A holistic modeling framework for estimating the influence of climate change on indoor air quality.

The IPCC 2021 report predicts rising global temperatures and more frequent extreme weather events in the future, which will have different effects on the regional climate and concentrations of ambient air pollutants. Consequently, changes in heat and mass transfer between the inside and outside of buildings will also have an increasing impact on indoor air quality. It is therefore surprising that indoor spaces and occupant well-being still play a subordinate role in the studies of climate change. To increase awareness for this topic, the Indoor Air Quality Climate Change (IAQCC) model system was developed, which allows short and long-term predictions of the indoor climate with respect to outdoor conditions. The IAQCC is a holistic model that combines different scenarios in the form of submodels: building physics, indoor emissions, chemical-physical reaction and transformation, mold growth, and indoor exposure. IAQCC allows simulation of indoor gas and particle concentrations with outdoor influences, indoor materials and activity emissions, particle deposition and coagulation, gas reactions, and SVOC partitioning. These key processes are fundamentally linked to temperature and relative humidity. With the aid of the building physics model, the indoor temperature and humidity, and pollutant transport in building zones can be simulated. The exposure model refers to the calculated concentrations and provides evaluations of indoor thermal comfort and exposure to gaseous, particulate, and microbial pollutants.

Deposition potential of 0.003-10 µm ambient particles in the humidified human respiratory tract: Contribution of new particle formation events in Beijing.

Ultrafine particles (UFPs) usually explosive growth during new particle formation (NPF) events. However, the risk of exposure to UFPs on NPF days has been ignored due to the prevalence of mass-based air quality standards. In this study, the daily deposited doses, i.e., the daily deposited particle number dose (DPNd), mass dose (DPMd), and surface area dose (DPSd), of ambient particles in the human respiratory tract in Beijing were evaluated based on the particle number size distribution (3 nm-10 µm) from June 2018 to May 2019 utilizing a Multiple-Path Particle Dosimetry Model (MPPD) after the hygroscopic growth of particles in the respiratory tract had been accounted for. Our observations showed a high frequency (72.6%) of NPF on excellent air quality days, with daily mean PM2.5 concentrations less than 35 µg m-3. The daily DPNd on excellent air quality days was comparable with that on polluted days, although the DPMd on excellent air quality days was as low as 15.6% of that on polluted days. The DPNd on NPF days was ~1.3 times that on non-NPF days. The DPNd in respiratory tract regions decreased in the order: tracheobronchial (TB) > pulmonary (PUL) > extrathoracic (ET) on NPF days, while it was PUL > TB > ET on non-NPF days. The number of deposited nucleation mode particles, which were deposited mainly in the TB region (45%), was 2 times higher on NPF days than that on non-NPF days. Our results demonstrated that the deposition potential due to UFPs in terms of particle number concentrations is high in Beijing regardless of the aerosol mass concentration. More toxicological studies related to UFPs on NPF days, especially those targeting tracheobronchial and pulmonary impairment, are required in the future.

Particle number emission rates of aerosol sources in 40 German households and their contributions to ultrafine and fine particle exposure.

More representative data on source-specific particle number emission rates and associated exposure in European households are needed. In this study, indoor and outdoor particle number size distributions (10-800 nm) were measured in 40 German households under real-use conditions in over 500 days. Particle number emission rates were derived for around 800 reported indoor source events. The highest emission rate was caused by burning candles (5.3 × 1013  h-1 ). Data were analyzed by the single-parameter approach (SPA) and the indoor aerosol dynamics model approach (IAM). Due to the consideration of particle deposition, coagulation, and time-dependent ventilation rates, the emission rates of the IAM approach were about twice as high as those of the SPA. Correction factors are proposed to convert the emission rates obtained from the SPA approach into more realistic values. Overall, indoor sources contributed ~ 56% of the daily-integrated particle number exposure in households under study. Burning candles and opening the window leads to seasonal differences in the contributions of indoor sources to residential exposure (70% and 40% in the cold and warm season, respectively). Application of the IAM approach allowed to attribute the contributions of outdoor particles to the penetration through building shell and entry through open windows (26% and 15%, respectively).

Technical control of nanoparticle emissions from desktop 3D printing.

Material extrusion (ME) desktop 3D printing is known to strongly emit nanoparticles (NP), and the need for risk management has been recognized widely. Four different engineering control measures were studied in real-life office conditions by means of online NP measurements and indoor aerosol modeling. The studied engineering control measures were general ventilation, local exhaust ventilation (LEV), retrofitted enclosure, and retrofitted enclosure with LEV. Efficiency between different control measures was compared based on particle number and surface area (SA) concentrations from which SA concentration was found to be more reliable. The study found out that for regular or long-time use of ME desktop 3D printers, the general ventilation is not sufficient control measure for NP emissions. Also, the LEV with canopy hood attached above the 3D printer did not control the emission remarkably and successful position of the hood in relation to the nozzle was found challenging. Retrofitted enclosure attached to the LEV reduced the NP emissions 96% based on SA concentration. Retrofitted enclosure is nearly as efficient as enclosure attached to the LEV (reduction of 89% based on SA concentration) but may be considered more practical solution than enclosure with LEV.

Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters.

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.

Input-Adaptive Proxy for Black Carbon as a Virtual Sensor.

Missing data has been a challenge in air quality measurement. In this study, we develop an input-adaptive proxy, which selects input variables of other air quality variables based on their correlation coefficients with the output variable. The proxy uses ordinary least squares regression model with robust optimization and limits the input variables to a maximum of three to avoid overfitting. The adaptive proxy learns from the data set and generates the best model evaluated by adjusted coefficient of determination (adjR2). In case of missing data in the input variables, the proposed adaptive proxy then uses the second-best model until all the missing data gaps are filled up. We estimated black carbon (BC) concentration by using the input-adaptive proxy in two sites in Helsinki, which respectively represent street canyon and urban background scenario, as a case study. Accumulation mode, traffic counts, nitrogen dioxide and lung deposited surface area are found as input variables in models with the top rank. In contrast to traditional proxy, which gives 20-80% of data, the input-adaptive proxy manages to give full continuous BC estimation. The newly developed adaptive proxy also gives generally accurate BC (street canyon: adjR2 = 0.86-0.94; urban background: adjR2 = 0.74-0.91) depending on different seasons and day of the week. Due to its flexibility and reliability, the adaptive proxy can be further extend to estimate other air quality parameters. It can also act as an air quality virtual sensor in support with on-site measurements in the future.

Characterization of exposure to carbon nanotubes in an industrial setting.

While production and use of carbon nanotubes (CNTs) is increasing, workers exposure to CNTs is expected to increase as well, with inhalation being potentially the main pathway for uptake. However, there have been few studies reporting results about workers' personal exposure to CNTs. In this study, worker exposure to single-walled CNTs (SWCNTs) during the production of conductive films in a modern up-scaling factory was assessed. Particulate matter concentrations (2.5-10 µm) and concentrations of CO and CO2 were monitored by using real-time instruments. Workers' exposure levels to SWCNTs were qualitatively estimated by analyzing particle samples by transmission electron microscopy (TEM). TEM samples identified high aspect ratio (length/width > 500) SWCNTs in workplace air. SWCNT concentrations estimated from micrographs varied during normal operation, reactor use without local exhaust ventilation (LEV), and cleaning between 1.7×10(-3), 5.6 and 6.0×10(-3) SWCNT cm(-3), respectively. However, during cleaning it was unclear whether the SWCNTs originated from the cleaning itself or from other reactor openings. We were unable to quantify the SWCNT emissions with online particle instrumentation due to the SWCNT low concentrations compared to background particle concentrations, which were on average 2.6±1.1×10(3)cm(-3). However, CO concentrations were verified as a good indicator of fugitive emissions of SWCNTs. During normal operation, exposure levels were well below proposed limit values (1.0×10(-2) fibers cm(-3) and 1 µg m(-3)) when LEV was used. Based on the results in this study, the analysis of TEM grids seems to be the only direct method to detect SWCNTs in workplace air.

Do small spores disperse further than large spores?

In species that disperse by airborne propagules an inverse relationship is often assumed between propagule size and dispersal distance. However, for microscopic spores the evidence for the relationship remains ambiguous. Lagrangian stochastic dispersion models that have been successful in predicting seed dispersal appear to predict similar dispersal for all spore sizes up to -40 microm diameter. However, these models have assumed that spore size affects only the downwards drift of particles due to gravitation and have largely omitted the highly size-sensitive deposition process to surfaces such as forest canopy. On the other hand, they have assumed that spores are certain to deposit when the air parcel carrying them touches the ground. Here, we supplement a Lagrangian stochastic dispersion model with a mechanistic deposition model parameterized by empirical deposition data for 1-10 microm spores. The inclusion of realistic deposition improved the ability of the model to predict empirical data on the dispersal of a wood-decay fungus (aerodynamic spore size 3.8 microm). Our model predicts that the dispersal of 1-10 microm spores is in fact highly sensitive to spore size, with 97-98% of 1 microm spores but only 12-58% of 10-microm spores dispersing beyond 2 km in the simulated range of wind and canopy conditions. Further, excluding the assumption of certain deposition at the ground greatly increased the expected dispersal distances throughout the studied spore size range. Our results suggest that by evolutionary adjustment of spore size, release height and timing of release, fungi and other organisms with microscopic spores can change the expected distribution of dispersal locations markedly. The complex interplay of wind and canopy conditions in determining deposition resulted in some counterintuitive predictions, such as that spores disperse furthest under intermediate wind, providing intriguing hypotheses to be tested empirically in future studies.

Constructing transferable and interpretable machine learning models for black carbon concentrations.

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Infectivity of exhaled SARS-CoV-2 aerosols is sufficient to transmit covid-19 within minutes.

Exhaled SARS-CoV-2-containing aerosols contributed significantly to the rapid and vast spread of covid-19. However, quantitative experimental data on the infectivity of such aerosols is missing. Here, we quantified emission rates of infectious viruses in exhaled aerosol from individuals within their first days after symptom onset from covid-19. Six aerosol samples from three individuals were culturable, of which five were successfully quantified using TCID50. The source strength of the three individuals was highest during singing, when they exhaled 4, 36, or 127 TCID50/s, respectively. Calculations with an indoor air transmission model showed that if an infected individual with this emission rate entered a room, a susceptible person would inhale an infectious dose within 6 to 37 min in a room with normal ventilation. Thus, our data show that exhaled aerosols from a single person can transmit covid-19 to others within minutes at normal indoor conditions.

Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe.

This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017-2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal patterns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated. The result showed that the peak concentrations of LDSA at UB and TR sites are commonly observed in the morning (06:00-8:00 UTC) and late evening (19:00-22:00 UTC), coinciding with traffic rush hours, biomass burning, and atmospheric stagnation periods. The only LDSA night-time peaks are observed on weekends. Due to the variability of emission sources and meteorology, the seasonal variability of the LDSA concentration revealed significant differences (p = 0.01) between the four seasons at all monitoring sites. Meanwhile, the correlations of LDSA with other pollutant metrics suggested that Aitken and accumulation mode particles play a significant role in the total LDSA concentration. The results also indicated that the main proportion of total LDSA is attributed to the ALV fraction (50 %), followed by the TB (34 %) and HA (16 %). Overall, this study provides valuable information of LDSA as a predictor in epidemiological studies and for the first time presenting total LDSA in a variety of European urban environments.

Indoor Exposure and Regional Inhaled Deposited Dose Rate during Smoking and Incense Stick Burning-The Jordanian Case as an Example for Eastern Mediterranean Conditions.

Tobacco smoking and incense burning are commonly used in Jordanian microenvironments. While smoking in Jordan is prohibited inside closed spaces, incense burning remains uncontrolled. In this study, particle size distributions (diameter 0.01-25 µm) were measured and inhaled deposited dose rates were calculated during typical smoking and incense stick-burning scenarios inside a closed room, and the exposure was summarized in terms of number and mass concentrations of submicron (PNSub) and fine particles (PM2.5). During cigarette smoking and incense stick-burning scenarios, the particle number concentrations exceeded 3 × 105 cm-3. They exceeded 5 × 105 cm-3 during shisha smoking. The emission rates were 1.9 × 1010, 6.8 × 1010, and 1.7 × 1010 particles/s, respectively, for incense, cigarettes, and shisha. That corresponded to about 7, 80, and 120 µg/s, respectively. Males received higher dose rates than females, with about 75% and 55% in the pulmonary/alveolar during walking and standing, respectively. The total dose rates were in the order of 1012-1013 #/h (103-104 µg/h), respectively, for PNSub and PM2.5. The above reported concentrations, emissions rates, and dose rates are considered seriously high, recalling the fact that aerosols emitted during such scenarios consist of a vast range of toxicant compounds.

Estimation of the Seasonal Inhaled Deposited Dose of Particulate Matter in the Respiratory System of Urban Individuals Living in an Eastern Mediterranean City.

In this study, we present an estimation for the inhaled deposited dose rate in adult males and females during common exposure scenarios to urban background aerosols in an Eastern Mediterranean city (Amman, Jordan) based on a one-year database of measured particle number size distribution. The dose rates show seasonal variations reflecting the physical characteristics (i.e., modal structure) of the particle number size distribution. An additional factor was the varying deposition fraction (DF) for different regions and different human activities (exercising versus resting). The total dose rate was 3 × 109-65 × 109 particles/h (PM2.5 and PM10 doses 1-22 µg/h and 9-210 µg/h; respectively) depending on the gender, activity, and season. Based on the particle number metrics, the inhaled deposited dose in the head, Tracheobronchial, and alveolar were 7-16%, 16-28%, and 56-76%; respectively. Based on the PM2.5 metric, the corresponding dose rate was 9-41%,13-19%; and 46-72% respectively. As for the PM10 metric, they were 25-75%, 7-35%, and 15-55%; respectively.

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan.

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5-40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant.

Improving the current air quality index with new particulate indicators using a robust statistical approach.

To convey the severity of ambient air pollution level to the public, air quality index (AQI) is used as a communication tool to reflect the concentrations of individual pollutants on a common scale. However, due to the enhanced air pollution control in recent years, air quality has improved, and the roles of some air pollutant species included in the existing AQI as urban air pollutants have diminished. In this study, we suggest the current AQI should be revised in a way that new air pollution indicators would be considered so that it would better represent the health effects caused by local combustion processes from traffic and residential burning. Based on the air quality data of 2017-2019 in three different sites in Helsinki metropolitan area, we assumed the statistical distributions of the current indicators (NO2 and PM2.5) and the proposed particulate indicators (BC, LDSA and PNC) were related as they have similar sources in urban regions despite the varying correlations between the current and proposed indicators (NO2: r = 0.5-0.85, PM2.5: r = 0.28-0.72). By fitting the data to an optimal distribution function, together with expert opinions, we improved the current Finnish AQI and determined the AQI breakpoints for the proposed indicators where this robust statistical approach is transferrable to other cities. The addition of the three proposed indicators to the current AQI would decrease the number of good air quality hours in all three environments (largest decrease in urban traffic site, ~22 %). The deterioration of air quality class appeared more severe during peak hours in the urban traffic site due to vehicular emission and evenings in the detached housing site where domestic wood combustion often takes place. The introduction of the AQI breakpoints of the three new indicators serve as a first step of improving the current AQI before further air quality guideline levels are updated.

Aerosol concentrations and size distributions during clinical dental procedures.

Background: Suspected aerosol-generating dental instruments may cause risks for operators by transmitting pathogens, such as the SARS-CoV-2 virus. The aim of our study was to measure aerosol generation in various dental procedures in clinical settings. Methods: The study population comprised of 84 patients who underwent 253 different dental procedures measured with Optical Particle Sizer in a dental office setting. Aerosol particles from 0.3 to 10 µm in diameter were measured. Dental procedures included oral examinations (N = 52), restorative procedures with air turbine handpiece (N = 8), high-speed (N = 6) and low-speed (N = 30) handpieces, ultrasonic scaling (N = 31), periodontal treatment using hand instruments (N = 60), endodontic treatment (N = 12), intraoral radiographs (N = 24), and dental local anesthesia (N = 31). Results: Air turbine handpieces significantly elevated <1 µm particle median (p = 0.013) and maximum (p = 0.016) aerosol number concentrations as well as aerosol particle mass concentrations (p = 0.046 and p = 0.006) compared to the background aerosol levels preceding the operation. Low-speed dental handpieces elevated >5 µm median (p = 0.023), maximum (p = 0.013) particle number concentrations,> 5 µm particle mass concentrations (p = 0.021) and maximum total particle mass concentrations (p = 0.022). High-speed dental handpieces elevated aerosol concentration levels compared to the levels produced during oral examination. Conclusions: Air turbine handpieces produced the highest levels of <1 µm aerosols and total particle number concentrations when compared to the other commonly used instruments. In addition, high- and low-speed dental handpieces and ultrasonic scalers elevated the aerosol concentration levels compared to the aerosol levels measured during oral examination. These aerosol-generating procedures, involving air turbine, high- and low-speed handpiece, and ultrasonic scaler, should be performed with caution. Clinical significance: Aerosol generating dental instruments, especially air turbine, should be used with adequate precautions (rubber dam, high-volume evacuation, FFP-respirators), because aerosols can cause a potential risk for operators and substitution of air turbine for high-speed dental handpiece in poor epidemic situations should be considered to reduce the risk of aerosol transmission.

Evaluating the Theoretical Background of STOFFENMANAGER® and the Advanced REACH Tool.

STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m-3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m-3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the 'operational analysis' level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson's correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

Nano-Magnetic NiFe2O4 and Its Photocatalytic Oxidation of Vanillyl Alcohol-Synthesis, Characterization, and Application in the Valorization of Lignin.

Here, we report on a phyto-mediated bimetallic (NiFe2O4) preparation using a Boswellia carterii extract, which was characterized by XRD, FT-IR, TGA, electron microscopy, magnetic spectroscopy, and Mössbauer spectroscopy measurements. The prepared nano-catalysts were tested for oxidation of lignin monomer molecules-vanillyl alcohol and cinnamyl alcohol. In comparison with previously reported methods, the nano NiFe2O4 catalysts showed high photocatalytic activity and selectivity, under visible light irradiation with a nitroxy radical initiator (2,2,6,6-tetramethylpiperidinyloxy or 2,2,6,6-tetramethylpiperidine 1-oxyl; TEMPO) at room temperature and aerobic conditions. The multifold advantages of the catalyst both in terms of reduced catalyst loading and ambient temperature conditions were manifested by higher conversion of the starting material.

COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting.

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves' occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

Indoor Model Simulation for COVID-19 Transport and Exposure.

Transmission of respiratory viruses is a complex process involving emission, deposition in the airways, and infection. Inhalation is often the most relevant transmission mode in indoor environments. For severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the risk of inhalation transmission is not yet fully understood. Here, we used an indoor aerosol model combined with a regional inhaled deposited dose model to examine the indoor transport of aerosols from an infected person with novel coronavirus disease (COVID-19) to a susceptible person and assess the potential inhaled dose rate of particles. Two scenarios with different ventilation rates were compared, as well as adult female versus male recipients. Assuming a source strength of 10 viruses/s, in a tightly closed room with poor ventilation (0.5 h-1), the respiratory tract deposited dose rate was 140-350 and 100-260 inhaled viruses/hour for males and females; respectively. With ventilation at 3 h-1 the dose rate was only 30-90 viruses/hour. Correcting for the half-life of SARS-CoV-2 in air, these numbers are reduced by a factor of 1.2-2.2 for poorly ventilated rooms and 1.1-1.4 for well-ventilated rooms. Combined with future determinations of virus emission rates, the size distribution of aerosols containing the virus, and the infectious dose, these results could play an important role in understanding the full picture of potential inhalation transmission in indoor environments.