CO2 in indoor environments: From environmental and health risk to potential renewable carbon source

A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation

This study presents a comprehensive investigation into the interplay between machine learning (ML) models, morphological features, and outdoor thermal comfort (OTC) across three key indices: Universal Thermal Climate Index (UTCI), Physiological Equivalent Temperature (PET), and Predicted Mean Vote (PMV). Based on a comprehensive field measurement for 173 urban canyons, proper dataset for summer outdoor thermal condition was provided. Concurrently, six distinct ML models were evaluated and optimized using Bayesian optimization (BO) technique, considering performance indicators like weighted accuracy, F1-Score, precision, and recall. Notable trends emerged, with the CatBoost Classifier demonstrating superior performance in UTCI prediction, the Random Forest classifier excelling in PET estimation, and the XGBoost Classifier achieving optimal PMV prediction. Furthermore, the study delved into the influence of morphological features on OTC, prioritizing factors using SHAP values. Results consistently identified 90-degree orientation, street width, and 180-degree orientation as pivotal factors influencing OTC, with varying degrees of sensitivity across different classifications of thermal stress. Analysis of binary SHAP values unveiled intricate relationships between urban features and OTC indices, emphasizing the critical influence of street orientation on regulating outdoor thermal environments for UTCI and PET scenarios. Surprisingly, street width emerged as the foremost influential factor within the PMV index, challenging established trends and highlighting the complexity of thermal comfort modeling. Additionally, current research delineates the multifaceted impact of street width on microclimate dynamics, enriching our understanding of urban thermal dynamics and emphasizing its role in mitigating thermal stress within urban environments.

A Comparative Study of CO2 Forecasting Strategies in School Classrooms: A Step Toward Improving Indoor Air Quality

This paper comprehensively investigates the performance of various strategies for predicting CO2 levels in school classrooms over different time horizons by using data collected through IoT devices. We gathered Indoor Air Quality (IAQ) data from fifteen schools in Navarra, Spain between 10 January and 3 April 2022, with measurements taken at 10-min intervals. Three prediction strategies divided into seven models were trained on the data and compared using statistical tests. The study confirms that simple methodologies are effective for short-term predictions, while Machine Learning (ML)-based models perform better over longer prediction horizons. Furthermore, this study demonstrates the feasibility of using low-cost devices combined with ML models for forecasting, which can help to improve IAQ in sensitive environments such as schools.

Implementation of an Internet of Things Architecture to Monitor Indoor Air Quality: A Case Study During Sleep Periods

Most human time is spent indoors, and due to the pandemic, monitoring indoor air quality (IAQ) has become more crucial. In this study, an IoT (Internet of Things) architecture is implemented to monitor IAQ parameters, including CO2 and particulate matter (PM). An ESP32-C6-based device is developed to measure sensor data and send them, using the MQTT protocol, to a remote InfluxDBv2 database instance, where the data are stored and visualized. The Python 3.11 scripting programming language is used to automate Flux queries to the database, allowing a more in-depth data interpretation. The implemented system allows to analyze two measured scenarios during sleep: one with the door slightly open and one with the door closed. Results indicate that sleeping with the door slightly open causes CO2 levels to ascend slowly and maintain lower concentrations compared to sleeping with the door closed, where CO2 levels ascend faster and the maximum recommended values are exceeded. This demonstrates the benefits of ventilation in maintaining IAQ. The developed system can be used for sensing in different environments, such as schools or offices, so an IAQ assessment can be made. Based on the generated data, predictive models can be designed to support decisions on intelligent natural ventilation systems, achieving an optimized, efficient, and ubiquitous solution to moderate the IAQ.

Sources, levels, and determinants of indoor air pollutants in Europe: A systematic review

Clean air is a requirement for life, and the quality of indoor air is a health determinant since people spend most of their daily time indoors. The aim of this study was to systematically review the available evidence regarding the sources, determinants and concentrations of indoor air pollutants in a set of scenarios under study in K-HEALTHinAIR project. To this end, a systematic review was performed to review the available studies published between the years 2013-2023, for several settings (schools, homes, hospitals, lecture halls, retirement homes, public transports and canteens), conducted in Europe, where sources and determinants of the indoor pollutants concentrations was assessed. After a two-stage screening in abstract and full-text, 148 papers were included for data extraction. For particulate matter, carbon dioxide and volatile organic compounds, several emission sources were identified (occupancy, human activities, resuspension, cleaning products, disinfectants, craft activities, cooking, smoking), with ventilation, number of occupants, building characteristics, being considered as important determinants. This review made also possible to discuss some of the actions that are already in place or should be implemented in the future to prevent and control the presence of pollutants indoors.

Relationships between the Surface Hydrophilicity of a Bismuth Electrode and the Product Selectivity of Electrocatalytic CO2 Reduction

Two types of bismuth films (micro-Bi and nano-Bi) were prepared, and their electrocatalytic behavior was studied in terms of reduction current and product selectivity in a potential range of -0.776 to -1.376 V vs RHE. CO2 and H2O molecules competed with each other for reduction on the surfaces of both types of films, and formate and H2 were the respective major products of reductive reactions. Under the same conditions, nano-Bi exhibited lower selectivity for formate and higher selectivity for H2 compared to the respective micro-Bi cases with bismuth films of similar thickness. This can be attributed to the higher hydrophilicity of bismuth film surfaces of nano-Bi due to surface nanoscale roughness and lower surface-carbon content compared with those of micro-Bi. Our results suggest a new strategy for controlling the selectivity of electrocatalytic CO2 reduction under aqueous electrolytes through the use of surface engineering.

Exploring MOF-Derived CuO/rGO Heterostructures for Highly Efficient Room Temperature CO2 Sensors

In response to the urgent need for advanced climate change mitigation tools, this study introduces an innovative CO2 gas sensor based on p-p-type heterostructures designed for effective operation at room temperature. This sensor represents a significant step forward, utilizing the synergistic effects of p-p heterojunctions to enhance the effective interfacial area, thereby improving sensitivity. The incorporation of CuO nanoparticles and rGO sheets also optimizes gas transport channels, enhancing the sensor's performance. Our CuO/rGO heterostructures, with 5 wt % rGO, have shown a notable maximum response of 39.6-500 ppm of CO2 at 25 °C, and a low detection limit of 2 ppm, indicating their potential as high-performance, room-temperature CO2 sensors. The prepared sensor demonstrates long-term stability, maintaining 98% of its initial performance over a 30-day period when tested at 1-day intervals. Additionally, the sensor remains stable under conditions of over 40% relative humidity. Furthermore, a first-principles study provides insights into the interaction mechanisms with CO2 molecules, enhancing our understanding of the sensor's operation. This research contributes to the development of CO2 monitoring solutions, offering a practical and cost-effective approach to environmental monitoring in the context of global climate change efforts.

The relationship between occupant behaviour and indoor air quality in Malaysian hospital outpatient departments: A multistage cross-sectional study

Introduction

Poor indoor air quality (IAQ) in healthcare settings may adversely impact occupants' well-being and promote transmission of infectious respiratory disease. However, evidence on its potentially modifiable determinants, including occupant behaviour, remains scarce. This study aims to determine the relationship between occupant behaviour and IAQ in Malaysian hospital outpatient departments (OPDs).

Methods

A multistage cross-sectional study of six randomly selected Malaysian public hospital OPDs was conducted. In stage one, IAQ parameters, including temperature, relative humidity (RH), air velocity (AV), carbon dioxide (CO2), total bacterial count (TBC), and total fungal count (TFC) were measured. In stage two, an observation form based on the Korsavi and Montazami tool for measuring adaptive behaviour was used to examine occupant density, activities, and operation of building envelopes and appliances. Simple correlation, partial correlation, and linear regression analyses were performed to examine the relationship between occupant behaviour and IAQ parameters.

Results

The IAQ of selected hospital OPDs complied with established standards, except for temperature and AV. Occupant density was positively correlated with temperature and CO2. Meanwhile, occupants' activities including slow walking and brisk walking were positively correlated with temperature, AV, CO2, TBC and TFC. Conversely, occupants' opening of windows and doors were positively correlated with temperature and AV but negatively correlated with CO2, TBC and TFC. Finally, turning on fans was positively correlated with AV but negatively correlated with TBC, whereas turning on air conditioner was positively correlated with CO2. Among occupants' behaviour, opening of windows and doors contributed the most to variation in IAQ parameters.

Conclusions

The study findings suggest that IAQ in hospital OPDs are influenced by occupant density, activities, and operation of doors, windows, and appliances. Prospective hospital IAQ guidelines should incorporate policies and measures targeting these factors to ensure occupants' best practices in maintaining healthy hospital indoor air environments.

Influence of Human Activity on Radon Concentration, Indoor Air Quality, and Thermal Comfort in Small Office Spaces

This article focuses on the influence of occupants on the concentration of contaminants (radon Rn-222 and CO2) as well as the thermal comfort parameters. A series of sensors were placed to measure the concentration of the contaminants, temperature, and relative humidity in the test room at the Institute of Nuclear Physics PAN in Krakow (IFJ PAN), Poland. The test room is an office that is typical of the offices used in the facility. The occupants that used the space kept a detailed diary of their entry, exit, and number of people entering. The results showed that the accumulation of contaminants in such spaces may be severe and risks the health and safety of occupants. The accumulation of CO2 was extremely noticeable and did not diminish to the background level between the re-entry of the occupants to the office. The same was consistent for the radon concentration. The study shows how ventilation methods and small changes in occupant work strategy may influence the contaminant concentration within a test zone.

2D MXenes and their composites; design, synthesis, and environmental sensing applications

Novel 2D layered MXene materials were first reported in 2011 at Drexel University. MXenes are widely used in multidisciplinary applications due to their anomalous electrical conductivity, high surface area, and chemical, mechanical, and physical properties. This review summarises MXene synthesis and applications in environmental sensing. The first section describes different methods for MXene synthesis, including fluorinated and non-fluorinated methods. MXene's layered structure, surface terminal groups, and the space between layers significantly impact its properties. Different methods to separate different MXene layers are also discussed using various intercalation reagents and commercially synthesized MXene without compromising the environment. This review also explains the effect of MXene's surface functionalization on its characteristics. The second section of the review describes gas and pesticide sensing applications of Mxenes and its composites. Its good conductivity, surface functionalization with negatively charged groups, intrinsic chemical nature, and good mechanical stability make it a prominent material for room temperature sensing of environmental samples, such as polar and nonpolar gases, volatile organic compounds, and pesticides. This review will enhance the young scientists' knowledge of MXene-based materials and stimulate their diversity and hybrid conformation in environmental sensing applications.

Physiology or Psychology: What Drives Human Emissions of Carbon Dioxide and Ammonia?

Humans are the primary sources of CO2 and NH3 indoors. Their emission rates may be influenced by human physiological and psychological status. This study investigated the impact of physiological and psychological engagements on the human emissions of CO2 and NH3. In a climate chamber, we measured CO2 and NH3 emissions from participants performing physical activities (walking and running at metabolic rates of 2.5 and 5 met, respectively) and psychological stimuli (meditation and cognitive tasks). Participants' physiological responses were recorded, including the skin temperature, electrodermal activity (EDA), and heart rate, and then analyzed for their relationship with CO2 and NH3 emissions. The results showed that physiological engagement considerably elevated per-person CO2 emission rates from 19.6 (seated) to 46.9 (2.5 met) and 115.4 L/h (5 met) and NH3 emission rates from 2.7 to 5.1 and 8.3 mg/h, respectively. CO2 emissions reduced when participants stopped running, whereas NH3 emissions continued to increase owing to their distinct emission mechanisms. Psychological engagement did not significantly alter participants' emissions of CO2 and NH3. Regression analysis revealed that CO2 emissions were predominantly correlated with heart rate, whereas NH3 emissions were mainly associated with skin temperature and EDA. These findings contribute to a deeper understanding of human metabolic emissions of CO2 and NH3.

Carbon footprint of low-energy buildings in the United Kingdom: Effects of mitigating technological pathways and decarbonization strategies

There is a limited comprehensive analysis of the effectiveness of adopted carbon mitigation strategies for buildings over their life cycle, that are concerned with temporal perspectives of emissions. Accordingly, this paper explores a life cycle assessment (LCA) to address the concerns regarding mitigating the carbon footprint of a UK timber-frame low-energy dwelling. In particular, it aims to investigate the potential greenhouse gas (GHG) emission reduction in terms of three different heating and ventilation options, and to analyze the influence of decarbonization of electricity production as well as the technological progress of the waste treatment of timber on the building's environmental performance. Thus, the whole life‑carbon of the building case studies was evaluated for a total of eight investigated prospective scenarios, and they were compared to the LCA results of the baseline scenario, where the existing technology and context remained constant over time. Results show that using a compact heat pump would lead to a significant whole life-cycle emission reduction of the dwelling, by 19 %; while GHG emission savings can be reinforced if the assessed systems are employed simultaneously with grid decarbonization, exhibiting a 25 %-60 % reduction compared to the baseline scenario. Moreover, technological changes in the waste treatments of timber products could substantially reduce the buildings' embodied emissions, representing 3 %-23 %. From these emission-saving measures, the contribution of material efficiency strategies to achieve more embodied carbon savings should be highlighted in future construction practices.

Solar-driven CO2 reduction using modified earth-abundant ilmenite catalysts

Photocatalytic CO₂ reduction is an alternative technology to the depletion of highly pollutant fossil fuels through the generation of renewable solar-based fuels. This technology requires that the photocatalysts be obtained directly from nature to scale up the process. Taking that into consideration, this work proposed the fabrication of sodium iron titanate (NaFeTiO₄) photocatalysts from earth-abundant ilmenite mineral. The photocatalysts exhibited full spectrum light response, good electron transfer due to its unique tunnel structure that favored the formation of rod-like morphology. These properties promoted the solar-driven CO₂ reduction to generate formic acid (HCOOH) with high selectivity (157 μmol g^-1 h^-1). It was found that higher synthesis temperatures promoted the formation of Fe³⁺ species, which decreased the efficiency for CO₂ reduction. Also, the possibility of reduced the CO₂ molecules in the air was studied with the NaFeTiO₄ samples, which resulted in an efficiency of up to 93 μmol g^-1 h^-1 of HCOOH under visible light. The stability of the solar-driven CO₂ reduction with the NaFeTiO₄ photocatalysts was confirmed after seven days of continuous evaluation.