Konstantinos Kourtidis

Spatiotemporal variability and contribution of different aerosol types to the aerosol optical depth over the Eastern Mediterranean

This study characterizes the spatiotemporal variability and relative contribution of different types of aerosols to the Aerosol Optical Depth (AOD) over the Eastern Mediterranean as derived from MODIS Terra (3/2000-12/2012) and Aqua (7/2002-12/2012) satellite instruments. For this purpose, a 0.1° × 0.1° gridded MODIS dataset was compiled and validated against sunphotometric observations from the AErosol RObotic NETwork (AERONET). The high spatial resolution and long temporal coverage of the dataset allows for the determination of local hot spots like megacities, medium sized cities, industrial zones, and power plant complexes, seasonal variabilities, and decadal averages. The average AOD at 550 nm (AOD550) for the entire region is ~ 0.22 ± 0.19 with maximum values in summer and seasonal variabilities that can be attributed to precipitation, photochemical production of secondary organic aerosols, transport of pollution and smoke from biomass burning in Central and Eastern Europe, and transport of dust from the Sahara Desert and the Middle East. The MODIS data were analyzed together with data from other satellite sensors, reanalysis projects and a chemistry-aerosol-transport model using an optimized algorithm tailored for the region and capable of estimating the contribution of different aerosol types to the total AOD550. The spatial and temporal variability of anthropogenic, dust and fine mode natural aerosols over land and anthropogenic, dust and marine aerosols over the sea is examined. The relative contribution of the different aerosol types to the total AOD550 exhibits a low/high seasonal variability over land/sea areas, respectively. Overall, anthropogenic aerosols, dust and fine mode natural aerosols account for ~ 51 %, ~ 34 % and ~ 15 % of the total AOD550 over land, while, anthropogenic aerosols, dust and marine aerosols account ~ 40 %, ~ 34 % and ~ 26 % of the total AOD550 over the sea, based on MODIS Terra and Aqua observations.

Towards Air Quality Estimation Using Collected Multimodal Environmental Data

This paper presents an open platform, which collects multimodal environmental data related to air quality from several sources including official open sources, social media and citizens. Collecting and fusing different sources of air quality data into a unified air quality indicator is a highly challenging problem, leveraging recent advances in image analysis, open hardware, machine learning and data fusion. The collection of data from multiple sources aims at having complementary information, which is expected to result in increased geographical coverage and temporal granularity of air quality data. This diversity of sources constitutes also the main novelty of the platform presented compared with the existing applications.

A 1-year remote sensing study of radiative effects of aerosol and clouds over the NE Mediterranean

Direct aerosol radiative forcing (ARF) and cloud radiative forcing (CRF) over the Aegean Sea are investigated using 1 year of almost continuous observations of aerosol optical properties, cloud data and Clouds and the Earths Radiant Energy System (CERES) satellite short-wave (SW) and long-wave (LW) fluxes during 2005–2006. Aerosol optical depth (AOD) values from moderate resolution imaging spectroradiometer (MODIS) over the Aegean Sea are found to be high during summer and spring months. This period of the year, the air masses come predominantly from the eastern and northeastern part of Europe. The monthly mean ARF at the top of the atmosphere (TOA) over the Aegean Sea resulted in net cooling and ranged from −6.0 to −15.8 W m−2, within the range of results from previous studies. The presence of fine particles greatly influenced the ARF. The CRF was also characterized by net cooling and ranged from −23 to −84 W m−2 and appeared to be driven primarily by changes in cloud fraction. An apparent aerosol weekly cycle with lower values during the weekend as well as a cloud fraction weekly cycle with lower values during the weekend is observed over the Aegean Sea, which, if confirmed, could result in net TOA forcing of around −20 W m−2.

A 3-D Evaluation of the MACC Reanalysis Dust Product Over Europe Using CALIOP/CALIPSO Satellite Observations

This work focuses on the evaluation of the MACC (Monitoring atmospheric composition and climate) reanalysis dust product over Europe. Europe receives significant amounts of dust on an annual basis primarily from the large neighboring area sources (Sahara Desert, Arabian Peninsula) and from smaller local sources. Dust affects a number of processes in the atmosphere modulating weather and climate and exerts an impact on human health and the economy. Hence, the ability of simulating adequately the amount of dust and its optical properties is essential. For the evaluation of the MACC reanalysis, pure dust satellite-based retrievals from CALIOP/CALIPSO are utilized for the period 2007–2012. Specifically, the CALIOP/CALIPSO data used here come from an optimized retrieval scheme that was originally developed within the framework of the LIVAS (Lidar Climatology of Vertical Aerosol Structure for Space-Based LIDAR Simulation Studies) project. The natural aerosol extinction coefficients from MACC and the dust optical depth patterns at 550 nm are validated against dust extinction coefficients and dust optical depth patterns at 532 nm from CALIOP/CALIPSO. Our results highlight the important role that space-based lidars may play in the improvement of the MACC aerosol product.

A new method for deriving trace gas emission inventories from satellite observations: The case of SO2 over China

A method is developed that allows the construction of spatial emission inventories. The method is applied for anthropogenic SO2 over China (0.25°×0.25°). The Enhancement Ratio Method (ERM) allows for the calculation of SO2 emissions using relationships between gridded satellite measurements of SO2 and NO2 at low wind speeds, and satellite-based NOx emission estimates. Here, we derive SO2 emissions for five years (2007-2011). A large decrease of emissions during 2007-2009 and a modest increase between 2010 and 2011 is observed. The evolution of emissions over time calculated here is in general agreement with bottom-up inventories, although differences exist, not only between the current inventory and other inventories but also among the bottom up inventories themselves. The ERM-derived emissions are consistent, spatially and temporally, with existing inventories.

Towards Improved Air Quality Monitoring Using Publicly Available Sky Images.

Air pollution causes nearly half a million premature deaths each year in Europe. Despite air quality directives that demand compliance with air pollution value limits, many urban populations continue being exposed to air pollution levels that exceed by far the guidelines. Unfortunately, official air quality sensors are sparse, limiting the accuracy of the provided air quality information. In this chapter, we explore the possibility of extending the number of air quality measurements that are fed into existing air quality monitoring systems by exploiting techniques that estimate air quality based on sky-depicting images. We first describe a comprehensive data collection mechanism and the results of an empirical study on the availability of sky images in social image sharing platforms and on webcam sites. In addition, we present a methodology for automatically detecting and extracting the sky part of the images leveraging deep learning models for concept detection and localization. Finally, we present an air quality estimation model that operates on statistics computed from the pixel color values of the detected sky regions.

Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations. Part I: ATSR (1995–2011) and MODIS C6.1 (2000–2017)

Aerosol optical depth (AOD) patterns and interannual and seasonal variations over China are discussed based on the AOD retrieved from the Along-Track Scanning Radiometer (ATSR-2, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite (2000–2017). The AOD products used were the ATSR Dual View (ADV) v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and deep blue (DB) AOD product. Together these datasets provide an AOD time series for 23 years, from 1995 to 2017. The difference between the AOD values retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and monthly aggregate comparisons as well as validation results. This allows for the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset correction. ADV and MODIS AOD validation results show similar high correlations with the Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while the corresponding bias is positive for MODIS (0.06) and negative for ADV (− 0.07). Validation of the AOD products in similar conditions, when ATSR and MODIS/Terra overpasses are within 90 min of each other and when both ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs better than MODIS in autumn, while MODIS performs slightly better in spring and summer. In winter, both ADV and MODIS underestimate the AERONET AOD. Similar AOD patterns are observed by ADV and MODIS in annual and seasonal aggregates as well as in time series. ADV–MODIS difference maps show that MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north. The agreement between sensors regarding year-to-year AOD changes is quite good. During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between 2006 and 2011 AOD did not change much, showing minor minima in 2008–2009. From 2011 onward AOD decreased in the SE of China. Similar patterns exist in year-toyear ADV and MODIS annual AOD tendencies in the overlapping period. However, regional differences between the ATSR and MODIS AODs are quite large. The consistency Published by Copernicus Publications on behalf of the European Geosciences Union. 11390 L. Sogacheva et al.: Spatial and seasonal variations of aerosols over China – Part 1 between ATSR and MODIS with regards to the AOD tendencies in the overlapping period is rather strong in summer, autumn and overall for the yearly average; however, in winter and spring, when there is a difference in coverage between the two instruments, the agreement between ATSR and MODIS is lower. AOD tendencies in China during the 1995–2017 period will be discussed in more detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and MODIS is introduced, and combined AOD time series are analyzed.

Evaluation of Regional Climate Model Surface Solar Radiation Patterns Over Europe Using Satellite-Based Observations and Radiative Transfer Calculations

The ability of RegCM4 regional climate model to simulate surface solar radiation (SSR) patterns over Europe is assessed through an evaluation of a decadal simulation against satellite-based observations from the Satellite Application Facility on Climate Monitoring (CM SAF). The model simulates adequately the SSR patterns over the region slightly overestimating SSR (bias of ~+2.5 % for the period 2000–2009). Cloud macrophysical and microphysical properties from RegCM4 such as cloud fractional cover (CFC), cloud optical thickness (COT) and cloud effective radius (Re) are evaluated against data from CM SAF. The same is done for aerosol optical properties such as aerosol optical depth (AOD), asymmetry factor (ASY), and single scattering albedo (SSA) using data from the MACv1 aerosol climatology, and other parameters, such as surface broadband albedo (ALB) using data from the CERES satellite sensors, and water vapor amount (WV) using data from the ERA-Interim reanalysis. The good agreement between RegCM4 and satellite-based SSR observations is a result of counterbalancing effects of these parameters. The contribution of each parameter to the RegCM4-CM SAF SSR deviations is estimated with the combined use of the aforementioned data and a radiative transfer model (SBDART). CFC, COT and AOD are the major determinants of these deviations.

Aerosol–Cloud Relations for Cloud Systems of Different Heights

Here we examine the annual and seasonal aerosol–cloud relations over three major urban clusters of China, at different cloud heights, using a decade of Aerosol Optical Depth at 550 nm (AOD), Cloud Cover (CC), Cloud Optical Depth (COD), Water Vapor (WV) and Cloud Top Pressure (CTP) data from the MODIS instrument. Over all regions and for all seasons, CC is found to increase with increasing AOD, WV and cloud height. Aerosols, at low WV environments and under constant CTP, have less impact on CC than at high WV environments. In addition, AOD has a varying influence on COD depending on CTP. Finally, COD is found to increase with height for low and middle height clouds, and with increasing AOD, especially at low AOD, the latter being in line with the expected first indirect effect.

ENVI4ALL: Personalised Air Quality Information Based on Open Environmental Data and User-Generated Information

Air pollution open data has a huge value for citizens, especially these belonging to vulnerable groups. Information on air quality can help them to take better informed decisions that safeguard their health. Although this information is available in multiple sources, in the form that the data is provided, it is difficult for citizens to extract the information they actually need. In addition, existing monitoring stations mainly cover only large cities, and fail to take into account differences in microclimates occurring within a specific area. ENVI4ALL will be an application that addresses these challenges by providing direct access to personalised and localised information on air quality (current, forecast, and historical), making use of diverse sources of large datasets of open air quality data, and crowdsourced information on the perception of app users about the current air quality. An empirical model will be also applied for the provision of air quality forecasts.