Search

Particulate matter (PM) is one of the most problematic pollutants in urban air. The effects of PM on human health, associated especially with PM of ≤2.5μm in diameter, include asthma, lung cancer and cardiovascular disease. Consequently, major urban centers commonly monitor PM2.5 as part of their air quality management strategies. The Chemical Transport models allow for a permanent monitoring and prediction of pollutant behavior for all the regions of interest, different to the sensor network where the concentration is just available in specific points. In this chapter a data assimilation system for the LOTOS-EUROS chemical transport model has been implemented to improve the simulation and forecast of Particulate Matter in a densely populated urban valley of the tropical Andes. The Aburrá Valley in Colombia was used as a case study, given data availability and current environmental issues related to population expansion. Using different experiments and observations sources, we shown how the Data Assimilation can improve the model representation of pollutants.

Chemical Transport Models (CTMs) simulate the emission, transformation, and transport of atmospheric chemical species, providing concentration and deposition estimates. While greatly sophisticated, these are still imperfect representations of reality. Data Assimilation (DA), a technique whereby observations are integrated into the simulations, helps alleviate the models' weaknesses, improving their simulation outputs and enabling parameter and state estimation. The variational DA method is an efficient approach for large-scale parameter and state estimation, but it is not straightforward to implement due to the need for a tangent linear matrix of the adjoint model forecast operator. To circumvent this difficulty, the ensemble-based 4DEnVar DA technique was used in this work.Daily NO2 observations from the TROPOspheric Monitoring Instrument (TROPOMI) at resolutions of 3x5 km were acquired for 2019 and assimilated into the LOTOS-EUROS CTM. Due to the scarcity of ground-based monitoring stations for atmospheric gases in Colombia, especially outside urban areas, satellite data provide an attractive alternative for DA.The 4DEnVar DA was first evaluated via the Design of Experiments (DOE) methodology with the Lorenz96 model assimilating synthetic data. Different parameters were changed (ensemble number, spread, forcing factor and width of the assimilation time window) according to a complete 24 factorial design followed by a Box Behnken design, providing an empirical model that guided the selection about how to modify those tuning parameters. The evaluation criteria used to test the 4DEnVar DA performance was the Root-Mean-Square (RMS) error between the analysis step and the synthetic data. Once this methodology was implemented, it was scaled up to the high-dimensional LOTOS-EUROS experiment.The setup for the LOTOS-EUROS DA experiment was simplified in terms of domain area, chemical species of interest, dominant dynamics and considerations about how to perturb the parameters or initial conditions. A range of ensemble-members generated from perturbed parameters or input initial states were studied in conjunction with ensemble inflation experiments and Singular Value Decomposition projections, characterizing the degeneracy of the Gaussian assumption through the time propagation of the ensemble. Additionally, a complimentary analysis of this Gaussian ensemble degeneration was performed using the Shapiro-Wilk and Kolmogorov-Smirnov normality tests, which permitted a rational selection of the spin-up time of the model before the start of the assimilation window and the DA window size.The assimilation of satellite NO2 observations into LOTOS-EUROS made possible the estimation of parameters and states. Before the DA, the non-assimilated model overestimated the magnitude of the observation, this technique improves the simulation in the sense that the analysis result approaches the observations reducing the RMS. Through this methodology, it was possible to circumvent the absence of an adjoint model associated with the chemical components of this CTM. To our knowledge, this is the first application of ensemble variational DA on a CTM for the Northwestern South America region.

Today, airspace control has the challenge of merging information from independent and heterogeneous systems in order to minimize air safety risks and facilitate the decision-making process. One of the main risks for air operations is meteorology because convective formations like Torre cumulus or cumulonimbus could generate several dangerous phenomena such as icing, wind gusts, and thunderstorms, among others, that can affect the air operation safety. Based on previous works that allow the automatic identification of convective phenomena through the fusion of multispectral satellite images and other sources as winds and Meteorological Aerodrome Report (METAR), and establishing a common georeferenced coordinates system like WGS-84, for all sources, it can generate a system that could calculate early alerts about hazardous weather conditions in the aircrafts proximality for air traffic control system. For this, a meteorological analysis system can generate information about convective clouds calculating area, heights, temperatures, risk level and position of the meteorological formation. Parallelly the convective cloud is surrounded by optimal elliptical forms centered on the convective formation, generating a meteorological object. On the other hand, there is a system responsible for monitoring the information of the surveillance sensors. This system fused the air traffic sensors available like primary and secondary radar signals and ADS-B sensors in a unique WGS-84 coordinates system. Finally, in a georeferenced raster-type graphing system or in a Geographic Information System (GIS), the meteorological and surveillance information is correlated projecting the track routes generates by air traffic system and traces generated by meteorological objects in order to establish times and high-risk areas, early. With this information, the Air Traffic Controller (ATC) system users, could minimize risk areas and reorganize the air traffic flow. This methodology then, would contribute to the decision-making process of ATC, facilitating the air flow reorganization and minimizing meteorological risks. For the development of this project a cooperative experimental methodology by subsystems was used. It was based on an operational knowledge and normal operating procedures of the Colombian Air Force, integrated with radar tracking technologies that implement decision trees. These alerts allow the air traffic controller to assess the risk and in accordance with the evaluation, if necessary, reorganize the air traffic flow for a specific area before the aircraft enter areas of bad weather mitigating the risks.

Biotechnological processes represent a challenge into the control field, due to its high non linearity. Particularly, continuous alcoholic fermentation by Zymomonas mobilis (Z.m) presents high ethanol performance, but it shows a highly oscillatory behavior in process variables, because of the influence of inhibition dynamics over Biomass, Substrate and Product concentrations; it is a significant challenge from the control perspective. In this work, a new and significant solution for control of biotechnological variables into the fermentation process, based on numerical methods and linear algebra is proposed. In addition, a state estimator of Biomass into the control loop is presented, using to reach this aim, the Non Linear Filtering based on the recursive application of the Bayes rule and Monte Carlo techniques. Specifically, the recursive Bayesian Filters are used. This methodology allows developing the controller design and a tested state estimator for Biomass in Z.m, without the use of complex calculations.