In August 2021, a historic heatwave was recorded in Greece which resulted in extreme wildfire events that strongly affected the air quality over the city of Athens. Saharan dust was also transferred over Greece on certain days of the same period due to the prevailing southern winds. The impact of these events on air quality and surface solar radiation is investigated in this study. Event characterization based on active and passive remote sensing instrumentation has been performed. The study shows that significantly increased levels of air pollution were recorded from the end of July to the first week of August. The smoke led to unusually high aerosol optical depth (AOD) values (up to 3.6 at 500 nm), high Ångström exponent (AE) (up to 2.4 at 440–870 nm), and a strong and negative dependence of single-scattering albedo (SSA) on wavelength that was observed to decrease from 0.93 at 440 nm to 0.86 at 1020 nm, while the dust event led to high AOD (up to 0.7 at 500 nm), low AE (up to 0.9 at 440–870 nm), and a positive dependence of SSA on wavelength that was observed to increase from 0.89 at 440 nm to 0.95 at 1020. Furthermore, the smoke plume was also detected over the PANhellenic GEophysical observatory of Antikythera on 7 August, which is about 240 km away from Athens. Increased AOD values (up to ∼ 0.90 at 500 nm) associated with a high fine-mode AOD (up to ∼ 0.85 at 500 nm) and decrease in SSA with wavelength suggested the dominance of fine biomass burning aerosols. The impact of dust and smoke on solar irradiance revealed significant differences in the spectral dependence of the attenuation caused by the two different aerosol types. The attenuation of solar irradiance in the ultraviolet (UV-B) spectrum was found to be much lower in the case of dust compared to smoke for similar AOD₅₀₀ values. Differences were less pronounced in the near-infrared and visible spectral regions. The large AODs during the wildfires resulted in a decrease in the noon UV index by up to 53 %, as well as in the daily effective doses for the production of vitamin D (up to 50 %), in the daily photosynthetically active radiation (up to 21 %) and in the daily global horizontal irradiance (up to 17 %), with serious implications for health, agriculture, and energy. This study highlights the wider impacts of wildfires that are part of the wider problem for Mediterranean countries, whose frequency is predicted to increase in view of the projected increasing occurrence of summer heatwaves.

Currently, 55 % of the world's population resides in urban areas and this number is projected to increase to 70 % by 2050. Urban agglomerations with a population over 10 million, characterized as megacities, are expected to be more than 100 by 2100. Such large concentrations of population could boost creativity and economic progress, but also raises several environmental challenges such as air quality degradation. In this study, we investigate the spatial and temporal variability of urban aerosol state of 81 cities with a population over 5 million, relying on daily satellite-based aerosol optical depth (AOD) retrievals, derived at fine spatial resolution ($\mathrm{0.1}{}^{\circ }\times \mathrm{0.1}{}^{\circ }$ ), over an 18-year period spanning from 2003 to 2020. According to our results, the lowest long-term mean AOD values worldwide were found in European and American cities (from 0.08 to 0.20). For almost all African and Asian cities, mean AOD ranged from 0.25 up to 0.90, but a considerable dust aerosol contribution (up to 70 %) was found for some of them with associated mean dust optical depth (DOD) values reaching up to 0.4. Mostly Chinese and Indian cities tend to have higher mean AOD values in the areas surrounding their center, while the opposite was found for most of the cities in the rest of the world. High intraannual AOD variability was revealed for the eastern American cities, while lower values were found in Chinese, eastern Indian and the eastern Mediterranean cities. During the study period, statistically significant negative AOD decadal trends were found for East Asian, European and North American cities, with the greatest decrease of −0.1 to −0.3 per decade recorded for the Chinese cities, in which the maximum mean AODs (0.45–0.91) are observed. In most of the US cities, where low mean AOD <0.17 was recorded, considerable declining AOD trends were found (−30 % to −50 % per decade). For the rest of Asian, African and South American cities, statistically significant AOD increase was found, with the greatest values of +0.07 to +0.16 per decade recorded for Indian cities. In Bengaluru (India), it is reported the lowest mean AOD value (0.2) and the maximum AOD increase (+69 %), which may be partially attributed to the population growth over the study period. The agreement of the satellite-derived AOD trends against those obtained from ground-based AERONET measurements was examined. For ground-based stations within the geographical limits of the contiguous urban area of the examined cities, a 0.93 correlation for the long-term means of AOD was found and ∼75 % of the derived trends agreed in sign. It was found that the spatial homogeneity within the examined satellite domain and the location of the surface station were key factors that determined their agreement. The present study highlights the vital and essential contribution of spaceborne products to monitor aerosol burden over megacities of the planet towards fulfilling the United Nations Sustainable Development Goal of “sustainable cities and communities”, dealing with urban air quality.

We examine the impact of atmospheric aerosols and clouds on the surface solar radiation and solar energy at Nainital, a high-altitude remote location in the central Gangetic Himalayan region (CGHR). For this purpose, we exploited the synergy of remote-sensed data in terms of ground-based AERONET Sun Photometer and satellite observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat Second Generation (MSG), with radiative transfer model (RTM) simulations and 1 day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Clouds and aerosols are one of the most common sources of solar irradiance attenuation and hence causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The outputs of RTM results presented with high accuracy under clear, cloudy sky and dust conditions for global horizontal (GHI) and beam horizontal irradiance (BHI). On an annual basis the total aerosol attenuation was found to be up to 105 kWh m⁻² for the GHI and 266 kWh m⁻² for BHI, respectively, while the cloud effect is much stronger with an attenuation of 245 and 271 kWh m⁻² on GHI and BHI. The results of this study will support the Indian solar energy producers and electricity handling entities in order to quantify the energy and financial losses due to cloud and aerosol presence.

Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m² for GHI and −103 to −76 W/m² for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions.

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations.