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Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. Results: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the

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Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage

Historically, risk perception studies have paid little attention to comparative assessments based on gender, race, ethnicity, religion or class. Yet these attri butes are crucial in understanding the local acceptability of risky technologies and activities. This paper uses a feminist analysis to evaluate the efficacy of the psychometric paradigm in distinguishing risk perceptions based on gender. We find some slight differences between the views of women and men, particularly with respect to the distrust, perceived catastrophic potential, and perceived potential for death of 33 technologies and activities. Women in the sample were slightly more pessimistic about technology than men and feared those hazards that are societal in nature (nuclear weapons, CFCS, commercial nuclear power). Though only a few minor differences were found between men and women, this is partially explained by shortcomings in the design of the research instru ment. Sensitizing modifications specific to gender analysis are suggested.

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Environmental problems in China are intensifying and it is vital to evaluate the environmental knowledge, attitudes and behaviors of the generation poised to inherit their management. This study examines a survey of environmental awareness among Chinese students (aged between 16 and 20 years). Considering the contrasting levels of regional economic development and environmental problems in the eastern/coastal and western/inland regions of China, we examine how environmental differences affect university students' environmental awareness. Data were analyzed statistically using nonparametric tests to compare a population of urban residents from a developed region against a similar population of urbanites from a less-developed region. Students in the samples possessed rather low levels of environmental knowledge, but had positive environmental attitudes and were willing to commit to environment-friendly behaviors. Students growing up in developed versus less-developed settings had significantly different levels of general environmental awareness despite their shared exposure to institutionalized environmental education.