Impacts of short-term lockdown during COVID-19 on air quality in Egypt

Egyptian Newspapers Coverage of COVID-19 Vaccines: A Theoretically Driven Content Analysis

The Egyptian government has acquired COVID-19 vaccines from different sources; however, the vaccination rates and vaccine acceptance among the public remained low. News media play an influential role in shaping the public's understanding of medical issues and promoting health behaviors such as vaccination. Guided by the Extended Parallel Processing Model (EPPM) and the framing theory, a content analysis of COVID-19 vaccines coverage in two established Egyptian newspapers in Arabic (Al-Goumhuria and Al-Masry Al-Youm) between January 2020 and November 2021 was conducted. Findings suggested that the Egyptian newspapers focused on the efficacy of the vaccines but downplayed the severity of COVID-19. Most articles from both newspapers did not use gain or loss frames, although Al-Goumhuria was most likely to use both (loss and gain) frames simultaneously. Specific vaccine information regarding its safety, side effects, and effectiveness was minimal in both newspapers. The differences in COVID-19 vaccine coverage between the two newspapers were limited, suggesting a high level of government control of COVID-19 related content, regardless of whether it is state- or private-owned newspaper. Theoretical and practical implications of the findings were discussed.

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

The COVID-19 pandemic in Germany in 2020 brought many regulations to impede its transmission such as lockdown. Hence, in this study, we compared the annual air pollutants (CO, NO, NO2, O3, PM10, PM2.5, and BC) in Augsburg in 2020 to the record data in 2010-2019. The annual air pollutants in 2020 were significantly (p < 0.001) lower than that in 2010-2019 except O3, which was significantly (p = 0.02) higher than that in 2010-2019. In a depth perspective, we explored how lockdown impacted air pollutants in Augsburg. We simulated air pollutants based on the meteorological data, traffic density, and weekday and weekend/holiday by using four different models (i.e. Random Forest, K-nearest Neighbors, Linear Regression, and Lasso Regression). According to the best fitting effects, Random Forest was used to predict air pollutants during two lockdown periods (16/03/2020-19/04/2020, 1st lockdown and 02/11/2020-31/12/2020, 2nd lockdown) to explore how lockdown measures impacted air pollutants. Compared to the predicted values, the measured CO, NO2, and BC significantly reduced 18.21%, 21.75%, and 48.92% in the 1st lockdown as well as 7.67%, 32.28%, and 79.08% in the 2nd lockdown. It could be owing to the reduction of traffic and industrial activities. O3 significantly increased 15.62% in the 1st lockdown but decreased 40.39% in the 2nd lockdown, which may have relations with the fluctuations the NO titration effect and photochemistry effect. PM10 and PM2.5 were significantly increased 18.23% an 10.06% in the 1st lockdown but reduced 34.37% and 30.62% in the 2nd lockdown, which could be owing to their complex generation mechanisms.

Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants

Implemented quarantine due to the ongoing novel coronavirus (agent of COVID-19) has an immense impact on human mobility and economic activities as well as on air quality. Since then, and due to the drastic reduction in pollution levels in cities across the world, a large discussion has been magnetized regarding if the lockdown is an adequate alternative counter-measure for enhancing air quality. This paper aimed at studying the Air Quality Index (AQI), PM2.5, and tropospheric NO2 levels in three lockdown phases (before, during, and after) among 21 cities around the world. Simple before/after comparison approach was carried out to capture the declining trend in air pollution levels caused by the lockdown restrictions. The results showed that the frequency distribution for NO2 is more variable than that for PM2.5, and the distribution is flatter from 2020 to the baseline 2018-2019 period. Besides, AQI, in most of the cities, has varied from high to mild pollution during the lockdown and was moderate before. Although during the lockdown, a reduction of 3 to 58% of daily NO2 concentrations was observed across the cities, an increase was detected in three cities including Abidjan (1%), Conakry (3%), and Chengdu (10%). Despite this mixed trend, the NO2 time series clearly showed the effect of the unlocking phase where the NO2 levels increased in almost all cities. Similarly, PM2.5 concentrations have increased in the post-lockdown period, with 50% of the cities reporting significant positive differences between the lock and the unlock phase. Then, the levels of PM2.5 were higher at the pre-lockdown phase than at any other time exhibiting a "U" shape. In addition, during Ramadan, it was noted that altered patterns of daily activities in some Islamic cities have a significant negative impact on air quality.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.

The COVID-19 pandemic and its impact on environment: the case of the major cities in Pakistan

In Wuhan city, China, a pneumonia-like disease of unknown origin triggered a catastrophe. This disease has spread to 215 nations, affecting a diverse variety of persons. It was formally called extreme acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as coronavirus disease, by the World Health Organization as a pandemic. This pandemic forced countries to enforce a socio-economic lockdown to avoid its widespread presence. This study focuses on how the pollution of particulate matter during the coronavirus pandemic in the period from 23 March 2020 to 31 December 2020 was reduced compared to the pre-pandemic situation in the country. The improvement in air quality and atmosphere due to the coronavirus pandemic in Pakistan was identified by both ground-based and satellite observations with a primary focus on the four provincial capitals and country capitals, namely, Peshawar, Karachi, Quetta, Lahore, and Islamabad, and statistically verified through paired Student's t test. Both datasets have shown a significant decrease in the levels of PM2.5 pollutions across Pakistan (ranging from 15 to 35% for satellite observations, while 27 to 61% for ground-based observations). The result shows that poor air quality is one of the key factors for a higher COVID-19 spread rate in major Pakistani cities. By extending the same investigation across the nation, there is a greater need to investigate the connections between COVID-19 spread and air pollution. However, both higher population density rates and frequent population exposure can be partially attributed to increased levels of PM2.5 concentrations before the pandemic of the coronavirus.

The Impact of COVID-19 Lockdowns on Air Quality—A Global Review

The outbreak of the COVID-19 pandemic has emerged as a serious public health threat and has had a tremendous impact on all spheres of the environment. The air quality across the world improved because of COVID-19 lockdowns. Since the outbreak of COVID-19, large numbers of studies have been carried out on the impact of lockdowns on air quality around the world, but no studies have been carried out on the systematic review on the impact of lockdowns on air quality. This study aims to systematically assess the bibliographic review on the impact of lockdowns on air quality around the globe. A total of 237 studies were identified after rigorous review, and 144 studies met the criteria for the review. The literature was surveyed from Scopus, Google Scholar, PubMed, Web of Science, and the Google search engine. The results reveal that (i) most of the studies were carried out on Asia (about 65%), followed by Europe (18%), North America (6%), South America (5%), and Africa (3%); (ii) in the case of countries, the highest number of studies was performed on India (29%), followed by China (23%), the U.S. (5%), the UK (4%), and Italy; (iii) more than 60% of the studies included NO2 for study, followed by PM2.5 (about 50%), PM10, SO2, and CO; (iv) most of the studies were published by Science of the Total Environment (29%), followed by Aerosol and Air Quality Research (23%), Air Quality, Atmosphere & Health (9%), and Environmental Pollution (5%); (v) the studies reveal that there were significant improvements in air quality during lockdowns in comparison with previous time periods. Thus, this diversified study conducted on the impact of lockdowns on air quality will surely assist in identifying any gaps, as it outlines the insights of the current scientific research.

Deep learning model for forecasting COVID-19 outbreak in Egypt

The World Health Organization has declared COVID-19 as a global pandemic in early 2020. A comprehensive understanding of the epidemiological characteristics of this virus is crucial to limit its spreading. Therefore, this research applies artificial intelligence-based models to predict the prevalence of the COVID-19 outbreak in Egypt. These models are long short-term memory network (LSTM), convolutional neural network, and multilayer perceptron neural network. They are trained and validated using the dataset records from 14 February 2020 to 15 August 2020. The results of the models are evaluated using the determination coefficient and root mean square error. The LSTM model exhibits the best performance in forecasting the cumulative infections for one week and one month ahead. Finally, the LSTM model with the optimal parameter values is applied to forecast the spread of this epidemic for one month ahead using the data from 14 February 2020 to 30 June 2021. The total size of infections, recoveries, and deaths is estimated to be 285,939, 234,747, and 17,251 cases on 31 July 2021. This study could assist the decision-makers in developing and monitoring policies to confront this disease.

How changes in human activities during the lockdown impacted air quality parameters: A review

The health emergency linked to the spread of COVID-19 has led to important reduction in industrial and logistics activities, as well as to a drastic changes in citizens' behaviors and habits. The restrictions on working activities, journeys and relationships imposed by the lockdown have had important consequences, including for environmental quality. This review aims to provide a structured and critical evaluation of the recent scientific bibliography that analyzed and described the impact of lockdown on human activities and on air quality. The results indicate an important effect of the lockdown during the first few months of 2020 on air pollution levels, compared to previous periods. The concentrations of particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide have decreased. Tropospheric ozone, on the other hand, has significantly increased. These results are important indicators that can become decision drivers for future policies and strategies in industrial and logistics activities (including the mobility sector) aimed at their environmental sustainability. The scenario imposed by COVID-19 has supported the understanding of the link between the reduction of polluting emissions and the state of air quality and will be able to support strategic choices for the future sustainable growth of the industrial and logistics sector.

Effect of large-scale social restriction (PSBB) during COVID-19 on outdoor air quality: Evidence from five cities in DKI Jakarta Province, Indonesia

The variation in the concentration of outdoor air pollutants during the COVID-19 lockdown was studied in Jakarta, Indonesia. The term lockdown was replaced by large-scale social restrictions (PSBB) in Indonesia by more flexible regulations to save the economy. Data on five air pollutants, namely, PM₁₀, SO₂, CO, O₃, and NO₂, from five monitoring stations located in five regions in Jakarta (West, East, Central, North, and South Jakarta) were utilized. We analyzed the changes in the concentrations of outdoor air pollutants before lockdown from January 1 to April 9, 2020, and during lockdown from April 10 to June 4, 2020. Overall, the CO concentration (39.9%) demonstrated the most significant reduction during lockdown, followed by NO₂ (7.5%) and then SO₂ (5.7%). However, we unexpectedly found that during lockdown, the PM₁₀ concentration in Jakarta increased by 10.9% due to the southwest monsoon during the seasonal change in Jakarta. Among the five cities in Jakarta, East and Central Jakarta experienced the maximum improvement in their air quality, whereas North Jakarta had the least air quality improvement. To the best of our knowledge, this research is the first to study the effect of lockdown on outdoor air quality improvement in Indonesia using ground-level measurement data. The findings of the study provide additional strategies to the regulatory bodies for the reduction of temporal air pollutants in Jakarta, Indonesia, by restricting people mobility as a supplementary initiative.

How do pollutants change post-pandemic? Evidence from changes in five key pollutants in nine Chinese cities most affected by the COVID-19

Under the COVID-19 global pandemic, China has weakened the large-scale spread of the epidemic through lockdown and other measures. At the same time, with the recovery of social production activities, China has become the only country which achieves positive growth in 2020 in the major economies. It entered the post pandemic period. These measures improved the local environmental quality. However, whether this improvement can be sustained is also a problem that needs to be solved. So, this study investigated the changes of five air pollutants (PM2.5, PM10, NO2, SO2, and CO) in the nine cities most severely affected by the pandemic in China during the lockdown and post pandemic period. We emphasized that when analyzing the changes of environmental quality during the epidemic, we must consider not only the impact of the day and short-term changesbut also the cumulative lag effect and sustainable development. Through a combination of qualitative and quantitative methods, it is found that the concentration of pollutants decreased significantly during the lockdown compared to the situation before the epidemic. PM10 and NO2 are falling most, which downs 39% and 46% respectively. During the lockdown period, the pollutant concentrations response to the pandemic has a lag of 3-7 days. More specifically, in the cities related to single pollutants, the impact on the pollutant shows a significant correlation when the measures are delayed for seven days. In the cities that are related to multiple pollutants, the correlation is usually highest in 3-5 days. This means that the impact of policy measures on the environment lasted for 3-5 days. Besides, Wuhan, Jingmen and Jingzhou have seen the most obvious improvement. However, this improvement did not last. In the post pandemic period, the pollutants rebounded, the growth rates of PM10 and NO2 reached 44% and 87% in September. When compared with the changes of pollutants concentration in the same period from 2017 to 2019, the decline rate has also been significantly slower, even higher than the average concentration of previous years. The research not only contributes to China's economic "green recovery" plan during the post epidemic period, but also provides references for environmental governance during economic recovery in other countries.