Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries

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

Estimating NOx LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO2 Assimilation

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

Experimental Approach for the Evaluation of the Performance of a Satellite Module in the CanSat Form Factor for In Situ Monitoring and Remote Sensing Applications

This article includes the phases of conceptualization and validation of a picosatellite prototype named Simple-2 for remote sensing activities using COTS (Commercial-Off-The-Shelf) components and the modular design methodology. To evaluate its performance and ensure the precision and accuracy of the measurements made by the satellite prototype, a methodology was designed and implemented for the characterization and qualification of CanSats (soda can satellites) through statistical tests and techniques of DoE (Design of Experiments) based on CubeSat aerospace standards and regulations, in the absence of official test procedures for these kinds of satellite form factor. For the above, two experimental units were used, and all the performance variables of the different satellite subsystems were discriminated. For the above, two experimental units were used, and all the performance variables of the different satellite subsystems were discriminated against. These were grouped according to the dependence of the treatments formulated in thermal and dynamic variables. For the tests of the first variables, a one-factor design was established using dependent samples on each of the treatments. Then, hypothesis tests were performed for equality of medians, using nonparametric analysis of the Kruskal-Wallis variance. Additionally, multivariate analysis of variance was carried out for nonparametric samples (nonparametric multivariate tests), and the application of post hoc multiple-range tests to identify the treatments that presented significant differences within a margin of acceptability. To know the dynamic response and ensure the structural integrity of the satellite module, shock, oscillation, and sinusoidal tests were applied through a shaker. Having applied the experimental methodology to the different units, the results of a real experiment are illustrated in which a high-altitude balloon was used through the application of nonparametric regression methods. This experiment’s interest measured thermodynamic variables and the concentration of pollutants in the stratosphere to corroborate the operating ranges planned in the above experiments using on-flight conditions and estimate the TLR (technology readiness level) of future prototypes

Estimating NO_x LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO₂ Assimilation

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

HIPAE Helicopter-borne in-situ Pollution Assessment Experiment: Plataforma alternativa para el monitoreo de contaminantes atmosféricos

La iniciativa científica MAUI (Medellín Air qUality Initiative) reúne proyectos de investigación y desarrollo tecnológico, con el propósito de impulsar la investigación sobre los problemas del medio ambiente, la sostenibilidad, y los impactos de la contaminación del aire en la salud humana, la agricultura y los ecosistemas. En la misión llamada HIPAE (Helicopter-borne In-situ Pollution Assessment Experiment) se desarrolló una prueba de concepto dentro de una aeronave de la Fuerza Aérea Colombiana, sobrevolando el Valle de Aburrá con el objetivo de obtener mediciones para ser utilizadas en el diseño de un sistema Lídar 4D, y complementar las mediciones satelitales de TROPOMI (TROPOspheric Monitoring Instrument) para la asimilación de datos con el modelo químico de transporte LOTOS-EUROS en alta y baja resolución, aumentando la capacidad para previsión del transporte de contaminantes en una escala local y regional.La aeronave transportó dos tipos de contadores de partículas PM2.5 y PM10, así como dos versiones de las plataformas de medición llamadas Simple para detectar 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 nanofiltros 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.Fue 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 en aeronaves comerciales o militares con el objetivo de suministrar, 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

Evaluation of the 3DVAR Operational Implementation of the Colombian Air Force for Aircraft Operations: A Case Study

This manuscript introduces an exploratory case study of the SIMFAC’s (Sistema de Información Meteorológica de la Fuerza Aérea Colombiana) operational implementation of the Weather Research and Forecasting (WRF) model with a 3DVAR (three-dimensional variational) data assimilation scheme that provides meteorological information for military, public, and private aviation. In particular, it investigates whether the assimilation scheme in SIMFAC’s implementation improves the prediction of the variables of interest compared to the implementation without data assimilation (CTRL). Consequently, this study compares SIMFAC’S 3DVAR-WRF operational implementation in Colombia with a CTRL with the same parameterization (without 3DVAR assimilation) against the ground and satellite observations in two operational forecast windows. The simulations are as long as an operational run, and the evaluation is performed using the root mean square error, the mean fractional bias, the percent bias, the correlation factor, and metrics based on contingency tables. It also evaluates the model’s results according to the regions of Colombia, accounting for the country’s topographical differences. The findings reveal that, in general, the operational forecast (3DVAR) is similar to the CTRL without data assimilation, indicating the need for further improvement of the 3DVAR-WRF implementation.Atmospheric Remote Sensin