Most of the world’s population is concentrated in accumulated spaces in the form of cities, making the concept of urban planning a significant issue for consideration by decision makers. Urban vulnerability is a major issue which arises in urban management, and is simply defined as how vulnerable various structures in a city are to different hazards. Reducing urban vulnerability and enhancing resilience are considered to be essential steps towards achieving urban sustainability. To date, a vast body of literature has focused on investigating urban systems’ vulnerabilities with regard to natural hazards. However, less attention has been paid to vulnerabilities resulting from man-made hazards. This study proposes to investigate the physical vulnerability of buildings in District 6 of Tehran, Iran, with respect to intentional explosion hazards. A total of 14 vulnerability criteria are identified according to the opinions of various experts, and standard maps for each of these criteria have been generated in a GIS environment. Ultimately, an ordered weighted averaging (OWA) technique was applied to generate vulnerability maps for different risk conditions. The results of the present study indicate that only about 25 percent of buildings in the study area have a low level of vulnerability under moderate risk conditions. Sensitivity analysis further illustrates the robustness of the results obtained. Finally, the paper concludes by arguing that local authorities must focus more on risk-reduction techniques in order to reduce physical vulnerability and achieve urban sustainability.

Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R² (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter.

Quantification of Surface Ecological Status (SES) changes is of great importance for understanding human exposure and adaptability to the environment. This study aims to assess the effect of urban growth on spatial and temporal changes of SES over a set of neighboring Iranian cities, Amol, Babol, Qaemshahr, and Sari, which are located in moderate and humid climate conditions. Firstly, the built-up footprint was prepared using Landsat images based on the Automatic Built-up Extraction Index (ABEI). Then, the surface biophysical characteristics were calculated. Secondly, the SES was modeled using the Remotely Sensed Ecological Index (RSEI), and the spatio-temporal changes of the SES were evaluated. The results revealed that the average RSEI for these cities increased from 0.48, 0.51, 0.53, and 0.55 in 1986 to 0.69, 0.77, 0.75, and 0.78 in 2022, respectively. The proportion of the poor ecological condition class in these cities rose from 10%, 3%, 5%, and 1% to 74%, 64%, 54%, and 41% during the 1986–2022 period. Our findings indicate that the SES of these cities significantly decreased while they experienced large physical growth. The findings and the methodical approach of the study provide a data-driven approach for monitoring SES in fast growing regions, which is required for studying the impact of climate change on society.

Noise pollution is one of the non-natural hazards in cities. Long-term exposure to this kind of pollution has severe destructive effects on human health, including mental illness, stress, anxiety, hormonal disorders, hypertension and therefore also cardiovascular disease. One of the primary sources of noise pollution in cities is transportation. The COVID-19 outbreak caused a significant change in the pattern of transportation in cities of Iran. In this article, we studied the spatial and temporal patterns of noise pollution levels in Tehran before and after the outbreak of this disease. An overall analysis from one year before until one year after the outbreak, which showed that noise pollution in residential areas of Tehran had increased by 7% over this period. In contrast, it had diminished by about 2% in the same period in the city centre and around Tehran’s Grand Bazaar. Apart from these changes, we observed no specific pattern in other city areas. However, a monthly data analysis based on the t-test, the results show that the early months of the virus outbreak were associated with a significant pollution reduction. However, this reduction in noise pollution was not sustained; instead a gradual increase in pollution occurred over the following months. In the months towards the end of the period analysed, noise pollution increased to a level even higher than before the outbreak. This increase can be attributed to the gradual reopening of businesses or people ignoring the prevailing conditions.