The present study proposes a novel data assimilation (DA) approach for estimating emission and wind direction parameters in an advection-diffusion model. This implementation aims to improve the prediction of a chemical transport model over long distances by updating the emission operator in the model using DA techniques. As a first step, we want to test the method in a small-scale scenario. A low-dimensional advection-diffusion model was utilized to evaluate the effectiveness of the proposed approach under various sampling observation numbers. The model’s emission and wind parameters are perturbed as a source of uncertainty. The parameters are sequentially estimated with the adjoint-free Ensemble Kalman filter with an augmented state vector. These sequential DA techniques exploit the ensemble of multiple model realizations to reduce uncertainty in the state and parameter representation. An associated stream function with a divergence-free condition controls the wind fields, and the estimation of this stream function through the assimilation process allows corrections of the wind fields without violating physical laws. The technique’s performance was compared against validation observations such as the Root-Mean Square (RMS), and it was found that the number of assimilated observations had a significant impact on the parameter estimations results. This study demonstrates the potential of the proposed DA approach for improving the prediction of transport in the advection-diffusion model through parameter estimation.

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The densest network for measuring air pollutant concentrations in Colombia is in Medellin, where most sensors are located in the heavily polluted lower parts of the valley. Measuring stations in the higher elevations on the mountains surrounding the valley are not available, which limits our understanding of the valley’s pollutant dynamics and hinders the effectiveness of data assimilation studies using chemical transport models such as LOTOS-EUROS. To address this gap in measurements, we have designed a new network of low-cost sensors to be installed at altitudes above 2000 m.a.s.l. The network consists of custom-built, solar-powered, and remotely connected sensors. Locations were strategically selected using the LOTOS-EUROS model driven by diverse meteorology-simulated fields to explore the effects of the valley wind representation on the transport of pollutants. The sensors transmit collected data to internet gateways for posterior analysis. Various tests to verify the critical characteristics of the equipment, such as long-range transmission modeling and experiments with an R score of 0.96 for the best propagation model, energy power system autonomy, and sensor calibration procedures, besides case exposure to dust and water experiments, to ensure IP certifications. An inter-calibration procedure was performed to characterize the sensors against reference sensors and describe the observation error to provide acceptable ranges for the data assimilation algorithm (<10% nominal). The design, installation, testing, and implementation of this air quality network, oriented towards data assimilation over the Aburrá Valley, constitute an initial experience for the simulation capabilities toward the system’s operative capabilities. Our solution approach adds value by removing the disadvantages of low-cost devices and offers a viable solution from a developing country’s perspective, employing hardware explicitly designed for the situation.@en

This work proposes a robust and non-Gaussian version of the shrinkage-based knowledge-aided EnKF implementation called Ensemble Time Local H_∞ Filter Knowledge-Aided (EnTLHF-KA). The EnTLHF-KA requires a target covariance matrix to integrate previously obtained information and knowledge directly into the data assimilation (DA). The proposed method is based on the robust H_∞ filter and on its ensemble time-local version the EnTLHF, using an adaptive inflation factor depending on the shrinkage covariance estimated matrix. This implies a theoretical and solid background to construct robust filters from the well-known covariance inflation technique. The proposed technique is implemented in a synthetic assimilation experiment, and in an air quality application using the LOTOS-EUROS model over the Aburrá Valley to evaluate its potential for non-linear and non-Gaussian large systems. In the spatial distribution of the PM_2.5 concentrations along the valley, the method outperforms the well-known Local Ensemble Transform Kalman Filter (LETKF), and the non-robust knowledge-aided Ensemble Kalman filter (EnKF-KA). In contrast to the other simulations, the ability to issue warnings for high concentration events is also increased. Finally, the simulation using EnTLHF-KA has lower error values than using EnKF-KA, indicating the advantages of robust approaches in high uncertainty systems.@en

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM_2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

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In order to avoid the adverse effects of air pollution, efforts have been made to monitor when air pollution reaches dangerous levels. A Chemical Transport Model (CTM) can simulate trace gases and particles concentration in specific areas. These models are not entirely reliable, owing to incomplete knowledge about emissions and meteorological conditions. Explaining and predicting variability in air quality models remains a challenge. In this thesis we want to demonstrate that data assimilation (DA) can reduce uncertainty in the model process. DA is a mathematical family of techniques in which observed values are combined with a dynamic model to improve the accuracy of the model. Standard DA methods have limitations when there is not a complete characterization of the uncertainties. In air quality applications, emission inventories’ accuracy is often low, and weather models often do not predict events very well. The problem is worse in developing countries where the knowledge available is sparse and of relatively low quality. The thesis’s main contribution is the development of a DA systems for improving the behavior of complex models in the presence of high uncertainty. The proposed methods and developments have been tested in the framework of the LOTOS-EUROS CTM with applications to forecast particular matter in the Aburrá Valley in Colombia. The use of a less expensive monitoring network is also discussed. The Aburrá valley represents a good testing scenario because of its current air quality issues, the difficulty of its terrain, the lack of a detailed emission inventory, and the operational availability of a low-cost monitoring network. Our first step was to apply the Ensemble Kalman Filter (EnKF) to assimilate the official air quality monitoring network. Evaluations of the system were performed by varying values of the covariance localization influence area. Moreover, various inheritance strategies were evaluated to optimize the assimilation window’s estimated information into the forecast window. Although the model’s performance could be improved with application of DA, there were still issues with the emission inventories, the low number of observations, and the model’s difficulties in capturing essential transport dynamics within the valley. Given the significant impact the Aburrá Valley emission inventory has on air quality modeling and perceived issues with the available inventory, we built a highresolution emission inventory for the Aburrá Valley metropolitan area. We also assessed the ability of a low-cost network’s available in the metropolitan area to track the dynamics of PMኼ.኿ correctly and use it as observations in the DA process. With recent developments in the production of low-cost sensors, it is possible to use these devices for DA. The DA system is composed by the EnKF, LOTOS-EUROS, the latest emission inventory, and the low-cost monitoring network. The high measurement density of this type of network is an advantage in the DA process, and it can be used in places that cannot afford a standard monitoring network. Finally, the city’s air quality was improved through the revised emission inventory. Combined with a new emission inventory and a denser observation network, we have proposed two ensemble-based DA methods to deal with the high uncertainties in the model. The first is a variant of the EnKF using a covariance-based estimator called Ensemble Kalman Filter Knowledge-Aided (EnKF-KA). The method’s novelty is that it allows for incorporating prior knowledge of the system directly in the assimilation process through a target covariance matrix. The second method, the Ensemble Time Local Hጼ Filter Knowledge-Aided (EnTLHF-KA) is a robust version of the EnKF-KA that incorporates an adaptive covariance inflation factor to reduce the impact of uncertainties. Both approaches were first analyzed using simple models to isolate the proposed technique’s advantages and drawbacks and to compare the results of this new method with traditional algorithms. The formulation of both new methods is sufficiently general to be applicable in other contexts. Finally, we implemented the proposed methods with the LE model and the lowcost monitoring network in the Aburrá valley. We used the target matrix to limit the influence of the observations, following the complex topography of the valley. This reduced the impact caused by a low resolution of the dynamics within the valley of the meteorological input. The results of the proposed methods were compared with the results of the Local Ensemble Transform Kalman Filter (LETKF) algorithm. Both new methods outperformed the LETKF and resulted in a more accurate spatial representation of the PM concentrations. Thus, by applying the DA method to the Aburrá Valley, the modeling and forecasting of air quality improved tremendously when compared with the observations. @en

In this work, we present the development of a 4D-Ensemble-Variational (4DEnVar) data assimilation technique to estimate NOx top-down emissions using the regional chemical transport model LOTOS-EUROS with the NO2 observations from the TROPOspheric Monitoring Instrument (TROPOMI). The assimilation was performed for a domain in the northwest of South America centered over Colombia, and includes regions in Panama, Venezuela and Ecuador. In the 4DEnVar approach, the implementation of the linearized and adjoint model are avoided by generating an ensemble of model simulations and by using this ensemble to approximate the nonlinear model and observation operator. Emission correction parameters’ locations were defined for positions where the model simulations showed significant discrepancies with the satellite observations. Using the 4DEnVar data assimilation method, optimal emission parameters for the LOTOS-EUROS model were estimated, allowing for corrections in areas where ground observations are unavailable and the region’s emission inventories do not correctly reflect the current emissions activities. The analyzed 4DEnVar concentrations were compared with the ground measurements of one local air quality monitoring network and the data retrieved by the satellite instrument Ozone Monitoring Instrument (OMI). The assimilation had a low impact on NO2 surface concentrations reducing the Mean Fractional Bias from 0.45 to 0.32, primordially enhancing the spatial and temporal variations in the simulated NO2 fields.@en

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.@en

The change in land use promotes climate change and the loss of diversity, producing effects on the atmosphere, ecosystems and human health. Land use change scenarios, together with transport chemistry models (CTM) are effective tools to analyze the causes and consequences of atmospheric dynamics in various spatial or temporal scenarios. The objective is to evaluate the variables of dry deposition of NOy and surface concentration of NOx, calculated by the LOTOS-EUROS transport chemistry model, in different proposed city scenarios in the Aburra Valley (AMVA), generating an approximation to evaluate and predict the consequences of the cover changes on the atmospheric dynamics of nitrogen in the AMVA and its possible effect on the surrounding ecosystems from a modeling perspective. A land use classification was made with the 23 categories of Global Land Cover (GLC) for Colombia resolution (0.3km ∗ 0.3km),

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In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation. Our filter implementation combines information brought by an ensemble of model realizations, and that based on our prior knowledge about the dynamical system of interest. We perform the combination of both sources of information via optimal shrinkage factors. The method exploits the rank-deficiency of ensemble covariance matrices to provide an efficient and practical implementation of the analysis step in EnKF based formulations. Localization and inflation aspects are discussed, as well. Experimental tests are performed to assess the accuracy of our proposed filter implementation by employing an Advection Diffusion Model and an Atmospheric General Circulation Model. The experimental results reveal that the use of our proposed filter implementation can mitigate the impact of sampling noise, and even more, it can avoid the impact of spurious correlations during assimilation steps.

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This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This square-root approximation serves as a control space onto which analysis increments can be computed. These are calculated via Line-Search (LS) optimization. We theoretically prove the convergence of the MLEF-MC. Experimental simulations were performed using an Atmospheric General Circulation Model (AT-GCM) and a highly nonlinear observation operator. The results reveal that the proposed method can obtain posterior error estimates within reasonable accuracies in terms of ℓ − 2 error norms. Furthermore, our analysis estimates are similar to those of the MLEF with large ensemble sizes and full observational networks.

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A data assimilation system for the LOTOS-EUROS chemical transport model has been implemented to improve the simulation and forecast of PM₁₀ and PM_2.5 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. The data assimilation system is an Ensemble Kalman filter with covariance localization based on specification of uncertainties in the emissions. Observations assimilated were obtained from a surface network for the period March–April of 2016, a period of one of the worst air quality crisis in recent history of the region. In a first series of experiments, the spatial length scale of the covariance localization and the temporal length scale of the stochastic model for the emission uncertainty were calibrated to optimize the assimilation system. The calibrated system was then used in a series of assimilation experiments, where simulation of particulate matter concentrations was strongly improved during the assimilation period, which also improved the ability to accurately forecast PM₁₀ and PM_2.5 concentrations over a period of several days.

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La denominada misión HIPAE (Helicopter-borne In-situ Pollution Assessment Experiment) desarrolló una prueba de concepto dentro de una aeronave de la Fuerza Aérea Colombiana, sobrevolando el Valle de Aburrá para transportar dos tipos de contadores de partículas PM2.5 y PM10, así como dos versiones de las plataformas en desarrollo llamadas Simple para medir variables meteorológicas (humedad relativa, presión barométrica, temperatura), altitud, geo-posición y ocho tipos de gases CO2, H2, NO2, NH3, C2H6OH, CH4, C4H10, C3H8. Adicionalmente, un experimento con nano filtros demostró su capacidad para capturar material particulado, el cual fue analizado mediante microscopía electrónica de barrido combinada con espectroscopía de rayos-X (EDX). Los resultados de EDX arrojaron información valiosa sobre la morfología y química a nivel de partícula en la atmósfera urbana por encima de la altura de las estaciones de medición tradicionales. Fué posible visualizar en los datos altas concentraciones de compuestos de aerosol y gases como CO, NO2 y CH4, cuyos valores fueron menores en áreas rurales y forestales en comparación con áreas urbanas según lo esperado. La plataforma Simple mostró un comportamiento adecuado manteniéndose dentro de sus niveles de incertidumbre, indicando la utilidad de los datos adquiridos como primer paso a siguiente ejercicio para ser utilizadas en aeronaves comerciales o militares con el objetivo de suministrar constantemente, a los modelos meteorológicos y químicos de transporte, información in-situ para actividades de asimilación de datos basadas en ensamble, tanto secuencial (EnKF) como variacionalmente (4DenVar), como en actividades de fusión de datos para la toma de decisiones. @en