Interest in Harmful Algal Bloom (HAB) detection
has grown in recent years for scientific, commercial and public health reasons.
Depending on the type of algae present, HABs have been shown to be dangerous to
sea life and to human health. There
is a significant interest in identifying environmental factors that
contribute
to the occurrence of HABs, so that these may be incorporated in bloom
prediction algorithms [4, 5, 6]. A regional study on the dinoflagellate
Karlodinium veneficum has been generating near-real-time maps of HABs in the
Chesapeake Bay using a hydrodynamic model and satellite data [5]. The methodology uses time salinity, and
sea-surface temperature to predict the abundance (low, medium, or high) of the
dinoflagellate.
Since the algal species responsible for red
tides have been identified and their effects are well documented, it is
possible to monitor bloom pre-conditions, predict their occurrence, and respond
accordingly. In Hong Kong the Red Tide Information Network combines periodic
water sampling, food sampling and reports from local fisheries to monitor the
likelihood of a harmful algal bloom event. In response to reports of elevated
algal populations or red tide sightings, the government can take action to
determine if a bloom is in fact hazardous and if so, respond accordingly
(Bushaw, 1999). Plans are in place for
protecting fish farm populations through early harvesting or relocation and
monitoring the safety of public beaches and seafood sources. In situations like
Hong Kong’s, knowledge of the nature of local HABs makes monitoring,
prediction, and reaction possible, but in other locations our understanding is
limited. With countless species of algae worldwide, scientists still don’t know
what effects different species have on humans or marine organisms, much less
understand what causes most algal blooms. Singapore is one such location where
recent events have prompted closer investigation in HABs. In January of 2010
more than 200,000 fish were killed at fish farms near Pasir Ris and Pulau Ubin
in Singapore, the biggest reported loss in 10 years with damages exceeding $2
million.The deaths were attributed to a plankton bloom that decreased oxygen
levels in the water, suffocating the fish. Without identifying the species responsible
though, it’s impossible to know what environmental factors contributed to the
bloom or how future blooms could be predicted (Chao et al., 2009).
CENSAM’s aim in the study of HABs in Singapore
is to better inform decision makers so that future harmful algal blooms can be
predicted or even prevented, and damages minimized. Oceanographic models need
to be combined with measured data to better understand how local algal blooms
develop. One immediate goal is to associate algal blooms in Singapore with
easily measured environmental variables such as temperature, salinity, and
dissolved oxygen (Chao et al., 2009). ASVs using CTDs and mass
spectrometers can measure these key parameters and associate them with the
chemical compounds present in the water. Collecting data in the vicinity of
algal bloom events in conjunction with lab analysis of water samples can tell
us what harmful algae species we need to be concerned about and what
pre-conditions can lead to their blooming (Chao et al., 2009).
Despite these sensing capabilities, data
collection from algal bloom events is predicated on finding algal blooms when
they do occur. Since so little is currently known about blooms in Singapore, it
is difficult to predict when they will occur in the future. Furthermore algal
blooms are often very localized and transient, making them that much harder to
find and study (Richardson, 1996) Time scales can be on the order of hours to
days and while some blooms might cover hundreds of kilometers, others can be just
a few hundred meters across. Blooms will also move with water currents, making
continued observation difficult while physically separating the bloom from its
original location and the factors that contributed to its formation there. ASVs
can only gather measurements at their current location and their slow speed
combined with strong currents around Singapore limit their ability to
efficiently search for algal blooms. A more effective solution for finding
algal blooms and guiding the deployment of ASVs is needed, especially in these
early stages of HAB study when prediction is difficult (Chao et al.,
2009).
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