Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO₂) and ozone (O₃) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005–2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM₁₀ (PM<10 microns), PM_2.5 (PM<2.5 microns), PM_2.5–10 (PM between 2.5 and 10 microns), NO₂, and O₃ for each squared kilometer of Sweden over the period 2005–2016. Our models were able to describe between 64% (PM₁₀) and 78% (O₃) of air pollutant variability in held-out observations, and between 37% (NO₂) and 61% (O₃) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigations.

This study validated MODIS (Moderate Resolution Imaging Spectroradiometer) of the National Aeronautics and Space Agency, USA, Aqua and Terra Collection 6.1, and MERRA-2 (Modern-ERA Retrospective Analysis for Research and Application) Version 2 of aerosol optical depth (AOD) at 550 nm against AERONET (Aerosol Robotic Network) ground-based sunphotometer observations over Turkey. AERONET AOD data were collected from three sites during the period between 2013 and 2017. Regression analysis showed that overall, seasonally and daily statistics of MODIS are better than MERRA-2 by the mean of coefficient of determination (R2), mean absolute error (MAE), and relative root mean square deviation (RMSDrel). MODIS combined Terra/Aqua AOD and MERRA-2 AOD corresponding to morning and noon hours resulted in better results than individual sub datasets. A clear annual cycle in AOD was detected by the three platforms. However, overall, MODIS and MERRA-2 tend to overestimate and underestimate AOD, respectively, in comparison with AERONET. MODIS showed higher efficiency in detecting extreme events than MERRA-2. There was no clear relation found between the accuracy in MODIS/MERRA-2 AOD and surface relative humidity (RH).

Aerosol optical thickness (AOT) has decreased substantially in Europe in the summer half year (April–September) since 1980, with almost a 50% reduction in Central and Eastern Europe, according to Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) reanalysis. At the same time, strong positive trends in ERA5 reanalysis surface solar radiation downward for all‐sky and clear‐sky conditions (SSRD and SSRDc, respectively) and temperature at 2 m are found for Europe in summer during the period 1979–2020. The GEBA observations show as well strong increases in SSRD during the latest four decades. Estimations of changes in SSRDc, using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model, show similarly strong increases when fed by MERRA‐2 AOT. The estimates of warming in this study, caused by increases in SSRD and SSRDc, are based on energy budget approximations and the Stefan Boltzmann law. The increases in near surface temperature, estimated both for clear‐sky and all‐sky conditions, are up to about 1°C for Central and Eastern Europe. The total warming over large parts of this region for clear‐sky conditions is however nearly double the global mean temperature increase of 1.1°C, while somewhat less for all‐sky conditions. The effects of aerosols on warming over the southerly Iberian Peninsula are weaker compared to countries further north. The rapid total warming over the Iberian Peninsula is probably caused by greenhouse warming, drier surface conditions, and to some degree decline in aerosols. Reduced cloud cover is found for large parts of Europe in summer during the latest four decades.

Long-term Aqua and Terra MODIS (MODerate resolution Imaging Spectroradiometer) Collections 5.1 and 6.1 (c051 and c061, respectively) aerosol data have been combined with AERONET (AERosol RObotic NETwork) ground-based sun photometer observations to examine trends in aerosol optical thickness (AOT, at 550 nm) over Northern Europe for the months April to September. For the 1927 and 1559 daily coincident measurements that were obtained for c051 and c061, respectively, MODIS AOT varied by 86 and 90%, respectively, within the predicted uncertainty of one standard deviation of the retrieval over land (ΔAOT = ±0.05 ± 0.15·AOT). For the coastal AERONET site Gustav Dalen Tower (GDT), Sweden, larger deviations were found for MODIS c051 and c061 (79% and 75%, respectively, within predicted uncertainty). The Baltic Sea provides substantially better statistical representation of AOT than the surrounding land areas and therefore favours the investigations of trends in AOT over the region. Negative trends of 1.5% and 1.2% per year in AOT, based on daily averaging, were found for the southwestern Baltic Sea from MODIS c051 and c061, respectively. This is in line with a decrease of 1.2% per year in AOT at the AERONET station Hamburg. For the western Gotland Basin area, Sweden, negative trends of 1.5%, 1.1% and 1.6% per year in AOT have been found for MODIS c051, MODIS c061 and AERONET GDT, respectively. The strongest trend of –1.8% per year in AOT was found for AERONET Belsk, Poland, which can be compared to –1.5% per day obtained from MODIS c051 over central Poland. The trends in MODIS and AERONET AOT are nearly all statistically significant at the 95% confidence level. The strongest aerosol sources are suggested to be located southwest, south and southeast of the investigation area, although the highest prevalence of pollution events is associated with air mass transport from southwest.

Aerosol effects on low-level clouds over the Nordic Countries are investigated by combining in situ ground-based aerosol measurements with remote sensing data of clouds and precipitation. Ten years of number size distribution data from two aerosol measurement stations (Vavihill, Sweden and Hyytiälä, Finland) provide aerosol number concentrations in the atmospheric boundary layer. This is combined with cloud satellite data from the Moderate Resolution Imaging Spectroradiometer and weather radar data from the Baltic Sea Experiment. Also, how the meteorological conditions affect the clouds is investigated using reanalysis data from the European Centre for Medium-Range Weather Forecasts. The cloud droplet effective radius is found to decrease when the aerosol number concentration increases, while the cloud optical thickness does not vary with boundary layer aerosol number concentrations. Furthermore, the aerosol–cloud interaction parameter (ACI), a measure of how the effective radius is influenced by the number concentration of cloud active particles, is found to be somewhere between 0.10 and 0.18 and the magnitude of the ACI is greatest when the number concentration of particles with a diameter larger than 130 nm is used. Lower precipitation intensity in the weather radar images is associated with higher aerosol number concentrations. In addition, at Hyytiälä the particle number concentrations is generally higher for non-precipitating cases than for precipitating cases. The apparent absence of the first indirect effect of aerosols on low-level clouds over land raises questions regarding the magnitude of the indirect aerosol radiative forcing.

The surface-wind speed influences on aerosol optical depth (AOD), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua daily observations over the central North Pacific during the period 2003–2016, have been investigated in this study. The cloud coverage is relatively low over the present investigation area compared to other marine areas, which favors AOD derived from passive remote sensing from space. In this study, we have combined MODIS AOD with 2 m wind speed (U_2m) on a satellite-pixel basis, which has been interpolated from National Centers for Environmental Prediction (NCEP) reanalysis. In addition, daily averaged AOD derived from Aerosol Robotic Network (AERONET) measurements in the free-troposphere at the Mauna Loa Observatory (3397 m above sea level), Hawaii, was subtracted from the MODIS column AOD values. The latter was to reduce the contribution of aerosols above the planetary boundary layer. This study shows relatively strong power-law relationships between MODIS mean AOD and surface-wind speed for marine background conditions in summer, fall and winter of the current period. However, previous established relationships between AOD and surface-wind speed deviate substantially. Even so, for similar marine conditions the present relationship agrees reasonable well with a power-law relationship derived for north-east Atlantic conditions. The present MODIS retrievals of AOD in the marine atmosphere agree reasonably well with ground-based remote sensing of AOD.