Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, IRAN.

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM2.5, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.

Risk Criteria in Hospital Site Selection: A Systematic Review.

INTRODUCTION: Hospitals should be safe and remain functional in emergencies and disasters as it is mentioned in the Sendai Framework. Proper selection of a hospital location has a direct effect on survival of affected population in disasters as well as cost and benefit of the hospital in non-emergency situation. Different studies applied different criteria for Hospital Site Selection (HSS). The present study through a systematic review aimed to find out a categorized criteria list that have been used for (HSS) in the literature. METHODS: In accordance with the PRISMA statement, "PubMed", "ScienceDirect", "Google Scholar", and "Scopus" were searched up to end of 2015. All English Articles that were published in peer-reviewed journals and had discussed site selection criteria for hospitals were included. Out of 41 articles, 15 met the inclusion criteria in which 39 general criteria for HSS were applied. These criteria were categorized in six main groups including cost, demand, environmental, administrative, disaster risk, and "other" concerns through a focus group discussion. RESULTS: Accordingly, the application percentage of cost, demand, environmental, administrative, disaster risk, and "other" concerns in the articles was 100, 93.3, 53.3, 33.3, 20.0, and 13.3 respectively. The least devoted attention was to disaster risk issues. DISCUSSION: Few researchers applied risk related criteria for HSS. Further consideration of "risk of hazards" and "burden of diseases" in comprehensive studies, is recommended for HSS to guide the decision makers for building more resilient hospitals. Keywords   Hospital, Site selection, Systematic review, Disaster risk.

Reprint of "Spatial modeling of Cutaneous Leishmaniasis in Iran from 1983 to 2013".

INTRODUCTION: Cutaneous Leshmaniasis (CL), a parasitic skin infection caused by Leishmania species, is endemic in some regions of Iran. In this study, the effect of location on the incidence and distribution of CL in Iran was studied. METHODS: We collected datas including the number of Cutaneous Leishmaniasis cases and populations at-risk of disease in Iran's different provinces reported by the Iranian ministry of health and the National Bureau of Statistics, respectively. Spatial modeling was performed using Arc GIS software. Descriptive maps, hotspot analysis, and high/low clustering analysis were used to demonstrate distribution of the cutaneous leishmaniasis, to determine regions at risk of disease's incidence, and to reach the most appropriate method for clustering of disease. RESULTS: The total number of cases of cutaneous leishmaniasis reported through the study period was 589,913. The annual incidence of CL was estimated to be 30.9 per 100,000 in Iranian population. We also demonstrated that Cutaneous leishmaniasis most prominently occurs in regions with dry and desert climates as well as in central parts of Iran. It affected the southwest of Iran between 1983 and 1997, and subsequently developed towards the center and the eastern between 1998 and 2013. Disease hotspots were focused in the provinces of Yazd, Khozestan and Kohgiloyeh-Boyer-Ahmad (p<0.05). No pattern of spatial clustering was observed. CONCLUSION: Cutaneous leishmaniasis is a major health problem which could be a serious threat for inhabitants who live in high-risk provinces of Iran; much more resources need to be allocated in these areas, to warrant the prevention as well as effectively management of this disease.

Spatial modeling of cutaneous leishmaniasis in Iran from 1983 to 2013.

INTRODUCTION: Cutaneous Leshmaniasis (CL), a parasitic skin infection caused by Leishmania species, is endemic in some regions of Iran. In this study, the effect of location on the incidence and distribution of CL in Iran was studied. METHODS: We collected datas including the number of Cutaneous Leishmaniasis cases and populations at-risk of disease in Iran's different provinces reported by the Iranian ministry of health and the National Bureau of Statistics, respectively. Spatial modeling was performed using Arc GIS software. Descriptive maps, hotspot analysis, and high/low clustering analysis were used to demonstrate distribution of the cutaneous leishmaniasis, to determine regions at risk of disease's incidence, and to reach the most appropriate method for clustering of disease. RESULTS: The total number of cases of cutaneous leishmaniasis reported through the study period was 589,913. The annual incidence of CL was estimated to be 30.9 per 100,000 in Iranian population. We also demonstrated that Cutaneous leishmaniasis most prominently occurs in regions with dry and desert climates as well as in central parts of Iran. It affected the southwest of Iran between 1983 and 1997, and subsequently developed towards the center and the eastern between 1998 and 2013. Disease hotspots were focused in the provinces of Yazd, Khozestan and Kohgiloyeh-Boyer-Ahmad (p<0.05). No pattern of spatial clustering was observed. CONCLUSION: Cutaneous leishmaniasis is a major health problem which could be a serious threat for inhabitants who live in high-risk provinces of Iran; much more resources need to be allocated in these areas, to warrant the prevention as well as effectively management of this disease.

Determining the most suitable areas for artificial groundwater recharge via an integrated PROMETHEE II-AHP method in GIS environment (case study: Garabaygan Basin, Iran).

Flood spreading is a suitable strategy for controlling and benefiting from floods. Selecting suitable areas for flood spreading and directing the floodwater into permeable formations are amongst the most effective strategies in flood spreading projects. Having combined geographic information systems (GIS) and multi-criteria decision analysis approaches, the present study sought to locate the most suitable areas for flood spreading operation in the Garabaygan Basin of Iran. To this end, the data layers relating to the eight effective factors were prepared in GIS environment. This stage was followed by elimination of the exclusionary areas for flood spreading while determining the potentially suitable ones. Having closely examined the potentially suitable areas using the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II and analytic hierarchy process (AHP) methods, the land suitability map for flood spreading was produced. The PROMETHEE II and AHP were used for ranking all the alternatives and weighting the criteria involved, respectively. The results of the study showed that most suitable areas for the artificial groundwater recharge are located in Quaternary Q(g) and Q(gsc) geologic units and in geomorphological units of pediment and Alluvial fans with slopes not exceeding 3%. Furthermore, significant correspondence between the produced map and the control areas, where the flood spreading projects were successfully performed, provided further evidence for the acceptable efficiency of the integrated PROMETHEE II-AHP method in locating suitable flood spreading areas.

Multi-Source Remotely Sensed Data Combination: Projection Transformation Gap-Fill Procedure.

In this work a new gap-fill technique entitled projection transformation has been developed and used for filling missed parts of remotely sensed imagery. In general techniques for filling missed area of an image are broken down into three main categories: multi-source techniques that take the advantages of other data sources (e.g. using cloud free images to reconstruct the cloudy areas of other images); the second ones fabricate the gap areas using non-gapped parts of an image itself, this group of techniques are referred to as single-source gap-fill procedures; and third group contains methods that make up a combination of both mentioned techniques, therefore they are called hybrid gap-fill procedures. Here a new developed multi-source methodology called projection transformation for filling a simulated gapped area in the Landsat7/ETM+ imagery is introduced. The auxiliary imagery to filling the gaps is an earlier obtained L7/ETM+ imagery. Ability of the technique was evaluated from three points of view: statistical accuracy measuring, visual comparison, and post classification accuracy assessment. These evaluation indicators are compared to the results obtained from a commonly used technique by the USGS as Local Linear Histogram Matching (LLHM) [1]. Results show the superiority of our technique over LLHM in almost all aspects of accuracy.