A comprehensive review of various approaches for treatment of tertiary wastewater with emerging contaminants: what do we know?

In the last few decades, environmental contaminants (ECs) have been introduced into the environment at an alarming rate. There is a risk to human health and aquatic ecosystems from trace levels of emerging contaminants, including hospital wastewater (HPWW), cosmetics, personal care products, endocrine system disruptors, and their transformation products. Despite the fact that these pollutants have been introduced or detected relatively recently, information about their characteristics, actions, and impacts is limited, as are the technologies to eliminate them efficiently. A wastewater recycling system is capable of providing irrigation water for crops and municipal sewage treatment, so removing ECs before wastewater reuse is essential. Water treatment processes containing advanced ions of biotic origin and ECs of biotic origin are highly recommended for contaminants. This study introduces the fundamentals of the treatment of tertiary wastewater, including membranes, filtration, UV (ultraviolet) irradiation, ozonation, chlorination, advanced oxidation processes, activated carbon (AC), and algae. Next, a detailed description of recent developments and innovations in each component of the emerging contaminant removal process is provided.

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

Acceptance of COVID-19 vaccination among parents of children with autism and other neurodevelopmental disorders in Saudi Arabia. (preprint)

Background: Acceptance of COVID-19 vaccination may be less common among parents of children with autism spectrum disorder (ASD) and other neurodevelopmental disorders. This study aimed to explore the beliefs and willingness of parents of children with neurodevelopmental disorders about COVID-19 vaccine and understand how certain factors influencing the vaccine decision-making process differ between them and other parents’ groups. Methods: A cross-sectional qualitative study was conducted between August to November 2021. 400 parents from all 6 major regions in Saudi Arabia participated in an Arabic online survey and shared their beliefs about the new COVID-19 vaccination for their children. Results: The Cronbach alpha for Arabic version score was 0.71. 381 participants were eligible to answer the survey (95.2%) from 400 participants. The total number of parents of children with neurodevelopmental disorder was 158 (41.5%). 85 (53.8%) of them were ready to vaccinate their children with COVID-19 vaccine. While 36 (22.8%) were hesitant, the rest 37 (23.4%) did not want to vaccinate their children at all. Only a small number 16 (10.1%) have beliefs of vaccines as a cause of their child’s neurodevelopmental disorder. A total of 79 out of 131 responses were received from both parents’ groups. Fear of long-term side-effects was the most common reason reported by 41 responders out of 64 (64.06%) from parents of healthy children and 38 responders out of 67 (56.71%) from parents of diagnosed children. Another reason reported by parents of younger children in both groups was the child’s age. Having a healthcare relative worker was significantly associated with the vaccine decision making (p < .001). Conclusion: Although the majority of the respondents were willing to vaccinate their children against COVID-19, there are a number of parents around 45.14% who strongly refuse vaccination or are undecided about it yet. More information about the importance and safety of the vaccine should be accessible to those parents.

COVID-19 Vaccination Behavior Among Frontline Healthcare Workers in Pakistan: The Theory of Planned Behavior, Perceived Susceptibility, and Anticipated Regret.

Healthcare workers in Pakistan are still fighting at the frontline to control the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and have been identified as the earliest beneficiaries for COVID-19 vaccination by the health authorities of the country. Besides, the high vaccination rates of frontline healthcare workers (FHWs) are essential to overcome the ongoing pandemic and reduce the vaccines hesitancy among the general population. The current research employed the theory of planned behavior (TPB) to investigate the COVID-19 vaccination behavior among FHWs in Pakistan as well as the predictors of such behavior. Following the epidemic control and prevention policies, a sample of 680 FHWs were accessed to fill in the questionnaire evaluating the components of the TPB. Moreover, the potential role of anticipated regret (AR) and perceived susceptibility (PS) on COVID-19 vaccination behavior was also assessed. The partial least square structural equation modeling (PLS-SEM) results revealed that the TPB components, as well as the AR, have positive associations with the COVID-19 vaccination behavior. The results further confirmed that PS positively affects the anticipated regret, attitude (ATT), and subjective norm (SN) to vaccinate against SARS-CoV-2. The perceived susceptibility also has a positive association with COVID-19 vaccination behavior through the mediation of anticipated regret, ATT, and SN. Our findings highlighted the importance of COVID-19 vaccination among healthcare workers, which can be applied to reduce vaccine hesitancy among the general public.

Effect of temperature and humidity on coronavirus infection in Pakistan.

Ongoing Coronavirus epidemic (COVID-19) identified first in Wuhan, China posed huge impact on public health and economy around the globe. Both cough and sneeze based droplets or aerosols encapsulated COVID-19 particles are responsible for airborne transmission of this virus and caused an unexpected escalation and high mortality worldwide. Current study intends to investigate the correlation of COVID-19 epidemic with meteorological parameters, particularly temperature and humidity. A data set of Epidemiological data of COVID-19 for highly infected provinces of Pakistan was collected from the official website of (https://www.covid.gov.pk/) and weather data was collected from (https://www.timeanddate.com/) during the time period of 1st March to 30th September 2020. The GrapPad prism 5 Software was used to calculate the mean and standard error of mean (SEM). In the current study the incident of daily covid cases is recorded higher in the month of June while the less number of case were reported in the month of May as compared to the other months (April, May, June, July, September and August) in the four province of Pakistan. We also find out that the incident of Covid19 were high at higher temperature (like the average temperature in the month of June 37 °C) while less cases were reported in May the average temperature was 29.5 °C. Furthermore the incident of covid cases were less reported at low humidity while more intendant with high humidity. Pearson's (r) determine the strength of the relationship between the variables. Pearson's correlation coefficient test employed for data analysis revealed that temperature average (TA) and average humidity is not a significant correlated with COVID-19 pandemic. The results obtained from the current analysis for selected parameters indirect correlation of COVID-19 transmission with temperature variation, and humidity. In the present study association of parameters is not correlated with COVID-19 pandemic, suggested need of more strict actions and control measures for highly populated cities. These findings will be helpful for health regulatory authorities and policy makers to take specific measures to combat COVID-19 epidemic in Pakistan.

Eco-Environmental Aspects of COVID-19 Pandemic and Potential Control Strategies.

A new coronavirus-strain from a zoonotic reservoir (probably bat)-termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-has recently claimed more than two million deaths worldwide. Consequently, a burst of scientific reports on epidemiology, symptoms, and diagnosis came out. However, a comprehensive understanding of eco-environmental aspects that may contribute to coronavirus disease 2019 (COVID-19) spread is still missing, and we therefore aim to focus here on these aspects. In addition to human-human direct SARS-CoV-2 transmission, eco-environmental sources, such as air aerosols, different public use objects, hospital wastes, livestock/pet animals, municipal wastes, ventilation facilities, soil and groundwater potentially contribute to SARS-CoV-2 transmission. Further, high temperature and humidity were found to limit the spread of COVID-19. Although the COVID-19 pandemic led to decrease air and noise pollution during the period of lockdown, increased use of masks and gloves is threatening the environment by water and soil pollutions. COVID-19 badly impacted all the socio-economic groups in different capacities, where women, slum dwellers, and the people lacking social protections are the most vulnerable. Finally, sustainable strategies, waste management, biodiversity reclaim, eco-friendly lifestyle, improved health infrastructure and public awareness, were proposed to minimize the COVID-19 impact on our society and environment. These strategies will seemingly be equally effective against any future outbreak.