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Vehicular traffic occupies a significant place among the sources of air pollution, due to population and urban growth that has led to an excessive increase in the vehicle fleet worldwide, and in Costa Rica as well. Vehicle emissions generate greenhouse gases (GHGs), particulate matter (PM), and heavy metals (HMs), due to combustion products from fossil-fuel engines, tire wear, and brake linings. HMs are important because they cannot be degraded or destroyed naturally; however, they can be diluted by physicochemical agents and be incorporated into trophic chains where they can be bioaccumulated causing significant negative effects on human well-being and ecological quality. This study aimed to assess the HM pollution load in biomonitors and road dust from vehicular emissions by chemical analyses and magnetic properties modeling. For this purpose, chemical and magnetic property analyses were carried out on samples of road dust and leaves of Cupressus lusitanica Mill. and Casuarina equisetifolia L., which were sampled during 2 different years in the Greater Metropolitan Area of Costa Rica known as GAM. Contamination factor (CF) and pollution load index (PLI) results showed significant metal pollution in some of the study sites. Contamination by the metals V, Cr, and Zn was most commonly present in the biomonitors, and for road dust, they were Cr, Zn, and Pb. The PLI estimates obtained with the validated support vector machine (SVM) magnetic properties models were consistent (sensitivity, specificity, and precision) with those obtained by chemical analysis, demonstrating the feasibility of this method for the identification of this index of contamination.

The increasing use of electric vehicles connected to the power grid gives rise to challenges in the vehicle charging coordination, cost management, and provision of potential services to the grid. Scheduling of the power in an electric vehicle charging station is a quite challenging task, considering time-variant prices, customers with different charging time preferences, and the impact on the grid operations. The latter aspect can be addressed by exploiting the vehicle charging flexibility. In this article, a specific definition of flexibility to be used for an electric vehicle charging station is provided. Two optimal charging strategies are then proposed and evaluated, with the purpose of determining which strategy can offer spinning reserve services to the electrical grid, reducing at the same time the operation costs of the charging station. These strategies are based on a novel formulation of an economic model predictive control algorithm, aimed at minimising the charging station operation cost, and on a novel formulation of the flexibility capacity maximisation, while reducing the operation costs. These formulations incorporate the uncertainty in the arrival time and state of charge of the electric vehicles at their arrival. Both strategies lead to a considerable reduction of the costs with respect to a simple minimum time charging strategy, taken as the benchmark. In particular, the strategy that also accounts for flexibility maximisation emerges as a new tool for maintaining the grid balance giving cost savings to the charging stations.

Hierarchical decision-making processes traditionally modeled as bilevel optimization problems are widespread in modern engineering and social systems. In this work, we deal with a leader with a population of followers in a hierarchical order of play. In general, this problem can be modeled as a leader-follower Stackelberg equilibrium problem using a mathematical program with equilibrium constraints. We propose two interconnected dynamical systems to dynamically solve a bilevel optimization problem between a leader and follower population in a single time scale by a predictive-sensitivity conditioning interconnection. For the leader's optimization problem, we developed a gradient descent algorithm based on the total derivative, and for the followers' optimization problem, we used the population dynamics framework to model a population of interacting strategic agents. We extended the concept of the Stackelberg population equilibrium to the differential Stackelberg population equilibrium for population dynamics. Theoretical guarantees for the stability of the proposed Stackelberg population learning dynamics are presented. Finally, a distributed energy resource coordination problem is solved via pricing dynamics based on the proposed approach. Some simulation experiments are presented to illustrate the effectiveness of the framework.

Metropolitan Mexico City (MMC) children and young adults exhibit overlapping Alzheimer and Parkinsons’ diseases (AD, PD) and TAR DNA-binding protein 43 pathology with magnetic ultrafine particulate matter (UFPM) and industrial nanoparticles (NPs). We studied magnetophoresis, electron microscopy and energy-dispersive X-ray spectrometry in 203 brain samples from 14 children, 27 adults, and 27 ALS cases/controls. Saturation isothermal remanent magnetization (SIRM), capturing magnetically unstable FeNPs ̴ 20nm, was higher in caudate, thalamus, hippocampus, putamen, and motor regions with subcortical vs. cortical higher SIRM in MMC ≤ 40y. Motion behavior was associated with magnetic exposures 25–100 mT and children exhibited IRM saturated curves at 50–300 mT associated to change in NPs position and/or orientation in situ . Targeted magnetic profiles moving under AC/AD magnetic fields could distinguish ALS vs. controls. Motor neuron magnetic NPs accumulation potentially interferes with action potentials, ion channels, nuclear pores and enhances the membrane insertion process when coated with lipopolysaccharides. TEM and EDX showed 7–20 nm NP Fe, Ti, Co, Ni, V, Hg, W, Al, Zn, Ag, Si, S, Br, Ce, La, and Pr in abnormal neural and vascular organelles. Brain accumulation of magnetic unstable particles start in childhood and cytotoxic, hyperthermia, free radical formation, and NPs motion associated to 30–50 μT (DC magnetic fields) are critical given ubiquitous electric and magnetic fields exposures could induce motion behavior and neural damage. Magnetic UFPM/NPs are a fatal brain cargo in children’s brains, and a preventable AD, PD, FTLD, ALS environmental threat. Billions of people are at risk. We are clearly poisoning ourselves.

Micro-grids have become the building block of modern energy systems, where distributed resources are the characterizing feature. The charging operation of electric vehicles can be exploited as a flexible load to achieve operational goals of the micro-grid. In the particular case of car-sharing fleets, the degrees of freedom in the charging procedures are reduced when compared to private users. In this work, we illustrate how a car sharing fleet can be incorporated as a flexible load in the micro-grid management system. A linear optimization problem is formulated, where the cost function makes a trade-off between the gain in flexibility in the micro-grid and the loss incurred by the car-sharing service for delaying the recharging procedure of the EV. The proposed approach is evaluated on a data set of charging events generated by a real car-sharing fleet showing that the EMS allows reducing the daily peak demand requested to the public grid and diminishes the operational costs.

Billions of people are exposed to fine particulate matter (PM2.5) levels above the USEPA’s annual standard of 9 μg/m3. Common emission sources are anthropogenic, producing complex aerosolized toxins. Ultrafine particulate matter (UFPM) and industrial nanoparticles (NPs) have major detrimental effects on the brain, but the USA does not measure UFPM on a routine basis. This review focuses on the development and progression of common neurodegenerative diseases, as diagnosed through neuropathology, among young residents in Metropolitan Mexico City (MMC). MMC is one of the most polluted megacities in the world, with a population of 22 million residents, many of whom are unaware of the brain effects caused by their polluted atmosphere. Fatal neurodegenerative diseases (such as Alzheimer’s and Parkinson’s) that begin in childhood in populations living in air polluted environments are preventable. We conclude that UFPM/NPs are capable of disrupting neural homeostasis and give rise to relentless neurodegenerative processes throughout the entire life of the highly exposed population in MMC. The paradigm of reaching old age to have neurodegeneration is no longer supported. Neurodegenerative changes start early in pediatric ages and are irreversible. It is time to invest in preventive medicine.

Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver’s chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum.

This paper presents a methodology for determining the optimal portfolio allocation for a demand response aggregator. The formulation is based on Day-Ahead electricity prices, in which the aggregator coordinates a set of residential consumers that are recruited through contracts. Four types of contracts are analyzed, considering both direct and indirect demand response programs. The objective is to compare different scenarios for contract portfolios in order to establish the benefits of each market agent. An optimization problem is formulated to capture the interactions between the aggregator and end consumers. The model is formulated as a mathematical program with equilibrium constraints: At the upper level, the aggregator maximizes its benefits, whereas the lower level represents the consumers’ contracts. By applying the developed methodology, the characterization of the consumers’ behavior is established in order to forecast their responses to the generation of punctual incentives, both for usual scenarios and peak events, as well as to evaluate the impact that direct and indirect control contracts have on the performance of the aggregator as the energy price varies.

Air pollution plays a key role in sleep disorders and neurodegeneration. Alzheimer’s disease (AD), Parkinson’s disease (PD), and/or transactive response DNA-binding protein TDP-43 neuropathology have been documented in children and young adult forensic autopsies in the metropolitan area of Mexico City (MMC), along with sleep disorders, cognitive deficits, and MRI brain atrophy in seemingly healthy young populations. Ultrafine particulate matter (UFPM) and industrial nanoparticles (NPs) reach urbanites’ brains through nasal/olfactory, lung, gastrointestinal tract, and placental barriers. We documented Fe UFPM/NPs in neurovascular units, as well as lateral hypothalamic nucleus orexinergic neurons, thalamus, medullary, pontine, and mesencephalic reticular formation, and in pinealocytes. We quantified ferromagnetic materials in sleep and arousal brain hubs and examined their motion behavior to low magnetic fields in MMC brain autopsy samples from nine children and 25 adults with AD, PD, and TDP-43 neuropathology. Saturated isothermal remanent magnetization curves at 50–300 mT were associated with UFPM/NP accumulation in sleep/awake hubs and their motion associated with 30–50 µT (DC magnetic fields) exposure. Brain samples exposed to anthropogenic PM pollution were found to be sensitive to low magnetic fields, with motion behaviors that were potentially linked to the early development and progression of fatal neurodegenerative diseases and sleep disorders. Single-domain magnetic UFPM/NPs in the orexin system, as well as arousal, sleep, and autonomic regions, are key to neurodegeneration, behavioral and cognitive impairment, and sleep disorders. We need to identify children at higher risk and monitor environmental UFPM and NP emissions and exposures to magnetic fields. Ubiquitous ferrimagnetic particles and low magnetic field exposures are a threat to global brain health.

Electric faults in photovoltaic (PV) systems cause negative economic and safety impacts, reducing their performance and causing unwanted electric connections that can be dangerous for the user. Line to line, ground and open circuit faults, are three of the main faults that happen in a photovoltaic array system. This work proposes a characterization of the equivalent circuits and the voltage-power (VP) curves at the output of multiple PV arrays under different topological configurations and fault conditions to evaluate the effects of these three main faults on the performance of a photovoltaic array system, taking into account the temperature and solar radiation influence. This work presents a validation of the characterization by measuring the output VP curves of a low-power photovoltaic array system under real outdoors conditions. This method can be useful in future works to develop low cost systems capable of detecting and classifying electric faults in photovoltaic array systems.