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Atmospheric aerosols are known to play a key role in Earth’s radiative budget, although the quantification of their climate forcing is still highly uncertain. In order to improve the scientific understanding of their climatic effect, in-situ ground-based aerosol properties observations are needed by the research community. Such data would also allow the global assessment of the effect of environmental policies over both the short and the long term.To develop a robust and consistent view over time of the worldwide variability of aerosol properties, data resulting from a fully-characterized value chain, including uncertainty estimation, is needed.The present work is part of a wider project, having among its goals the investigation of the variability of climate-relevant aerosol properties observed at all sites connected to the Global Atmospheric Watch network, whose data are publicly available from the World Data Centre for Aerosols and follow the aforementioned specifications.This work focuses on aerosol optical proprieties, i.e. the aerosol light scattering coefficient (σsp), the aerosol light absorption coefficient (σap), single scattering albedo (ωo) and both scattering and absorption Ångström exponents (åsp and åap).The analysis includes 108 yearly datasets collected either during 2016 or 2017 at different sites: 53 for absorption and 55 for scattering coefficient datasets, respectively. For 29 of these sites it was also possible to compute single scattering albedo.The spatial variability in extensive and intensive optical properties was analysed in terms of each site’s geographical location (either polar, continental, coastal or mountain) and its footprint (from pristine to urban, representing increasing levels of anthropogenic influence).The results highlight the impact of anthropogenic emissions and biomass burning on absolute levels and annual variability. The effect of sea spray or long range transport of dust is also evident for several sites, along with the influence of regional emissions. The largest seasonality in aerosol loading was observed at mountain sites under mixed footprint conditions, while the lowest seasonality occurred at urban sites. Urban sites also exhibited the highest σsp and σap values. The lowest levels in σsp and σap were observed at some polar sites, along with few coastal and mountain sites, despite their typically mixed footprint.AcknowledgementsThe authors acknowledge WMO-GAW World Data Centre on Aerosol for providing data available at http://ebas.nilu.no

Most of worldwide population lives in urban areas, demanding for air quality information with a high spatio-temporal resolution. The most promising approaches for estimating urban air quality within the complex urban topography are small sensor networks and simulation models.The TRAFAIR project focuses on understanding the role of traffic emissions on urban air quality by the combination of dispersion modelling, space- and time-resolved gas monitoring by lower cost sensors and realistic traffic flow rates by dynamic traffic model based on real time traffic data. Test cities of TRAFAIR are Modena, Florence, Pisa, Livorno, Zaragoza and Santiago de Compostela.Depending on the size of the urban area, from 6 to 13 sensors units are deployed across each city since August 2019, providing estimates of NO, NO2, CO and O3, along with RH and temperature. Metal oxide sensors are deployed in Tuscany (Florence, Pisa, Livorno) and electrochemical cells are used elsewhere. The units are calibrated on a regular basis by co-location at the air quality regulatory stations and subsequently deployed across the town to monitor several representative locations (e.g. Low Emission Zones, hospital surroundings). For each sensor the raw readings (e.g. mV for electrochemical cells) are collected and a regression model (e.g. Random Forest) is applied to derive a calibration function, exploiting the data from the regulatory stations during co-location periods; for instance in Modena, the first short-term calibration provided a model with a Mean Absolute Error between 5 – 6 ppb and 2 – 4 ppb for NO and NO2 respectively.The sensors are used for both real-time urban air quality mapping and to test and validate the 24hr forecast service of NOx by the microscale lagrangian dispersion model GRAL. The simulation domains, covering the urban area of each TRAFAIR city, have a horizontal resolution of 4 m and allow to account for the presence of buildings. The dispersion model mainly focuses on NOx by traffic emissions, although domestic heating will be also included in the analysis. Vehicular emissions are based either upon historical traffic data (e.g. induction loops), or upon previously available traffic flow simulation, or upon traffic pattern reconstruction using a traffic flow model followed by a cluster analysis to group streets with similar pattern.The final goal of the project is the development of a tool to support local policymakers and to inform citizenship about the quality of air and the impact of urban emission sources, particularly traffic. A secondary goal of the project is the development of a valuable QA/QC protocol for small sensor units and the optimization of the modelling chain for the forecast of traffic and domestic heating impact on local air quality at the urban scale.

In order to support environmental policies, epidemiological studies and urban mobility planning, a multi-scale modelling system was developed to provide hourly NOx (NO + NO2) concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley (Italy). The modelling system relied on two different models: the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), which is able to compute concentration fields over regional domain by considering specific emission scenarios, and Parallel Micro SWIFT SPRAY (PMSS), a Lagrangian particle model accounting for dispersion phenomena within the urban area. PMSS was used to simulate at building-scale resolution the NOx dispersion produced by urban traffic flows in the city of Modena. Conversely, WRF-Chem was selected to estimate the NOx background concentrations over three nested domains with resolution of 15, 3 and 1 km in order to take into account emissions both at regional and local scale by excluding traffic emissions sources over the city of Modena. The estimation of traffic emissions in the urban area of Modena was based on a bottom-up approach relying on the Emission Factors suggested by the European Monitoring and Evaluation Programme (EMEP/EEA) and traffic fluxes estimated by the PTV VISUM model. By contrast, other anthropogenic emissions were taken from the TNO-MACC III inventory at the scales resolved by the WRF-Chem model. Simulation was performed between 28 October and 8 November 2016, the same period whereby a direct vehicle flow measurement campaign was carried out continuously, with 4 Doppler radar counters in a four-lane road in Modena, to reproduce the hourly modulation rates of the emissions. The performances of the model chain were finally assessed by comparing modelled NOx concentrations with observations at two air quality monitoring stations located inside the urban domain. Simulated and observed NOx hourly concentrations exhibit a large agreement, in particular for urban traffic site where detailed traffic emissions estimation (real traffic modulation combined with a bottom-up approach) proved to be very successful in reproducing the observed NOx pattern. At the urban background station, notwithstanding a general underestimation of the observed concentrations (more pronounced than at the urban traffic site), the analysis of hourly daily modelled concentrations shows that PMSS combined with WRF-Chem provided a daily pattern in line with observations. These features highlight the strength of this modelling chain in representing urban air quality, in particular at traffic sites, whose concentration levels make them the most critical area of the city; characteristics that chemical transport models alone cannot express, due to the coarser resolution to which they operate and to their inability to reproduce street canyons and urban structures.

Aerosol particles are essential constituents of the Earth’s atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence time resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in-situ near-surface segment of the atmospheric observations system. This paper will provide the widest effort so far to document variability of climate-relevant in-situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High quality data from more than 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single scattering albedo and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information including data provision procedures, quality control and analysis, data policy and usage of the ground-based aerosol measurements network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully-characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.

Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE

The limits to atmospheric pollutant concentration set by the European Commission provide a challenging target for the municipalities in the Po Valley, because of the characteristic climatic conditions and high population density of this region. In order to assess climatology and trends in the concentration of atmospheric particles in the Po Valley, a data set of PM10 data from 41 sites across the Po Valley have been analysed, including both traffic and background sites (either urban, suburban or rural). Of these 41 sites, 18 with 10 yr or longer record have been analysed for long-term trend in deseasonalized monthly means, in annual quantiles and in monthly frequency distribution. A widespread significant decreasing trend has been observed at most sites, up to a few percent per year, by a generalized least squares and Theil–Sen method. All 41 sites have been tested for significant weekly periodicity by Kruskal–Wallis test for mean anomalies and by Wilcoxon test for weekend effect magnitude. A significant weekly periodicity has been observed for most PM10 series, particularly in summer and ascribed mainly to anthropic particulate emissions. A cluster analysis has been applied in order to highlight stations sharing similar pollution conditions over the reference period. Five clusters have been found, two encompassing the metropolitan areas of Turin and Milan and their respective nearby sites and the other three clusters gathering northeast, northwest and central Po Valley sites respectively. Finally, the observed trends in atmospheric PM10 have been compared to trends in provincial emissions of particulates and PM precursors, and analysed along with data on vehicular fleet age, composition and fuel sales. A significant basin-wide drop in emissions occurred for gaseous pollutants, contrarily to emissions of PM10 and PM2.5, whose drop was low and restricted to a few provinces. It is not clear whether the decrease for only gaseous emissions is sufficient to explain the observed drop in atmospheric PM10, or if the low drop in particulate emissions is indeed due to the uncertainty in the emission inventory data for this species.

The Po Valley is one of the largest European regions with a remarkably high concentration level of atmospheric pollutants, both for particulate and gaseous compounds. In the last decade stringent regulations on air quality standards and on anthropogenic emissions have been set by the European Commission, including also for PM2.5 and its main components since 2008. These regulations have led to an overall improvement in air quality across Europe, including the Po Valley and specifically PM10, as shown in a previous study by Bigi and Ghermandi (2014). In order to assess the trend and variability in PM2.5 in the Po Valley and its role in the decrease in PM10, we analysed daily gravimetric equivalent concentration of PM2.5 and of PM10–2.5 at 44 and 15 sites respectively across the Po Valley. The duration of the times series investigated in this work ranges from 7 to 10 years. For both PM sizes, the trend in deseasonalized monthly means, annual quantiles and in monthly frequency distribution was estimated: this showed a significant decreasing trend at several sites for both size fractions and mostly occurring in winter. All series were tested for a significant weekly periodicity (a proxy to estimate the impact of primary anthropogenic emissions), yielding positive results for summer PM2.5 and for summer and winter PM10–2.5. Hierarchical cluster analysis showed moderate variability in PM2.5 across the valley, with two to three main clusters, dividing the area in western, eastern and southern/Apennines foothill sectors. The trend in atmospheric concentration was compared with the time series of local emissions, vehicular fleet details and fuel sales, suggesting that the decrease in PM2.5 and in PM10 originates from a drop both in primary and in precursors of secondary inorganic aerosol emissions, largely ascribed to vehicular traffic. Potentially, the increase in biomass burning emissions in winter and the modest decrease in NH3 weaken an otherwise even larger drop in atmospheric concentrations.

The limits to atmospheric pollutant concentration set by the European Commission provide a challenging target for the municipalities in the Po Valley, because of the characteristic climatic conditions and high population density of this region. In order to assess climatology and trends in the concentration of atmospheric particles in the Po Valley, a dataset of PM10 data from 41 sites across the Po Valley have been analysed, including both traffic and background sites (either urban, suburban or rural). Of these 41 sites, 18 with 10 yr or longer record have been analysed for long term trend in de-seasonalized monthly means, in annual quantiles and in monthly frequency distribution. A widespread significant decreasing trend has been observed at most sites, up to few percent per year, by Generalised Least Square and Theil-Sen method. All 41 sites have been tested for significant weekly periodicity by Kruskal–Wallis test for mean anomalies and by Wilcoxon test for weekend effect magnitude. A significant weekly periodicity has been observed for most PM10 series, particularly in summer and ascribed mainly to anthropic particulate emissions. A cluster analysis has been applied in order to highlight stations sharing similar pollution conditions over the reference period. Five clusters have been found, two gathering the metropolitan areas of Torino and Milano and their respective nearby sites and the other three clusters gathering north-east, north-west and central Po Valley sites respectively. Finally the observed trends in atmospheric PM10 have been compared to trends in provincial emissions of particulates and PM precursors, and analysed along with data on vehicular fleet age, composition and fuel sales. Significant basin-wide drop in emissions occurred for gaseous pollutants, contrarily to emissions of PM10 and PM2.5, whose drop resulted low and restricted to few provinces. It is not clear whether the decrease for only gaseous emissions is sufficient to explain the observed drop in atmospheric PM10, or if the low drop in particulate emissions is indeed due to the uncertainty in the emission inventory data for this species.