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

Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island

Greater Cairo, Egypt, which lies in the apex of the Nile Delta, is one of the most populated regions in the world. Air pollution is a profound environmental issue prevailing in the urban/rural landscapes of this crowded megacity. The objectives of the present study were to utilize remotely sensed data in order to address the seasonal variations of the nocturnal surface urban heat island intensity (SUHII) as extracted from the American Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and the related seasonal distribution of selected air pollutants, including nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) as extracted from the European TROPOspheric Monitoring Instrument (TROPOMI) for the period from 2018 to 2021. It is observed that there is clear nocturnal urban heat island over Greater Cairo, particularly at the administrative districts dominated by urban land use with high density of population and at the industrial and power generation locations. The highest SUHII is observed during winter. On the other hand, the selected pollutants also represent an urban pollution island (UPI) capping the regions of high SUHII. At the seasonal level, the highest NO2 correlation with the SUHII occurs during spring (R2 = 0.59), while the CO correlates maximum during winter (R2 = 0.51). Nonetheless, the seasonal SO2 distribution is poorly related to the SUHII as this specific pollutant is significantly associated with the industrial land use. Climatic and topographic factors could intensify the distribution of air pollution in the study area. Results of this study demonstrate the significance of geospatial technology tools in the subtle analysis and addressing regional air pollution. The outputs are also of a paramount implication on the management of urban environment and the adaptation of urban air quality.

Impacts of short-term lockdown during COVID-19 on air quality in Egypt

COVID-19 is a pandemic disease that is actively spread over the globe in a few months. Most of the Nations took the appropriate measures including lockdown to reduce the risk of spreading and safe human health and life. Egypt took the measures of partial and complete lockdown from 15th March till 30th June 2020. Such short-term lockdown has had a significant impact on the reduction of emissions from transportation, industrial and human activities. This research used multi-data sensors from space to map the changes of air quality over Egypt in the first 6 months from January to June 2020 due to the lockdown and compare with previous years of 2018 and 2019. It is clearly observed that the air quality over the whole country is improved as a result of reducing pollutants emissions, with NO₂ reduced by 45.5%, CO emissions reduced by 46.23%, Ozone concentration decreased by about 61.1%, and AOD reduced by 68.5% compared to the previous 2 years. It is found that the lockdown is an effective mitigation measure against air pollution to improve air quality and reduce the air pollution that creates pressure on the human health and health system. It might be difficult to implement long lockdown, as a mitigation measure, due to its direct impact on social and economic needs. However, we recommend a complete lockdown for 2–3 days (long weekend) every at least 2-3 months, on national and/or global level, which will significantly enhance our air quality and improve the health environment of the planet.

An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models’ performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (ψ), the mean absolute percentage difference (|ψ|)) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R2)).

Investigation of Aerosol Climatology, Optical Characteristics and Variability over Egypt Based on Satellite Observations and In-Situ Measurements

Egypt experiences high rates of air pollution, which is a major threat to human health and the eco-environment and therefore needs to be tackled by defining major causes to hinder or mitigate their impacts. The major driving forces of air pollution are either of local and/or regional origin. In addition, seasonal aerosols may be natural, such as dust particles transported from the western desert, or anthropogenic aerosols which are transported from industrial areas and smoke particles from seasonal biomass burning. Monitoring the optical properties of aerosols and their pattern in the atmosphere on a daily basis requires a robust source of information and professional analytical tools. This research explored the potential of using time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Robotic Network (AERONET) data to comprehensively investigate the aerosol optical depth (AOD) and variability for the period 2012–2018 on a daily basis. The data show that spring, summer and autumn seasons experienced the highest anomaly originating from regional and national sources. The high AOD in spring associated with a low Ångström exponent (AE) indicates the presence of coarse particles which naturally originate from desert dust or sea spray. In contrast, the high AE in summer and autumn demonstrated the dominance of fine anthropogenic aerosols such as smoke particles from local biomass burning. The observation of a high number of fire incidents over Egypt in October and November 2018, during the months of rice crop harvesting, showed that these incidents contribute to the presence of autumn aerosols across the country. In-situ measurements of Particulate Matter (PM10) from local stations from an environmental based network as well as the AERONET AOD were used to validate the MODIS AOD, providing a high correlation coefficient of r = 0.73.

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

In September 2015, the members of United Nations adopted the 2030 Agenda for Sustainable Development with universal applicability of 17 Sustainable Development Goals (SDGs) and 169 targets. The SDGs are consequential for the development of the countries in the Nile watershed, which are affected by water scarcity and experiencing rapid urbanization associated with population growth. Earth Observation (EO) has become an important tool to monitor the progress and implementation of specific SDG targets through its wide accessibility and global coverage. In addition, the advancement of algorithms and tools deployed in cloud computing platforms provide an equal opportunity to use EO for developing countries with limited technological capacity. This study applies EO and cloud computing in support of the SDG 6 “clean water and sanitation” and SDG 11 “sustainable cities and communities” in the seven Nile watershed countries through investigations of EO data related to indicators of water stress (Indicator 6.4.2) and urbanization and living conditions (Indicators 11.3.1 and 11.1.1), respectively. Multiple approaches including harmonic, time series and correlational analysis are used to assess and evaluate these indicators. In addition, a contemporary deep-learning classifier, fully convolution neural networks (FCNN), was trained to classify the percentage of impervious surface areas. The results show the spatial and temporal water recharge pattern among different regions in the Nile watershed, as well as the urbanization in selected cities of the region. It is noted that the classifier trained from the developed countries (i.e., the United States) is effective in identifying modern communities yet limited in monitoring rural and slum regions.

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

With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM_2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM_2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM_2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM_2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM_2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM_2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM_2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.

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

Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies.