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Volume 27, Issue 3 (Fall 2023)
Abstract
Tehran is one of the most polluted cities in the world. The air pollution is caused by different factors such as centralization, increased traffic and incorrect location of the spatial pattern, consumption of fossil fuels, lack of rules and regulations for limiting industrial areas and nonobservance to the environmental guidelines. Sometimes, these factors are intensified because of the climatic factors. The aim of this study is to identify the relationships between the residential urban factors and Tehran's air pollution and to determine the extent of their importance. The intended techniques found through radar data showed that the physical pattern and the consequences of the urban planning system are considered some of the effective structures in increasing the air pollution in Tehran. Hence, the correlation coefficient between the height environment of urban buildings and constructions (the third dimension of the city) and Tehran's air pollution was calculated to be 0.86. In other words, in the case of proper planning or with the possibility of modifying the structure of the height environment in city up to 86%, it is possible to adjust and control the air pollution. The results were in line with the spatial pattern of PM2.5 particles and carbon monoxide regarding urban land use, industries, commercial centers, transportation, organizations and health centers and had the highest coefficient of determination with the spatial pattern of Tehran' air pollution. Considering the traffic, the index had the highest correlation with the spatial pattern of PM2.5 particles. Meanwhile, in the dangerous and unhealthy parts of the spatial pattern, a large number of nodal points with little distances formed traffic channels. Thus, by identifying these channels and managing urban traffic, the air pollution can be controlled to a larger extent. It should be mentioned that because of the impossibility of decreasing or eliminating driving forces in the creation or intensification of the air pollution, the residential environment of Tehran can be directed toward an appropriate environmental pattern by changing or maintaining the structures and functions through a change in the patterns of macro-urban management and the urban planning models consistent with the human functions and spatial pattern.
Volume 28, Issue 2 (6-2024)
Abstract
The deficiency of surface water in arid and semi-arid territories has exacerbated the dependence on groundwater resources, resulting in considerable reductions in groundwater levels. This phenomenon has been particularly pronounced in numerous plains throughout Iran, where the diminution has exacerbated issues related to land subsidence. A comprehensive understanding of groundwater level variations is imperative for enhancing water management strategies and alleviating the associated hazards. A range of statistical, mathematical, and machine-learning methodologies have been utilized to model the dynamics of groundwater aquifers. Recently, deep neural network algorithms have gained prominence in the investigation of surface and groundwater resources, particularly in light of the spatiotemporal characteristics inherent to groundwater.
In the present investigation, a hybrid spatiotemporal data mining framework, denoted as Wavelet-PCA, was employed to analyze data acquired from 44 piezometric wells situated in the Qahavand plain over a span of three decades (1988-2018) for the purpose of elucidating temporal and spatial patterns associated with fluctuations in groundwater levels. Subsequently, a sophisticated deep recurrent neural network architecture incorporating Long Short-Term Memory (LSTM) was implemented to model the time series data resulting from the data mining procedure. Various degrees of wavelet transformation were applied to effectively capture the intricate trends in groundwater levels. The LSTM model exhibited a coefficient of determination (R²) of 0.85 for the training dataset while achieving an R² of 0.62 for the testing dataset.
The research additionally examined regional patterns of land subsidence utilizing radar interferometry data obtained from the Sentinel-1 satellite during the period from 2014 to 2019. The results revealed an average maximum subsidence measurement of 9 centimeters, with the most pronounced subsidence noted in regions that are undergoing the most substantial declines in groundwater levels. This observed relationship between groundwater depletion and land subsidence underscores the necessity for judicious land use planning and the implementation of effective water resource management strategies in analogous regions.
