the time interval
[t
1
,t
1
+ t] in the grid
τ
j,k
= t
k
+ j · dτ, j = 0,. ..,N
where dτ = t/N denotes the discretization step.
To do this, at each time τ
j
the evaluations of
U(r(τ
j,k
),τ
j,k
) and η(r(τ
j,k
),τ
j,k
) are required. How-
ever, these values are provided by ROMS only at
some discrete time instants, and on a discrete irregu-
lar three-dimensional grid. Such a grid can be thought
of as the set of vertexes of a finite number of poly-
hedrons V
i
(cells). These cells are all topologically
homeomorphic to a cube and their union is the space
domain of U.
Figure 1: Example of cell. The cells form the irregular grid
where the values of U are known.
An example of the form of such cells is in Fig-
ure 1. Notice that the irregular polyhedron is defined
by assigning its eight vertexes.
Possible choices of interpolants, which do not
need any information about the mesh (i.e. the so-
called mesh-free methods) are the radial basis func-
tions methods (Cuomo et al., 2013; Fasshauer, 2007).
However, when some structure of the grid is assigned,
i.e. when a smart subdivision of the domain in geo-
metrical polyhedral structures is known, one can take
advantage of this fact and so several kinds of inter-
polants, exploiting the geometry of the cells that form
the mesh, can be defined (Galletti and Maratea, 2016;
Cuomo et al., 2014a; Cuomo et al., 2014b; Cuomo
et al., 2015). In this case we choose a simple trilinear
interpolation approach using barycentric coordinates.
In particular, by referring to the notations introduced
in Figure 1, we compute velocities at any spatial lo-
cation, e.g. at particle position r and desired time, by
the linear interpolation of velocities made available
by ROMS at the vertexes of any grid cell and regular
time intervals.
In order to assign the stochastic fluctuations, Wa-
ComM relies on the standard ‘K-theory’, based on
a diffusion coefficient which is estimated by pre-
processed ROMS data. An exponential decay which
uses the T90 parameter (the time required to de-
grade 90% of the biodegradable matter in a given
environment) is applied to takes into account de-
caying processes. A sedimentation velocity, w
sed
=
(0,0, w
sed
), is added to the deterministic component
of velocity to simulate settling particles. At the end
of each suitably chosen time interval, a scaled con-
centration field C
i, j,k
is computed by simply counting
the number of particles found within each grid cell.
4 IMPLEMENTATION
The modeling system can be used in an ex-ante fash-
ion, as a decision support tool to aid in the selection
of the best suitable areas for farming activity deploy-
ment, or in an ex-post fashion, in order to achieve a
better management of offshore activities. We tested
the system on several case studies where pollutants
are spilled out from well known punctual sources lo-
cated along the coasts of Campania region.
As arguing from the numerical approach, the
model is computing intensive and parallelization is
needed for its usage in real-world applications. The
problem size increases with the number of emission
sources and the number of emitted particles. More-
over, the computing time is influenced by the integra-
tion time step which should be short enough to cor-
rectly represent the turbulent diffusion processes.
Although consistent results can be guaranteed us-
ing the sequential implementation of the WaComM
model, the wall-clock performance actually makes the
production unfeasible.
Hence, the growing need of on-demand results,
which involves a large computational effort for Wa-
ComM, suggests to use general purpose GPUs in or-
der to efficiently perform computing intensive tasks.
In particular, a GPU implementation is considered
for the WaComM main cycles involved for the inter-
polation and evaluation steps of the 3D momentum
and dispersion parameters. This concerns with a par-
allel design schema hierarchical and heterogeneous.
The implementation of the GPGPU enabled code
is realized with the NVIDIA CUDA programming
model, and using heterogeneously both the CPU and
GPU (Ortega et al., 2016) supported by an MPI based
distributed memory approach. Such an implementa-
tion considers a dynamical load balancing on the par-
ticle number.
More in detail, the distributed memory paralleliza-
tion has been introduced in the hourly inner cycle of
WaComM in order to enhance the performance. Such
a cycle computes the path of each particle and, since
no interaction between particles is assumed, each par-
ticle path can be virtually tracked independently of
the others. As explained in the design section, the
interpolation stage is time consuming. The problem
size scales with the input data (the resolution of the
momentum, the sea current 3D vector components
- U, V, W - and the vertical T-diffusion - AKT -
grid). Then, previous discussion justifies even more
the shared memory parallel approach for the evalua-
tion of the interpolation model.
5 DATA CROWDSOURCING
In the field of ocean modeling, the need for compu-
tational and storage resources is currently satisfied by
the availability of cloud based services that reduce the
total cost of ownership and accelerate the democra-
tization of science (Foster, 2011). Nevertheless, to
have more robust and accurate models, there is a need
for detailed, high resolution, spatio-temporal data for
initialization and validation. While data can be hard
to obtain from traditional sources, due the lack of
available public data in some coastal areas, the chal-
lenges of surveying large areas and other technical
constraints, this data can be easily obtained using in-
ternet of things based crowdsourcing tools like Fair-
Wind
(Montella et al., 2016c).
FairWind is an integrated, multifunctional, navi-
gation software based on open technologies designed
and developed by a very interdisciplinary team in or-
der to maximize the benefits and the advantages. It
is a marine single board computer device leveraging
a custom-built infrastructure on top of stable and well
documented mobile and cloud technologies.
From the marine electronics point of view, the
most remarkable innovation introduced by FairWind
are the Boat Apps that extend the FairWind basic fea-
tures, integrating with already present onboard instru-
ments and straightforwardly interacting with indus-
trial or self - made internet of things based instru-
ments. The board dataset, collected by FairWind, is
a scientifically intriguing source of huge amounts of
geolocated data (big-data) about marine coastal envi-
ronment (weather and sea conditions, surface sea cur-
rents, water temperature, water depth, etc.), boat en-
gine status, boat performances (speed, heading, pitch,
roll), presence of board water and waste management,
fuel consumptions and, above all, safety at sea and
search and rescue systems (Figure 2).
Data is collected on board and, when possible,
sent to cloud storage and computing facilities using
reliable, affordable, and safe technologies such as the
Globus data transport services. Users can choose
what data to share in a named or anonymous way.
Operationally, once data is collected, it will be ana-
lyzed and processed in order to extract sensor calibra-
tion using big-data algorithms, evaluated with a qual-
ity model comparing it with data acquired by trustful
equipment and, finally, made available as open data
for ocean model initialization and/or validation.
6 EVALUATION
This section describes the WaCoMM model use case
for the Campania Region (Italy), applied to prevent
the consumption of contaminated food harvest in
mussel farms. This farms have, generally, a long lines
organization in which the mussels are attached to sub-
merged ropes hung from a back-bone supported by
large plastic floats.
In 2006, the Campania Region published the
Guidelines for mussel farms: classification and
monitoring system of mussels production and relay-
ing areas to delineate the guideline to mussel farms
monitoring.
This document identified the skills to perform
the analysis provided by the Experimental Zoo-
prophylactic Institute of Southern Italy (IZSM) for
mussels samples and by the Campania Regional En-
vironmental Protection Agency (ARPAC) for water
samples. The in situ analysis also included the com-
pulsory microbiological parameters: Escherichia Coli
and Salmonella spp. In accordance with the current
European legislation (2073/2005EC), the concentra-
tion of E. Coli and Salmonella spp in mussels must be
less than 230 MPN/100g (Most Probable Number per
100 grams) and zero respectively.
The considered mussels rows (MF
PT
in Figure 3)
are located about 500 m distance to the coast in Punta
Terone-Capo Miseno and cover an area of about 257
m
2
, with a depth of about 20 m. In this case, the
reared mussels are mostly Mytilus Galloprovincialis.
The MF
PT
mussels are allowed to be placed on the
market for human consumption directly, without fur-
Figure 2: Smart devices in a context of internet of things afloat make data crowdsourcing affordable. FairWind equipped
professional and leisure boats can contribute to model setup improvement and data assimilation.
ther treatment in a purification centre or after relaying
(class type A). This makes the mussels quality very
important to human diseases prevention.
In some cases flooding events can bring too much
pollutants to shellfish farms, banning them from har-
vesting.
In this context, a forecasting system of the meteo-
rological and hydrodynamic circulation, coupled with
the WaComM model, can be a valid support to the
mussel farm management.
WaComM is included in a scientific workflow to
ingest the forecast input data needed to initialize the
simulation and track particles trajectories.
The Weather Research and Forecasting Model
(WRF) (Skamarock et al., 2001) simulates the
weather conditions for driving the ROMS model.
The WRF model has already been used to simulate
weather conditions on the Campania region (Barone
et al., 2000; Ascione et al., 2006). WaComM model
domain is 715×712×11 grid points (Lat
min
=40.48N,
Lat
max
=40.95N; Lon
min
=13.91E, Lon
max
=14.52E).
The pollutant sources in the Gulf of Naples are con-
sidered as points all along the coast, spilling out 100
particles for each simulation hour. Actually we used
50 particle sources. Our system has been tested com-
paring the numerical forecast and mussel microbio-
logical analysis. The simulation spanned the time
interval 07/12/2015 Z00 21/12/2015 Z23 and the
output was stored at a hourly interval. On days
09/12/2015 and 21/12/2015 the local authorities car-
ried out the microbiological analysis on Mytilus Gal-
loprovincialis in Punta Terone mussel farm. Results
showed a concentration of E. Coli much greater than
the legal limits in the first day (5400 MPN/100g) and
lower in the second day (45 MPN/100g); Salmonella
spp was absent in both samples.
The mean wind direction started blowing from
NW from 7 to 9 December 2015; in this period parti-
Figure 3: Mussel farms locations in the Bay of Pozzuoli. The studied mussel farm is in Punta Terone (MF
PT
) area.
cles spilled out by sources in the eastern part moved
towards the center, while particles emitted by sources
in the western part remained close to the coast (see
Figure 4). After, the mean wind shifted to NE and all
particles moved towards the center of the gulf with a
progressive increase in the concentration of the tracer
in the area surrounding the two mussel farms with a
maximum value on day 15/12/2015. Subsequently,
the rotating ocean currents contributed to the disper-
sion of the tracer away from the mussel farms. This
picture is also confirmed by microbiological analy-
sis carried out on Mytilus Galloprovincialis mussels
(Figure 5). The comparison between numerical fore-
cast and microbiological analysis showed a remark-
able similarity in trends, although this kind of analysis
have to be performed in a more extensively fashion.
That confirmed the possibility to use the system as a
decision maker tool for applications correlated with
sea quality and as a support system for experimental
observations and controls.
7 CONCLUSION
The quality of coastal marine waters depends strictly
on the impact of human activities. Urban settlements,
industries, agriculture, livestock farming and weather
conditions produce effects which, individually or to-
gether, can heavily compromise or even disrupt the
equilibrium of aquatic ecosystems.
In this paper we presented our research efforts in
designing and developing WaComM, a community
water quality model, with the main aim, but not lim-
ited to, to develop a forecast system and perform op-
erational numerical predictions in the context of mus-
sel farms management, in order to prevent E. Coli
and Salmonella human diseases with a strong effort
in data dissemination for local management decision
support (Montella et al., 2007).
WaComM is under continuous active develop-
ment.
From the implementation point of view a deep
refactoring is needed in order to better exploit the hi-
erarchical parallelism. The current implementation
leverages on a naive application designed load bal-
ancing (Laccetti et al., 2013). A Portable, Extensible
Toolkit for Scientific Computation (PETSc) approach
could enhance the overall perforance and, above all,
the problem scalability (Carracciuolo et al., 2015).
A short-term goal of our project is to extend the
studied area to the whole coasts of the Campania
Region in order to promote its use as an effective
tool dedicated to improve the management of coastal
farms. In order to achieve this target, we need to im-
prove the robustness of the WaComM model and the
scalability of the offline coupling system.
From the scientific point of view, we will en-
hance the simulation quality with data collected us-
ing the FairWind technology as depicted above im-
proving the data acquisition from boat sensor as inter-
connected smart devices using the Internet of Things
afloat technologies. This issue is a source of novelty
even because the whole FairWind ecosystem is based
on the SignalK marine data interchange open proto-
col (http://signalk.org). The proposed system is ex-
tensible in order to collect data from other sensors
as, but not limited to, surface ph and salinity sen-
sors that could improve the simulation quality and the
model validation process and, finally, if supported by
Figure 4: Sea surface currents (vectors) and pollutants concentration (red=high; yellow=medium; green=low; blue=very low;
white=absent) in in Gulf of Pozzuoli (Campania Region, Italy) in days 08/12/2015 Z12 (figure A), 09/12/2015 Z12 (figure
B), 15/12/2015 Z12 (figure C) and 20/12/2015 Z23 (figure D). The red dotted line is the area of Study with mussel farms in
Punta Terone (number 1 in figure A) area.
a ground true based on better microbiological analy-
sis and consistent epidemiological studies on mussels
originated enterogastric diseases (Suffredini et al.,
2014), enhance the overall system trustability.
Figure 5: Forecasted averaged particles concentration timeseries in the study area.
ACKNOWLEDGEMENTS
This work is supported in part by the Euro-
pean Union Grant Agreement number 644312-
RAPID–H2020-ICT-2014/H2020-ICT-2014-1 “Het-
erogeneous Secure Multi-level Remote Acceleration
Service for Low-Power Integrated Systems and De-
vices”, using GVirtuS open source software compo-
nents; project “Development of a software for model-
ing pollution events based on high-performance and
distributed computing technologies with advanced
web interface”; project MytiluSE - Modelling
mytilus farming System with Enhanced web tech-
nologies”’; NSF cyberSEES program award ACI-
1331782; NSF Decision Making Under Uncertainty
program award 0951576; DOE contract DE-AC02-
06CH11357. IBM-P8 resources were generously pro-
vided through an agreement between IBM and the
University of Naples “Parthenope” - Department of
Science and Technology - High Performance Scien-
tific Computing Smart-Lab.
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