Large eddy simulation of sneeze plumes and particles in a poorly ventilated outdoor air condition: A case study of the University of Houston main campus.

Since the outbreak of the COVID-19 pandemic, many previous studies using computational fluid dynamics (CFD) have focused on the dynamics of air masses, which are believed to be the carriers of respiratory diseases, in enclosed indoor environments. Although outdoor air may seem to provide smaller exposure risks, it may not necessarily offer adequate ventilation that varies with different micro-climate settings. To comprehensively assess the fluid dynamics in outdoor environments and the efficiency of outdoor ventilation, we simulated the outdoor transmission of a sneeze plume in "hot spots" or areas in which the air is not quickly ventilated. We began by simulating the airflow over buildings at the University of Houston using an OpenFOAM computational fluid dynamics solver that utilized the 2019 seasonal atmospheric velocity profile from an on-site station. Next, we calculated the length of time an existing fluid is replaced by new fresh air in the domain by defining a new variable and selecting the hot spots. Finally, we conducted a large-eddy simulation of a sneeze in outdoor conditions and then simulated a sneeze plume and particles in a hot spot. The results show that fresh incoming air takes as long as 1000 s to ventilate the hot spot area in some specific regions on campus. We also found that even the slightest upward wind causes a sneeze plume to dissipate almost instantaneously at lower elevations. However, downward wind provides a stable condition for the plume, and forward wind can carry a plume even beyond six feet, the recommended social distance for preventing infection. Additionally, the simulation of sneeze droplets shows that the majority of the particles adhered to the ground or body immediately, and airborne particles can be transported more than six feet, even in a minimal amount of ambient air.

Influence of natural ventilation design on the dispersion of pathogen-laden droplets in a coach bus.

Natural ventilation is an energy-efficient design approach to reduce infection risk (IR), but its optimized design in a coach bus environment is less studied. Based on a COVID-19 outbreak in a bus in Hunan, China, the indoor-outdoor coupled CFD modeling approach is adopted to comprehensively explore how optimized bus natural ventilation (e.g., opening/closing status of front/middle/rear windows (FW/MW/RW)) and ceiling wind catcher (WCH) affect the dispersion of pathogen-laden droplets (tracer gas, 5 µm, 50 µm) and IR. Other key influential factors including bus speed, infector's location, and ambient temperature (Tref) are also considered. Buses have unique natural ventilation airflow patterns: from bus rear to front, and air change rate per hour (ACH) increases linearly with bus speed. When driving at 60 km/h, ACH is only 6.14 h-1 and intake fractions of tracer gas (IFg) and 5 µm droplets (IFd) are up to 3372 ppm and 1394 ppm with ventilation through leakages on skylights and no windows open. When FW and RW are both open, ACH increases by 43.5 times to 267.50 h-1, and IFg and IFd drop rapidly by 1-2 orders of magnitude compared to when no windows are open. Utilizing a wind catcher and opening front windows significantly increases ACH (up to 8.8 times) and reduces IF (5-30 times) compared to only opening front windows. When the infector locates at the bus front with FW open, IFg and IFd of all passengers are <10 ppm. More droplets suspend and further spread in a higher Tref environment. It is recommended to open two pairs of windows or open front windows and utilize the wind catcher to reduce IR in coach buses.

Numerical simulation of social distancing of preventing airborne transmission in open space with lateral wind direction, taking into account temperature of human body and floor surface.

This paper presents the numerical results of particle propagation in open space, taking into account the temperature of the human body and the surface of the ground. And also, the settling of particles or droplets under the action of gravitational force and transport in the open air is taken into account, taking into account the temperature during the process of breathing and sneezing or coughing. The temperature of the body and the surface of the ground, different rates of particle emission from the mouth, such as breathing and coughing or sneezing, are numerically investigated. The effect of temperature, cross-inlet wind, and the velocity of particle ejection from a person's mouth on social distancing is being investigated using a numerical calculation. The variable temperature of the human body forms a thermal plume, which affects the increase in the trajectory of the particle propagation, taking into account the lateral air flow. The thermal plume affects the particles in the breathing zone and spreads the particles over long distances in the direction of the airflow. The result of this work shows that in open space, taking into account the temperature of the body and the surface of the ground, a 2-m social distance may be insufficient for the process of sneezing and social distance must be observed depending on the breathing mode.

Return connectedness and multiscale spillovers across clean energy indices and grain commodity markets around COVID-19 crisis.

The aim of this paper is to examine the return connectedness and multiscale spillovers between the Clean Energy Index and the grain commodity market around COVID-19. Using daily data from January 4, 2017 to July 1, 2022, a time-varying parametric vector autoregressive (TVP-VAR) connectedness approach is first used to reveal connectedness patterns before and during COVID-19. We further used Baruník and Krehlík (2018)'s frequency domain spillover method to assess connectedness in different domain horizons. Our results show spillover effects over time and frequency, with COVID-19 significantly affecting the connectedness of the whole system. Dynamic connectedness peaks significantly after sudden bursts of COVID-19, validating the reported uncertainty. We also documented higher spillover levels in the short term than in the medium and long term. In addition, We find that (i) most clean energy indices are positively affected by the COVID-19 outbreak; (ii) the NASDAQ OMX Bio/Clean Fuels Index and NASDAQ OMX Geothermal Index send spillovers to all grain commodities throughout the sample period, while the WilderHill Clean Energy Index and NASDAQ OMX Wind Energy Index indices are the largest recipients of spillovers from other markets regardless of time horizon; (iii) the OMX Bio/Clean Fuels and OMX Geothermal Energy indices dominate spillover shocks to grain commodity markets. This is the first study to analyse the connectedness and time-frequency dynamics of returns in the green energy index and the grain commodity market. These results provide valuable insights to investors and key policymakers, especially at a time of more significant uncertainty.

Modeling and Preliminary Analysis of the Impact of Meteorological Conditions on the COVID-19 Epidemic.

Since the COVID-19 epidemic outbreak at the end of 2019, many studies regarding the impact of meteorological factors on the attack have been carried out, and inconsistent conclusions have been reached, indicating the issue's complexity. To more accurately identify the effects and patterns of meteorological factors on the epidemic, we used a combination of logistic regression (LgR) and partial least squares regression (PLSR) modeling to investigate the possible effects of common meteorological factors, including air temperature, relative humidity, wind speed, and surface pressure, on the transmission of the COVID-19 epidemic. Our analysis shows that: (1) Different countries and regions show spatial heterogeneity in the number of diagnosed patients of the epidemic, but this can be roughly classified into three types: "continuous growth", "staged shock", and "finished"; (2) Air temperature is the most significant meteorological factor influencing the transmission of the COVID-19 epidemic. Except for a few areas, regional air temperature changes and the transmission of the epidemic show a significant positive correlation, i.e., an increase in air temperature is conducive to the spread of the epidemic; (3) In different countries and regions studied, wind speed, relative humidity, and surface pressure show inconsistent correlation (and significance) with the number of diagnosed cases but show some regularity.

The impact of weather on COVID-19 pandemic.

Rising temperature levels during spring and summer are often argued to enable lifting of strict containment measures even in the absence of herd immunity. Despite broad scholarly interest in the relationship between weather and coronavirus spread, previous studies come to very mixed results. To contribute to this puzzle, the paper examines the impact of weather on the COVID-19 pandemic using a unique granular dataset of over 1.2 million daily observations covering over 3700 counties in nine countries for all seasons of 2020. Our results show that temperature and wind speed have a robust negative effect on virus spread after controlling for a range of potential confounding factors. These effects, however, are substantially larger during mealtimes, as well as in periods of high mobility and low containment, suggesting an important role for social behaviour.

Effect of meteorological factors on the COVID-19 cases: a case study related to three major cities of the Kingdom of Saudi Arabia.

The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.

The impact of meteorological factors and PM2.5 on COVID-19 transmission.

In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system.

Dominance of the residential sector in Chinese black carbon emissions as identified from downwind atmospheric observations during the COVID-19 pandemic.

Emissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44-82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb-Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (- 18%) during Feb-Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (- 35%) in the period Feb-Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48-100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.

A wind speed threshold for increased outdoor transmission of coronavirus: an ecological study.

BACKGROUND: To examine whether outdoor transmission may contribute to the COVID-19 epidemic, we hypothesized that slower outdoor wind speed is associated with increased risk of transmission when individuals socialize outside. METHODS: Daily COVID-19 incidence reported in Suffolk County, NY, between March 16th and December 31st, 2020, was the outcome. Average wind speed and maximal daily temperature were collated by the National Oceanic and Atmospheric Administration. Negative binomial regression was used to model incidence rates while adjusting for susceptible population size. RESULTS: Cases were very high in the initial wave but diminished once lockdown procedures were enacted. Most days between May 1st, 2020, and October 24th, 2020, had temperatures 16-28 °C and wind speed diminished slowly over the year and began to increase again in December 2020. Unadjusted and multivariable-adjusted analyses revealed that days with temperatures ranging between 16 and 28 °C where wind speed was < 8.85 km per hour (KPH) had increased COVID-19 incidence (aIRR = 1.45, 95% C.I. = [1.28-1.64], P < 0.001) as compared to days with average wind speed ≥ 8.85 KPH. CONCLUSION: Throughout the U.S. epidemic, the role of outdoor shared spaces such as parks and beaches has been a topic of considerable interest. This study suggests that outdoor transmission of COVID-19 may occur by noting that the risk of transmission of COVID-19 in the summer was higher on days with low wind speed. Outdoor use of increased physical distance between individuals, improved air circulation, and use of masks may be helpful in some outdoor environments where airflow is limited.

Life cycle assessment of the use of decommissioned wind blades in second life applications.

53,000 tonnes of blade waste from on-shore wind farms will potentially be generated in Ireland by 2040. The recycling of blades, which are made from composite material, is costly and thus far no high volume recycling solution exists. Repurposing blades into second life structures is an alternative which is gaining in popularity, but has many challenges. Green Public Procurement has the potential to help drive demand for blade products in Irish public works. The Re-Wind project has generated a Design Atlas with 47 blade product concepts and these are screened for their ability to overcome repurposing challenges. Three Irish scenarios are developed based on this ranking, maximal utilization of the blade, and on the end customer. Life Cycle Assessment is used to determine the marginal environmental impacts of the raw material substitution provided by the use of blade material. Focusing on greenhouse gas emissions, an estimated 342 kg CO2 e can be saved for every tonne of blade waste used in these scenarios. Blade substitution of steel products was found to provide the most impact, followed by substitution of concrete products. Although repurposing is unlikely to offer an end-of-life solution for all Irish blade waste, the use of 20% of this material annually would divert 315 tonnes of blade waste from landfill, as well as avoiding emissions of 71,820 kg CO2 e. Green procurement has the potential to create a demand for repurposed blade products, which in turn could create jobs in high unemployment areas. Utilization of repurposed, local material could contribute to creating resiliency in supply chains. Both job creation and supply chain resiliency are essential for a post-Covid recovery in Ireland.

Effect of Train-Induced Wind on the Transmission of COVID-19: A New Insight into Potential Infectious Risks.

The unprecedented COVID-19 pandemic has caused a traffic tie-up across the world. In addition to home quarantine orders and travel bans, the social distance guideline of about six feet was enacted to reduce the risk of contagion. However, with recent life gradually returning to normal, the crisis is not over. In this research, a moving train test and a Gaussian puff model were employed to investigate the impact of wind raised by a train running on the transmission and dispersion of SARS-CoV-2 from infected individuals. Our findings suggest that the 2 m social distance guideline may not be enough; under train-induced wind action, human respiratory disease-carrier droplets may travel to unexpected places. However, there are deficiencies in passenger safety guidelines and it is necessary to improve the quantitative research in the relationship between train-induced wind and virus transmission. All these findings could provide a fresh insight to contain the spread of COVID-19 and provide a basis for preventing and controlling the pandemic virus, and probe into strategies for control of the disease in the future.

The link between COVID-19 mortality and PM2.5 emissions in rural and medium-size municipalities considering population density, dust events, and wind speed.

One contemporary issue is how environmental pollution and climate can affect the dissemination and severity of COVID-19 in humans. We documented the first case of association between particulate matter ≤2.5 µm (PM2.5) and COVID-19 mortality rates that involved rural and medium-sized municipalities in northwestern Mexico, where direct air quality monitoring is absent. Alternatively, anthropogenic PM2.5 emissions were used to estimate the PM2.5 exposure in each municipality using two scenarios: 1) considering the fraction derived from combustion of vehicle fuel; and 2) the one derived from modeled anthropogenic sources. This study provides insights to better understand and face future pandemics by examining the relation between PM2.5 pollution and COVID-19 mortality considering the population density and the wind speed. The main findings are: (i) municipalities with high PM2.5 emissions and high population density have a higher COVID-19 mortality rate; (ii) the exceptionally high COVID-19 mortality rates of the rural municipalities could be associated to dust events, which are common in these regions where soils without vegetation are dominant; and (iii) the influence of wind speed on COVID-19 mortality rate was evidenced only in municipalities with <100 inhabitants per km2. These results confirm the suggestion that high levels of air pollutants associated with high population density and an elevated frequency of dust events may promote an extended prevalence and severity of viral particles in the polluted air of urban, suburban, and rural communities. This supports an additional means of dissemination of the coronavirus SARS-CoV-2, in addition to the direct human-to-human transmission.

Airborne infection risk during open-air cardiopulmonary resuscitation.

AIM: Cardiopulmonary resuscitation (CPR) is an emergency procedure where interpersonal distance cannot be maintained. There are and will always be outbreaks of infection from airborne diseases. Our objective was to assess the potential risk of airborne virus transmission during CPR in open-air conditions. METHODS: We performed advanced high-fidelity three-dimensional modelling and simulations to predict airborne transmission during out-of-hospital hands-only CPR. The computational model considers complex fluid dynamics and heat transfer phenomena such as aerosol evaporation, breakup, coalescence, turbulence, and local interactions between the aerosol and the surrounding fluid. Furthermore, we incorporated the effects of the wind speed/direction, the air temperature and relative humidity on the transport of contaminated saliva particles emitted from a victim during a resuscitation process based on an Airborne Infection Risk (AIR) Index. RESULTS: The results reveal low-risk conditions that include wind direction and high relative humidity and temperature. High-risk situations include wind directed to the rescuer, low humidity and temperature. Combinations of other conditions have an intermediate AIR Index and risk for the rescue team. CONCLUSIONS: The fluid dynamics, simulation-based AIR Index provides a classification of the risk of contagion by victim's aerosol in the case of hands-only CPR considering environmental factors such as wind speed and direction, relative humidity and temperature. Therefore, we recommend that rescuers perform a quick assessment of their airborne infectious risk before starting CPR in the open air and positioning themselves to avoid wind directed to their faces.

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID-19) in a hot, arid climate.

The rapid outbreak of the coronavirus disease (COVID-19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions), and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-19 cases and atmospheric variables, such as temperature, relative humidity, wind speed, visibility, and solar radiation, were collected for the period February 24 to May 30, 2020. Stochastic models were employed to analyze how atmospheric variables can affect the transmission of SARS-CoV-2. The normal and lognormal probability and cumulative density functions (PDF and CDF) were applied to analyze the relationship between atmospheric variables and COVID-19 cases. The Spearman's rank correlation test and multiple regression model were used to investigate the correlation of the studied variables with the transmission of SARS-CoV-2 and to confirm the findings obtained from the stochastic models. The results indicate that relative humidity had a significant negative correlation with the number of COVID-19 cases, whereas positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, whereas inversely related to the humidity. The lowest growth rate and longest doubling time of the COVID-19 infection occurred during the full lockdown period. The results in this study may help the World Health Organization (WHO) make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. Integr Environ Assess Manag 2022;18:500-516. © 2021 SETAC.

A systematic review and meta-analysis on correlation of weather with COVID-19.

This study presents a systematic review and meta-analysis over the findings of significance of correlations between weather parameters (temperature, humidity, rainfall, ultra violet radiation, wind speed) and COVID-19. The meta-analysis was performed by using 'meta' package in R studio. We found significant correlation between temperature (0.11 [95% CI 0.01-0.22], 0.22 [95% CI, 0.16-0.28] for fixed effect death rate and incidence, respectively), humidity (0.14 [95% CI 0.07-0.20] for fixed effect incidence) and wind speed (0.58 [95% CI 0.49-0.66] for fixed effect incidence) with the death rate and incidence of COVID-19 (p < 0.01). The study included 11 articles that carried extensive research work on more than 110 country-wise data set. Thus, we can show that weather can be considered as an important element regarding the correlation with COVID-19.

Non-linear link between temperature difference and COVID-19: Excluding the effect of population density.

INTRODUCTION: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. METHODOLOGY: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. RESULTS: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. CONCLUSIONS: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.