ObjectivesMost of the studies on the effect of heat stress on preterm birth (PTB) are conducted in temperate climates. Evidence on this effect in hot and arid countries with low and middle income is limited. This paper describes the short-term effect of exposure to the hot and cold environment on a daily number of PTB in Iran.MethodsThe daily number of PTB was obtained from all hospitals of the city. Meteorological and air pollution data from 2011 to 2017 were obtained from a metrological station in the city. A semi-parametric generalized additive model following a quasi-Poisson distribution with the distributed lag non-linear model was selected as a modeling framework for time-series analysis to simultaneously model the short-term and lagged effect of heat stress on PTB in the Sabzevar city.ResultsThe minimum and maximum daily temperature were − 11.2 and 45.4 °C respectively. The highest risk estimate at extreme cold temperature was found for apparent temperature (relative risk (RR) 1.83; 95% CI 1.61: 2.09). This pattern was seen for both models. For extreme hot temperatures, the model with mean temperature showed the highest risk increase for both the main model and air pollution adjusted model (RR 1.36; 95% CI 1.25: 1.49). The lowest risk estimate in extremely cold conditions was found in the model with mean temperature. However, for extremely hot temperature conditions, the lowest risk estimate was found for both maximum and apparent temperature.ConclusionObstetricians working in semi-arid areas should be aware of the influence of environmental extreme temperature on the incidence of PTB.Electronic supplementary materialThe online version of this article (10.1186/s12199-018-0760-x) contains supplementary material, which is available to authorized users.

Introduction Social factors can affect the vulnerability of disaster‐prone communities. This review aimed to identify and categorize social vulnerability indicators in the COVID‐19 pandemic. Methods This systematic review was conducted in February 2021. Bibliographies, citation databases, and other available records were investigated based on the aim of the study. The Joanna Briggs Institute (JBI) critical appraisal tools were applied for assessing the included articles retrieved through the comprehensive and systematic literature search. Descriptive and thematic analyses were done to extract the indicators affecting social vulnerability in the COVID‐19 pandemic. Results Thirty‐one eligible articles were included and 85 indicators of social vulnerability were extracted. The indicators were categorized in seven main categories, including; Household, community composition; Race, minority status and language; Socioeconomic status; Community health status; Public health infra‐structures; Education; Information, technology and communication. Conclusion Regions with higher social vulnerability experienced greater mortality rates during the COVID‐19 pandemic. Additional research is needed to measure the social vulnerability index in pandemics to prioritize distribution of scarce resources and ensure effectiveness and equity for all regions of countries.

The disasters can result in considerable damages to urban structures. In vulnerable cities, the amount of economic damages and fatalities is highlighted. The review of the literature indicates the urban vulnerabilities related to building qualification and the society situation. In this paper, some factors that can affect the vulnerability of old cities in Iran are listed. For evaluating the importance of each factor in identifying the vulnerability of Fahadan district, the AHP method is used. Twenty-five academic-related disaster management experts helped to achieve the priority of factors. Two vulnerability maps of the district were achieved by using the weight of factors obtained from the AHP model and geographic information system (GIS). Maps' analysis shows that places with high vulnerability cover all parts of the district, and residential parts of district suffer from susceptibility. Also, the results show that less vulnerable parts of the district surround the district. Reconstruction and rehabilitation measures can decrease the vulnerability value of Fahadan district.

In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018–2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018–2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 °C in the areas that were changed from gardens to buildings.

One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.