Case Study Of Tehran Research Articles

Spatial heterogeneity modeling of city prosperity using GWt-test: The case study of Tehran

City prosperity index (CPI) is an instrument for measuring sustainability in cities. This instrument has a conceptual model called wheel of urban prosperity, which includes productivity, infrastructure equity and social inclusion, quality of life and environmental sustainability. Current methods of calculating CPI use global methods for different CPI dimensions, and in this study, we have suggested a local model of geographically weighting - called GWt-test - for measuring the CPI that can model spatial heterogeneity. The GWt-test method is an expansion of the classical hypothesis t-test models. In this method, for each feature, the hypothesis test is calculated individually. Geographically weighted matrixes are used to select samples. At first, the randomness and normalization of the selected set are tested. If confirmed, a GWt-test is performed on samples. The GWt-test can be non-directional or directional based on the p-value hypothesis; accept and reject. The results on CPI in Tehran highlighted spatial heterogeneities int two parts including a) north and south, and b) northeast to southwest. The proposed model examines spatial heterogeneity at the local level based on context and relationships between locations and provides better comparisons of neighborhoods. From this point if view, it provides a better understanding of urban issues. As a result, better strategies and policies are produced in urban plans and programs that ensure a long-term vision of urban development.

Estimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3km MODIS AODs: case study of Tehran, Iran.

In this study we aimed to develop an optimized prediction model to estimate a fine-resolution grid of ground-level PM2.5 levels over Tehran. Using remote sensing data to obtain fine-resolution grids of particulate levels in highly polluted environments in areas such as Middle East with the abundance of brightly reflecting deserts is challenging. Different prediction models implementing 3km AOD products from the MODIS collection 6 and various effective parameters were used to obtain a reliable model to estimate ground-level PM2.5 concentrations. In this regards, the linear mixed effect model (LME), multi-variable linear regression model (MLR), Gaussian process model (GPM), artificial neural network (ANN) and support vector regression (SVR) were developed and their performance were compared. Since the LME and GPM outperformed other models, they were further optimized based on meteorological and topographical variables. These models were used to estimate PM2.5 values over the highly polluted megacity, Tehran, Iran. Moreover, the influence of site effect term on the performance of different shapes of LME models was evaluated by considering the random intercept for sites. Results showed LME models without the site effect term were able to explain ground-level variabilities of PM2.5 concentrations in ranges of 60-66% (RMSE = 9.6 to 10.3μg/m3) and 35-41% (RMSE = 12.7 to 13.3μg/m3) during the model-fitting and cross-validation, respectively. By considering the site effect term, the performance of LME models during calibrations and validations improved by 20% and 50% on average, respectively (18.5% and 17% decrease in the RSME) as compared to LME models without the site effect term. The optimized shape of LME models had a good agreement during both model-fitting (R2 of 0.76) and cross-validation (R2 of 0.6). Site-specific and seasonal performances of all types of models revealed that LME models had highest R2 values over all monitoring stations and all seasons during the cross-validation. LME models had the best performance in May and March compared to other months during the model-fitting and cross-validation. However, LME models had a significant weakness in predicting extreme values of PM2.5 during the cross-validation. Among all other types of models, GPM with the R2 value of 0.59 and the RMSE of 10.2μg/m3 had the best performance during the cross-validation. While the best shape of LME and GPM had similar and reliable performances in predicting ground-level PM2.5 values during the cross-validation, GPM was able to predict extreme values of ground-level PM2.5 concentrations, which was the weakness of LME models and was an important issue in urban polluted environments. In this respect, GPM could be a good alternative for LME models for high levels of PM2.5 concentrations. The spatial distribution of estimated PM2.5 values represented that central parts of Tehran were the most polluted area over the studied region which was consistent with the ground-level recording PM2.5 data over monitoring stations.

Estimation of highway capacity under environmental constraints vs. conventional traffic flow criteria: A case study of Tehran

Abstract In this paper, the concept of environmental capacity is developed to identify a convenient maximum traffic volume which will not reduce the life quality of residents. The presented method investigates the idea of traffic capacity under environmental constraints by calculating the maximum number of vehicles allowed on roads based on acceptable levels of air and noise pollutants. In this study, the permissible noise pollution level and permissible levels of CO and NOx pollution are considered for determining environmental capacity. Results show the significant difference between environmental capacity and functional traffic capacity, introduced by the highway capacity manual (HCM) as a conventional method for estimating functional capacity. Thus, maximum allowed pollution is considered a constraint on a vehicle flow rate, which shows the proper traffic flow for selected streets in Tehran, Iran's capital. The paper concludes that traffic capacity under noise and air pollution constraints is much less (approximately one-fourth and one-eighth for noise and air pollution respectively) than the current highway capacity estimated using HCM guidelines. Therefore, to save the cities like Tehran from noise and air pollution, traffic flows should be limited to the level of environmental capacity by implementing some travel demand management (TDM) policies like road pricing.

The effect of air pollution on drivers' safety performance.

Numerous studies have determined the adverse effects of air pollution on the health, welfare, and comfort of humans. More recently, the effects of air pollution on cognitive performance of humans are also focused. However, as a group in direct exposure to air pollution, drivers are rarely studied. The present study aims to evaluate the effect of air pollution on the performance of drivers. Their performance is evaluated by observing the number of recorded accidents. The effect of pollutant concentration (primarily PM2.5) on the number of accidents is investigated in a case study in Tehran. The temperature inversion phenomenon is used as an instrumental variable in a two-stage least squares method. The results indicate that temperature inversion had a significant positive correlation with the concentration of pollutants. Considering temperature inversion as an instrumental variable for these pollutants, it is observed that 1 μg/m3 increase in concentration of PM2.5, increased the number of accidents approximately by 4% in 1 day, for the range of parameters studied. No significant relationship was observed regarding the effect of NO2 and CO on accidents during the study period.

Feasibility Study of Using Adsorbent Plants to Remove Heavy Metals from Wastewater of Petrochemical Industry (Razi Case Study of Tehran)

Feasibility Study of Using Adsorbent Plants to Remove Heavy Metals from Wastewater of Petrochemical Industry (Razi Case Study of Tehran)

Multi-year load growth-based optimal planning of grid-connected microgrid considering long-term load demand forecasting: A case study of Tehran, Iran

Although much efforts have been devoted to the optimal design of the energy systems, there is a research gap about the multi-year load growth-based optimal planning of microgrids. This paper tries to fill such a research gap by developing a novel method for the optimal design of the grid-connected microgrids based on the long-term load demand forecasting. The multilayer perceptron artificial neural network is used for time-series load prediction. The impacts of the annual load growth are analyzed under various cases based on the consideration and determination methods of yearly load growth. The proposed method is applied to an actual microgrid in Tehran, Iran, using HOMER (Hybrid Optimization of Multiple Energy Resources) software. The load modeling’s capabilities of HOMER software, as a well-known software for the optimal design of energy systems, are used, which have received less attention. Since most existing research works in Iran focused on the off-grid operating mode, the study of an actual microgrid under grid-connected operating mode is one of the most contributions of this paper. The comparison of the obtained results and other available methods illustrate the impacts of the adequately precise estimation of annual load growth in the design of energy systems.

A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning

Natural disasters such as earthquakes always threaten human societies. Therefore, preparedness planning seems to be necessary to reduce casualties and accelerate relief efforts. Decisions in the preparedness phase include determining the optimal location for distribution centers and suppliers, allocating them to affected areas, and determining the amount of inventory of distribution centers. In this research, a robust simulation-optimization approach is presented for the planning in the preparedness phase. Although the demand for relief commodities has been considered as a given parameters. Due to our best knowledge there is no research in which the demand for relief commodities including drinking water, food, blood, medicine, tents and non-drinking water, during the disaster is related to the interactive relations of critical infrastructures of the city affected by the earthquake, and the power of the earthquake. Most of the unplanned occasions are occurred when co-incidences of probabilities are happen together. When a critical urban infrastructure fails due to earthquake it may cause some difficulties in the other infrastructures. Co-incidences may cause some horrible tragedy. First, the critical infrastructures of the city affected by the earthquake are identified and the interactions of these infrastructures are simulated. Considering the interactions of critical infrastructures in a city, various scenarios for earthquake are designed and tested through simulation approach in order to estimate the mean and variance of demand for relief commodities. The stochastic demand behavior which is the output of the simulation approach is assumed as a stochastic parameter of a mathematical model for multi-period location–allocation–Inventory problem. Robust optimization approach is used to deal with uncertainty. The mathematical model determines the location of distribution centers and suppliers, and how they are allocated to the affected areas. The proposed mathematical model is solved using a customized genetic algorithm for a case study in Tehran.

Comparison of the effect of temperature parameter on tracking and fixed photovoltaic systems: a case study in Tehran, Iran

The production of energy by renewable energy, including photovoltaic systems, is always dependent on the environmental and geographical parameters at which the system is installed. Temperature is one of the most important environmental parameters affecting the performance of photovoltaic systems. The effect of this parameter on the fixed and tracking photovoltaic system is not the same. The tracking photovoltaic system, because it is exposed to the sun from sunrise to sunset, has a higher temperature at the surface of the panels than the fixed photovoltaic systems. The result obtained from the experiments in this study shows that the temperature-induced efficiency drop in the fixed and tracking photovoltaic systems is more than 7.98% and 10.02%, respectively. According to calculations, the temperature-induced efficiency drop in tracking photovoltaic systems is about 25.55% higher than that of fixed photovoltaic systems. Observations show that this temperature difference is most extent at sunrise and sunset, and as we approach noon this difference is reduced and minimized

Developing an urban congestion pricing model by considering sustainability improvement and using a multi-objective optimization approach

This paper introduces a Travel Demand Management (TDM) model in order to decrease the transportation externalities by affecting on passengers’travel choices. Thus, a bi-objective bi-modal optimization model for road pricing is developed aiming to enhance environmental and social sustainability by considering to minimize the air pollution and maximize the social welfare as its objectives. This model determines optimal prices (bus fare and car toll) and optimal bus frequency simultaneously in an integrated model. The model is based on discrete choice theory and consideres the modes’ utility functions in its formulation. The proposed model is solved by two meta-heuristic methods (Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objectives Harmony Search (MOHS)) and the numerical results of a case study in Tehran are presented. The main managerial insights resulted from this case study is that its results support the idea of “free public transportation” or subsidizing the public transport as an effective way to decrease the transport related air pollution

Landmark Prediction of Survival for Breast Cancer Patients: A Case Study in Tehran, Iran

Breast cancer is the first non-cutaneous malignancy in women and the second cause of death due to cancer all over the world. There are situations where researchers are interested in dynamic prediction of survival of patients where traditional models might fail to achieve this goal. We aimed to use a dynamic prediction model in analyzing survival of breast cancer patients. We used a data set originates from a retrospective cohort (registry-based) study conducted in 2014 in Tehran, Iran, information of 550 patients were available analyzed. A method of landmarking was utilized for dynamic prediction of survival of the patients. The criteria of time-dependent area under the curve and prediction error curve were used to evaluate the performance of the model. An index of risk score (prognostic index) was calculated according to the available covariates based on Cox proportional hazards. Therefore, hazard of dying for a high-risk patient with breast cancer within the next five years was 2.69 to 3.04 times of that for a low-risk patient. The value of the dynamic C-index was 0.89 using prognostic index as covariate. Generally, the landmark model showed promising performance in predicting survival or probability of dying for breast cancer patients in this study in a predefined window. Therefore, this model can be used in other studies as a useful model for investigating the survival of breast cancer patients.

Experimental analysis and modeling of weather condition effects on photovoltaic systems’ performance: Tehran case study

ABSTRACT Increased reliability in grid-independent photovoltaic systems is an important factor in the prevalence and greater use of solar systems. The present study was conducted in Iran. It is one of the countries with high potential in the field of solar energy with average radiation of 5–5.5 kWh/m2 intensity. On the other hand, due to the wide desert areas, it is important to investigate the effect of dust deposition on photovoltaic panels. An experimental study on the performance of photovoltaic panels over a 90-day period from May 25 to August 22, 2018, in Tehran, and under real conditions, to investigate the effect of dust deposition and climate change on their output power was performed. Because of its large population, political and economic conditions, Tehran has the highest importance amongst other cities in Iran. The results showed that the dirty panels produced 10% less power than the clean panels. Utilization of the data achieved from the experiment, the output power of the clean and dirty panels considering the air temperature and solar radiation is modeled using the MLR method. In order to establish the relationship between the reductions of the output power of the panel, due to the deposition of walnut dust, the soiling ratio (SR) is used. This ratio is gained by dividing the output power of the dirty panel by the output power of the clean panel to obtain a coefficient to determine the reduction of the output power of the desired photovoltaic panel in terms of air temperature and solar radiation. The coefficient earned with the correlation coefficient of 0.9 (R2) and the error of the mean root squares 7.94 (RMSE) can well predict the loss of power output of the panel due to dust sediment on the surface of the panel. The uncertainty range of experimental results is between of 7.83% to 8.08%.

Quantitative Assessment of Safety and Health Risks in HAZMAT Road Transport Using a Hybrid Approach: A Case Study in Tehran

Given the catastrophic consequences of hazardous materials’ leakage from trucks, it has become necessary to adopt a coherent approach for quantitative risk assessment in this process. Therefore, th...

Spatial Analysis of Security and Insecurity in Urban Parks: A Case Study of Tehran, Iran

Urban security is of great importance, especially in public spaces like parks, which are considered a place for relaxation, comfort, and recreation. Various crimes occur in such places due to problems such as lack of supervision, large numbers of people, and reduced lighting, which increase the number of defenseless and private places. To analyze the security status of public parks, this study employed geographic information systems (GIS)‐based spatial analysis of crimes in the Tehran metropolis, the capital of Iran. The study includes twenty-four crimes occurring in forty-one parks in Tehran in 2015. Required information was collected by contacting the police department and the weight of each index was determined using an analytical network process. To rank the parks, the PROMETHEE model was applied and graphical statistical methods were used in a GIS environment to detect and display the spatial pattern of crimes. The results of the analysis show that crime is low in central, northern, and southern parts of the city, whereas it is high in the western and eastern parts of Tehran. In addition, there is a significant positive relationship between two variables such as area and park ranks in terms of security. It is essential to detect the place conditions resulting in crime opportunities or simplify it by relying on geographic science principles. Results of this research are of great importance for several organizations in Tehran, such as the Municipality of Tehran, police department, justice system, and other relevant organizations to increase safety in parks and for the development of GIScience in the context of crime analysis.

Investigating the relationship between housing policy and accessibility, based on developing a multi-perspectives accessibility index: a case study in Tehran, Iran

Quality of life has become a challenge for today’s world, which is associated with the transportation and land-use requirements, especially housing. Accessibility-based planning as a land-use transportation integration planning has turned growing attention in urban decision making in recent decades. Since there is a strong relationship between accessibility and housing policies such as housing price, this study tries to cover these relationships. In this context, we proposed a novel four-stage methodology. Firstly, a comprehensive, multi-perspectives accessibility index is being developed based on GIS for spatial analyses and GMCDM for aggregating experts’ views and then the index is compared with housing price. In order to verify the comparison and functionality of the parameters on accessibility and housing policy, sensitivity analysis is conducted. The results indicate, keeping structural features constant, the housing price shows a moderate negative correlation of -0.552 with accessibility in the study area. Although the GAHP weights and SA analysis results showed a positive correlation, the housing policy did not follow the same. The results of this research are meaningful in explaining the effect of especially neighbourhood characteristics for city policymaking and housing market analyses.

Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: a case study of Tehran, Iran

ABSTRACTSpatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated to simulate the ULST based on independent factors, i.e., land use/land cover (LULC), solar radiation, altitude, aspect, distance to major roads, and Normalized Difference Vegetation Index (NDVI). Additionally, the spatial influence and the main interactions among the influential factors of the ULST are explored. Landsat-8 is the main source for data extraction and Tehran metropolitan area in Iran is selected as the study area. Results show that NDVI, LULC, and altitude explained 86% of the ULST °C variation. Unexpectedly, lower LST is observed near the major roads, which was due to the presence of vegetation along the streets and highways in Tehran. The results also revealed that variation in the ULST was influenced by the interaction between altitude – NDVI, altitude – road, and LULC – altitude. This indicates that the individual examination of the underlying factors of the ULST variation might be unilluminating. Performance evaluation of the four models reveals a close performance in which their R2 and Root Mean Square Error (RMSE) fall between 60.6–62.1% and 2.56–2.60 °C, respectively. However, the difference between the models is not statistically significant. This study evaluated the predictive performance of several models for ULST simulation and enhanced our understanding of the spatial influence and interactions among the underlying driving forces of the ULST variations.

Patterns of Urban Park Use and Their Relationship to Factors of Quality: A Case Study of Tehran, Iran

Urban parks play an important role in increasing the quality of urban life. Thus, a variety of approaches to planning and decision-making regarding sustainable open spaces and urban parks is needed, especially when models from one location are borrowed to another with a very different culture. In many developing countries cities are expanding without proper planning of public green spaces which places pressure on the quality of life. In common with many Middle Eastern cities, Tehran, the capital of Iran is a large sprawling metropolis which has experienced fast growth since 1970 and with little in the way of comprehensive planning of green spaces as an organised system. To date there has been no comprehensive investigation of urban parks regarding their level of use, range of activities, quality of maintenance, evidence of anti-social activities or the demographic character of their catchment—important information for effective park planning. In this study a representative sample of 16 parks in Tehran was studied using a combination of site assessments and user observations. We found that a combination of factors concerning the quality of the parks such as presence of facilities, existence of water and other special attractions, as well as how well-lit they are, how well maintained and safe they are, has a lot to do with their level of use. Some pleasant parks with good facilities are less well used because of, e.g., evidence of anti-social behaviour, poor maintenance and accessibility. A key aspect we found is the difference between male and female users, with some parks being much less-well-used by women. Much more attention is needed for planning and management of parks in Tehran. The approach applied here has potential to help other cities in similar areas to learn more about their green space systems for planning purposes.

Providing Optimal Pricing Model for Urban Bus Services (Tehran Case Study)

Providing Optimal Pricing Model for Urban Bus Services (Tehran Case Study)

Seeking Substantiality: Evaluation of Public Attitudes toward Resilient Wastewater Reuse Management

Because of Iran’s limited water resources, maximum usage of available water resources is a high priority. In this case study of Tehran, Iran, academic an…

PM2.5 concentration forecasting using ANFIS, EEMD-GRNN, MLP, and MLR models: a case study of Tehran, Iran

Tehran, the capital city of Iran, is among the world’s most polluted cities. Tehran is exposed to different types of pollutants, one of which is the suspended particles of PM2.5. One of the steps that should be taken to reduce hazardous effects of this pollution on the health of society is timely prediction and announcement of its increased levels. Different methods can be used for predicting PM2.5 concentration. This study used a variety of models for predicting PM2.5 concentrations, including linear, nonlinear, and hybrid models. More specifically, the models which were used consisted of multiple linear regression, multi-layer perceptron (nonlinear model), and a combination of ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) (hybrid of nonlinear models). The independent variables in the current study were air quality parameters, which were measured in reference to PM2.5, PM10, SO2, NO2, CO, and O3 and meteorological data which included average atmospheric pressure (AP), average maximum temperature (Max T), average minimum temperature (Min T), daily relative humidity level of the air (RH), daily total precipitation (TP), and daily wind speed (WS) in 2016 in Tehran. The results indicated that the ANFIS model exhibited the most accurate prediction in the training phase (R2 = 0.99, RMSE (root mean square error) = 0.4794 and MAE (mean absolute error) = 0.1305) and in the testing phase (R2 = 0.82, RMSE = 3.2979 and MAE = 2.1668). As it can be concluded, in comparison with a linear model, hybrid models are of higher precision in predicting PM2.5 concentration.

Contribution of Iraqi and Syrian dust storms on particulate matter concentration during a dust storm episode in receptor cities: Case study of Tehran

Frequent dust storms originated from Middle Eastern deserts impact major cities in Iran, including Tehran. Previous studies identified Iraqi and Syrian deserts as the main cross-border contributors to Tehran Particulate Matter (PM) levels. In this study, the contribution of Iraqi and Syrian dust storms to Tehran's PM10 and PM2.5 concentration were assessed during a dust storm episode. Initially, a dust storm event was identified through the statistical analysis of the recorded air pollution data at Tehran's monitoring stations and the visual inspection of the satellite images. A combination of CMAQ/WRF/SMOKE modeling systems was used to simulate PM concentrations from Tehran local sources and dust storms formed in Iraq and Syria. An emission inventory of Tehran anthropogenic and biogenic pollutant sources was also developed and distributed throughout the city. In order to obtain the contribution of the different sources to Tehran PM10 and PM2.5 concentration, several sets of simulations were performed using different emission groups including local sources, Iraqi dust storms, Syrian dust storms and both Iraqi-Syrian dust storms. The results showed local emissions comprised most of the PM concentrations, however cross-border dust sources heavily influenced PM levels at all monitoring stations during the dust event. Nearly 28% of the average PM concentrations were dust particles formed in Iraq and Syria. The simulation of the dust event showed Iraqi's desert were responsible for nearly twice amount of observed PM concentrations in Tehran compared to Syria's desert during the episode. The results of this study could be used for planning effective measures to reduce dust related pollution problems in Tehran.