ABSTRACT
The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.
ABSTRACT
Most coastal cities have been experiencing unprecedented urbanization-induced flood risk, climatic events, and haphazard anthropogenic activities, jeopardizing residents' lives and building environments. Despite mounting flood-related studies, analyzing the correlation between the spatiotemporal dynamics of Built-up Expansion patterns (BE) and flood risk remains unknown and holds divergent perspectives. In this context, the coastal city of Alexandria, Egypt, characterized by multiple urban patterns and experiencing heavy rainfall annually, was selected as a testbed. Our method defined the spatiotemporal rates of BE from 1995 to 2023, quantified flood risk spatially, and finally investigated the correlation between BE and flood risk through spatial and statistical analysis. Our results show the built-up area occupied 30.32 % of the total city area till 2023, and the infilling pattern dominated the BE growth by 45.21 % of the total built-up area, followed by leapfrogging and edge expansion by 33.25 % and 21.55 %, respectively. The unplanned-infilling pattern is predominantly highly correlated with the flood-vulnerable peaks (correlation coefficient (rk) = 0.975, p-value < 0.05) and lowers dramatically towards planned-infilling regions with flood protections. Meanwhile, a spatial mismatch exists between high-risk peaks and leapfrogging and edge expansion (rk = 0.118 and 0.662, respectively, with a p-value < 0.01), indicating that controlling the built-up amount is inadequate for mitigating flood risk. Porosity-based urban configuration and spatial distribution of built-up patches in harmony with nature-based solutions are recommended for shaping flood-resilient and effective urban planning.
ABSTRACT
Urban sewer system management is challenging due to its higher vulnerability to flooding caused by rapid urbanization and climate change. For local decision-makers, storm water management is essential for urban planning and development. Therefore, the main objective of this study is to develop a numerical model for the sewerage network of the central catchment area of Algiers since it has experienced frequent overflows during the winter season. For this purpose, to model the sewerage networks, the model was built by coupling ArcGIS with MIKE URBAN. Its calibration and validation were performed using real-time measurements with a time step of 15 min. The model was evaluated by several statistical indicators, such as the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). The model results showed acceptable model performance, with an NSE superior to 0.50, R2 of approximately 0.63, RMSE of 7%, and PBIAS of 10% during the validation of the model. The performance parameters prove the reliability of the developed model. The employed model can be applied in other regions and could be helpful for policymakers and managers to improve flood mitigation measures based on the model prediction of the sewerage network.
Subject(s)
Floods , Urbanization , Algeria , Reproducibility of Results , Climate ChangeABSTRACT
Cities have experienced rapid urbanization-induced harsh climatic events, especially flooding, inevitably resulting in negative and irreversible consequences for urban resilience and endangering residents' lives. Numerous studies have analyzed the effects of anthropogenic practices (land use changes and urbanization) on flood forecasting. However, non-structural mitigation's effectiveness, like Nature-Based Solutions (NBS), has yet to receive adequate attention, particularly in the Middle East and North Africa (MENA) region, which have become increasingly significant and indispensable for operationalizing cities efficiently. Therefore, our study investigated the predictive influence of incorporating one of the most common NBS strategies called low-impact development tools (LID) (such as rain gardens, bio-retention cells, green roofs, infiltration trenches, permeable pavement, and vegetative swale) during the urban planning of Alexandria, Egypt, which experiences the harshest rainfall annually and includes various urban patterns. City characteristics-dependent 14 LID scenarios were simulated with recurrence intervals ranging from 2 to 100 years using the LID Treatment Train Tool (LID TTT), depending on calibrated data from 2015 to 2020, by the Nash-Sutcliffe efficiency index and deterministic coefficient, and root-mean-square error with values of 0.97, 0.91, and 0.31, respectively. Our findings confirmed the significant effectiveness of combined LID tools on total flood runoff volume reduction by 73.7%, revealing that different urban patterns can be used in flood-prone cities, provided LID tools are considered in city planning besides grey infrastructure to achieve optimal mitigation. These results, which combined multiple disciplines and were not explicitly mentioned in similar studies in developing countries, may assist municipalities' policymakers in planning flood-resistant, sustainable cities.
