The spatio-temporal dynamics of aerosols in the Marmara region and impact of land cover/use on atmospheric environment

Abstract

The impacts of urbanization and industrialization on air quality are well known, and air pollution control strategies have been implemented, but the effectiveness of these strategies is limited to developed countries. Developing countries continue to rely on fossil fuels and experience air pollution from agriculture emissions, crop residue burning, biomass fuel, and low-quality coal combustion. Air pollution contributes to health, visibility, ecosystems, and climate change. Solid air pollutants, also known as particulate matter (PM) consist of tiny particles suspended in the air has the most severe health impacts. Governments need to measure, monitor, and control the concentration of air pollutants and maintain the pollution levels under the values defined by the World Health Organization (WHO). There have been some efforts to reduce air pollution in developing countries. However, there is still a long way to go in terms of clean air in many developing countries. The data collected from air quality monitoring (AQM) stations are commonly used to study the impacts of air pollution on human health. However, the initial investment and maintenance of ground-based AQM stations are expensive. The limited quantity of air quality monitoring stations on the ground poses a hindrance to conducting thorough research into the effects of elevated levels of air pollution on the environment. Measurements from earth-observing satellites can be a useful substitute for ground-based data in environmental studies. Although ground-based data are often more detailed and accurate, satellite data provide a broader view of larger spatial extents. The advantages of satellite data collection over a lengthy period allow for long-term monitoring of environmental trends and changes. Aerosols, tiny particles that are suspended in the Earth's atmosphere can be of natural or human-made origin. The impact of atmospheric aerosol loading on various aspects of the Earth's climate and environment cannot be overstated. Besides its adverse effect on human health, it affects global temperature, atmospheric radiation, the Earth's albedo, and terrestrial heat budget, as well as clouds and precipitation processes, and ecological systems. Therefore, understanding atmospheric aerosols is essential for comprehending Earth's climate and ecosystem. The columnar aerosol pollution or the number of microscopic aerosol particles in a vertical slice of the atmosphere can be measured by satellite sensors. The main remotely sensed geophysical quantity and column-effective particle property is total column aerosol optical depth (AOD). The AOD is a measure of how much sunlight is absorbed or scattered by aerosol particles in the atmosphere, and it provides information on the concentration and distribution of atmospheric aerosols. The concentrations of PM and AOD are important measures of air pollution. They represent the amount of particulate matter present in the air and are strongly correlated with each other. While ground-based air pollutant concentration records show the concentration of pollutants near the Earth`s surface, satellite-based and ground-measured AOD data provide a measure of the total column amount of aerosols from the Earth's surface up to the top of the atmosphere. Satellite-based AOD measurements can identify hotspots of particle pollution and short-term spikes, which can then be targeted for more detailed ground-based measurements of air pollution. Space-based remote sensing plays a vital role in characterizing the spatial and temporal distributions of atmospheric aerosols from the local to global scale. The popularity of satellite aerosol products has led to the development of various aerosol retrieval algorithms. To ensure the accuracy of satellite aerosol data in interpreting regional and global aerosol patterns, it's crucial to evaluate the performance of the retrieval algorithms. Validation using accurate ground-based AOD measurements is employed for this purpose. AErosol RObotic NETwork (AERONET) program is a federation of ground-based remote sensing aerosol networks, a vital worldwide network for monitoring aerosols that employ numerous sun-photometers stationed across the globe to measure various aerosol optical and microphysical characteristics, such as AOD. It's important to note that the efficacy of satellite aerosol products may vary depending on factors such as geography, climate, and weather conditions. Thus, it's essential to identify the most reliable algorithm by validating satellite AOD retrievals against the nearest ground-based data to the region of interest. The primary aim of this research is to better understand the behavior of aerosols in the atmosphere and gain insight into the spatial distribution and temporal variability of aerosols in the region, as well as to identify the factors that influence the magnitude of AOD and its correlation with land cover/use (LCU). This understanding will aid in devising an efficient strategy to manage environmental air pollution. Furthermore, by gathering, verifying, and analyzing appropriate data through particular techniques, the alterations in aerosol concentrations over a prolonged period will be assessed. For this major objective, the study is divided into three key sections. First, various aerosol products are evaluated for their accuracy and reliability by comparing satellite-derived AODs with AERONET AOD measurements at three different sites. The most effective AOD product were determined by comparing the performances of aerosol data sets. In this context, multiple Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD products were compared with AERONET AOD measurements at various surface classes/types (i.e. land, ocean, coastal) in the eastern Mediterranean over five years (2014-2018) and the results were validated. MODIS aerosol products based on Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) are available at different spatial resolutions (1 km, 3 km, and 10 km), while the VIIRS aerosol product has a resolution of 6 km. To obtain VIIRS AOD values, the improved DB approach was used for land and the Satellite Ocean Aerosol Retrieval (SOAR) method for the ocean. The best-performing products over the urban/land surface are the MODIS MAIAC and VIIRS DB aerosol products with Root-Mean-Squared Errors (RMSE) of 0.048-0.061 and a higher percentage (81%-89%) of retrievals falling within the expected error for land. This is while the MODIS MAIAC AOD data also has the advantage that it has a better spatial resolution of 1 km, which enables it to capture sub-grid aerosol features and a higher number of AOD retrievals. In the second section, I focus on the spatiotemporal variation of MODIS MAIAC AOD over the Marmara region. The objective of this section is to pinpoint areas within a region that have high AOD levels and examine how various environmental factors such as seasonal weather patterns, local emission sources, and LCU are linked to the presence of aerosol pollution in those areas. Our study area was the Marmara region because it is subject to the production of aerosols originating from diverse sources, both natural and anthropogenic. The Marmara Region is the country's most populous region, despite being the second smallest geographically. This is due to Istanbul being located there. The region also contains other important developing cities like Bursa, Kocaeli, and Tekirdag. It is a significant economic center with a lot of agricultural, commercial, and industrial activity. Aerosol formation in the region is influenced by a range of sources, including sea salt, agricultural practices, maritime transport, industrial activities, and the seasonal transportation of dust from the Sahara Desert. To accomplish the objective of the study's second part, I analyzed the MODIS MAIAC AOD data at annual, monthly, and seasonal scales between 2000 and 2021 to investigate the spatiotemporal variability of AOD in the Marmara region. The monthly mean AOD increases gradually from January to May and fluctuates between May and August and reaches its highest value in August. The monthly mean AOD decreases after August and reaches its lowest monthly mean at the end of the year during December. Seasonal variation in AOD is significant: summer (0.148) > spring (0.136) > autumn (0.116) > winter (0.09). According to an analysis of the AOD's multi-year variation, there were two maxima for the AOD between 2000 and 2010 with values of 0.146 and 0.137 in 2002 and 2008, respectively. AOD exhibits a decreasing tendency from 2000 to 2021, with a 0.005/yearly decline. Using the MODIS MAIAC AOD data at a 1-km scale, I performed a comprehensive assessment of aerosol loading and statistical-visual analyses highlighting the influence of land use/cover on aerosol properties. The findings revealed that there are significant regions with high aerosol concentrations across the region and that these regions show significant temporal variations. The aerosol loading was higher over the western side of the Marmara region (Edirne, Tekirdag, Kırklareli, Canakkale, Istanbul, Kocaeli) until 2011 while the eastern part of the region (Bursa, Sakarya, and Balikesir) was exposed to higher aerosol concentrations after 2016. From 2000 to 2021 the largest number of days with lower aerosol pollution is seen in Bilecik (≈ 35%) while Edirne experienced the highest percentage of days with the highest aerosol pollution level (≈ 9%). In the region, aerosols are mostly generated by urban activities and industries, as well as mineral aerosols originating from soils. Finally, a novel approach to LCU classification is proposed. The last part of the paper proposes a strategy to identify and distinguish different LCU patterns in Mediterranean cities. The focus was on separating built-up areas from bare land, which can be challenging due to urban landscape complexity and heterogeneity. Separation is also necessary since urbanized/industrialized zones and bare soil areas contribute significantly to atmospheric aerosol pollution. For this purpose, the separation of these two classes was well-addressed by using the proposed multi-index methods on satellite image data. The multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) showed outstanding overall performance with 93% accuracy and a 0.91 kappa value for all LCU classes. The improvement achieved in separating built-up regions from bare land is of substantial significance, as it significantly reduces the misclassification of bare land as built-up regions. This is particularly important for aerosol studies in the study region, where the two factors with the highest impact on aerosol loading are found. The enhanced accuracy of land cover classification provided by this improvement can greatly enhance the reliability and precision of aerosol studies in the area.