Low Concentration Ozone for Surface Disinfection against Sars-Cov-2 and Feline Coronavirus

Intro: Surface and environment disinfection is an important part of infection control strategies, especially in the ongoing COVID-19 pandemic. Ozone, a highly reactive oxidant, is a widely used disinfectant in many industries including food, healthcare and water treatment. It has a broad-spectrum activity and leaves no harmful residues. However, most demonstrated efficacy has been at high ozone levels (>1ppm) which can be harmful to humans in case of exposure. Here, we undertook a study to evaluate if exposure to ozone is effective in inactivating SARS-CoV-2 and feline coronavirus (FCoV) even at low concentrations. Method(s): Ozone at 0.07, 0.1 and 1.2 ppm were evaluated for its virucidal activity against SARS-CoV-2 and FCoV. An ozone gas generator (Medklinn Air + Surface Sterilizer (CerafusionTM Technology), Medklinn, Malaysia) supplied controlled levels of ozone to a custom-built chamber of 1.5 ft3 (1.5ft x 1ft x 1ft) where dry virus films containing 1 x 104 PFU of test virus were exposed to ozone gas for 0.5h, 1h, 3h, 5h, and 8h. The experiment was performed at ambient temperature (23-24oC) and relative humidity (RH) of 55% (FCoV only) and 85% (SARS-CoV-2 and FCoV). Finding(s): At low level of ozone of 0.1ppm, >90% reduction of both viruses was achieved after 3h exposure at 85% and 55% humidity. At 1.2ppm, >90% reduction of both viruses was achieved after 0.5h exposure at 85% humidity. Ozone at 0.07ppm, however, did not show good efficacy as reduction not exceeding 90% was achieved only after 8h exposure at 85% and 55% humidity. Conclusion(s): The study demonstrated that low concentration of ozone of at least 0.1 ppm reduced SARS-CoV-2 and FCoV by >90% when used at 85% humidity. The use of low level ozone presents a safer alternative for disinfecting enclosed spaces and greatly reduces any potential harmful health effects in case of accidental exposure.Copyright © 2023

Influence of Meteorological Factors on Covid-19 Incidence in the Conditions of Ukrain

Objective: The coronavirus disease (COVID-19) is a problem for the health care systems of many countries around the world. Seasonal nature of influenza and other the respiratory viral diseases is commonly known. The nature of the relationship between the frequency of registration of cases of COVID-19 and natural factors is still being studied by researchers. The purpose is to determine the influence of air temperature, relative humidity, wind speed, and atmospheric pressure on the incidence of the coronavirus disease COVID-19 in the conditions of Ukraine. Materials and methods. Official reports of the Ministry of Health of Ukraine and data from daily monitoring of meteorological indicators conducted by the Sumy Regional Hydrometeorology Center were used in the paper. Descriptive and analytical ways of epidemiological method of investigation were applied. The search for parameters of interrelation between the frequency of registration of COVID-19 cases and meteorological cases took place using of program "Statistica", namely the relevant tools of this program: "Analysis"/ "Multiple regression". Results and Discussion: In the period under study from March 25, 2020 to December 31, 2021 in Sumy Oblast of Ukraine, three waves of rise in the incidence were registered. In the third wave of rise in the incidence, in autumn 2021 the frequency of registration of COVID-19 cases reached 1684.9 per 100 thousand of people, despite the fact that almost 70 % of the population had already recovered or were vaccinated. Meteorological factors in the conditions of Ukraine have little influence on the rate of spread of COVID-19. The value of multiple correlation coefficients was within those limits, which are considered moderate in terms of influence. A moderate inverse correlation was established between the frequency of registration of COVID-19 cases and indicators of air temperature, and a direct correlations-with indicators of relative air humidity. Conclusion(s): In the conditions of Ukraine, the studied meteorological factors (air temperature, relative humidity, wind speed, atmospheric pressure) indirectly influenced the intensity of the epidemic process of COVID-19. the strength of this influence was either weak or moderate.Copyright © 2023, Ibn Sina Trust. All rights reserved.

Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study.

BACKGROUND: Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs). OBJECTIVE: To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs. METHODS: We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model. RESULTS: There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites. CONCLUSION: Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.

Climate influences on COVID‐19 prevalence rates: An application of a panel data spatial model

The present study aims to measure the impact of climate characteristics on the prevalence rate of coronavirus disease 2019 (COVID‐19) in Brazilian states given the exogenous nature of these variables. We used a daily panel for the period from March 10 to April 10, 2020, the first phase of the pandemic, as there were few intervention policies to contain the spread of COVID‐19 during that period, and it was estimated through generalized least squares (GLS) spatial models to control the presence of spatial spillover, first‐order autoregressive errors, and correlation between cross‐sections. Considering the COVID‐19 incubation period and the time it takes for COVID‐19 symptoms to manifest, the econometric models were estimated using the 14‐, 11‐, and 7‐day moving averages of the climate variables. The results showed that increases of 1% in the solar incidence, average temperature, and relative humidity of the air reduced COVID‐19 prevalence rates by 0.16%, 0.049%, and 0.22%, respectively, considering the 11‐day moving average.Alternate :El presente estudio tiene como objetivo medir el impacto de las características climáticas en la prevalencia de la enfermedad por coronavirus 2019 (COVID‐19) en los estados brasileños, dada la naturaleza exógena de estas variables. Se utilizó un panel diario para el período comprendido entre el 10 de marzo y el 10 de abril de 2020, la primera fase de la pandemia, ya que hubo pocas políticas de intervención para contener la propagación de COVID‐19 durante ese período, y se estimó mediante modelos espaciales de mínimos cuadrados generalizados (GLS) para controlar la presencia de spillover espacial, errores autorregresivos de primer orden y la correlación entre muestras representativas. Teniendo en cuenta el periodo de incubación de COVID‐19 y el tiempo que tardan en manifestarse los síntomas de COVID‐19, los modelos econométricos se estimaron utilizando las medias móviles de 14, 11 y 7 días de las variables climáticas. Los resultados mostraron que aumentos del 1% en la incidencia solar, la temperatura media y la humedad relativa del aire redujeron la prevalencia de COVID‐19 en un 0,16%, 0,049% y 0,22%, respectivamente, teniendo en cuenta la media móvil de 11 días.Alternate :抄録本稿では、ブラジルの各州における新型コロナウイルス感染症 (COVID‐19)の罹患率に対する気候特性の影響を、これらの変数の外因性を考慮して、測定する。パンデミックの第一波である2020年3月10日~4月10日の期間は、COVID‐19の拡散を封じ込めるための介入政策がほとんどなかったが、この期間の毎日のパネルデータを使用し、一般化最小二乗法 (GLS)による空間モデルを用いて、空間スピルオーバー、1次自己回帰のエラー、および横断面間の相関の存在を制御して、推定した。COVID‐19の潜伏期間と発症までにかかる時間を考慮し、気候変数の14、11、7日間の移動平均を用いて計量経済モデルを推定した。結果から、11日間の移動平均を考慮すると、太陽光の入射、平均気温、空気中の相対湿度の1%増加が、それぞれCOVID‐19罹患率を0.16%、 0.049%、0.22%減少させることが示された。

Effects of Visitor Influx on the Indoor Climate of the Milan Cathedral

The indoor climate of non-climatized churches is usually subject to cyclical fluctuations of temperature and relative humidity induced by external climate conditions which might be dampened by the high thermal capacity of their envelope. However, several phenomena affect their indoor climate (e.g., internal gains due to people and artificial lighting, air infiltration, etc.), which lead to environmental variations that might jeopardize the artworks contained within. In particular, one of the most influential parameters that may affect non-climatized churches is the massive and intermittent presence of people who constantly visit their spaces. In such regard, long-term monitoring allows the collection of environmental data with different building operation conditions and visitor fluxes. This paper analyses the indoor climate of the Milan Cathedral (Duomo di Milano) in Italy for three continuous years (including the lockdown period that occurred in 2020 caused by the COVID-19 pandemic), with a focus on visitors' effects on the indoor environment and the conservation of the main artworks contained within. The results of the analysis have shown that spaces with huge volume are most influenced by the opening of the doors rather than the hygrothermal contribution of the intermittent presence of massive crowds. Moreover, the absence of visitors for a prolonged period correlates with an improvement in the indoor conservation conditions for artworks, especially those made of hygroscopic materials, due to the reduction in short, rapid climate fluctuations.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Knowledge regarding heat stress and practice of personal protective equipment use among healthcare workers during the COVID 19 pandemic

Introduction: The need for healthcare workers (HCWs) to wear personal protective equipment (PPE) during the coronavirus disease 2019 (COVID-19) pandemic heightens their risk of thermal stress. Objective(s): To determine knowledge regarding heat stress and practice of PPE among HCWs during the COVID-19 pandemic. Material(s) and Method(s): A cross-sectional observational study was done among 115 HCWs using a self-designed questionnaire to identify their thoughts on heat stress caused by PPE wear during the COVID-19 pandemic. The ambient temperature, relative humidity (RH), and dew point in their respective locations were recorded using data loggers (EL-USB-2-LCD+, Lascar Electronics, Salisbury, UK). R version 3.6.2 was used to examine the frequency and descriptive statistics from the survey findings. Pearson's chi-square was used to determine associations. A p value of <0.05 was considered statistically significant. Result(s): The average wet bulb globe temperature (WBG) reported was 30.0oC (IQR 29.1-31.8oC) according to the heat exposure data. The majority of the 115 HCWs were involved in medical consulting (39%), followed by nursing (29%). The researchers discovered that 50% of the workers (n=57) were wearing 5-6 PPEs and had a fourteen-fold increased risk of developing an undesirable thermal health symptom (95 % CI: 0.77 to 280.24;p=0.0737). In addition, 36% of the participants (n=42) worked between 8 and 10 hours every shift with little or no rest. It was also important to note that 76% (n=88) of them worked in non-air-conditioned environments, and 33% (n=38) of them had excessive workloads, which could add to the employees' heat burden. More than half of them agreed that wearing PPE is extremely inconvenient, and they avoided taking breaks to maintain infection control. The impression of thermal stress was reported by 96% of the participants (n=111), and it was found to be substantially linked with the job they were engaged [OR 0.0654 (95% CI: 0.003- 1.246) P=0.001]. More than half of the participants strongly agreed that appropriate rest and hydration before work could help them lower their thermal stress. Conclusion(s): HCWs are cognizant of the effects of heat stress but might not adopt best practices due to various constraints. Ice slurry ingestion could be a practical and effective method for promoting thermal comfort. Adverse effects of heat stress on productivity and judgment of HCWs warrant further investigation.

Saliva Droplet Evaporation Experiment and Simple Correlation of Evaporation-falling Curve under Different Temperatures and RH

In the present study, water and saliva droplet evaporation rates are experimentally evaluated in various environmental conditions: temperature and relative humidity. We found that the ratio of saliva residue size to saliva initial droplet size is 0.216. We also found that the saliva correction factor which is defined as a ratio of water evaporation coefficient to saliva evaporation coefficient was not affected by environmental conditions and was determined as 0.857. By considering the evaporation and gravitational settling effects, the saliva airborne lifetime, i.e., the evaporation-falling curve is numerically calculated and characterized. In addition to this numerical calculation results, we present the simple correlation equation for obtaining the evaporation-falling curve under different temperatures and relative humidity. © 2023, AAGR Aerosol and Air Quality Research. All rights reserved.

Intelligent Ventilation System for COVID-19 Prevention using the IoT and Decision Tree

COVID-19 can be spread through the air, caused by inhaling smaller droplets1 containing SARS-CoV-2 in an indoor environment. In particular, both people with symptoms and people without symptoms make many small droplets and respiratory droplets when they breathe, sneeze, cough, or speak. When these tiny droplets are exposed to the air around them, they can react with the particles (PM) and stay in the air for a long time. The survival period depends on various conditions, including the type of surface, temperature, and relative humidity. The ventilation system is one way to solve this problem. This research makes a significant contribution to the development of intelligent ventilation systems utilizing the Internet of Things (IoT) and decision tree algorithms. The function of this system is to get a clean room environment from viruses by spraying disinfectant automatically and controlling the temperature and humidity of the air. Based on the results of this study, the system is able to control and categorize room conditions based on temperature and humidity factors. The classification value using the C4.5 decision tree algorithm is 92.33% with an average temperature and humidity value of 25oC and 49%. © 2022 IEEE.

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study.

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO2 ). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.

Associations between indoor relative humidity and global COVID-19 outcomes.

Globally, the spread and severity of COVID-19 have been distinctly non-uniform. Seasonality was suggested as a contributor to regional variability, but the relationship between weather and COVID-19 remains unclear and the focus of attention has been on outdoor conditions. Because humans spend most of their time indoors and because most transmission occurs indoors, we here, instead, investigate the hypothesis that indoor climate-particularly indoor relative humidity (RH)-may be the more relevant modulator of outbreaks. To study this association, we combined population-based COVID-19 statistics and meteorological measurements from 121 countries. We rigorously processed epidemiological data to reduce bias, then developed and experimentally validated a computational workflow to estimate indoor conditions based on outdoor weather data and standard indoor comfort conditions. Our comprehensive analysis shows robust and systematic relationships between regional outbreaks and indoor RH. In particular, we found intermediate RH (40-60%) to be robustly associated with better COVID-19 outbreak outcomes (versus RH < 40% or >60%). Together, these results suggest that indoor conditions, particularly indoor RH, modulate the spread and severity of COVID-19 outbreaks.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis.

BACKGROUND: The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory. OBJECTIVE: The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States. METHODS: This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation. RESULTS: Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States. SIGNIFICANCE: The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.

Influence of Hospital Environmental Variables on Thermometric Measurements and Level of Concordance: A Cross-Sectional Descriptive Study.

During a pandemic, and given the need to quickly screen febrile and non-febrile humans, it is necessary to know the concordance between different thermometers (TMs) and understand how environmental factors influence the measurements made by these instruments. OBJECTIVE: The objective of this study is to identify the potential influence of environmental factors on the measurements made by four different TMs and the concordance between these instruments in a hospital setting. METHOD: The study employed a cross-sectional observational methodology. The participants were patients who had been hospitalised in the traumatology unit. The variables were body temperature, room temperature, room relative humidity, light, and noise. The instruments used were a Non Contract Infrared TM, Axillary Electronic TM, Gallium TM, and Tympanic TM. A lux meter, a sound level meter, and a thermohygrometer measured the ambient variables. RESULTS: The study sample included 288 participants. Weak significant relationships were found between noise and body temperature measured with Tympanic Infrared TM, r = -0.146 (p < 0.01) and likewise between environmental temperature and this same TM, r = 0.133 (p < 0.05). The concordance between the measurements made by the four different TMs showed an Intraclass Correlation Coefficient (ICC) of 0.479. CONCLUSIONS: The concordance between the four TMs was considered "fair".

Effect of environmental factors on the concentration distribution of bioaerosols with different particle sizes in an enclosed space

COVID-19 has alerted us about the need to quantify the effect of different environmental factors on the concentration distribution of bioaerosols. An experimental investigation was carried out to evaluate the effect of environmental factors, including air temperature, relative humidity, airflow speed and ultraviolet (UV) radiation, on the potential dispersion risk of bioaerosols in an enclosed space by tracking the Serratia marcescens as the tiny organisms. Research results indicated that the concentration of bioaerosols is the highest at the indoor air temperature of 25°C among the tested conditions (20°C, 25°C, 30°C and 35°C). The particle size of bioaerosols can be influenced by temperature, resulting in changes in the amount of settling. Increasing relative humidity from 50% to 80% and airflow speed from 1.5 m/s to 2.2 m/s have a negative impact on the dispersion of bioaerosols as the amount of particle settlement increases accordingly. As for the UV radiation parameters, a better disinfection efficiency was achieved at a radiation distance of 40 cm in the tested range of 20–50 cm and a radiation exposure time of 30 min in the tested range of 10–50 min. This study delivered novel data for the concentration distribution of bioaerosol under different environmental factors for creating a safe indoor environment. © The Author(s) 2022.

Influence of Indoor Relative Humidity on the Number Concentration, Size Distribution, and Trajectory of Sneeze Droplets: Effects on Social Distancing Guidelines

The spread of the COVID-19 pandemic is mainly due to the direct transmission routes of SARS-CoV-2 virus-carrying aerosols in indoor environments. In this study, the effect of indoor relative humidity (RH∞) on the number concentration, size distribution, and trajectory of sneeze droplets was studied in a confined space experimentally and numerically. Computational fluid dynamics (CFD) simulations using the renormalization group k-ε turbulence model by considering the one-way and two-way (humidity) coupling models were performed to assess the effects of humidity fields on the propagation of droplets. Number concentration profiles indicated that the RH∞ affected the dispersion modes of droplets differently for the puff, droplet cloud, fully-dispersed, and dilute-dispersed droplets phases identified by the shadowgraph imaging technique. The two-way (humidity) coupling model led to a close agreement with the experimental data in all phases. In particular, the two-way coupling provided better agreement with the data in the puff phase compared to the one-way coupling model. However, the one-way coupling model was sufficient for studying the motion of airborne droplets in the other phases. The velocity fields in the droplet cloud were more sensitive to RH∞ than the puff and fully-dispersed droplets phases. Also, the effect of RH∞ on the maximum spreading distance of droplets, dmax,sp, in the puff was insignificant, while its effect became dominant in the dilute-dispersed droplets phase. A dynamic change in the velocity profile of the sneeze jet was seen at a critical relative humidity RH∞,crit of about 48%. At RH∞< RH∞,crit, the number concentration of aerosolized droplets increases, significantly affecting the size distribution and the velocity of droplets. At RH∞≥ RH∞,crit, the effect of evaporation time on the number concentration, and diameter of droplets was negligible. At RH∞ of 24 and 64%, dmax,sp was 2.14 m (7 feet) and 3.05 m (10 feet), respectively. However, a dry indoor environment led to an increase in evaporation rate and more than four times number concentration of aerosolized droplets compared to a humid environment. Thus, the risk of direct transmission of Covid-19 in a humid indoor environment was higher than the dry conditions, which suggested the requirements for incorporating the RH∞ effect in the social distancing guideline.

The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe.

The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.

Aerial roots elevate indoor plant health: Physiological and morphological responses of three high-humidity adapted Araceae species to indoor humidity levels.

Heightened by the COVID-19 pandemic there has been a global increase in urban greenspace appreciation. Indoor plants are equally important for improving mental health and air quality but despite evolving in humid (sub)tropical environments with aerial root types, planting systems ignore aerial resource supply. This study directly compared nutrient uptake preferences of aerial and soil-formed roots of three common houseplant species under high and ambient relative humidities. Growth and physiology parameters were measured weekly for Anthurium andreanum, Epipremnum aureum and Philodendron scandens grown in custom made growth chambers. Both aerial and soil-formed roots were then fed mixtures of nitrate, ammonium and glycine, with one source labelled with 15 N to determine uptake rates and maximum capacities. Aerial roots were consistently better at nitrogen uptake than soil roots but no species, root type or humidity condition showed a preference for a particular nitrogen source. All three species grew more in high humidity, with aerial roots demonstrating the greatest biomass increase. Higher humidities for indoor niches, together with fertiliser applications to aerial roots will support indoor plant growth, creating lush calming indoor environments for people inhabitants.

SARS-CoV-2 persistence on common food covering materials: plastic wrap, fruit wax, and cardboard takeout containers.

AIMS: Assess the persistence of infectious SARS-CoV-2 virus and virus genomic material on three common food coverings. METHODS AND RESULTS: The stability of infectious virus and genomic material on plastic wrap, fruit wax, and cardboard takeout containers was measured. SARS-CoV-2 in simulated saliva was applied to the surface of these materials and allowed to dry. Samples were stored at 4°C or 20°C and a relative humidity of 30%, 50%, 65%, or 70% for up to 7 days. Viability was measured by TCID50 and the half-life for infectious virus was determined to be ~24 hours and ~8 hours at 4°C and 20°C, respectively, on all surfaces and RH tested. There was no loss of virus genomic material as measured by qRT-PCR at all conditions evaluated. CONCLUSIONS: SARS-CoV-2 virus remains infectious on food coverings for hours to days. It is estimated that a 99.9% reduction in titer requires 10 days at 4°C and 3 days at 20°C for all RH tested. SARS-CoV-2 genomic material showed no loss when assayed by qRT-PCR. Significance and Impact of Study: SARS-CoV-2 virus on food coverings loses infectivity over a certain period, but PCR assays can still detect virus genomic material throughout the same time. Thus, testing and controls may need to consider the fact that virus genomic material may still be detected when no infectious virus is present.

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

Environmental parameters have a significant impact on the spread of respiratory viral diseases (temperature (T), relative humidity (RH), and air saturation state). T and RH are strongly correlated with viral inactivation in the air, whereas supersaturated air can promote droplet deposition in the respiratory tract. This study introduces a new concept, the dynamic virus deposition ratio (α), that reflects the dynamic changes in viral inactivation and droplet deposition under varying ambient environments. A non-steady-state-modified Wells-Riley model is established to predict the infection risk of shared air space and highlight the high-risk environmental conditions. Findings reveal that a rise in T would significantly reduce the transmission of COVID-19 in the cold season, while the effect is not significant in the hot season. The infection risk under low-T and high-RH conditions, such as the frozen seafood market, is substantially underestimated, which should be taken seriously. The study encourages selected containment measures against high-risk environmental conditions and cross-discipline management in the public health crisis based on meteorology, government, and medical research.

Association Between Air Pollution, Climate Change, and COVID-19 Pandemic: A Review of the Recent Scientific Evidence

Background: Recent studies indicated the possible relationship between climate change, environmental pollution, and Coronavirus Disease 2019 (COVID-19) pandemic. This study reviewed the effects of air pollution, climate parameters, and lockdown on the number of cases and deaths related to COVID-19. Methods: The present review was performed to determine the effects of weather and air pollution on the number of cases and deaths related to COVID-19 during the lockdown. Articles were collected by searching the existing online databases, such as PubMed, Science Direct, and Google Scholar, with no limitations on publication dates. Afterwards, this review focused on outdoor air pollution, including PM2.5, PM10, NO2, SO2, and O-3, and weather conditions affecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19. Results: Most reviewed investigations in the present study showed that exposure to air pollutants, particularly PM2.5 and NO2, is positively related to COVID-19 patients and mortality. Moreover, these studies showed that air pollution could be essential in transmitting COVID-19. Local meteorology plays a vital role in coronavirus spread and mortality. Temperature and humidity variables are negatively correlated with virus transmission. The evidence demonstrated that air pollution could lead to COVID-19 transmission. These results support decision-makers in curbing potential new outbreaks. Conclusions: Overall, in environmental perspective-based COVID-19 studies, efforts should be accelerated regarding effective policies for reducing human emissions, bringing about air pollution and weather change. Therefore, using clean and renewable energy sources will increase public health and environmental quality by improving global air quality.