Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R values of 0.96-0.97. In addition, the improvement in MAE (34.71-49.65%) and RMSE (32.82-48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction.

Aerosols such as PM and PM can have an immense impact on human health. With the outbreak of SARS-CoV-2, it is urgent to filter aerosols by media filtration technology. Electrospun nanofibers are a promising material for achieving high efficiency, low resistance, light weight, and environmentally friendly air filtration. But research on filtration theory and computer simulation of nanofiber media is still lacking. The traditional method involving computational fluid dynamics (CFD) and Maxwell's first-order slip boundary overestimates the slip velocity on the fiber surface. In this study, a new modified slip boundary was proposed, which introduced a slip velocity coefficient on the basis of the no-slip boundary to address the slip wall. Our simulation results were compared with the experimental pressure drop and particle capture efficiency of real polyacrylonitrile (PAN) nanofiber media. The computational accuracy on pressure drop of the modified slip boundary improved 24.6% and 11.2% compared with that of the no-slip boundary and Maxwell's first-order slip boundary, respectively. It was found that the particle capture efficiency near the most-penetrating particle size (MPPS) was significantly increased when slip effect occurred. This may be explained by the slip velocity on the fiber surface, which would make particles more accessible to the fiber surface and captured by interception.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory illness called the novel coronavirus 2019 (COVID-19). COVID-19 was declared a pandemic on March 11, 2020. Bow tie analysis (BTA) was applied to analyze the hazard of SARS-CoV-2 for three receptor groups: patient or family member at the IWK Health Centre in acute care, staff member at a British Columbia Forest Safety Council (BCFSC) wood pellet facility, and staff member at the Suncor refinery in Sarnia, Ontario. An inherently safer design (ISD) protocol for BTA was used as a guide for evaluating COVID-19 barriers, and additional COVID-19 controls were recommended. Two communication tools were developed from the IWK bow tie diagram to disseminate the research findings. This research provides lessons learned about the barriers implemented to protect people from contracting COVID-19, and about the use of bow tie diagrams as communication tools. This research has also developed additional example-based guidance that can be used for the COVID-19 pandemic or future respiratory illness pandemics. Recommended future work is the application of BTA to additional industries, the consideration of ISD principles in other control types in the hierarchy of controls (HOC), and further consideration of human and organizational factors (HOF) in BTA.

Particulate matter (PMs) from combustion emissions (traffic, power plant, and industries) and the novel coronavirus (COVID-19) pandemic have recently enhanced the development of personal protective equipment against airborne pathogens to protect humans' respiratory system. However, most commercial face masks still cannot simultaneously achieve breathability and high filtration of PMs, bacteria, and viruses. This study used the electrospinning method with polyimide (PI) and polyethersulfone (PES) solutions to form a nanofiber membrane with low-pressure loss and high biocompatibility for high-efficiency bacteria, viruses, and nano-aerosol removal. Conclusively, the optimized nano-sized PI/PES membrane (0.1625 m/g basis weight) exhibited conspicuous performance for the highest filtration efficiency towards PM from 50 to 500 nm (99.74 %), good filter quality of nano-aerosol (3.27 Pa), exceptional interception ratio against 100-nm airborne COVID-19 (over 99 %), and non-toxic effect on the human body (107 % cell viability). The PI/PES nanofiber membrane required potential advantage to form a medical face mask because of its averaged 97 % BEF on filiation and ultra-low pressure loss of 0.98 Pa by referring ASTM F2101-01. The non-toxic PI/PES filters provide a new perspective on designing excellent performance for nano-aerosols from air pollution and airborne COVID-19 with easy and comfortable breathing under ultra-low air flow resistance.

Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO), ozone (O), sulfur dioxide (SO), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020-15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021-1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO, SO, CO and Rn, and significant negative correlations, especially for spring-summer periods between ground O levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020-1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.

Increased demand for single-use personal protective equipment (PPE) during the COVID-19 pandemic has resulted in a marked increase in the amount of PPE waste and associated environmental pollution. Developing efficient and environmentally safe technologies to manage and dispose of this PPE waste stream is imperative. We designed and evaluated a hydrothermal deconstruction technology to reduce PPE waste by up to 99% in weight. Hydrothermal deconstruction of single-use PPE waste was modelled using experimental data in Aspen Plus. Techno-economic and sensitivity analyses were conducted, and the results showed that plant scale, plant lifetime, discount rate, and labour costs were the key factors affecting overall processing costs. For a 200 kg/batch plant under optimal conditions, the cost of processing PPE waste was found to be 10 NZD/kg (6 USD/kg), which is comparable to the conventional practice of autoclaving followed by landfilling. The potential environmental impacts of this process were found to be negligible; meanwhile, this practice significantly reduced the use of limited landfill space.

The objective of this study was to evaluate the moderating effect of Occupational Health and Safety actions based on the World Health Organization (WHO) recommendations to mitigate the negative effect of COVID-19 on the operational, logistical, marketing (OLMP), and health and safety performance (OHSP) of workers in multinational industries. The development of surveys in companies was the method adopted, which had confirmatory evaluations through Structural Equations Modelling (SEM). As a result, it was confirmed that this is one of the few scientific studies that expectedly validates that the COVID-19 pandemic has severely impacted operational, logistical, market, and Occupational Health and Safety (OHS) performance. This is also one of the few research projects to assess the moderating effect of OHS practices based on WHO to mitigate the effects of COVID-19. According to our findings, those practices were able to reduce by at least 50% the effect of the COVID-19 crisis on operational, logistical, and marketing performance. However, they minimize by only 1.8% the negative effects of health and safety performance for the worker, generating absenteeism increasingly due to physical and mental problems. This number could be higher if the social distance could be provided in public transportation and if employees were more aware of the risks of COVID-19 contamination during their social activities.

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry.

The COVID-19 outbreak has significantly impacted the construction industry. The pandemic can exacerbate an already dire safety and health situation in the industry and negatively impact construction employees and employers. The present study investigates the safety and health measures implemented by construction firms in the United States (US), their effectiveness and usefulness, and workers' satisfaction with these COVID-19 measures. A questionnaire survey was developed and distributed to construction fieldworkers in the US to collect their perspectives on the implemented COVID-19 measures in the construction industry. A total of 187 valid responses were received and analyzed to achieve the aim of the study. Results revealed that strategies implemented to increase social distance and minimize group gathering to 10 persons in certain workstations were perceived to be substantially more effective than job-site screening strategies. Furthermore, smaller contractors implemented fewer safety measures and perceived them to be significantly less effective than those used by medium- and large-sized contractors. Fieldworkers were favorably disposed toward using technologies, such as video-conferencing apps and wearable sensing devices, to slow the spread of COVID-19 on construction job sites. The present study contributes to the body of knowledge by identifying safety and health measures to mitigate the spread of COVID-19 in construction. Practically, the study findings provide valuable insights to inform the successful implementation of safety strategies in the construction industry during a pandemic. The results are crucial for industry practitioners responsible for developing and revising pre- and post-pandemic safety and health plans.

Establishing an accurate and efficient prediction model is of great significance for governments and other social organizations to formulate prevention and control policies and curb the explosive spread of the pandemic. To improve prediction accuracy of cumulative COVID-19 confirmed data, a new hybrid prediction model based on gradient-based optimizer variational mode decomposition (GVMD), extreme learning machine (ELM), and autoregressive integrated moving average (ARIMA), named GVMD-ELM-ARIMA, is proposed. To solve the problem of selecting the value and the penalty factor in variational mode decomposition (VMD), this paper proposes gradient-based optimizer variational mode decomposition (GVMD), which realizes the self-adaptive determination of value and value. Firstly, GVMD decomposes the cumulative COVID-19 confirmed data into some intrinsic mode functions (IMFs) and a residual component (IMFr). Secondly, IMFs are predicted by ELM. Then, IMFr is predicted by ARIMA. Finally, the final prediction results are obtained by reconstructing the prediction result of IMFs and IMFr. The cumulative COVID-19 confirmed data of the United States, India and Russia is used to verify its effectiveness. Taking the United States as an example, compared with the average MAPE, RMSE and MAE of the single model, the average MAPE of the hybrid model is reduced by 47.27%, the average RMSE is reduced by 44.50%, and the average MAE is reduced by 55.34%. Compared with GVMD-ELM-ELM, GVMD-ELM-ARIMA proposed in this paper reduces the MAPE by 60%, the RMSE by 56.85%, and the MAE by 61.61%. The experimental results show that GVMD-ELM-ARIMA has best prediction accuracy, and it provides a new method for predicting the cumulative COVID-19 confirmed data.

The Coronavirus disease (COVID-19) has spread over the world, resulting in more than 225 million patients, and 4.7 million deaths in September 2021. It also caused panic and terror, halted numerous activities, and resulted in the world economy deteriorates. It altered human behavior and compelled people to alter their lifestyles to avoid infection. Air conditioning systems are one of the most important sectors that must be considered because of the pandemic SARS-CoV-2 all over the world. Air is used as a heat transfer medium in heating, ventilation, and air conditioning (HVAC) systems. The air contains a variety of pollutants, viruses, and bacteria, all of which have an impact on and destroy human life. Significantly in summer, people spend more time in air conditioners which results in lower levels of vitamin D and melatonin which may affect the functioning of their immune system and are susceptible to receiving SARS-CoV-2 from other individuals. As an important component of air conditioning and ventilation systems, the air filter plays a significant role. As a result, researchers must work harder to improve its design to prevent the ultra-small particles loaded with COVID-19. This paper contributes to the design of existing HVAC systems in terms of their suitability and impact on the spread of the hybrid SARS-CoV-2 epidemic, as well as efforts to obtain a highly efficient air filter to remove super-sized particles for protection against epidemic infection. In addition, important guideline recommendations have been extracted to limit the spread of the SARS-CoV-2 throughout the world and to get the highest quality indoor air in air-conditioned places.

On 24 April 2021, a disastrous fire in an Iraqi hospital took the lives of 82 people. Since the outbreak of the pandemic in March 2020, incidents of oxygen-related hospital fires in various countries around the world have caused over 200 deaths, the majority of whom were patients extremely ill with the novel Coronavirus. Fires involving medical oxygen are not a new phenomenon but are more common in the operating theatre where oxygen is routinely administered. In these settings, strict safety protocols are normally enforced and surgical staff are well trained in dealing with oxygen hazards. It appears that some hospitals may not have been fully prepared for the elevated risk of oxygen-related fire in intensive care units due to the high demand for oxygen therapy in severely ill Covid-19 patients. Indeed, gas producers and public health authorities were also slow to recognize and alert hospitals to the potential dangers. Oxygen is essential to life and generally makes up about 21 % of the gases in the air we breathe. Pure oxygen reacts with common materials such as oil and grease to cause fires, and even explosions, when released at high pressures. A leaking valve or hose, and openings at interfaces of masks and tubes, when in a confined space or where air circulation is low, can quickly increase the oxygen concentration to a dangerous level. Even a small increase in the oxygen level in the air to 24 % can create a fire hazard. In an oxygen-enriched environment, materials become easier to ignite and fires will burn hotter and more fiercely than in normal air. There is also a potentially heightened risk of using ethanol-based and organic solvents as cleaning agents in an oxygen rich atmospheres. This paper will provide an overview of oxygen accident scenarios that may be relevant for hospital intensive care units, with particular reference to recent events and similar accidents that have occurred in the past. The paper will recommend that hospitals recognize their chemical risks as part of their risk governance responsibility and assign chemical risk management a prominent role in their overall management. Investigation of dangerous events to extract causes and lessons learned should be utilized to highlight opportunities for prevention as well as emergency response. The industrial gas industry also needs to actively support hospitals in adoption of more rigorous risk management approaches, building on lessons learned in chemical process safety for managing flammable and explosive atmospheres.

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.

COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial( = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.

On March 11, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. The outbreak caused a worldwide impact, becoming a health threat to the general population and its professionals. To date, there are no specific antiviral treatments or vaccines for the COVID-19 infection, however, some drugs are being clinically tested. The use of these drugs on large scale raises great concern about their imminent environmental risk, since the elimination of these compounds by feces and urine associated with the inefficiency of sewage treatment plants in their removal can result in their persistence in the environment, putting in risk the health of humans and of other species. Thus, the goal of this work was to conduct a review of other studies that evaluated the presence of the drugs chloroquine, hydroxychloroquine, azithromycin, ivermectin, dexamethasone, remdesivir, favipiravir and some HIV antivirals in the environment. The research indicated the presence of these drugs in the environment in different regions, with concentration data that could serve as a basis for further comparative studies following the pandemic.

This work involves the application of process safety concepts to other fields, specifically bow tie analysis and inherently safer design (ISD) to COVID-19. An analysis framework was designed for stakeholders to develop COVID-19 risk management plans for specific scenarios and receptor groups. This tool is based on the incorporation of the hierarchy of controls (HOC) within bow tie analysis to identify priority barriers. The analysis framework incorporates inherently safer design (ISD) principles allowing stakeholders to assess the adequacy of controls along with the consideration of degradation factors and controls. A checklist has also been developed to help stakeholders identify opportunities to apply the ISD principles of minimization, substitution, moderation, and simplification. This work also considers barrier effectiveness with respect to human and organization factors (HOF) in degradation factors and controls. This paper includes a collection of bow tie elements to develop bow tie diagrams for specific receptor groups and scenarios in Nova Scotia, Canada. The pandemic stage (At-Peak or Post-Peak) and its influence on different scenarios or settings is also considered in this work. Bow tie diagrams were developed for numerous receptor groups; bow tie diagrams modelling a generally healthy individual, a paramedic and a hair salon patron contracting COVID-19 are presented in this work.

Like several countries, Spain experienced a multi wave pattern of COVID-19 pandemic over more than one year period, between spring 2020 and spring 2021. The transmission of SARS-CoV-2 pandemics is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation.This study aims to quantify the impact of climate and air pollution factors seasonality on incidence and severity of COVID-19 disease waves in Madrid metropolitan region in Spain. We employed descriptive statistics and Spearman rank correlation tests for analysis of daily in-situ and geospatial time-series of air quality and climate data to investigate the associations with COVID-19 incidence and lethality in Madrid under different synoptic meteorological patterns. During the analyzed period (1 January 2020-28 February 2021), with one month before each of three COVID-19 waves were recorded anomalous anticyclonic circulations in the mid-troposphere, with positive anomalies of geopotential heights at 500 mb and favorable stability conditions for SARS-CoV-2 fast diffusion. In addition, the results reveal that air temperature, Planetary Boundary Layer height, ground level ozone have a significant negative relationship with daily new COVID-19 confirmed cases and deaths. The findings of this study provide useful information to the public health authorities and policymakers for optimizing interventions during pandemics.

The containment of infectious diseases is challenging due to complex transmutation in the biological system, intricate global interactions, intense mobility, and multiple transmission modes. An emergent disease has the potential to turn into a pandemic impacting millions of people with loss of life, mental health, and severe economic impairment. Multifarious approaches to risk management have been explored for combating an epidemic spread. This work presents the implementation of engineering safety principles to pandemic risk management. We have assessed the pandemic risk using Paté-Cornell's six levels of uncertainty. The susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD), an advanced mechanistic model, along with the Monte Carlo simulation, has been used to estimate the fatality risk. The risk minimization strategies have been categorized into hierarchical safety measures. We have developed an event tree model of pandemic risk management for distinct risk-reducing strategies realized due to natural evolution, government interventions, societal responses, and individual practices. The roles of distinct interventions have also been investigated for an infected individual's survivability with the existing healthcare facilities. We have studied the Corona Virus Disease of 2019 (COVID-19) for pandemic risk management using the proposed framework. The results highlight effectiveness of the proposed strategies in containing a pandemic.

The new coronavirus disease (COVID-19), which first appeared in China in December 2019, has pervaded throughout the world. Because the epidemic started later in Turkey than other European countries, it has the least number of deaths according to the current data. Outbreak management in COVID-19 is of great importance for public safety and public health. For this reason, prediction models can decide the precautionary warning to control the spread of the disease. Therefore, this study aims to develop a forecasting model, considering statistical data for Turkey. Box-Jenkins Methods (ARIMA), Brown's Exponential Smoothing model and RNN-LSTM are employed. ARIMA was selected with the lowest AIC values (12.0342, -2.51411, 12.0253, 3.67729, -4.24405, and 3.66077) as the best fit for the number of total case, the growth rate of total cases, the number of new cases, the number of total death, the growth rate of total deaths and the number of new deaths, respectively. The forecast values of the number of each indicator are stable over time. In the near future, it will not show an increasing trend in the number of cases for Turkey. In addition, the pandemic will become a steady state and an increase in mortality rates will not be expected between 17-31 May. ARIMA models can be used in fresh outbreak situations to ensure health and safety. It is vital to make quick and accurate decisions on the precautions for epidemic preparedness and management, so corrective and preventive actions can be updated considering obtained values.

Nitrogen dioxide (NO) can have harmful effects on human health and can act as a precursor for the formation of other air pollutants in urban environment such as secondary PM and ozone. The lockdown measures for CoViD-19 allowed to simulate on a large scale the massive and prolonged reduction of road traffic (the main source for NO in urban environment). This work aims to selectively assess the maximum impact that total traffic blocking measures can have on NO. For this reason, three megacities (London, Milan and Paris) were chosen which had similar characteristics in terms of climatic conditions, population, policies of urban traffic management and lockdown measures. 52 air quality control units have been used to compare data measured in lockdown and in the same periods of previous years, highlighting a significant decrease in NO concentration due to traffic (London: 71.1 % - 80.8 %; Milan: 8.6 % - 42.4 %; Paris: 65.7 % - 79.8 %). In 2020 the contribution of traffic in London, Milan and Paris dropped to 3.3 ± 1.3 μg m, 6.1 ± 0.8 μg m, and 13.4 ± 1.5 μg m, respectively. Despite the significant reduction in the NO concentration, in UT stations average NO concentrations higher than 40 μg m were registered for several days. In order to reduce the pollution, the limitation of road traffic could be not enough, but a vision also aimed at rethink the vehicles and their polluting effects should be developed.