Comparative cancer risk assessment of THMs in drinking water from well water sources and surface water sources

This research evaluates the lifetime cancer risks from trihalomethanes in Tehran's drinking water. The Trihalomethanes were measured in seven different water districts. Sixty-three samples were taken from tap water across the city for 7 months. The samples were analyzed for trihalomethanes using US EPA method 524.2. The average concentration of total trihalomethanes in different districts were between 0.81 and 9.0 μg/L, and the highest concentrations were detected in district 2 at 19.5 μg/L. Total lifetime cancer risks assessment from exposure to trihalomethanes in drinking water (ingestion, inhalation, and skin routes) were performed for people living in different districts in Tehran. The lifetime cancer risk was 7.19 × 10(-5) in district 2 (a more affluent neighborhood) where mostly surface water sources is used to supply drinking water and 9.38 × 10(-6) in district 7 (a less affluent neighborhood) which is mainly supplied with well water sources. Based on the population data, the total expected lifetime cancer cases from exposure to trihalomethanes are 104, 108, 81, 81, 41, 27, and three for districts 1 through 7, respectively. The average lifetime cancer risk was 4.33 × 10(-5) which means a total of 606 lifetime cancer cases for the entire province of Tehran. The highest risk from THMs seems to be from the inhalation route followed by ingestion and dermal contacts.     

High-risk behaviors while driving: A population-based study from Iran

Objective: Traffic injuries are becoming one of the most important challenges of public health systems. Because these injuries are mostly preventable, the aim of this study is to evaluate the four main high-risk behaviors while driving.

Methods: This cross-sectional study was conducted on a random sample from the population of Mashhad, Iran, in 2014. A checklist and a previously validated questionnaire for the transtheoretical stages of change model (TTM) were used for data collection. Statistical analyses were performed using SPSS 11.5 software with P <.05 statistically significant.

Results: Totally 431 individuals were included with a mean age of 30 ± 11.3 years. Forty-three percent (183) were male. The TTM model revealed that participants were mostly in pre-actional phases regarding not using a cell phone while driving (80%), fastening the driver's seat belt (66%), front seat belt (68%), and rear seat belt (85%) The penalty was a protective factor only for using cellphone (odd ratio [OR] = 0.82, 95% confidence interval [CI], 0.68–0.98). Lower education (OR = 0.12, 95% CI, 0.01–0.94) and male gender (OR = 0.35, 95% CI, 0.14–0.83) were indicative of lower rates of fastening the front and rear seat belts.

Conclusion: The stages of change model among study participants is a proper reflection of the effectiveness of the current policies. More serious actions regarding these high-risk behaviors should be considered in legislation.     

Drivers of local people’s participation in sustainable natural resource management: a case study in central Iran

The driving forces behind natural resource management (NRM) vary among countries. Most NRM programmes focus on biophysical drivers such as soil, water and vegetation, with little attention directed towards the nuanced sociocultural and religious drivers of sustainable natural resource management (SNRM) practices. This paper explores those understudied drivers that influence local people’s participation (LPP) in SNRM in Isfahan, Iran. Using a multi-stage stratified sampling method, we selected 200 natural resource experts and natural resource users to complete a questionnaire about their perceptions of SNRM. Results reveal that sociocultural and religious beliefs are the major drivers of SNRM. The results also indicate that subsidiary drivers include: a sense of responsibility towards SNRM; the conviction that natural resources belong to God and should therefore be preserved; participation to preserve natural resources because of training courses and media influence; a long-established custom of preserving natural resources; and the specific impact of environmental television programmes. Demographic analysis finds a significant relationship between educational level and LPP in SNRM. This study’s results therefore suggest that natural resource managers would benefit from a deeper understanding of the local sociocultural and religious contexts that motivate people to participate in SNRM.     

Critical reflections on the theory and practice of social sustainability in the built environment – a meta-analysis

This article presents a critical reflection on the theory and practice of social sustainability in the built environment, identifies areas of agreement and disagreement, explores theoretical and conceptual gaps and challenges, and suggests practical implications for future research and urban policy. It argues that despite revisionist approaches which challenge the tripartite structure of sustainable development, social dimension of sustainability remains an essential valid pillar. Utilising a qualitative meta-analysis methodology for undertaking critical analysis of previous research and publications on the topic, key themes of theory and practice of social sustainability are identified and critically examined. Accordingly, 10 key formative characteristics of social sustainability and their research and policy implications are introduced. The article concludes with institutional observations for policy-makers to achieve greater success in addressing largely underestimated dimensions of social sustainability in urban settings.     

Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites

In this research, an experimental study of reinforcing alumina nano-particles into the aluminum alloy matrix was implemented to verify the accuracy of modeling results obtained by feed forward neural networks. Artificial neural network combined with numerical technique were used to predict the various parameters of mechanical properties such as hardness, tensile and compressive yield stress, UTS and elongation percentage. Much experimentation were taken to discover a suitable number of hidden neurons, avoid detraction from the trainability and enable feed forward neural networks to solve more complex problems. The predictions were found to be consistent with experimental measurements.     

Assessing the effects of dam building on land degradation in central Iran with Landsat LST and LULC time series

This study aimed to analyze the impact of Zayandehrood Dam on desertification using the spatio-temporal dynamics of land use/land cover (LULC) and land surface temperature (LST) in an arid environment in central Iran from 1987 to 2014. The LULC and LST images were calculated from Landsat TM, ETM+, and OLI data, and their accuracies were assessed against reference data using error matrix and linear regression analysis. Results showed that salty and bare lands increased up to 57,302 ha, while agricultural lands declined substantially (28,275.58 ha) in the region. The changes in LULC classes resulted in dramatic variations in LST values. The average temperature showed a 5.03 °C increase, and the minimum temperature increased by 5.66 °C. LST had an increasing trend in bare lands (8.74 °C), poor rangelands (6.8 °C), agricultural lands (9.46 °C), salty lands (9.6 °C), and residential areas (3.18 °C) in this 27-year period. Rainfall and temperature trend analysis revealed that the main cause of these extreme changes in LULC and LST was largely attributed to the drying up of Zayandehrood River due to dam construction and allocating water mainly for industrial sectors. Results indicate that in addition to LULC changes, the spatio-temporal variations of LST can be used as an effective index in desertification assessment and monitoring in arid environments.     

Contaminants of emerging concern: a review of new approach in AOP technologies

The presence of contaminants of emerging concern (CECs) such as pharmaceuticals and personal care products (PPCPs), endocrine-disrupting compounds (EDCs), flame retardants (FRs), pesticides, and artificial sweeteners (ASWs) in the aquatic environments remains a major challenge to the environment and human health. In this review, the classification and occurrence of emerging contaminants in aquatic environments were discussed in detail. It is well documented that CECs are susceptible to poor removal during the conventional wastewater treatment plants, which introduce them back to the environment ranging from nanogram per liter (e.g., carbamazepine) up to milligram per liter (e.g., acesulfame) concentration level. Meanwhile, a deep insight into the application of advanced oxidation processes (AOPs) on mitigation of the CECs from aquatic environment was presented. In this regard, the utilization of various treatment technologies based on AOPs including ozonation, Fenton processes, sonochemical, and TiO₂ heterogeneous photocatalysis was reviewed. Additionally, some innovations (e.g., visible light heterogeneous photocatalysis, electro-Fenton) concerning the AOPs and the combined utilization of AOPs (e.g., sono-Fenton) were documented.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).