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.

Psyche is a Discovery mission that will visit the asteroid (16) Psyche to determine if it is the metallic core of a once larger differentiated body or otherwise was formed from accretion of unmelted metal-rich material. The spacecraft will launch in August 2022 and arrive at the asteroid in January 2026. Psyche will carry three science instruments: a gamma ray and neutron spectrometer, a magnetometer, and a multispectral imager. Additionally, the spacecraft will host the Deep Space Optical Communications payload, which is a technology demonstration not required to meet Psyche's science objectives. The magnetometer is composed of two identical high-sensitivity magnetic field fluxgate sensors mounted in a gradiometer configuration that enables the rejection of meter-scale stray fields from the spacecraft. The instrument is key to meeting mission objectives since measurements of a strong asteroid remanent magnetic field will unambiguously indicate that (16) Psyche is an iron core. The magnetic signature from the spacecraft is the main source of noise for the magnetometer, both for DC and AC magnetic fields. Limiting and characterizing spacecraft-generated magnetic fields is therefore essential to the mission. This is the objective of the Psyche's magnetics control program described in this paper. The first step towards a successful program was to establish a set of magnetic cleanliness requirements directly derived from the magnetometer science performance and Psyche's range of expected fields. Test and modeling efforts of DC and AC fields of spacecraft components were then put in place to characterize and understand the spacecraft fields and enable verification of the cleanliness requirements. In this paper we describe the derivation of these requirements, test and analyses methods, and more generally the processes and procedures that govern the magnetics program for Psyche. The paper concludes with a discussion of the challenges and work to go and a comparison with the magnetic control processes of other missions with similar magnetic cleanliness constraints.

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.

In this work, we present the development of a 4D-Ensemble-Variational (4DEnVar) data assimilation technique to estimate NO${}_x$ top-down emissions using the regional chemical transport model LOTOS-EUROS with the NO${}_2$ 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 NO${}_2$ surface concentrations reducing the Mean Fractional Bias from 0.45 to 0.32, primordially enhancing the spatial and temporal variations in the simulated NO${}_2$ fields.