Human health risk of nitrate in groundwater of Tehran-Karaj plain, Iran.

Groundwater pollution by nitrate has is a major concern in the Tehran-Karaj aquifer, Iran, where the wells provide up to 80% of the water supply for a population of more than 18 million-yet detailed human health risks associated with nitrate are unknown due to the lack of accessible data to adequately cover the aquifer in both place and time. Here, using a rich dataset measured annually in more than 75 wells, we mapped the non-carcinogenic risk of nitrate in the aquifer between 2007 and 2018, a window with the most extensive anthropogenic activities in this region. Nitrate concentration varied from ~ 6 to ~ 150 mg/L, around three times greater than the standard level for drinking use, i.e. 50 mg/L. Samples with a non-carcinogenic risk of nitrate, which mainly located in the eastern parts of the study region, threatened children's health, the most vulnerable age group, in almost all of the years during the study period. Our findings revealed that the number of samples with a positive risk of nitrate for adults decreased in the aquifer from 2007 (17 wells) to 2018 (6 wells). Although we hypothesized that unsustainable agricultural practices, the growing population, and increased industrial activities could have increased the nitrate level in the Tehran-Karaj aquifer, improved sanitation infrastructures helped to prevent the intensification of nitrate pollution in the aquifer during the study period. Our compilation of annually mapped non-carcinogenic risks of nitrate is beneficial for local authorities to understand the high-risk zones in the aquifer and for the formulation of policy actions to protect the human health of people who use groundwater for drinking and other purposes in this densely populated region.

Integrated model for land-use transformation analysis based on multi-layer perception neural network and agent-based model.

The efficacy of land-use changes on aquatic ecosystems has been extensively studied in recent decades. Water resource management needs to understand the relationship between land-use change patterns and water quality, especially in urban areas. Hence, recognizing spatial-temporal changes in land use is required for sustainable development and proper water resource management. This research has developed an integrated model based on agent-based model (ABM) and multi-layer perceptron (MLP) neural network technique to predict the future land-use transformation tested on the North Ahvaz watershed, Iran. Random forest-supervised classification technique was applied to derive the land-use maps using Landsat 1989, 2004, and 2019 images in the Google Earth Engine (GEE) platform. The overall accuracy of classified land-use images was 0.82, 0.81, and 0.84, respectively, with the kappa coefficient of 0.74, 0.72, and 0.78. Land-use change analysis and generating transition potential maps were carried out in land change modeler (LCM) through MLP based on seven driving factors. Then, the land-use map for 2019 (for validation) and 2040 was simulated using the transition potential map and an agent-based approach. The ABM scenario was farmers' and urban landowners' decisions to convert undeveloped and unprotected lands to residential lands. The results showed that residential areas and pasture lands would grow by 67.96 km2 and 64.63 km2, and agricultural and barren lands would degrade about 84.19 km2 and 47.98 km2 during 2019-2040, respectively. Predicting land-use change through the integrated MLP-ABM model may be used to evaluate the effects of land-use change coming out of human decision-making.

Optimization of influential parameters of hydrocarbon recovery from waste oily sludge by solvent extraction using solvent blend.

The influence of experimental factors in solvent extraction for hydrocarbon recovery from oil sludge was investigated using an orthogonal experimental design. The results indicated that temperature, solvent type, solvent blend, solvent to sludge ratio, and treatment duration had significant effects on hydrocarbon recovery. In this investigation, a response surface methodology used to optimize the conditions required to recover hydrocarbons from oily sludge. In addition, the effectiveness of solvent extraction using solvent blends of methyl ethyl ketone (MEK) and xylene for recovery and upgrading of oily sludge has been determined. Oily sludge used here consisted of 63% of its weight petroleum hydrocarbons. Based on the experimental data, a full quadratic model was generated. Optimization of influential parameters was conducted through the fitted model. As determined by the fitted model, the factors such as temperature, mixing time, MEK, and xylene to sludge ratios for optimum oil sludge recovery with solvent extraction method must be fixed at 49.28 °C, 22 min, 6.04 to 1, and 6.4 to 1, respectively. Under the optimum conditions, 64.04% extraction yield achieved using solvent blends of MEK and xylene according to the model. Using the combination of MEK and xylene for recovery of oil sludge, a maximum extraction yield of 61.44% as petroleum hydrocarbons obtained during experiments. Based on the results, combination of MEK and xylene as solvent blend demonstrated significant effect on hydrocarbon recovery in 6 to 1 and higher ratios.

Study of speciation and spatial variation of pollutants in Anzali Wetland (Iran) using linear regression, Kriging and multivariate analysis.

Multivariate statistical techniques and geostatistical methods are among the important tools used in surface water quality management. They are widely used in interpreting data, identifying the pollution sources, understanding the spatial variation of parameters, and determining the places of monitoring stations. Therefore, in this study, spatial variation of water quality and pollutants in the Anzali Wetland water (Iran) was evaluated using multivariate statistical and Kriging methods. The values of different water quality parameters measured in six stations in the wetland water were subjected to cluster analysis (CA) and principal component analysis (PCA). Cluster analysis reduced the number of stations from six to four. The results of PCA showed that industrial and agricultural pollution sources could be responsible for the Anzali Wetland water quality. Then, the spatial variation maps of the PCA scores were generated using Kriging geostatistical method in the geographical information system (GIS) to investigate the pollution sources affecting the wetland parts. These maps illustrated that a great part of the wetland body was under the effect of agricultural sources, while the industrial sources affected the outlet and central parts. Finally, a comparison between two models (multiple linear regression (MLR) and Kriging) was made to assess their ability in predicting water quality parameters in the study area. The results showed the improvement of prediction using MLR, which was by 25%-97%, compared with Kriging. The results of the present study can be effectively used in the planning and implementation of future monitoring networks in the Anzali Wetland and other similar aquatic systems.

Conceptual change of disaster management models: A thematic analysis.

Different models have been proposed for disaster management by researchers and agencies. Despite their efficiency in some locations, disasters are still a fundamental challenge in the way of sustainable development. The purpose of this research is developing a comprehensive conceptual model for disaster management using thematic analysis. In this regard, first, disaster management models are collected. In the next stage, the themes of each model are extracted and categorised in three phases. In the first phase that is descriptive coding, available elements in each model are extracted as code and the basic themes are recognised. Then, in the phase of interpretive coding, basic themes are classified in three categories that are called organising themes (i.e. hazard assessment, risk management and management actions). In the final phase, strategic management is selected as the global or overarching theme to integrate all the other themes. Based on thematic analysis, it can be concluded that disaster management has three main elements that are the three organising themes. Therefore, comprehensive model of disaster management should include these three elements and their sub-basic themes that is called the ideal or criterion type. Results showed that some scientists have looked at disaster management one dimensionally (one theme). Even in two-dimensional models, one dimension has advantage over the other one. While the proposed typology in this study showed that the comprehensive model should include all the three mentioned elements.

Disaster Management Structure of Universities: Case Study of the Central Campus of the University of Tehran.

OBJECTIVE: Research on the disaster management plans of renowned universities worldwide shows that such plans are generally compiled in 3 categories: structural, nonstructural, and organizational sections. The importance of earthquakes in Tehran and the high vulnerability of the University of Tehran to earthquakes encouraged us to challenge the university's plans concerning disaster management. METHODS: An initial attempt was made to analyze the disaster management of 23 renowned universities worldwide and their structure compared with the present organizational structure of the University of Tehran. Then an expert opinion study was done to determine the appropriate management structure of the Incident Command System. RESULTS: These efforts resulted in an adhocratic system as the proper one for emergency situations after an earthquake. Furthermore, the results of the comparative study led to a general management structure that may be considered as a global pattern. CONCLUSIONS: An appropriate organizational structure is proposed for the disaster management of the University of Tehran, which may be used as an appropriate disaster management structure for other universities. (Disaster Med Public Health Preparedness. 2017;11:681-693).

Mathematical modeling in municipal solid waste management: case study of Tehran.

BACKGROUND: Solid Waste Management (SWM) in metropolises with systematic methods and following environmental issues, is one of the most important subjects in the area of urban management. In this regard, it is regarded as a legal entity so that its activities are not overshadowed by other urban activities. In this paper, a linear mathematical programming model has been designed for integrated SWM. Using Lingo software and required data from Tehran, the proposed model has been applied for Tehran SWM system as a case study. RESULTS: To determine the optimal status of the available system for Tehran's Solid Waste Management System (SWMS), a novel linear programming model is applied. Tehran has 22 municipal regions with 11 transfer stations and 10 processing units. By running of the model, the transfer stations and processing units are decreased to 10 and 6 units, respectively. CONCLUSIONS: The proposed model is an alternative method for improvement the SWMS by decreasing the transfer stations and processing units.

Temporary site selection and decision-making methods: a case study of Tehran, Iran.

Decisions on selecting an appropriate site for temporary shelter used to be taken in a limited amount of time after a disaster. The need for a systematic method in this area inspired the MADM (multi-attribute decision making) for complex disaster management decisions. This research proposes a model for appropriate and systematic site selection for temporary shelters, before an earthquake, using a geographical information system and MADM based on an earthquake damage assessment. After the effective criteria for site selection of temporary shelters are determined, the geographical layers of these criteria are prepared for Municipal District No.1 of Greater Tehran, the capital of Iran. Given these attributes and the required shelter area (415-610 hectares), 14 zones are proposed initially. Various MADM methods are used for the final selection. The mean of the aggregated ranking results are determined, and 10 of the 14 initial zones are ranked.