Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements

Abstract

This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations of multiple parameters, including solar radiation, air temperature, humidity, and UV exposure. Scarce ground PM10 data were collected and compared against satellite products, yet owed to their discrete nature of availability, our approach made use of the Random Decision Forest (RDF) model to convert satellite-based AOD and other meteorological parameters to predict PM10. The RDF model with inputs from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Global Land Data Assimilation System (GLDAS) datasets improves the performance of using AOD products to estimate PM10 values. The connection between climate variability and aerosol intensity, as well as their impact on health-related PM2.5 over Egypt is also demonstrated.

Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data

“…Both regions are rich in dust and industrial pollutant while the Mediterranean coastal regions are also more likely affected by the transporting marine aerosols such as sea salt. The lower R 2 may be due to the mixture of different aerosol types in this region, where marine aerosols are usually mixed with dust and smoke yet occasionally dominating in some emerging aerosol episodes [68]. Different aerosol sources have different impacts on the urban human health related parameters, including solar radiation, air temperature, humidity, and UV exposure.…”

“…The analysis proved the DNN model can effectively optimize the results from the available AOD products. Similarly, the usage of DNN also has been proved to be effective to predict and estimate the other parameters related to human health and living conditions such as PM10 and precipitation [67,68].…”

Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

“…More than 26% of the land is covered by deserts where the main sources of dust storms are located. Recent studies have indicated that satellite-based PM 2.5 concentration data can be used to understand the spatio-temporal variability of atmospheric pollutants in arid region [35][36][37][38][39]. For example, Munir et al [39] used satellite-derived PM 2.5 concentration data to analyze the spatial and temporal variability of PM 2.5 in Saudi Arabia, finding that remote sensing can help better understand the spatial variability of atmospheric pollutants, especially on a large scale.…”

Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries

“…For instance, Donchyts et al [20] employed the GEE for mapping global surface water changes over the past 30 years with a high spatial resolution (30 m), an effort that would not have been possible without the powerful processing and analytical capabilities from cloud computing. GEE is also widely used for environmental research in the Nile watershed and adjacent regions [14,[21][22][23][24][25]. In particular, it is a promising tool that allows researchers from developing countries (e.g., Nile Basin countries) to have the same ability to undertake state-of-art studies as those in the most advanced nations.…”