Indoor air最新文献

Quantitative microbiological risk assessment (QMRA) is a method to estimate the risk of infectious disease transmission from human exposure to pathogens. QMRA is a helpful tool to inform health policies to control the impact of infectious disease transmission from human-to-human transmissible infectious respiratory diseases. QMRA combines an estimate of pathogen exposure with information on the probability of infection given the dose. The infection risk of respiratory diseases is generally assumed to depend on the interpersonal distance between the infectious person (index) and the exposed recipient. To account for close-proximity exposure in QMRA, specific generic models are required. To be helpful in policy information, these models should be sufficiently accurate in describing elevated air concentrations of pathogens near the index. On the other hand, they should be sufficiently generic and flexible to be applied in generalized situations without requiring very specific and detailed situational information. In this work, we identified different models to account for near-field exposure in the literature: multizone, diffusion, and jet models. These methods were tested with respect to their applicability in QMRA. We evaluated them on the criteria of ease of use, the availability of parameter values for generic application, and their ability to describe air concentrations in realistic situations as replicated in experiments. It was found that only diffusion modelling appeared to be both flexible enough to describe experimental data and to be supported by sufficient information to allow for parametrization in a wide variety of situations. The multizone models were found to be easy to use but difficult to parametrize given the arbitrariness of aspects of the modelling method. The jet models were found to be more complex to implement and adapt to specific exposure scenarios.

This study evaluates the effects of Advanced ActivePure technology, a photocatalytic air purification system, on the health status of mice housed in a controlled environment. This novel technology is promising due to its ability to eliminate the risk of ozone release into the environment, making it safer compared to other photocatalytic oxidation technologies. Utilizing 300 6-month-old mice over a period of 3 months, this research investigated various health parameters, including haematological, serum biochemical, redox, and inflammatory indicators. The experiment demonstrated no significant alteration in the majority of tested parameters between the control and Advanced ActivePure-exposed groups. Until now, no study has analyzed the health safety of living organisms exposed to Advanced ActivePure in such a detailed manner. Histopathological analyses of nasal and eye tissues showed no adverse changes attributable to Advanced ActivePure exposure. These findings suggest that Advanced ActivePure technology does not negatively impact the overall health of mice, including biochemical markers and respiratory histopathology.

The children’s sensitivity to high concentrations of pollutants in schools has led to increased attention to indoor air quality in recent years. Hence, the study was aimed at determining the concentration of air pollutants in primary school classrooms in Tehran during one academic year. For this purpose, the research team identified and evaluated primary schools in different regions of the Tehran megacity. Schools with similar characteristics were selected to sample CO₂, PM₁, PM_2.5, and PM₁₀ pollutants in the indoor air of Tehran primary school classrooms. A total of 30 classrooms were sampled in five schools during the autumn, winter, and spring seasons of one academic year, spanning from September 2016 to May 2017. Data analysis was conducted using Python-based tools, including Seaborn and Matplotlib, to generate visual insights. The results showed that the concentration of the pollutants studied differs significantly in winter compared to autumn and spring in the indoor air of the classrooms. The highest CO₂, PM₁, PM_2.5, PM₁₀, and PM_Total concentrations were observed in the north, center, south, south, and south of Tehran, especially in the ground-floor classrooms, respectively. The results of distance from green space, private space, and floors in winter emphasize an internal source of pollution; conversely, in spring and autumn, they show the influence of external sources on the concentration of pollutants inside the classrooms in various areas of Tehran. To summarize, the results of this study showed that the indoor air quality of primary school classrooms in Tehran requires careful investigation and urgent measures to reduce pollutants and improve environmental conditions to maintain the health and comfort of students and school board members.

This observational experimental study was aimed at measuring SARS-CoV-2 dispersion via air and deposition onto surfaces in indoor spaces at short range (0.8 m) and long range (4.0 m) during the speaking and singing of mildly symptomatic COVID-19 patients. Ten patients were invited to sing and speak in unventilated rooms. Air and surface samples were taken and analyzed for SARS-CoV-2 by qPCR and cell culture assay. Seventy-three of 120 air samples and 2 of 80 surface samples tested positive by qPCR. Concentrations were too low to be quantified. Culturing to confirm infectivity was unsuccessful for all samples. High nasal virus concentration in patients, a high number of symptoms, and short symptom duration correlated with a higher probability of PCR-positive air samples. Impingers were significantly more effective air samplers than impactors. No significant effect was found for patient age, oropharyngeal virus concentration, the presence of systemic symptoms, vaccination status, the number of coughs during measurements, room temperature, humidity, time, proximity, respiratory activity, or voice amplitude during experiments. Two supporting experiments were performed on aerosol dispersion and sampler equipment tests. They confirmed that aerosols spread throughout the room homogeneously and that selected sampler equipment can detect genetic material from environmental samples. This study adds to the body of evidence regarding the dispersion of SARS-CoV-2 RNA in range of a few meters indoors.

The SARS-CoV-2 pandemic has highlighted the importance of maintaining a healthy indoor environment, particularly in healthcare facilities where strict infection control is essential. Airborne infection isolation rooms (AIIRs) are designed to isolate infectious patients and prevent the spread of airborne pathogens. However, additional microbial contamination control measures are necessary to ensure safe indoor air quality for both healthcare workers and patients. In this study, the disinfection performance of upper-room ultraviolet germicidal irradiation (UR-UVGI) was experimentally evaluated in a full-scale AIIR environment. Experiments were conducted under the AIIR minimum operational conditions (i.e., ≥ 6 air changes per hour (ACH)), using Bacillus subtilis (ATCC 6633) as the microbial contaminant. To simulate practical conditions, two microbial source scenarios were considered: (1) outdoor sources, wherein the microbes infiltrated from the anteroom into the ward, and (2) indoor sources, wherein the microbes were generated directly at the patient’s respiratory position. The results indicate that for outdoor sources, UR-UVGI reduced airborne contaminants by approximately 20% at the ward center and 28% at the patient’s respiratory position, but these reductions were not statistically significant (p > 0.05). By contrast, for indoor sources, UR-UVGI achieved a statistically significant reduction of approximately 23% at the ward center and 25% at the ward exhaust (p < 0.05). These findings suggest that UR-UVGI serves as a supplementary disinfection method in AIIRs. In addition, the relatively low disinfection efficacy observed at high ventilation rates (≥ 6 ACH) indicates the need for optimized UR-UVGI placement strategies to enhance disinfection performance. Future research will focus on microbial dispersion and deposition patterns, incorporating computational fluid dynamics modeling to assess UR-UVGI effectiveness under various environmental conditions.

This study investigates the indoor air quality (IAQ) conditions in green-certified buildings and examines the factors influencing them. An integrated IoT sensing system was implemented indoors and outdoors to assess the levels of particulate matter, nitrogen dioxide, and ozone at five Leadership in Energy and Environmental Design (LEED)-certified and five non-LEED educational buildings in Central Florida. Building-related characteristics were collected through walk-through surveys, BACnet systems, and construction drawings. An algorithm model based on support vector machine (SVM) and nonnegative matrix factorization (NMF) was developed to analyze the features of pollutants and the relative contribution of different influencing factors. The findings reveal that concentrations of target pollutants are generally lower in LEED buildings compared to non-LEED buildings. Although IAQ influencing factors are generally similar between LEED and non-LEED buildings, the weighted contribution ratios of specific factors, particularly for indoor nitrogen dioxide and ozone, vary significantly. The concentration of pollutants in non-LEED buildings is more susceptible to adverse environmental factors. The SVM-NMF model demonstrates significant advantages in nonlinear feature extraction and handling multicollinearity issues. It surpasses multiple linear regression and backpropagation neural network models in analyzing multidimensional indoor air data by 26.9% and 18% (p < 0.001), respectively. The robustness of the model was validated through fit comparison, cross-validation, and residual analysis. This study provides a foundational information base and effective technical means for subsequent research on IAQ management.

Indoor air quality is a crucial factor affecting human health, with high levels of CO₂ impairing cognition and ozone reacting with human skin to produce volatile organic compounds (VOCs), such as geranyl acetone (Ga), 6-methyl-5-hepten-2-one (6-MHO), and 4-oxopentanal (4-OPA), which can cause irritation to the respiratory tract and skin. In this study, the indoor air quality of a university classroom was monitored using home-built air quality boxes (AQBs) comprising low-cost sensors for various gas species, including CO₂, ozone, and NO_x. The interaction processes between indoor and outdoor air and human interference were investigated using box model simulation of CO₂ and ozone profiles. The results indicate both indoor CO₂ and ozone were significantly affected by the ventilation and number of occupants. The simulation of CO₂ profiles retrieves an air exchange rate constant of ~1.05 h⁻¹ for one door opening, in addition to the room ventilator of 1.20 h⁻¹. With the derived parameters, the study estimated that ozone, mainly transported from the outdoors and consumed by room and human surfaces, has deposition velocities of 0.019 ± 0.005 and 0.45 ± 0.15 cm s⁻¹ for room and human surfaces, respectively, consistent with the literature. The simulation also suggests that VOCs such as Ga, 6-MHO, and 4-OPA from ozone consumption on human surfaces might accumulate indoors to several parts per billion by volume in a crowded room with poor ventilation. The integration of observation using low-cost sensors with the model simulation quantified the physical and chemical processes controlling indoor ozone concentration and organic ozonolysis. Furthermore, the study suggests that the retrieved parameters from the model could guide proper ventilation strategies to maintain good indoor air quality with energy efficiency based on the number of occupants.

The microbial contamination levels in the apartment transition spaces, frequently traversed by pedestrians, are closely related to resident health. This study analyzed the microbial distribution in these spaces and modeled and assessed the microbial exposure risk faced by residents following different flow paths. The results showed that the dominant genera of airborne microbes, settling microbes, wall microbes, and ground microbes were Staphylococcus, Bacillus, and Micrococcus, collectively accounting for over 70% of the total microbial population. The concentration of settling microbes in noncorridor spaces was 9.7 times higher than in corridor spaces, necessitating targeted disinfection of settling microbes in noncorridor spaces. The analysis of biodiversity indices elucidates the extent to which the biodiversity of different types of microbes is affected by variations in pedestrian flow, with airborne microbes being the most affected and ground microbes the least affected. This study also constructed a microbial exposure risk assessment model during residents’ mobility in the apartment transitional spaces. Based on this model, it was confirmed that nonfirst-floor residents using the elevator to enter and exit their homes face the highest exposure risk to airborne microbes and wall microbes, while those using the stairwell face the highest exposure risk to settling microbes and ground microbes. First-floor residents face the lowest microbial exposure risk when entering and exiting their homes. The research results not only establish a microbial exposure risk assessment system but also provide important theoretical reference for evaluating and improving the environmental quality of other similar scenarios.

Older people are more susceptible to health risks from indoor air pollution, even at low pollution levels. This study aimed to improve ventilation and air quality of densely occupied multipurpose rooms in elderly care centers (ECCs). The specific objectives were to investigate the impact of different ventilation types on ventilation performance and air quality in the ECC’s multipurpose room, including mixing (MV), displacement (DV), zone (ZV), stratum (SV), and underfloor ventilation (UV); analyze the influence of ventilation on CO₂ concentration; and discuss appropriate ventilation design comprehensively considering air velocity, CO₂ level, air change efficiency (ACE), mean age of air (MAA), contaminant removal effectiveness (CRE), and predicted mean vote (PMV). First, an experimental study was conducted, and then 11 potential optimization models were proposed based on the experiment’s results. Finally, quantitative results were obtained through computational fluid dynamics (CFD) analysis. Analysis revealed that model ZV2 with wall outlets at 1.1 m and ceiling inlets proved to be the optimal ventilation type. It had fewer air circulation and stagnation areas, and it maintained its effectiveness regardless of furniture configurations or occupant positioning (caregivers and elderly people), factors that could potentially compromise airflow in other models. Compared to model ZV2, CO₂ concentrations in the sitting breathing zone of other models increased from a minimal increase of 0.2% to a substantial increase of 38.9%. Analysis also showed that the seats in the first row consistently maintained low pollutant concentration environments. These new results offer valuable insights for ECC stakeholders by assessing ventilation and air quality in crowded spaces for older people at two different breathing heights (sitting and standing).

Awareness of how buildings interact with the occupant experience—especially human performance—is becoming more prevalent, as seen by increasing interest and investment in healthy built environments. However, there is a need to synthesize the wide array of existing indoor environmental assessment and performance research in a way that can translate directly to building design and operation. Existing research in this area typically focuses on a single isolated metric and has not focused on making the results utilizable by building practitioners. The aim of this research is to investigate existing office performance literature through meta-analyses and produce regression models for four indoor environmental quality (IEQ) metrics to support critical decision-making for building operation and renovation. To reach this aim, a literature review was conducted to identify studies that measure the impact of changing ventilation rate, temperature, horizontal illuminance, and noise level in offices on occupant task performance. This repository of field and laboratory studies was analyzed to visualize the trends between the selected IEQ metrics and task performance. The temperature, ventilation rate, and horizontal illuminance regression models showed clear improvement potential when modifying indoor conditions toward the defined high-performance range, while the regression model for noise level was inconclusive. The discussion notes the importance of designing holistically for all components of these IEQ categories to utilize the results, for example, good filtration on outdoor air for quantifying ventilation impact and uniform overhead lighting with low contrast for quantifying horizontal illuminance impact. The novelty of this work is in considering multiple facets of the indoor environment under a single, unified analysis schema and producing IEQ-based performance gains that can directly inform cost-benefit analyses of building design and renovation.