Air exchange rates from atmospheric CO2 daily cycle

Estimating Indoor Pollutant Loss Using Mass Balances and Unsupervised Clustering to Recognize Decays

Low-cost air quality monitors are increasingly being deployed in various indoor environments. However, data of high temporal resolution from those sensors are often summarized into a single mean value, with information about pollutant dynamics discarded. Further, low-cost sensors often suffer from limitations such as a lack of absolute accuracy and drift over time. There is a growing interest in utilizing data science and machine learning techniques to overcome those limitations and take full advantage of low-cost sensors. In this study, we developed an unsupervised machine learning model for automatically recognizing decay periods from concentration time series data and estimating pollutant loss rates. The model uses k-means and DBSCAN clustering to extract decays and then mass balance equations to estimate loss rates. Applications on data collected from various environments suggest that the CO₂ loss rate was consistently lower than the PM_2.5 loss rate in the same environment, while both varied spatially and temporally. Further, detailed protocols were established to select optimal model hyperparameters and filter out results with high uncertainty. Overall, this model provides a novel solution to monitoring pollutant removal rates with potentially wide applications such as evaluating filtration and ventilation and characterizing indoor emission sources.

Assessment of ventilation rates inside educational buildings in Southwestern Europe: Analysis of implemented strategic measures

The pandemic caused by COVID-19 has highlighted the need to ensure good indoor air quality. Public buildings (educational buildings in particular) have come under the spotlight because students, teachers and staff spend long periods of the day indoors. This study presents a measurement campaign for the assessment of ventilation rate (VR) and ventilation strategies in educational buildings in Southwestern Europe, Portugal and Spain. A representative sample of the teaching spaces of the Azurém Campus (Guimarães, Portugal) and the Fuentenueva Campus (Granada, Spain) have been analyzed. Natural ventilation is the predominant ventilation strategy in these spaces, being the most common strategy in educational buildings in Europe. VR was estimated under different configurations, using the CO₂ decay method. Subsequently, the CO₂ concentration was estimated according to occupancy and the probability of infection risk was calculated using the Wells-Riley equation. The obtained VR varied between 2.9 and 20.1 air change per hour (ACH) for natural cross ventilation, 2.0 to 5.1 ACH for single-sided ventilation and 1.8 to 3.5 for mechanically ventilated classrooms. Large differences in CO₂ concentrations were verified, depending on the analyzed ventilation strategy, ranging from 475 to 3903 ppm for the different scenarios. However, the probability of risk was less than 1% in almost all of the classrooms analyzed. The results obtained from the measurement campaign showed that the selection of an appropriate ventilation strategy can provide sufficient air renewal and maintain a low risk of infection. Ventilation strategies need to be reconsidered as a consequence of the health emergency arising from the COVID-19 pandemic.

Exhaled CO2 as a COVID-19 Infection Risk Proxy for Different Indoor Environments and Activities

CO2 is co-exhaled with aerosols containing SARS-CoV-2 by COVID-19-infected people and can be used as a proxy of SARS-CoV-2 concentrations indoors. Indoor CO2 measurements by low-cost sensors hold promise for mass monitoring of indoor aerosol transmission risk for COVID-19 and other respiratory diseases. We derive analytical expressions of CO2-based risk proxies and apply them to various typical indoor environments. The relative infection risk in a given environment scales with excess CO2 level, and thus, keeping CO2 as low as feasible in a space allows optimization of the protection provided by ventilation. We show that the CO2 level corresponding to a given absolute infection risk varies by >2 orders of magnitude for different environments and activities. Although large uncertainties, mainly from virus exhalation rates, are still associated with infection risk estimates, our study provides more specific and practical recommendations for low-cost CO2-based indoor infection risk monitoring.

Multisensor IoT Platform for Optimising IAQ Levels in Buildings through a Smart Ventilation System

Indoor Air Quality (IAQ) issues have a direct impact on the health and comfort of building occupants. In this paper, an experimental low-cost system has been developed to address IAQ issues by using a distributed internet of things platform to control and monitor the indoor environment in building spaces while adopting a data-driven approach. The system is based on several real-time sensor data to model the indoor air quality and accurately control the ventilation system through algorithms to maintain a comfortable level of IAQ by balancing indoor and outdoor pollutant concentrations using the Indoor Air Quality Index approach. This paper describes hardware and software details of the system as well as the algorithms, models, and control strategies of the proposed solution which can be integrated in detached ventilation systems. Furthermore, a mobile app has been developed to inform, in real time, different-expertise-user profiles showing indoor and outdoor IAQ conditions. The system is implemented in a small prototype box and early-validated with different test cases considering various pollutant concentrations, reaching a Technology Readiness Level (TRL) of 3–4.

Urban Pollutant Transport and Infiltration into Buildings Using Perfluorocarbon Tracers

People spend the majority of their time indoors and therefore the quality of indoor air is worthy of investigation; indoor air quality is affected by indoor sources of pollutants and from pollutants entering buildings from outdoors. In this study, unique perfluorocarbon tracers were released in five experiments at a 100 m and ~2 km distance from a large university building in Manchester, UK and tracer was also released inside the building to measure the amount of outdoor material penetrating into buildings and the flow of material within the building itself. Air samples of the tracer were taken in several rooms within the building, and a CO₂ tracer was used within the building to estimate air-exchange rates. Air-exchange rates were found to vary between 0.57 and 10.90 per hour. Indoor perfluorocarbon tracer concentrations were paired to outdoor tracer concentrations, and in-out ratios were found to vary between 0.01 and 3.6. The largest room with the lowest air-exchange rate exhibited elevated tracer concentrations for over 60 min after the release had finished, but generally had the lowest concentrations, the room with the highest ventilation rates had the highest concentration over 30 min, but the peak decayed more rapidly. Tracer concentrations indoors compared to outdoors imply that pollutants remain within buildings after they have cleared outside, which must be considered when evaluating human exposure to outdoor pollutants.

Review and Extension of CO₂-Based Methods to Determine Ventilation Rates with Application to School Classrooms

The ventilation rate (VR) is a key parameter affecting indoor environmental quality (IEQ) and the energy consumption of buildings. This paper reviews the use of CO₂ as a “natural” tracer gas for estimating VRs, focusing on applications in school classrooms. It provides details and guidance for the steady-state, build-up, decay and transient mass balance methods. An extension to the build-up method and an analysis of the post-exercise recovery period that can increase CO₂ generation rates are presented. Measurements in four mechanically-ventilated school buildings demonstrate the methods and highlight issues affecting their applicability. VRs during the school day fell below recommended minimum levels, and VRs during evening and early morning were on the order of 0.1 h⁻¹, reflecting shutdown of the ventilation systems. The transient mass balance method was the most flexible and advantageous method given the low air change rates and dynamic occupancy patterns observed in the classrooms. While the extension to the build-up method improved stability and consistency, the accuracy of this and the steady-state method may be limited. Decay-based methods did not reflect the VR during the school day due to heating, ventilation and air conditioning (HVAC) system shutdown. Since the number of occupants in classrooms changes over the day, the VR expressed on a per person basis (e.g., L·s⁻¹·person⁻¹) depends on the occupancy metric. If occupancy measurements can be obtained, then the transient mass balance method likely will provide the most consistent and accurate results among the CO₂-based methods. Improved VR measurements can benefit many applications, including research examining the linkage between ventilation and health.