The Smart Drifter Cluster: Monitoring Sea Currents and Marine Litter Transport Using Consumer IoT Technologies.

The study of marine Lagrangian transport holds significant importance from a scientific perspective as well as for practical applications such as environmental-pollution responses and prevention (e.g., oil spills, dispersion/accumulation of plastic debris, etc.). In this regard, this concept paper introduces the Smart Drifter Cluster: an innovative approach that leverages modern "consumer" IoT technologies and notions. This approach enables the remote acquisition of information on Lagrangian transport and important ocean variables, similar to standard drifters. However, it offers potential benefits such as reduced hardware costs, minimal maintenance expenses, and significantly lower power consumption compared to systems relying on independent drifters with satellite communication. By combining low power consumption with an optimized, compact integrated marine photovoltaic system, the drifters achieve unlimited operational autonomy. With the introduction of these new characteristics, the Smart Drifter Cluster goes beyond its primary function of mesoscale monitoring of marine currents. It becomes readily applicable to numerous civil applications, including recovering individuals and materials at sea, addressing pollutant spills, and tracking the dispersion of marine litter. An additional advantage of this remote monitoring and sensing system is its open-source hardware and software architecture. This fosters a citizen-science approach, enabling citizens to replicate, utilize, and contribute to the improvement of the system. Thus, within certain constraints of procedures and protocols, citizens can actively contribute to the generation of valuable data in this critical field.

Feasibility Study for the Development of a Low-Cost, Compact, and Fast Sensor for the Detection and Classification of Microplastics in the Marine Environment.

The detection and classification of microplastics in the marine environment is a complex task that implies the use of delicate and expensive instrumentation. In this paper, we present a preliminary feasibility study for the development of a low-cost, compact microplastics sensor that could be mounted, in principle, on a float of drifters, for the monitoring of large marine surfaces. The preliminary results of the study indicate that a simple sensor equipped with three infrared-sensitive photodiodes can reach classification accuracies around 90% for the most-diffused floating microplastics in the marine environment (polyethylene and polypropylene).

Marine Litter Tracking System: A Case Study with Open-Source Technology and a Citizen Science-Based Approach.

It is well established that most of the plastic pollution found in the oceans is transported via rivers. Unfortunately, the main processes contributing to plastic and debris displacement through riparian systems is still poorly understood. The Marine Litter Drifter project from the Arno River aims at using modern consumer software and hardware technologies to track the movements of real anthropogenic marine debris (AMD) from rivers. The innovative "Marine Litter Trackers" (MLT) were utilized as they are reliable, robust, self-powered and they present almost no maintenance costs. Furthermore, they can be built not only by those trained in the field but also by those with no specific expertise, including high school students, simply by following the instructions. Five dispersion experiments were successfully conducted from April 2021 to December 2021, using different types of trackers in different seasons and weather conditions. The maximum distance tracked was 2845 km for a period of 94 days. The activity at sea was integrated by use of Lagrangian numerical models that also assisted in planning the deployments and the recovery of drifters. The observed tracking data in turn were used for calibration and validation, recursively improving their quality. The dynamics of marine litter (ML) dispersion in the Tyrrhenian Sea is also discussed, along with the potential for open-source approaches including the "citizen science" perspective for both improving big data collection and educating/awareness-raising on AMD issues.

On the determination of the optimal parameters in the CAM model.

In the field of complex systems, it is often possible to arrive at some simple stochastic or chaotic Low Order Models (LOMs) exploiting the time scale separation between leading modes of interest and fast fluctuations. These LOMs, although approximate, might provide interesting qualitative insights regarding some important aspects like the average time between two extreme events. Recently, the simplest example of a LOM with multiplicative noise, namely, a linear system with a linearly state dependent noise [also called correlated additive and multiplicative (CAM) model], has been considered as archetypal for numerous phenomena that present markedly non-Gaussian statistics. We show in this paper that the determination of the parameters of a CAM model from the (few) available data is far from trivial and that the actual most likely parameters might differ substantially from the ones determined directly from a (necessarily limited) short sequence of observations. We illustrate how this problem can be tackled, at least to the extent possible, using an approach that is based on Bayes' theorem. We shall focus on a CAM modeling the El Niño Southern Oscillation but the methodology can be extended to any phenomenon that can be described by a simplified LOM similar to the one examined here and where the available sequence of data is relatively short. We conclude that indeed a Bayesian approach can fix the problem.

Estimate of the average timing for strong El Niño events using the recharge oscillator model with a multiplicative perturbation.

El Niño Southern Oscillation (ENSO) is the leading mode of tropical Pacific variability at interannual timescales. Through atmospheric teleconnections, ENSO exerts large influences worldwide, so that improved understanding of this phenomenon can be of critical societal relevance. Extreme ENSO events, in particular, have been associated with devastating weather events in many parts of the world, so that the ability to assess their frequency and probability of occurrence is extremely important. In this study, we describe the ENSO phenomenon in terms of the Recharge Oscillator Model perturbed by multiplicative deterministic chaotic forcing, and use methodologies from the field of Statistical Mechanics to determine the average time between El Niño events of given strengths. This is achieved by describing the system in terms of its probability density function, which is governed by a Fokker Planck equation, and then using the Mean First Passage Time technique for the determination of the mean time between extreme events. The ability to obtain analytical solutions to the problem allows a clear identification of the most relevant model parameters for controlling the frequency of extreme events. The key parameter is the strength of the multiplicative component of the stochastic perturbation, but the decorrelation timescale of the stochastic forcing is also very influential. Results obtained with this approach suggest an average waiting time between extreme events of only some tens of years.

Ordinary chemical reaction process induced by a unidimensional map.

In this paper we show the results of the numerical simulation of a "standard" reaction process obtained by a "non standard" system: a reactant oscillator (a harmonic oscillator with an energy threshold) weakly interacting with a unidimensional tent map. According to the paper by M. Bianucci, R. Mannella, B.J. West, and P. Grigolini [Phys. Rev. E 51, 3002 (1995)]], the action of such a map on the reactant system should be equivalent, in some sense, to that of a thermal bath, where the values of the temperature and of the friction depend on the normalized correlation function and on the response function of the map. Here this prediction is confirmed by the numerical simulation. The numerical results are fitted very well by an Arrhenius law with the predicted temperature and friction values. Notice that this is a strict test of the theory because the reaction rate strongly depends on the fine level statistics, i.e., a small deviation in the cue of the distribution would result in a large deviation in the value of the rate.