Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.Alternate :Plain Language SummaryHigh‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future.

Computational mathematics applied in the identification of the risk of indirect contagion of COVID-19 in university classrooms

This exploratory study evaluated the risk of contagion from airborne diseases, such as coronaviruses, in schools. For three days, the concentration of carbon dioxide in two university classrooms was monitored for 90 minutes, while the students took their math classes. We use these values to validate a first-order model for carbon dioxide concentration and calculate the air exchange rate indirectly (avoiding the need for expensive measurement equipment). The air exchange rate obtained allowed us to assess whether the usual ventilation systems (both natural and mechanical) are sufficient to guarantee a low risk of contagion of aerosols due to respiration. The results show that the risk of contagion is low if three factors are considered: the level of conversation within the classroom, the usage of a moisture extraction system, and the lecture duration. The risk is low if the lecture time is less than 50 minutes, the level of conversation is moderate, and a moisture extraction system is available. If these conditions are not met the risk is considerably higher even if mechanical ventilation is employed.

The effect of ventilation performance on risk of indoor airborne transmission of SARS-CoV-2

Ventilation performance plays a significant role in distributing contaminants and airborne infections indoors. Thus, poorly ventilated public spaces may be at high risk due to the presence of both infectious and susceptible people. Adapting HVAC ventilation systems to mitigate virus transmission requires considering ventilation rate, airflow patterns, air balancing, occupancy, and feature placement. The study aims to identify poorly ventilated spaces where airborne transmission of pathogens such as SARS-CoV-2 could be critical. This study is focused on evaluating the ventilation performance of the building stock and the safety of using the facilities based on measured indoor CO2. The results revealed the spaces with the potential risk of indoor airborne transmission of COVID-19. The study proposes recommendations for utilising air ventilation systems in different use cases. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Evaluation of Optimal Mechanical Ventilation Strategies for Schools for Reducing Risks of Airborne Viral Infection

Ventilation systems are one of the most effective strategies to reduce the risk of viral infection transmission in buildings. However, insufficient ventilation rates in crowded spaces, such as schools, would lead to high risks of infection transmission. On the other hand, excessive ventilation rates might significantly increase cooling energy consumption. Therefore, energy-efficient control methods, such as Demand Control Ventilation systems (DCV), are typically considered to maintain acceptable indoor air quality. However, it is unclear if the DCV-based controls can supply adequate ventilation rates to minimize the probability of infection (POI) in indoor spaces. This paper investigates the benefits of optimized ventilation strategies, including conventional mechanical systems (MV) and DCV, in reducing the POI and cooling energy consumption through a detailed sensitivity analysis. The study also evaluates the impact of the ventilation rate, social distancing, and number of infectors on the performance of the ventilation systems. A coupling approach of a calibrated energy model of a school building in Jeddah, KSA, with a validated Wells–Riley model is implemented. Based on the findings of this study, proper adjustment of the DCV set point is necessary to supply adequate ventilation rates and reduce POI levels. Moreover, optimal values of 2 ACH for ventilation rate and 2 m for social distance are recommended to deliver acceptable POI levels, cooling energy use, and indoor CO2 concentration for the school building. Finally, this study confirms that increasing the ventilation rate is more effective than increasing social distancing in reducing the POI levels. However, this POI reduction is achieved at the cost of a higher increase in the cooling energy.

Real-time Control of the Air Volume in Ventilation Facilities by Limiting Concentration with Cluster Algorithms

Ventilation improves indoor air quality and reduces airborne infections. It is particularly important at present because of the COVID-19 pandemic. Commercially available ventilation facilities can only be instantly turned on/off or at a set time with adjustable air volumes (high, middle, and low). However, maintaining the indoor carbon dioxide concentration while reducing the energy consumption of these facilities is challenging. Hence, this study developed clustering algorithms to determine the carbon dioxide concentration limit thus enabling real-time air volume adjustment. These limit values were set using the existing energy recovery ventilation (ERV) controller. In the experiment, dual estimation was adopted, and the constructing building energy models from data were sampled at a low rate to compare that the ventilation facilities are only turned on/off. In addition, switching control is closely related to fuzzy control;that is, fuzzy control can be considered a smooth version of switching control. The experimental results indicated that the limits of 600 and 700 ppm were suitable to effectively control the real-time air volume based on the ERV operation. An ERV-based carbon dioxide concentration limit reduced the energy consumption of ventilation facilities by 11%implications of this study. IEEE

Measurement and rapid assessment of indoor air quality at mass gathering events to assess ventilation performance and reduce aerosol transmission of SARS-CoV-2

To assess risk factors for COVID-19 transmission and address the closure of mass gathering events since March 2020, the UK Government ran the Events Research Programme (ERP), following which it reopened live events in sports, music, and culture in July 2021. We report the rapid post-occupancy evaluation of Indoor Air Quality (IAQ) and associated long-range airborne transmission risk conducted in the Environmental Study of the ERP. Ten large venues around the UK were monitored with CO2 sensors at a high spatial and temporal resolution during 90 events. An IAQ Index based on CO2 concentration was developed, and all monitored spaces were classified in bands from A to G based on their average and maximum CO2 concentrations from all events. High resolution monitoring and the IAQ Index depicted the overall state of ventilation at live events, and allowed identification of issues with ventilation effectiveness and distribution, and of spaces with poor ventilation and the settings in which long-range airborne transmission risk may be increased. In numerous settings, CO2 concentrations were found to follow patterns relating to event management and specific occupancy of spaces around the venues. Good ventilation was observed in 90% of spaces monitored for given occupancies. Practical applications: High-resolution monitoring of indoor CO2 concentrations is necessary to detect the spatial variation of indoor air quality (IAQ) in large mass gathering event venues. The paper summarises COVID-19 ventilation guidance for buildings and defines a methodology for measurement and rapid assessment of IAQ during occupancy at live events that can be implemented by venue managers. Comparisons of the CO2 concentrations measured during the events identified the spaces at high risk of long-range transmission of airborne pathogens. Building operators should be mindful of the ventilation strategies used relative to the total occupancy in different spaces and the occupant's activities.

Luftqualität: Eine günstige CO2‐Ampel für den Klassenraum

Im Zuge der anstehenden Öffnungen von Schulen für den Präsenzunterricht steht im Rahmen der Covid‐19‐Pandemie die Qualität von Raumluft besonders im Vordergrund. Die Kohlenstoffdioxid‐Konzentration ist dabei ein guter Indikator für die Luftqualität und Keimbelastung und kann vergleichsweise einfach erfasst werden. Im Artikel wird beschrieben, wie die CO2‐Konzentration mit der digitalen Messstation LabPi erfasst werden kann. Zusätzlich wird eine weitere LabPi‐Variante im Sinne einer CO2‐Ampel vorgestellt, die mit einfachen Materialien und kostenfreier Software selbst angefertigt werden kann.Alternate abstract:Translation abstractIn the course of the upcoming (re‐)opening of schools for presence teaching, the quality of indoor air is of particular concern in the context of the Covid 19 pandemic. The carbon dioxide concentration is a good indicator for the air quality and microbial load and can be measured quite easily. This article describes how the CO2 concentration can be recorded with the LabPi digital measuring station. In addition, another LabPi variant in the form of a CO2 indicator is presented, which can be built with simple materials and free software.

EVALUATION ON INDOOR ENVIRONMENT AND ALTERNATIVE VENTILATION METHODS IN A SCHOOL CLASSROOM IN A COLD REGION UNDER COVID-19 PANDEMIC

COVID-19 caused a global pandemic. The possibility of aerosol transmission has been pointed out as a possible route of infection, and there are reports that conventional infection control measures are insufficient to counteract aerosol transmission. Therefore, this report presents the results of an actual survey at a high school, including measurement of CO2 concentration and a questionnaire survey, and the results of an experiment to evaluate the attenuation of particle concentration by an air cleaner based on this survey.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe.We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands).We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE.We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures ("plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well.We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at 10.18160/20Z1-AYJ2 .

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN.

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time in any space is termed as R-Event. Real-time data for the office environment was gathered in the spring of 2022 in a naturally ventilated office room in Roorkee, India, under composite climatic conditions. To ascertain the merit of the proposed ANN and curve-fitting models, the performances of the ANN approach were compared against the curve fitting model regarding conventional statistical indicators, i.e., correlation coefficient, root mean square error, mean absolute error, Nash-Sutcliffe efficiency index, mean absolute percentage error, and a20-index. Eleven input parameters namely indoor temperature ( $T_{In}$ ), indoor relative humidity ( $RH_{In}$ ), area of opening ( $A_{O}$ ), number of occupants ( $O$ ), area per person ( $A_{P}$ ), volume per person ( $V_{P}$ ), $CO_{2}$ concentration ( $CO_{2}$ ), air quality index ( $AQI$ ), outer wind speed ( $W_{S}$ ), outdoor temperature ( $T_{Out}$ ), outdoor humidity ( $RH_{Out}$ ) were used in this study to predict the R-Event value as an output. The primary goal of this research is to establish the link between $CO_{2}$ concentration and R-Event value;eventually providing a model for prediction purposes. In this case study, the correlation coefficient of the ANN model and curve-fitting model were 0.9992 and 0.9557, respectively. It shows the ANN model's higher accuracy than the curve-fitting model in R-Event prediction. Results indicate the proposed ANN prediction performance (R = 0.9992, RMSE = 0.0018708, MAE = 0.0006675, MAPE = 0.8643816, NS = 0.9984365, and a20-index = 0.9984300) is reliable and highly accurate to predict the R-event for offices.

Experimental studies of carbon dioxide concentration in the space under the face mask protecting against Covid-19 - Pilot studies.

Masks are the primary tool used to prevent the spread of COVID-19 in the current pandemic. The use of masks may result in some discomfort, which may be caused by the accumulation of carbon dioxide in the inner space of the mask. This paper presents tests of carbon dioxide concentration in the inner space of the mask during work at a computer, for various flat and convex masks. Five different masks were used in the tests. Convex masks showed a greater accumulation of carbon dioxide than flat masks. The concentration of carbon dioxide was also higher for masks made of more layers. The dependence of the average values of carbon dioxide concentrations under the masks for selected people depending on the BMI and the type of mask was determined, as well as the measurements of carbon dioxide concentrations without the mask. An increase in carbon dioxide concentration was observed with increasing BMI. The development of effective self-defense tools against the virus, including masks, is essential to contain the spread of COVID-19.

A Carbon Nanotube-Functional Polymer Composite Film for Low-Power Indoor CO₂ Monitoring

Indoor air quality (IAQ) has been a growing concern in recent years, only to be expedited by the COVID-19 pandemic. A common provisional measure for IAQ is carbon dioxide (CO2), which is commonly used to inform the ventilation control of buildings. However, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. This work presents a polymer composite-based chemiresistive CO2 sensor that leverages branched poly(ethylenimine) (PEI) and poly(ethylene glycol) (PEG) as the CO2 absorbing layer. This polymer blend was incorporated with single wall carbon nanotubes (CNT), which serve as the charge carriers. Prototype sensors were assessed in a bench-top environmental test chamber which varied temperature (22–26 °C), relative humidity level (20–80%), CO2 concentration (400–20,000 ppm), as well as other gas constituents to simulate typical and extreme indoor conditions. The results indicate that the proposed system could ultimately serve as a low-power alternative to current commercially available technologies for indoor CO2 monitoring.

Atmospheric CO2 concentration and its influence factors during 2020 COVID-19 pandemic in Nanchang: Journal of Nanjing University of Information Science & Technology

The atmospheric CO2 concentrations are mainly influenced by regional sinks/sources and atmospheric transport processes, thus observations in urban area contain essential information of anthropogenic CO2 emissions. To investigate the effect of COVID-19 on atmospheric CO2 concentration and its anthropogenic emissions, this study chose Nanchang city as the study area and used a priori emission inventory with WRF-STILT (Stochastic Time-Inverted Lagrangian Transport) atmospheric transport model to simulate hourly CO2 concentrations from January 24th to April 30th, 2020. In accordance with the government measures to control COVID-19 epidemic, the whole study period was divided into two periods of Level 1 period (from January 24th to March 11th) and Level 2 period (from March 12th to April 30th). Results indicate the model can well capture hourly variations of CO2 concentration, but it overestimated nighttime concentrations due to the negligence of emission source height. During Level 1 period, the observed and simulated afternoon (12:00-18:00) CO2 mole fractions were 433. 63×10-6 and 438. 22×10-6, respectively,in which the anthropogenic emissions were 21.9% overestimated by simulation compared with observations. While during Level 2 period, the observation and simulation were very close as 432. 06×10-6 and 432. 24 × 10-6. The above comparisons indicate that the CO2 emissions can be represented by a priori CO2 emission inventory in Level 2 period, but was overestimated by 21.9% in Level 1 period, and the discrepancy was mainly due to government measures to control COVID-19 pandemic during this period. Besides, the average biological NEE enhancements were generally lower than 2×10-6, indicating a small contribution compared with anthropogenic emissions. The higher PBLH (Planetary Boundary Layer Height) in Level 2 period also offset the enhancement in CO2 emissions, which was also the main reason for the close observations during two periods. Our findings can provide scientific method supports for greenhouse gas emission inversions at urban scale.

USING FACE MASKS IN THE CLASSROOM: THE EFFECT ON THE INDOOR ENVIRONMENT PARAMETERS AND FACE THERMOPHYSIOLOGICAL REACTIONS

Masks have been recommended as a protective tool for effectively combating the COVID-19 pandemic. In many countries, masks are required indoors, but the obligation temporarily and sporadically extends to all public places indoors and outdoors in some regions. Our study investigated the effect of wearing face masks in the classroom on the indoor environmental parameters and the human body experimentally. The study was performed at the Technical University of Sofia with 14 volunteers during regular lecture classes. Two stages were considered: with and without face masks. Measurement of the indoor environment parameters, oxygen (O-2) and carbon dioxide (CO2) concentration was continuously performed. Thermal image analysis was used to obtain the face thermograms of the participants. The results clearly showed the retention effect of the face masks on the exhaled air, leading to lower CO2 concentration in the classroom and higher O-2 concentration and humidity. It was also found that the continuous wear of a face mask for 40-45 min provoked an increment of the facial skin temperature under the mask to 37 degrees CC and even more. The rise of the temperature of the inner cantus of the eye showed that the face mask triggered the body's thermoregulation, causing thermophysiological reactions.

Role of Microalgae in Global CO2 Sequestration: Physiological Mechanism, Recent Development, Challenges, and Future Prospective

The rising concentration of global atmospheric carbon dioxide (CO2) has severely affected our planet’s homeostasis. Efforts are being made worldwide to curb carbon dioxide emissions, but there is still no strategy or technology available to date that is widely accepted. Two basic strategies are employed for reducing CO2 emissions, viz. (i) a decrease in fossil fuel use, and increased use of renewable energy sources;and (ii) carbon sequestration by various biological, chemical, or physical methods. This review has explored microalgae’s role in carbon sequestration, the physiological apparatus, with special emphasis on the carbon concentration mechanism (CCM). A CCM is a specialized mechanism of microalgae. In this process, a sub-cellular organelle known as pyrenoid, containing a high concentration of Ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco), helps in the fixation of CO2. One type of carbon concentration mechanism in Chlamydomonas reinhardtii and the association of pyrenoid tubules with thylakoids membrane is represented through a typical graphical model. Various environmental factors influencing carbon sequestration in microalgae and associated techno-economic challenges are analyzed critically.

Natural Ventilation Characterization in a Classroom under Different Scenarios.

The COVID-19 pandemic has pointed to the need to increase our knowledge in fields related to human breathing. In the present study, temperature, relative humidity, carbon dioxide (CO2) concentration, and median particle size diameter measurements were taken into account. These parameters were analyzed in a computer classroom with 15 subjects during a normal 90-minute class; all the subjects wore surgical masks. For measurements, Arduino YUN, Arduino UNO, and APS-3321 devices were used. Natural ventilation efficiency was checked in two different ventilation scenarios: only windows open and windows and doors open. The results show how ventilation affects the temperature, CO2 concentration, and median particle diameter size parameters. By contrast, the relative humidity depends more on the outdoor meteorological conditions. Both ventilation scenarios tend to create the same room conditions in terms of temperature, humidity, CO2 concentration, and particle size. Additionally, the evolution of CO2 concentration as well as the particle size distribution along the time was studied. Finally, the particulate matter (PM2.5) was investigated together with particle concentration. Both parameters showed a similar trend during the time of the experiments.

A Rapidly Deployable Test Suite for Respiratory Protective Devices in the COVID-19 Pandemic.

INTRODUCTION: The current COVID-19 pandemic has caused large shortages in personal protective equipment, leading to hospitals buying their supplies from alternative suppliers or even reusing single-use items. Equipment from these alternative sources first needs to be tested to ensure that they properly protect the clinicians that depend on them. This work demonstrates a test suite for protective face masks that can be realized rapidly and cost effectively, using mainly off-the-shelf as well as 3D printing components. MATERIALS AND METHODS: The proposed test suite was designed and evaluated in order to assess its safety and proper functioning according to the criteria that are stated in the European standard norm EN149:2001+A1 7. These include a breathing resistance test, a CO2 build-up test, and a penetration test. Measurements were performed for a variety of commercially available protective face masks for validation. RESULTS: The results obtained with the rapidly deployable test suite agree with conventional test methods, demonstrating that this setup can be used to assess the filtering properties of protective masks when conventional equipment is not available. DISCUSSION: The presented test suite can serve as a starting point for the rapid deployment of more testing facilities for respiratory protective equipment. This could greatly increase the testing capacity and ultimately improve the safety of healthcare workers battling the COVID-19 pandemic.