Adam Gauci

Comparative Modeling Approaches for Personal Exposure to Particle-Associated PAH

Several models for simulation of personal exposure (PE) to particle-associated polycyclic aromatic hydrocarbons (PAH) have been developed and tested. The modeling approaches include linear regression models (Model 1), time activity weighted models (Models 2 and 3), a hybrid model (Model 4), a univariate linear model (Model 5), and machine learning technique models (Model 6 and 7). The hybrid model (Model 4), which utilizes microenvironment data derived from time-activity diaries (TAD) with the implementation of add-on variables to account for external factors that might affect PE, proved to be the best regression model (R(2) for B(a)P = 0.346, p < 0.01; N = 68). This model was compared with results from two machine learning techniques, namely decision trees (Model 6) and neural networks (Model 7), which represent an innovative approach to PE modeling. The neural network model was promising in giving higher correlation coefficient results for all PAH (R(2) for B(a)P = 0.567, p < 0.01; N = 68) and good performance with the smaller test data set (R(2) for B(a)P = 0.640, p < 0.01; N = 23). Decision tree accuracies (Model 6) which assess how precisely the algorithm can determine the correct classification of a PE concentration range indicate good performance, but this is not comparable to the other models through R(2) values. Using neural networks (Model 7) showed significant improvements over the performance of hybrid Model 4 and the univariate general linear Model 5 for test samples (not used in developing the models). The worst performance was given by linear regression Models 1 to 3 based solely on home and workplace concentrations and time-activity data.

A Machine Learning approach for automatic land cover mapping from DSLR images over the Maltese Islands

Abstract High resolution raster data for land cover mapping or change analysis are normally acquired through satellite or aerial imagery. Apart from the incurred costs, the available files might not have the required temporal resolution. Moreover, cloud cover and atmospheric absorptions may limit the applicability of existing algorithms or reduce their accuracy. This paper presents a novel technique that is capable of mapping garrigue and/or phrygana vegetation as well as karst or ground-armour terrain in photos captured by a digital camera. By including a reference pattern in every frame, the automated method estimates the total area covered by each land type. Pixel based classification is performed by supervised decision tree methods. Although the intention is not to replace traditional surface cover analysis, the proposed technique allows accurate land cover mapping with quantitative estimates to be obtained. Since no expensive hardware or specialised personnel are required, vegetation monitoring of local sites can be carried out cheaply and frequently. The developed proof of concept is tested on photos taken in thirteen different sites across the Maltese Islands.

Comparative median grain size assessment through three different techniques for sandy beach deposits on the Maltese Islands (Central Mediterranean)

ABSTRACT Deidun, A., Gauci, R., Schembri, J.A., Šegina, E., Gauci, A., Gianni, J., Gutierrez, J.A., Sciberras, A. and Sciberras, J., 2013. Comparative median grain size assessment through three different techniques for sandy beach deposits on the Maltese Islands (Central Mediterranean) It is estimated that sandy beaches cover only 2.2 per cent of the Maltese coastline. Although small in size, Maltese pocket beaches exhibit complex geomorphological interactions. A granulometric analysis of Maltese beach deposits may thus shed further light on the understanding of these interactive processes and provide baseline information on how beach sediment size may relate geo-spatially and morphometrically. Surface (0-10cm) sand samples were collected from ninety sandy beaches in Sicily, circum-Sicilian islands and the Maltese Islands. The median grain size of these sediment samples was assessed through three different techniques: the conventional sieving technique, observation through stereo microscopy and through image processing. The two primary objectives of such work were firstly, to construct a repository of median grain size values for the entire stretch of Maltese sedimentary coastline (the first study ever to be done on such a comprehensive spatial scale), and secondly, to evaluate the degree of concordance between the three techniques. The highest Pearson correlation value (0.90) was recorded for the sieving-scanning match, although in many cases differences were large enough to result in a different sediment type classification. The highest level of agreement between the scanning and sieving technique was registered for the medium-fine (1.5–2.5phi) and very coarse categories (−0.5–0.0 phi). Median particle diameters measured through microscopy were those which diverged most from those of other techniques. This maybe due to the relatively small number of sediment grains which were analysed within such a technique.

A first attempt at testing correlation between MODIS ocean colour data and in situ chlorophyll-a measurements within Maltese coastal waters

The study of spatio-temporal trends for key water quality parameters in the Maltese coastal waters is hindered by the lack of systematic observations spanning over the full domain and for sufficiently long time periods. Satellite data offers an alternative source of information, but requires ground truthing against in situ measurements. The aim of this study is to attempt the statistical comparison of MODIS ocean colour data, for a near-shore marine area off the north-east coastline of Malta, with in situ surface chlorophyll-a measurements, and to extract a twelve-month ocean colour data series for the same marine area. Peaks in surface chlorophyll-a concentration occurred in the January-February period, with lowest values being recorded during the early spring period. Log bias values indicate that the MODIS dataset under-estimates the surface chlorophyll-a values, whilst RMSD and r2 values suggest that the match-up between satellite and in situ values is only partly consistent.

Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.

Optimal SKA antenna configuration using genetic algorithms

The Square Kilometre Array (SKA) is a radio telescope designed to operate between 70MHz and 10GHz. Due to this large bandwidth, the SKA will be built out of different collectors, namely antennas and dishes to cover the frequency range adequately. In order to deal with this bandwidth, innovative feeds and detectors must be designed and introduced in the initial phases of development. Moreover, the required level of resolution may only be achieved through a novel configuration of dishes and antennas. Due to the large collecting area and the specifications required for the SKA to deliver the promised science, the configuration of the dishes and the antennas within stations is an important question. This research investigates the applicability of machine learning techniques to determine an optimum configuration for the elements within an aperture array station. Genetic algorithms are primarily used to search a large space of optimum layouts. Fitness functions based on estimates of the main lobe to maximum side lobe ratio, the side lobes fall off rate, the main lobe area to side lobes area ratio as well as the kurtosis of residuals from polynomial fits of the main beam, are employed.

Optimising beached litter monitoring protocols through aerial imagery

The monitoring of beached litter along the coast is an onerous obligation enshrined within a number of legislative frameworks (e.g. the MSFD) and which requires substantial human resources in the field. Through this study, we have optimised the protocol for the monitoring of the same litter along coastal stretches within an MPA in the Maltese Islands through aerial drones, with the aim of generating density maps for the beached litter, of assisting in the identification of the same litter and of mainstreaming this type of methodology within national and regional monitoring programmes for marine litter. Concurrent and concomitant in situ monitoring of beached litter enabled us to ground truth the aerial imagery results. Results were finally discussed within the context of current and future MSFD monitoring obligations, with considerations made on possible future policy implications.

Is citizen science a valid tool to monitor the occurrence of jellyfish? The spot the jellyfish case study from the Maltese Islands

ABSTRACT Olsen, W.S.; Figueiredo, S.A.; Albuquerque, M.G., and Calliari, L.J., 2018. The Role of Local Geomorphology Influencing Coastal Response to Sea Level Rise. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 311–315. Coconut Creek (Florida), ISSN 0749-0208. Considering projected climate change scenarios with accelerated rates of mean sea level rise, Cassino Beach in Southern Brazil, a wave dominated low-gradient coastal plain, is inherently at a very high risk in relation to its impacts. Additionally, the presence of small-scale creeks (washouts) can increase coastal susceptibility to erosion in two ways: (i) changing substrate morphology by lowering foredune height; (ii) displacing sediments causing a local deficit. In order to compare coastal response under sea level rise in the presence of a washout versus at well-established foredune, two independent sets of simulation experiments were designed. Simulations were executed using Random Shoreface Translation Model (RanSTM) and considered two sea level rise scenarios (RCP 2.6 and RCP 8.5) projected for the year 2030. Experiment 1 quantified the effects of washouts channels presence (changes in morphology + sediment deficit), compared to its absence (foredune) while experiment 2 focused on isolating the effects of changes on substrate morphology from sediment budget. The results showed higher mean coastal retreat for washout substrate compared to foredune. Further data analysis indicated that changes in sediment budget, due to washout presence, exerted higher control under total coastal retreat compared to onshore topography differences in both scenarios.

Multi-frame blind image deconvolution through split frequency - phase recovery

Accurate information extraction from images can only be realised if the data is blur free and contains no artificial artefacts. In astronomical images, apart from hardware limitations, biases are introduced by phenomena beyond control such as atmospheric turbulence. The induced blur function does vary in both time and space depending on the astronomical “seeing” conditions as well as the wavelengths being recorded. Multi-frame blind image deconvolution attempts to recover a sharp latent image from an image sequence of blurry and noisy observations without knowledge of the blur applied to each image within the recorded sequence. Finding a solution to this inverse problem that estimates the original scene from convolved data is a heavily ill-posed problem. In this paper we describe a novel multi-frame blind deconvolution algorithm, that performs image restoration by recovering the frequency and phase information of the latent sharp image in two separate steps. For every given image in the sequence a point-spread function (PSF) is estimated that allows iterative refinement of our latent sharp image estimate. The datasets generated for testing purposes assume Moffat or complex Kolmogorov blur kernels. The results from our implemented prototype are promising and encourage further research.

Classification of the coastal water bodies of the Maltese Islands through the assessment of a decadal ocean colour data set

ABSTRACT Azzopardi J., Deidun A., Gianni F., Gauci A. P., Angulo Pan, B. and Cioffi M., 2013. Classification of the coastal water bodies of the Maltese Islands through the assessment of decadal ocean colour data set Nine coastal water bodies off the Maltese Islands (Central Mediterranean) were identified within the Water Framework Directive. The degree of spatial and seasonal variability in the ocean colour chlorophyll-a values (monthly re-analysed values originating from MODIS, MERIS and SeaWiFS sensors available from the MyOcean Marine Core Service) for these water bodies in the 2003–2011 period was evaluated graphically and statistically. Weekly values from the same satellite platforms were only available for the period 2010–2011 and these were analysed separately. The nine coastal water bodies were characterized by ocean colour values consistent with an oligotrophic water body, with seasonal mean values ranging from 0.06 to 0.35 mg/m3. The same nine coastal water bodies were classified according to an arbitrary ocean colour index based on seasonal mean values calculated over the entire 2003–2011 period. The seasonal pattern of variability within ocean colour values across the different coastal water bodies over a single year was highly homogenous, with highest values being recorded during the December–February period, and lowest values being recorded during the May–August period, with very few exceptions (solely recorded in 2010 and 2011). Although statistical analyses (PERMANOVA) showed significant inter-annual differences between seasonal ocean colour values of the different water bodies, spatial variations among the same values were statistically significant only in the spring and summer seasons, over the entire nine-year period. However, the pairwise tests revealed that most of these significantly different comparisons were registered during the 2010–2011 summer seasons. These spatial differences could either be due to an artefact (the 2010 and 2011 ocean colour data sets were derived using a different chlorophyll-a algorithm and different satellites than the 2003–2009 ones) or else they could be real (e.g. some of the coastal water bodies exhibiting higher ocean colour values are optically more complex since they host large coastal embayments, intensive aquaculture activities, treated sewage discharges and other anthropogenic activities).