mohammad h. vahidnia

Among the various air pollutants in Tehran, particulate matter with a diameter of less than 2.5 micron is considered one of the most important air pollutants that is created through fuel combustion and can cause various diseases and increase the mortality of people. Due to the importance of the subject, so far, various researches have been conducted on the effects of PM2.5 pollutants that most of them have only investigated statistical and epidemiological effects. In this study, the aim is to investigate and show the effect of PM2.5 pollutant concentration with the help of ABM, where the effect of pollution on a car as a moving agent and showing the effects of pollution in the form of color change on it was considered. For this purpose, PM2.5 pollutant distribution map for tow days with different pollution intensity was prepared in ArcGIS software environment. For doing this job, the information related to the concentration of pollution on the December 24, 2022 and January 1, 2023 was used and the IDW interpolation method was used to interpolate and prepare the dispersion map. After that, from the obtained data, the concentration of pollution and its integration with the location of the streets and squares of the studied area, the agent's movement environment was created in Netlogo software and the relevant environment was called. By determining the origin and destination, the route and the speed of the car, the model was implemented for two days, December 24, 2022 and January 1, 2023. The results of the model implementation showed that due to the higher intensity of pollution on the Januay 1, the effect of pollution on the agent was much higher than on the December 24. With the implementation of the model on the January 1, the amount of pollution in a short period of time reached above 100, which is an unhealthy range for sensitive groups. According to the AQI index, this level of concentration causes serious breathing problems for the elderly and children.

Rice is one of the most strategic plants in Iran. On the other hand, agriculture makes a wide variety of environmental amenities and problems. Thus researches that help the production and sustainable development in this area are significant. The main purpose of this research is the design and development of a scalable remote sensing-based paddy yield model.

In this study, we used several different images available in Google Earth Engine (GEE) to estimate paddy yield at various temporal (growing seasons) and spatial scales (from 30 m resolution to regional scales). Then, a remote sensing-based light use efficiency (LUE) model integrated with inanimate environmental stressors, was implemented. This operational model was assessed against actual field-level yield data in 2016, 2017, and 2019 growing seasons across more than 691 paddy fields in Gilan province.The efficiency of the current model was evaluated through different statistical measures. The results showed a positive correlation and a signed agreement between the estimated and measured values so that in the studied growing seasons, the average correlation coefficient (R) and agreement index (d) was equal to 0.55. The average RMSE equal to 500 kg/ha, the average MAE equal to 440 kg/ha, and the average NRMSE equal to 0.12, all indicate that the accuracy of the model in estimating crop yield in these locations and years is satisfactory. Also, the submitted model showed the appropriate variability of yield values at the farm scale.

In general, this new approach has confirmed that the use of remote sensing in the GEE is appropriate for estimating crop yield at various temporal and spatial scales, as the current model can be utilized in a wide range of applications such as agricultural management and insurance.

Many cities in our country, including Tehran, are facing the phenomenon of obsolescence and inefficiency of urban neighborhoods. Such areas can have devastating environmental, economic, or social effects. In the current era, proper management of such urban spaces requires up-to-date and valid data. Due to the large size of outdated and inefficient neighborhoods, collecting and updating this amount of data by organizations is time-consuming and costly. Therefore, using the capacity of citizen participation is a good opportunity for city managers, which is the main purpose of this research.

Citizen-centered GIS strategy has been introduced in recent years, and in many applications, enterprise location data acquisition has been replaced by volunteered geographic information (VGI). This research presents an object-oriented design for collecting spatial information about urban deterioration. Accordingly, a web-based implementation based on open-source components is on the agenda.

With the help of the proposed solution, first, spatial information can be collected in a categorized and thematic manner. According to the definition of worn-out texture, ten different classes were considered in the object-oriented approach, which include undesirable paths, unsafe and old buildings, ruined and dirty lands, narrow access networks, and places for offenders and addicts. A web-based system was also implemented based on open-source frameworks such as Django and components such as GeoDjango, PostGIS, and OpenLayers.

Citizen science is a new approach to collecting spatial information on worn-out tissue. The result of the implementation of the method used is the rapid and low-cost provider of information and enhances the role of experts in municipalities and urban design organizations, mainly professional supervisors instead of data providers. Cost-benefit analysis showed that in a one-year period, this approach could lead to an approximate savings of 15%.

Among all the natural hazards in the country, according to recorded statistics and observations, floods have been the most destructive, and it has the highest frequency of occurrence. Floods are one of the known natural disasters that cause a lot of financial and human losses. This phenomenon can be controlled by identifying flood-prone areas and proper management. In the current era, due to human encroachment on rivers and land-use change or destruction of vegetation, flood damage has increased. These factors cause, in addition to increasing human and financial losses, damages such as soil erosion upstream and sedimentation downstream. In this research, using agent-based modeling in the NetLogo simulation environment, flood-prone areas in Shush city have been identified. The most important input was topography (digital elevation model) and then dynamic and temporal simulation was done by performing tessellation on the area and considering the rainfall in each cell as an agent. Using spatial analysis in ArcGIS software and comparing the simulation results with the location of the city and land use maps of the region, the possible causes of floods in this region have been investigated. Agent-based models with the incorporation of geospatial information systems (GIS) can be used as a new solution to solve spatial problems such as natural crises, destructive environmental impacts, and so on. Finally, preventive measures to prevent floods in this area are proposed.

The study of statistics and information on annual damages caused by floods in Iran indicates the extent of flood damage to natural resources. The use of geospatial information and remote sensing technologies can be very helpful in reducing these damages and better planning. In the present study, these technologies have been used for flood zoning in the Karun River in Ahvaz city. The zoning of the study area was done by combining hydraulic model and ArcGIS software through HEC-Geo RAS extension along 33 km of this river. In order to extract the border of the river banks in the urban area of Ahvaz, the supervised classification and Landsat satellite images were used. After introducing the geometric information to the model, the value of the Manning roughness coefficient is determined for different periods and then the return periods of 2, 50, and 100 years are introduced. The maximum speeds in the return periods were 2.6, 6.15, and 7.2 m/s, respectively, which are within the allowable flood speeds. This means that if the river banks are organized and fenced, scouring, erosion, and sedimentation will not occur and water will flow in the main canal without destruction. Preparation of flood warning systems based on precipitation with a high return period that leads to extreme floods, walling, beach construction, and river management in unstable and erodible sections of the river and improving the slope of the bed and canal dredging in the required sections, including the proposed executive solutions for organizing Karun River.

Routing is one of the most sensitive parts of the design of power transmission lines. Any mistake in choosing a route can lead to high costs or costs for regional water companies at runtime or line operation. In this research, to solve the problem of determining the optimal route of water pipelines using spatial analysis, a multi-criteria decision making and raster-based shortest path algorithm were used from Ardak to Mashhad. For this purpose, firstly, the effective parameters in determining the route, including slope, geology, land use, residential areas, Waterways, roads, faults, and protected areas were determined and after weighting processes in AHP, TOPSIS and Fuzzy Logic methods, overlaid in the form of information layers in the GIS environment in order to generate cost mapping. Then, using the lowest cost algorithm in raster, the optimal path was determined. Finally, a comparison was made between the traditional way provided by the consultant and the proposed route with the lowest cost algorithm. The paths obtained from the lowest cost algorithm based on AND, GAMA, AHP, and TOPSIS were equal to 44.798, 46.755, 45.731, and 45.531 km, respectively. On the advised paths of the consultant, Route 1 has a length of 72.306, a suggested track 2 has a length of 63.108 and the third one was 48.690. Finally, the TOPSIS route model was selected as the best route due to the consideration of the forbidden areas.

In recent years, the use of smart mobile devices due to easy access, portability and also simpler user interface, compared to desktop computers has been met with unparalleled acceptance by Iranian users, managers and sellers of goods. That's why application development for smart devices in the internet businesses has become essential. The aim of this study is to find a solution based on location awareness to provide smart targeted advertising and services to users.

This study provides a model that first handles and categorizes information from some service centers and quickly exchanges data between smart media (such as mobile, tablet) and data centers based upon online map services (Google Maps). Second, based on the components of location-based services (LBS), in order to provide effective advertising to users, the components of selecting the service center and advertising and navigation of the user to it were put on the agenda based on loose coupling strategy.

In order to have the features of recommendation, three criteria of desirability, namely the introduction of the final option based on the shortest route in the transportation network, or based on user scores or based on scores allocated by the relevant service unit class were considered.  Finally, the "Location Finder" application was implemented under the Android operating system using the new technologies of the mobile and web-based spatial information system through the proposed model.

The results of the questionnaire were statistically analyzed using the frequency of the respondents and significantly showed the users' satisfaction with the program, with 55 percent rating it as a great choice for quality, and its compliance with the set goals so that it could fill part of the gap caused by the advertising software.

One of the most important components of disease prevention has always been having access to information on distribution of patients, their gender and age. With this information, we find the areas in which further prevention or care programs need to be implemented. In addition, this information helps in determining the effects of factors contributing to the spread of a disease in different areas. Thus, using new technologies such as GIS in such a field can be quite advantageous. Methods of spatial-statistical analysis can help us in mapping disease diffusion, its predicted future trend, and factors affecting that disease. Therefore, public health organizations have recently used such technologies to develop more appropriate health, prevention, and treatment plans. As a major health problem, gastric cancer is reported to be the most common type of cancer and the second leading cause of cancer-related deaths in the world. Following cardiovascular disease, cancer is the second leading cause of death in Iran. GIS capabilities have made it possible to use a variety of spatial-statistical models for gastric cancer. The present study seeks to analyze the spatial distribution of gastric cancer in Hamedan province, identify the incidence and prevalence of this disease based on gender and age of patients, and map the geographical factors affecting this disease. In this regard, methods of interpolation, classification, and clustering of highly affected points are used. Spatial correlation is also calculated using regression methods.

In order to prepare spatial distribution maps of gastric cancer in Hamadan province, related data on all gastric cancer patients between 2011 and 2015 were collected from the Population-Based Cancer Registry of Hamadan Medical Sciences University. Collected data included age, gender, and address of patients. Data were first classified based on the address registered for each patient in each year. Then, geocoding process was used to convert addresses into positions using Google Maps and create related layers in GIS software. Descriptions were then assigned to the layer of cities and 1157 points were produced for these 5 years and added to the maps. According to the obtained points, spatial distribution maps were prepared based on age and gender of patients. Age distribution of gastric cancer patients was also calculated using interpolation analysis and the IDW method. In the next step, maps of provinces in critical situation were prepared according to the Hotspot method using the Getis index. In cooperation with Water and Sewage Authority, data were also collected on water pollution (including nitrate, and lead in water and water hardness) to determine the relationship between the severity of the disease and environmental variables (water pollution). Ecological analysis was then performed based on regression analysis using ordinary least squares (OLS) method.

Results of the study and age distribution maps indicated lack of any significant clustering in the studied cities. Moreover, the age groups were sparsely distributed in each year. However, statistical analysis of patients’ age and gender showed a higher incidence of gastric cancer in men and in over 70-year age group during the reference period. Cluster analysis of areas with higher incidence of gastric cancer based on Hotspot method identified Qahavand as one of the cities having a critical situation regarding gastric cancer during the reference period with 99% confidence interval. Hamadan City was ranked second with 95% confidence interval. Laljin was ranked third with 90% confidence during the reference period. Regression analysis performed to determine the relationship between disease severity and environmental variables (water pollution) indicated the presence of a positive relationship between the level of lead and nitrate in drinking water and cancer incidences. However, an ideal fit was not reached for the regression model due to unavailability of recently collected data, small sample size, and inadequate data distribution.

Since a different life style is considered to be the most crucial basis of combating a disease, education, empowerment, policymaking and enactment of laws and regulations can create an appropriate environment to promote healthy lifestyle and behaviors. In fact, a useful intervention in society can eliminate or reduce the impact of many risk factors. Cooperation between the fields of geography and medical sciences can result in designing and implementing an acceptable system in the society. GIS is one of the technologies used in this regard to provide health warnings for people at risk based on proper analysis. The present study showed the efficiency of techniques such as classifications, interpolation, hotspot, and regression analysis in assessing disease severity, and factors affecting its incidence.

Studying vegetation status is one of the most important environmental issues. Using contemporary and field methods in this regard will cost a lot of time and money. Remote sensing technology and satellite image processing is a solution that can facilitate such difficult processes. In this study, remote sensing technology will be integrated with web geographic services to provide users with adequate analysis of the growth of the plants and their changes based on vegetation indices in the shortest possible time. Important technologies used for this purpose are ENVI IDL, Google Map API, JavaScript, and ASP.NET. The service was successfully implemented and was employed for Landsat satellite images. In order to evaluate the performance of the service at different points and with a different number of images, the timeliness of the NDVI vegetation index was prepared. The processing time evaluation also showed that by increasing the number of satellite images the processing time will increase linearly.