ABSTRACT
This study aims to analyze the correlation between the daily confirmed COVID-19 cases in Jordan and metrological parameters including the average daily temperature (°C), maximum ambient temperature (°C), relative humidity (%), wind speed (m/s), pressure (kPa), and average daily solar radiation (W/m2). This covers the first and the second waves in Jordan. The data were obtained from both the Jordanian Ministry of health and the Jordan Metrological Department. In this work, the Spearman correlation test was used for data analysis, since the normality assumption was not fulfilled. It was found that the most effective weather parameters on the active cases of COVID-19 in the initial wave transmission was the average daily solar radiation (r = - 0.503; p = 0.000), while all other tests for other parameters failed. In the second wave of COVID-19 transmission, it was found that the most effective weather parameter on the active cases of COVID-19 was the maximum temperature (r = 0.394; p = 0.028). This was followed by wind speed (r = 0.477; p = 0.007), pressure (r = - 0.429; p = 0.016), and average daily solar radiation (r = - 0.757; p = 0.000). Furthermore, the independent variable importance of multilayer perceptron showed that wind speed has a direct relationship with active cases. Conversely, areas characterized by low values of pressure and daily solar radiation exposure have a high rate of infection. Finally, a global sensitivity analysis using Sobol analysis showed that daily solar radiation has a high rate of active cases that support the virus' survival in both wave transmissions.
Subject(s)
COVID-19 , Humans , Humidity , Jordan , SARS-CoV-2 , Temperature , WeatherABSTRACT
To control the spread of COVID-19 disease and reduce its mortality, an early and precise diagnose of this disease is of significant importance. Emerging research data show that the current COVID-19 pandemic may be affected by environmental conditions. Therefore, the impact of weather parameters on COVID-19 distribution should be explored to predict its development in the next few months. This research aims to study the association between the daily confirmed COVID-19 cases in the three major cities of Jordan; Amman, Zarqa, and Irbid and climate indicators to include the average daily temperature (°C), wind speed (m/s), relative humidity (%), pressure (kPa), and the concentration of four pollutants (CO, NO2, PM10, and SO2). The data were obtained from the World Air Quality Project website and the Jordanian Ministry of Environment. A total of 305 samples for each city was used to conduct the data analysis using multiple linear regression and a feedforward artificial neural network. It was concluded that the multiple linear regression and feedforward artificial neural network could forecast the COVID-19 confirmed cases in the case studies; Amman, Irbid, and Zarqa. Finally, global sensitivity analysis using Sobol analysis indicated that pressure in Amman and Zarqa and the concentration of NO2 in Irbid has a high rate of positive cases that supports the virus's spread.
ABSTRACT
The COVID-19 pandemic has forced almost all universities in the world to switch from face-to-face to an online teaching format. In response to this sudden transition, the Mechanical Engineering department at the Al-Zaytoonah University in Jordan had to adapt to this change within two weeks in mid-March 2020. In addition, the department had to comply with the strict ABET accreditation regulations, among which is the indirect assessment of each course by the students each semester. They judge to what degree ABET-mandated student learning outcomes (SO) are actually met in any course designated to deliver the outcomes. Another ABET accreditation regulation is direct assessment, which is conducted by the instructor and relies on an instructor’s own evaluation of tasks designated for certain student learning outcomes. The aim of this study is to determine if, during the COVID-19 pandemic, students’ assessments changed significantly, in accordance with the ABET mandate, by using statistical analyses of students’ responses on an online completed survey. An independent samples t-test was used to evaluate the statistical importance of the observed differences. The SO coverage rates were quite high during the pandemic, although, generally, somewhat lower than their pre-pandemic equivalents. In this study, the Air Conditioning and Heating (ACH) course was selected to compare direct and indirect student learning outcomes before and after the COVID-19 pandemic. Consequently, our findings revealed a positive effect on SO performance during the COVID-19 pandemic and due to e-learning teaching.
ABSTRACT
This study aims to explore the real-time impact of the COVID-19 pandemic on measured air pollution in the three largest cities of Jordan (Amman, Irbid and Zarqa). It is hypothesized that a sharp decrease in the emitted amounts of particulate matter (PM10), CO, NO2 and SO2 during COVID-19 pandemic will be obtained, this corresponds with the reduced traffic due to mandated business closures. To achieve this exploration a paired sample t-test is used to compare the concentration of these four pollutants in the three cities over the period from 15 March to 30 June during the years from 2016 to 2020. It is found that there is a significant difference between the emitted concentrations mean values of CO, PM10, SO2 and NO2 during the period of study. This was indicated by the values of p for each species, which was less than 5 % for all these pollutants. The maximum reduction in SO2 and NO2 concentration during the lockdown period was in Zarqa. Irbid city witnessed the highest percentage reduction in CO and PM10. Furthermore, the correlation test, independent variable importance of multilayer perceptron and global sensitivity analysis using Sobol analysis showed that metrological data (Humidity, wind speed, average temperature and pressure) have a direct relationship with concentrations of CO, PM10, SO2 and NO2 in Amman, Irbid and Zarqa before and after COVID-19 pandemic.