Atmospheric aerosols play a crucial role in the Earth's system, but their role is not completely understood, partly because of the large variability in their properties resulting from a large number of possible aerosol sources. Recently developed lidar-based techniques were able to retrieve the height distributions of optical and microphysical properties of fine-mode and coarse-mode particles, providing the types of the aerosols. One such technique is based on artificial neural networks (ANNs). In this article, a Neural Network Aerosol Typing Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosol type from a set of multispectral lidar data. The algorithm was adjusted to run on the EARLINET 3β + 2α( + 1δ) profiles. The NATALI algorithm is based on the ability of specialized ANNs to resolve the overlapping values of the intensive optical parameters, calculated for each identified layer in the multiwavelength Raman lidar profiles. The ANNs were trained using synthetic data, for which a new aerosol model was developed. Two parallel typing schemes were implemented in order to accommodate data sets containing (or not) the measured linear particle depolarization ratios (LPDRs): (a) identification of 14 aerosol mixtures (high-resolution typing) if the LPDR is available in the input data files, and (b) identification of five predominant aerosol types (low-resolution typing) if the LPDR is not provided. For each scheme, three ANNs were run simultaneously, and a voting procedure selects the most probable aerosol type. The whole algorithm has been integrated into a Python application. The limitation of NATALI is that the results are strongly dependent on the input data, and thus the outputs should be understood accordingly. Additional applications of NATALI are feasible, e.g. testing the quality of the optical data and identifying incorrect calibration or insufficient cloud screening. Blind tests on EARLINET data samples showed the capability of NATALI to retrieve the aerosol type from a large variety of data, with different levels of quality and physical content.

Multi-wavelength depolarization Raman lidar measurements from Magurele, Romania are used in this study along with simulated mass-extinction efficiencies to calculate the mass concentration profiles of different atmospheric components, due to their different depolarization contribution to the 532 nm backscatter coefficient. Linear particle depolarization ratio (δ^part) was computed using the relative amplification factor and the system-dependent molecular depolarization. The low depolarizing component was considered as urban/smoke, with a mean δ^part of 3%, while for the high depolarizing component (mineral dust) a mean δ^part of 35% was assumed. For this study 11 months of lidar measurements were analysed. Two study cases are presented in details: one for a typical Saharan dust aerosol intrusion, 10 June 2012 and one for 12 July 2012 when a lofted layer consisting of biomass burning smoke extended from 3 to 4.5 km height. Optical Properties of Aerosols and Clouds software package (OPAC) classification and conversion factors were used to calculate mass concentrations. We found that calibrated depolarization measurements are critical in distinguishing between smoke-reach aerosol during the winter and dust-reach aerosol during the summer, as well as between elevated aerosol layers having different origins. Good agreement was found between lidar retrievals and DREAM- Dust REgional Atmospheric Model forecasts in cases of Saharan dust. Our method was also compared against LIRIC (The Lidar/Radiometer Inversion Code) and very small differences were observed.

The Poland-AOD aerosol research network was established in 2011 to improve aerosol–climate interaction knowledge and provide a real-time and historical, comprehensive, and quantitative database for the aerosol optical properties distribution over Poland. The network consists of research institutions and private owners operating 10 measurement stations and an organization responsible for aerosol model transport simulations. Poland-AOD collaboration provides observations of spectral aerosol optical depth (AOD), Ångstrom Exponent (AE), incoming shortwave (SW) and longwave (LW) radiation fluxes, vertical profiles of aerosol optical properties and surface aerosol scattering and absorption coefficient, as well as microphysical particle properties. Based on the radiative transfer model (RTM), the aerosol radiative forcing (ARF) and the heating rate are simulated. In addition, results from GEM-AQ and WRF-Chem models (e.g., aerosol mass mixing ratio and optical properties for several particle chemical components), and HYSPLIT back-trajectories are used to interpret the results of observation and to describe the 3D aerosol optical properties distribution. Results of Poland-AOD research indicate progressive improvement of air quality and at mospheric turbidity during the last decade. The AOD was reduced by about 0.02/10 yr (at 550 nm), which corresponds to positive trends in ARF. The estimated clear-sky ARF trend is 0.34 W/m²/10 yr and 0.68 W/m²/10 yr, respectively, at TOA and at Earth’s surface. Therefore, reduction in aerosol load observed in Poland can significantly contribute to climate warming.

The satellite based monitoring initiative for regional air quality (SAMIRA) initiative was set up to demonstrate the exploitation of existing satellite data for monitoring regional and urban scale air quality. The project was carried out between May 2016 and December 2019 and focused on aerosol optical depth (AOD), particulate matter (PM), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). SAMIRA was built around several research tasks: 1. The spinning enhanced visible and infrared imager (SEVIRI) AOD optimal estimation algorithm was improved and geographically extended from Poland to Romania, the Czech Republic and Southern Norway. A near real-time retrieval was implemented and is currently operational. Correlation coefficients of 0.61 and 0.62 were found between SEVIRI AOD and ground-based sun-photometer for Romania and Poland, respectively. 2. A retrieval for ground-level concentrations of PM_2.5 was implemented using the SEVIRI AOD in combination with WRF-Chem output. For representative sites a correlation of 0.56 and 0.49 between satellite-based PM_2.5 and in situ PM_2.5 was found for Poland and the Czech Republic, respectively. 3. An operational algorithm for data fusion was extended to make use of various satellite-based air quality products (NO₂, SO₂, AOD, PM_2.5 and PM₁₀). For the Czech Republic inclusion of satellite data improved mapping of NO₂ in rural areas and on an annual basis in urban background areas. It slightly improved mapping of rural and urban background SO₂. The use of satellites based AOD or PM_2.5 improved mapping results for PM_2.5 and PM₁₀. 4. A geostatistical downscaling algorithm for satellite-based air quality products was developed to bridge the gap towards urban-scale applications. Initial testing using synthetic data was followed by applying the algorithm to OMI NO₂ data with a direct comparison against high-resolution TROPOMI NO₂ as a reference, thus allowing for a quantitative assessment of the algorithm performance and demonstrating significant accuracy improvements after downscaling. We can conclude that SAMIRA demonstrated the added value of using satellite data for regional- and urban-scale air quality monitoring.

Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High urban concentrations are due to the proximity of sources such as traffic or residential heating, and to urban density with the presence of buildings that reduce street ventilation. This urban complexity makes fine-scale mapping challenging, even for regulated pollutants such as NO₂ and PM_2.5. In this study we apply state-of-the-art empirical and deterministic modeling approaches to produce high-resolution (<100 m) pollution maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city mapping capturing intra-urban gradients of concentrations. Depending on the methodology, regulated pollutants (NO₂, PM₂.₅) and/or emerging pollutants (black carbon (BC) and ultrafine particles (UFP characterized here by particulate number concentration PNC)) are considered. For deterministic modelling, different approaches are presented: a multi-scale Eulerian modelling chain down to the street scale with chemistry/aerosol dynamics at all scales, multi-scale hybrid models with Eulerian regional dispersion and Gaussian subgrid dispersion, and a Gaussian-based model. Empirical land use regression models were developed based upon mobile monitoring. To compare the relative performance of the methodologies and to evaluate their performance and limitations, the modelling results are compared to fixed measurement stations. We introduce a standardized metric to quantify spatial and seasonal variability and assess each method's capacity to reproduce fine-scale urban heterogeneity. We also evaluate how data assimilation affects both concentration accuracy and variability representation-particularly relevant for emerging pollutants where measurement data are sparse. We confirm established seasonal and spatial patterns: spatial variability is more pronounced for PNC, NO₂ and BC than PM_2.5, and concentrations are higher during the winter periods. We also observe reduced spatial variability in winter for PM₂. ₅ (linked to residential heating) and for BC in cities with significant wood burning emissions. This study adds unique value by evaluating these patterns using fixed measurement stations, and quantifying them across entire urban areas at very fine spatial resolution (<100 m). Furthermore, important methodological strengths and limitations are pointed out, providing practical guidance for the selection and improvement of urban exposure mapping methods, supporting the implementation of the new EU Air Quality Directive.