Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-The-Art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025°ĝ€¯×ĝ€¯0.025° covering an area surrounding the North Sea for the years 2019-2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

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When considering air quality, notably in South America, it seems that we are falling behind more developed regions in exacerbating the issue. This shortfall serves not just as observation, but as a warning, as air quality problems here are rapidly escalating. Nevertheless, by examining how other countries have addressed similar issues, we can prepare ourselves to tackle our own challenges. In this thesis we demonstrate how utilizing Data Assimilation DA we can reduce the uncertainty in some model uncertain parameters in an air quality model such as the LOTOS-EUROS Chemical Transport Model (CTM).....@en

Chemical transport models (CTM) are crucial for simulating the distribution of air pollutants, such as particulate matter, and evaluating their impact on the environment and human health. However, these models rely heavily on accurate emission inventory and meteorological inputs, usually obtained from reanalyzed weather data, such as the European Centre for Medium-Range Weather Forecasts (ECMWF). These inputs do not accurately reflect the complex topography and micro-scale meteorology in tropical regions where air pollution can pose a severe public health threat. We propose coupling the LOTOS–EUROS CTM model and the weather research and forecasting (WRF) model to improve LOTOS–EUROS representation. Using WRF as a meteorological driver provides high-resolution inputs for accurate pollutant simulation. We compared LOTOS–EUROS results when WRF and ECMWF provided the meteorological inputs during low and high pollutant concentration periods. The findings indicate that the WRF–LOTOS–EUROS coupling offers a more precise representation of the meteorology and pollutant dispersion than the default input of ECMWF. The simulations also capture the spatio-temporal variability of pollutant concentration and emphasize the importance of accounting for micro-scale meteorology and topography in air pollution modelling.@en

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.@en

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

With the aim of understanding the impact of air pollution on human health and ecosystems in the tropical Andes region (TAR), we aim to couple the Weather Research and Forecasting Model (WRF) with the chemical transport models (CTM) Long-Term Ozone Simulation and European Operational Smog (LOTOS–EUROS), at high and regional resolutions, with and without assimilation. The factors set for WRF, are based on the optimized estimates of climate and weather in cities and urban heat islands in the TAR region. It is well known in the weather research and forecasting field, that the uncertainty of non-linear models is a major issue, thus making a sensitivity analysis essential. Consequently, this paper seeks to quantify the performance of the WRF model in the presence of disturbances to the initial conditions (IC), for an arbitrary set of state-space variables (pressure and temperature), simulating a disruption in the inputs of the model. To this aim, we considered three distributions over the error term: a normal standard distribution, a normal distribution, and an exponential distribution. We analyze the sensitivity of the outputs of the WRF model by employing non-parametric and robust statistical techniques, such as kernel distribution estimates, rank tests, and bootstrap. The results show that the WRF model is sensitive in time, space, and vertical levels to changes in the IC. Finally, we demonstrate that the error distribution of the output differs from the error distribution induced over the input data, especially for Gaussian distributions.@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

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

This chapter book presents Medellín Air qUality Initiative or MAUI Project; it tells a brief story of this teamwork, their scientific and technological directions. The modeling work focuses on the ecosystems and human health impact due to the exposition of several pollutants transported from long-range places and deposited. For this objective, the WRF and LOTOS-EUROS were configurated and implemented over the región of interest previously updating some input conditions like land use and orography. By other side, a spinoff initiative named SimpleSpace was also born during this time, developing, through this instrumentation branch a very compact and modular low-cost sensor to deploy in new air quality networks over the study domain. For testing this instrument and find an alternative way to measure pollutants in the vertical layers, the Helicopter In-Situ Pollution Assessment Experiment HIPAE misión was developed to take data through the overflight of a helicopter over Medellín. From the data obtained from the Simple units and other experiments in the payload, a citogenotoxicity analysis quantify the cellular damage caused by the exposition of the pollutants.@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 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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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