The coastal zone of the Nile Delta lodges highly populated cities (e.g. Alexandria, Port Said) together with small towns and villages. It represents the major industrial, agricultural, and economic resource of the country. The area has been subjected to extensive and excessive unplanned developmental projects to foster the economic status of the local communities that, however, negatively impacted both land use and land cover characteristics. Satellite images were processed to identify the dominant land use/cover classes (from 1990 to 2014) and GIS techniques spatially analyzed and quantified the rate of changes. Five dominant classes were identified and their changes monitored against the unprecedented human activities. Results demonstrated a significant rate of land forms transformation within the last 25years, in favor of developing of fish farming (+13%) and urban (+22%). Such development of these land use classes was unfortunately at the expense of the agricultural land (−15%), coastal dune and barren land (−13%) and water bodies (−7%). In addition, the coastal processes have made significant changes in the shoreline creating areas of erosion (maximum of 24–36m/year) and others of accretion (a total accreted area of 3.14km2) with an overall retreat of 2.7km2 along the shoreline. The expected changes in climate and sea level rise will worsen the erosion rate and the general status of the coastal zone. Based on the IPCC scenarios (i.e. the 59cm sea level rise by the end of this century) and the proposed land subsidence (2.5mm/year), one fifth of the Nile Delta will be seriously vulnerable to inundation

The ecological and biological status of Lake Burullus was examined in 2006 to determine its water quality and eutrophication status in response to the quality and quantity of drain water entering it. The lake suffers from excessive nutrient concentrations. Chlorophyll a showed wide variations over the sampling period with maximum levels (85–122 mg m−3) in the western part in response to the nutrient-rich water inputs from the surrounding farmlands. Nutrients were at maximum levels in the southern and western parts of the lake, which were affected by wastewater discharged into the lake through the drains system. A dense (up to c. 8 × 103 cells ml−1) and more diverse phytoplankton community (about 160 taxa) was found, with a four- to eight-fold increase in species abundance compared to that over the past two decades. Low Secchi transparency (0.15–0.50 m) and high total suspended matter were recorded when phytoplankton was abundant. According to the trophic state index, Lake Burullus could be classified as h...

Water shortage in the arid region is an existing and future severe problem that threatens this part of the world. Egypt has a limited budget of 55 BM³ per year from the river Nile basin that has always been stored in the Lake Nasser reservoir, southern Egypt. It has been estimated that the water losses from the lake ranges from 10 to 20 BM³ a year⁻¹. This paper discusses the possibility of estimating the surface evaporation from Lake Nasser using optical remotely sensed data. The surface energy balance algorithm for land (SEBAL) algorithm was used to estimate the monthly evaporation rate using National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images based on the energy balance components. The main variable within these components is the surface temperature, which was calculated by a calibrated split window equation from the remotely sensed data. Twenty-four satellite images during the year 2008 were used to estimate the maximum and minimum evaporative losses from the lake at 16.3 and 12.5 BM³ year⁻¹, respectively. When compared with the Penman-Monteith mathematical formula, a high correlation was obtained with r ²=0.78. The study demonstrated that remotely sensed data can provide a robust estimate of evaporative losses from Lake Nasser that can aid decision makers in better management and Digital Earth studies.

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.