Modeling climate change adaptation for sustainable coastal zones using GIS and AHP.

The world is currently confronting one of its biggest environmental challenges: combating climate change. Coastal zones are one of the areas thought to be most sensitive to current and future climate change threats. The paper integrates Remote Sensing (RS), Geographic Information System (GIS) techniques, and Multi-Criteria Decision Analysis (MCDA) to detect vulnerable areas from climate change impacts in coastal zones in order to recommend adaptation systems in new coastal zones that can withstand various climatic changes. The proposed decision-making framework was developed in three phases: 1) climate data collection and processing; 2) Coastal Climate Impact Assessment (CCIA) model development; and 3) implementation and adaptation system selection. The climate data collection and processing phase involves determining the most significant climate change parameters and their indicators that affect coastal zone stability, extracting climatic data indicators from different climate database sources, and prioritizing the selected indicators. The indicators' weights were estimated using the Analytical Hierarchy Process (AHP) through a questionnaire survey shared with experts in climate change impacts. A CCIA model development phase involves the formulation of the proposed model using GIS technique to discover the vulnerable areas according to the most dominant impact. The implementation and adaptation system selection phase involves the application of the framework to Al-Alamein New City in Egypt. A sensitivity analysis was conducted to measure the behavior of several climate change parameters to identify the most critical parameter for climate change in Al-Alamein New City. The results showed that the geology of the region is the most crucial component influenced by climate change. It is capable of producing a very sensitive area in the coastal zone while also taking other factors into account. When creating new urban neighborhoods, the erosion of the shoreline is the least important factor to consider. This is because coastal deterioration is caused by both the influence of metrological data on the region and the impact of human activity. Shoreline deterioration will be reduced if climate conditions are maintained while limiting the impact of human activities. To adapt to the long-term effects of climate change on coastal zones, a combination of soft and hard protection systems should be considered.

Toxicity of Heavy Metals and Recent Advances in Their Removal: A Review.

Natural and anthropogenic sources of metals in the ecosystem are perpetually increasing; consequently, heavy metal (HM) accumulation has become a major environmental concern. Human exposure to HMs has increased dramatically due to the industrial activities of the 20th century. Mercury, arsenic lead, chrome, and cadmium have been the most prevalent HMs that have caused human toxicity. Poisonings can be acute or chronic following exposure via water, air, or food. The bioaccumulation of these HMs results in a variety of toxic effects on various tissues and organs. Comparing the mechanisms of action reveals that these metals induce toxicity via similar pathways, including the production of reactive oxygen species, the inactivation of enzymes, and oxidative stress. The conventional techniques employed for the elimination of HMs are deemed inadequate when the HM concentration is less than 100 mg/L. In addition, these methods exhibit certain limitations, including the production of secondary pollutants, a high demand for energy and chemicals, and reduced cost-effectiveness. As a result, the employment of microbial bioremediation for the purpose of HM detoxification has emerged as a viable solution, given that microorganisms, including fungi and bacteria, exhibit superior biosorption and bio-accumulation capabilities. This review deals with HM uptake and toxicity mechanisms associated with HMs, and will increase our knowledge on their toxic effects on the body organs, leading to better management of metal poisoning. This review aims to enhance comprehension and offer sources for the judicious selection of microbial remediation technology for the detoxification of HMs. Microbial-based solutions that are sustainable could potentially offer crucial and cost-effective methods for reducing the toxicity of HMs.

Assessment of COVID-19 impact on sustainable development goals indicators in Egypt using fuzzy analytic hierarchy process.

COVID-19 significantly influences the Sustainable Development Goals (SDGs) in both developed and developing countries. Within the 2030 agenda, Egypt is likely to face enormous negative ramifications from the virus spread. As a result, efficient control of the adverse repercussions of this virus is critical to achieving this objective. This research assesses indicators of specific SDGs in reflecting COVID-19 impact by conducting several questionnaire surveys among experts in Egypt. The scope of this research is limited to addressing poverty alleviation (SDG1), hunger abatement (SDG2), healthcare promotion (SDG3), sustainable economic growth (SDG8), and climate change mitigation (SDG13). The indicators are prioritized using the relative importance index, weighted aggregated sum product assessment technique for order preference by similarity to an ideal solution, and fuzzy analytic hierarchy process. The rankings are finally aggregated using an approach based on the half-quadratic theory. The results reveal that the most significant indicators in reflecting the COVID-19 impact are the share of population living below the international poverty line, undernourishment prevalence, official health sector support, annual gross domestic product per capita growth rate, and number of disaster deaths for SDG1, SDG2, SDG3, SDG8, and SDG13, respectively. Recognizing and ranking the indicators could help decision-makers understand the behavior of SDG indicators in light of COVID-19. The research findings could assist policymakers in making informed decisions to reduce the pandemic effects and sustain achieving SDGs by 2030.

Modeling COVID-19 effects on SDGs using system dynamics in Egypt.

The coronavirus disease 2019 (COVID-19) poses a significant threat to achieving the Sustainable Development Goals (SDGs). To address this challenge, a thorough examination of the pandemic's influence on four SDGs in Egypt is presented in a system dynamic model. The addressed goals are related to no poverty (SDG 1), zero hunger (SDG 2), decent work and economic growth (SDG 8), and climate action (SDG 13). The model is simulated over 35 years extending from 2015 to 2050. Furthermore, a web-based interactive learning environment is developed to analyze the interdependencies among public health activities and study the impacts of possible intervention countermeasures or prevention policies. Indicators including poverty line, food insecurity, gross domestic product (GDP) growth rate, and greenhouse gas (GHG) emissions are evaluated to track Egypt's performance in relation to SDGs 1, 2, 8, and 13. According to the simulation model, the poverty line will continue to decline until it reaches around 16% by 2050. According to the significant governmental efforts to follow its vision of 2030, Egypt can achieve a decreasing percentage of food insecurity, reaching 3% in 2030, and this percentage will continue to decrease until it reaches full sufficiency by 2050. The GDP growth rate will rise every year until it reaches 13.71% in 2050. With respect to climate, GHG emissions are predicted to fall to roughly 97 Mt CO2-equivalents by 2050. This approach revitalizes debates about the achievement of SDGs amid the crisis and acts as a powerful tool that aids decision-makers in identifying leverage points to avoid the long-term negative repercussions of the crisis on the economy, people, and environment.

Deep learning model for forecasting COVID-19 outbreak in Egypt.

The World Health Organization has declared COVID-19 as a global pandemic in early 2020. A comprehensive understanding of the epidemiological characteristics of this virus is crucial to limit its spreading. Therefore, this research applies artificial intelligence-based models to predict the prevalence of the COVID-19 outbreak in Egypt. These models are long short-term memory network (LSTM), convolutional neural network, and multilayer perceptron neural network. They are trained and validated using the dataset records from 14 February 2020 to 15 August 2020. The results of the models are evaluated using the determination coefficient and root mean square error. The LSTM model exhibits the best performance in forecasting the cumulative infections for one week and one month ahead. Finally, the LSTM model with the optimal parameter values is applied to forecast the spread of this epidemic for one month ahead using the data from 14 February 2020 to 30 June 2021. The total size of infections, recoveries, and deaths is estimated to be 285,939, 234,747, and 17,251 cases on 31 July 2021. This study could assist the decision-makers in developing and monitoring policies to confront this disease.