In recent years, models of land-use change and urban growth have become important tools for city planners, economists, ecologists, and resource managers. In most models, future land-use changes are forecasted based on past development pattern and expansion to periphery. While today, metropolitan areas employ smart-growth strategies. The main objectives in this study are according to the smart-growth infill. In this approach, transmission of incompatible land uses to the outside of the city boundary, redevelopment, improvement, and renovation of urban old district and worn-out texture and reuse of abandoned land to new urban development are considered. In fact, the objective is the using of the infill development pattern to modeling approach for simulating urban future development using potentials inside the city.

Lake Urmia (LU) is considered as the largest salt water lake in Iran and has severe restrictions on water resources and becoming a salt lake increasingly. The LU drought will Couse ecological, health, social and economic problems. Land-use change and the increasing of salt areas evaluated in this work using satellite imagery. We evaluated the present situation and changes of the lake area in the past and further changes until 2025. The results indicated that from 1987 to 2000, the process of change has slowed down and less than 2% of the lake’s water area was reduced, and from 2000 to 2010, these shrinking processes were faster and more than 28% of the lake water area disappeared. The intensity of the shrinking from 2010 to 2014 is very severe. Using the Land Transformation Model, the continuation of the changes was modeled until 2025. The results of the modeling indicate the conversion of the water lake to salt lake in this period, and in the north part, the shallow waters occupy 0.7% of the total lake area. The result shows that climate change was not the significant factors for drying up of the lake but human factors such as building dams to store water for irrigation, increasing groundwater use by established deeper wells for agricultural irrigation were the important factors for drying. With changing of management of the waters leading to the lake and the transfer of new water resources to the lake between 2014 and 2016, the area of the lake increased to a double. It was evident that by proper planning and managing of water resources, the lake’s restoration can be achieved.

Due to the health effects caused by airborne pollutants in urban areas, forecasting of air quality parameters is one of the most important topics of air quality research. During recent years, statistical models based on artificial neural networks (ANNs) have been increasingly applied and evaluated for forecasting of air quality. The development of ANN and multiple linear regressions (MLRs) has been applied to short-term prediction of the NO2 and NO x concentrations as a function of meteorological conditions. The optimum structure of ANN was determined by a trial and error method. We used hourly NO x and NO2 concentrations and metrological parameters, automatic monitoring network during October and November 2012 for two monitoring sites (Abrasan and Farmandari sites) in Tabriz, Iran. Designing of the network architecture is based on the approximation theory of Kolmogorov, and the structure of ANN with 30 neurons had the best performance. ANN trained by scaled-conjugate-gradient (trainscg) training algorithm has implemented to model. It also demonstrates that MLP neural networks offer several advantages over linear MLR models. The results show that the correlation coefficient (R 2) values are 0.92 and 0/94 for NO2 and NO x concentrations, respectively. But in MLR model, R 2 values were 0.41 and 0.44 for NO2 and NO x concentrations, respectively. This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO2 and NO x concentrations in an urban environment compared to linear models.

Population growth is a significant challenge in developing countries because it leads to the destruction of green infrastructure and urban agriculture lands around cities, resulting in dissatisfaction with the environment. This study aims to revitalize abandoned agricultural lands within city limits due to urban planning, which can be considered a kind of green infrastructure. In Tabriz, Iran, urban agriculture and urban texture coexist, and the effect of native plant-crop combinations on micrometeorological conditions and thermal comfort on agricultural land was examined. The ENVI-MET model simulated the research area, and the output parameters were validated by comparing their values to field data. The model identified seven proposed scenarios that demonstrated higher performance in improving outdoor thermal comfort. The results showed that edible functions of deciduous plants increased with canopy height and diameter, which improved thermal comfort in high-temperature conditions. Adopting this form of planting would rejuvenate abandoned agricultural fields. Consequently, outdoor thermal comfort may be improved [1.42 predicted mean value (PMV) reduction in the land without plants and a 1.37 PMV reduction in the agriculture land simulation], leading to the rehabilitation of urban agricultural lands that have lost their function in developing cities. These research findings can be utilized to help address the impacts of population growth on green infrastructure and urban agriculture lands.

Social capital is the effective contribution of social groups through providing a context for cooperation, sense of identity, and perception of social norms. Urban parks are important components of cities, helps building the social capital within urban societies. This study examines the social capital of important urban parks of Tehran, Iran, using three main criteria: informal social control, social cohesion, and social leverage. A stratified random sample of 330 users were selected and asked to rank the social capital criteria using a questionnaire involving five-point Likert scale questions. The results show mutual relationship between informal social control and social leverage (r = 0.62, α = 0.00), and also inter-relationship between design indicators and perceived social capital. People from lower age group and higher educational level show highest perception of social capital. Perceptual difference were observed between genders. Women experience higher esthetic perception (α = 0.00), security (α = 0.01), and accessibility (α = 0.03). The study, while proving the relationship between social indicators and design features, and the impact of personal characteristics on the perception of social capital, indicates social inequality in citizens’ equal benefit of social capital. Measures must be taken to increase social capital in society and solve the significant lower perceptions of some social capital indicators among specific groups.