The coronavirus disease 2019 (COVID-19) pandemic triggered an increase in remote work-from-home for office workers. Given that many homes now function as offices despite not being designed to support office work, it is critical to research the impact of indoor air quality (IAQ) in homes on the cognitive performance of people working from home. In this study, we followed 206 office workers across the U.S. over one year under remote or hybrid-remote settings during 2021-2022. Participants placed two real-time, consumer-grade indoor environmental monitors in their home workstation area and bedroom. Using a custom smartphone application geofenced to their residential address, participants responded to surveys and periodic cognitive function tests, including the Stroop color-word interference test, Arithmetic two-digit addition/subtraction test, and Compound Remote Associates Task (cRAT). Exposures assessed included carbon dioxide (CO) and thermal conditions (indoor heat index: a combination of temperature and relative humidity) averaged over 30 minutes prior to each cognitive test. In fully adjusted longitudinal mixed models (≤121), we found that indoor thermal conditions at home were associated with cognitive function outcomes non-linearly (<0.05), with poorer cognitive performance on the Stroop test and poorer creative problem-solving on the cRAT when conditions were either too warm or too cool. Most indoor CO levels were <640 ppm, but there was still a slight association between higher CO and poorer cognitive performance on Stroop (=0.09). Our findings highlight the need to enhance home indoor environmental quality for optimal cognitive function during remote work, with benefits for both employees and employers.

Ultra-Clean-Air (UCA) operating theatres aim to minimise surgical instrument contamination and wound infection through high flow rates of ultra-clean air, reducing the presence of Microbe Carrying Particles (MCPs). This study investigates the airflow patterns and ventilation characteristics of a UCA operating theatre (OT) under standard ventilation system operating conditions, considering both empty and partially occupied scenarios. Utilising a precise computational model, quasi-Direct Numerical Simulations (qDNS) were conducted to delineate flow velocity profiles, energy spectra, distributions of turbulent kinetic energy, energy dissipation rate, local Kolmogorov scales, and pressure-based coherent structures. These results were also complemented by a tracer gas decay analysis following ASHRAE standard guidelines. Simulations showed that contrary to the intended laminar regime, the OT's geometry inherently fosters a predominantly turbulent airflow, sustained until evacuation through the exhaust vents, and facilitating recirculation zones irrespective of occupancy level. Notably, the occupied scenario demonstrated superior ventilation efficiency, a phenomenon attributed to enhanced kinetic energy induced by the additional obstructions. The findings underscore the critical role of UCA-OT design in mitigating MCP dissemination, highlighting the potential to augment the design to optimise airflow across a broader theatre spectrum, thereby diminishing recirculation zones and consequently reducing the propensity for Surgical Site Infections (SSIs). The study advocates for design refinements to harness the turbulent dynamics beneficially, steering towards a safer surgical environment.

Passive design characteristics (PDC) play a pivotal role in reducing the energy burden on households without imposing additional financial constraints on project stakeholders. However, the scarcity of PDC data has posed a challenge in previous studies when assessing their energy-saving impact. To tackle this issue, this research introduces an innovative approach that combines deep learning-powered computer vision with machine learning techniques to examine the relationship between PDC and energy burden in residential buildings. In this study, we employ a convolutional neural network computer vision model to identify and measure key indicators, including window-to-wall ratio (WWR), external shading, and operable window types, using Google Street View images within the Chicago metropolitan area as our case study. Subsequently, we utilize the derived passive design features in conjunction with demographic characteristics to train and compare various machine learning methods. These methods encompass Decision Tree Regression, Random Forest Regression, and Support Vector Regression, culminating in the development of a comprehensive model for energy burden prediction. Our framework achieves a 74.2% accuracy in forecasting the average energy burden. These results yield invaluable insights for policymakers and urban planners, paving the way toward the realization of smart and sustainable cities.

It is now widely recognised that aerosol transport is major vector for transmission of diseases such as COVID-19, and quantification of aerosol transport in the built environment is critical to risk analysis and management. Understanding the effects of door motion and human movement on the dispersion of virus-laden aerosols under pressure-equilibrium conditions is of great significance to the evaluation of infection risks and development of mitigation strategies. This study uses novel numerical simulation techniques to quantify the impact of these motions upon aerosol transport and provides valuable insights into the wake dynamics of swinging doors and human movement. The results show that the wake flow of an opening swinging door delays aerosol escape, while that of a person walking out entrains aerosol out of the room. Aerosol escape caused by door motion mainly happens during the closing sequence which pushes the aerosols out. Parametric studies show that while an increased door swinging speed or human movement speed can enhance air exchange across the doorway, the cumulative aerosol exchange across the doorway is not clearly affected by the speeds.

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers () of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2, the virus that causes the coronavirus disease (COVID)-19, is primarily transmitted through respiratory droplets which linger in enclosed spaces, often exacerbated by HVAC systems. Although research to improve HVAC handling of SARS-CoV-2 is progressing, currently installed HVAC systems cause problems because they recirculate air and use ineffective filters against virus. This paper details the process of developing a novel method of eliminating air pollutants and suspended pathogens in enclosed spaces using Photocatalytic Oxidation (PCO) technology. It has been previously employed to remove organic contaminants and compounds from air streams using the irradiation of titanium dioxide (TiO) surfaces with ultraviolet (UV) lights causing the disintegration of organic compounds by reactions with oxygen (O) and hydroxyl radicals (OH). The outcome was two functional prototypes that demonstrate the operation of PCO-based air purification principle. These prototypes comprise a novel TiO coated fibre mop system, which provide very large surface area for UV irradiation. Four commercially accessible materials were used for the construction of the mop: Tampico, Brass, Coco, and Natural synthetic. Two types of UV lights were used: 365 nm (UVA) and 270 nm (UVC). A series of tests were conducted that proved the prototype's functionality and its efficiency in lowering volatile organic compounds (VOCs) and formaldehyde (HCHO). The results shown that a MopFan with rotary mop constructed with Coco fibres and utilising UVC light achieves the best VOC and HCHO purification performance. Within 2 h, this combination lowered HCHO by 50% and VOCs by 23% approximately.

People in cities use elevators daily. With the COVID-19 pandemic, there are more worries about elevator safety, since elevators are often small and crowded. This study used a proven CFD model to see how the virus could spread in elevators. We simulated five people taking in an elevator for 2 min and analyzed the effect of different factors on the amount of virus that could be inhaled, such as the infected person's location, the standing positions of the persons, and the air flow rate. We found that the position of the infected person and the direction they stood greatly impacted virus transmission in the elevator. The use of mechanical ventilation with a flow rate of 30 ACH (air changes per hour) was effective in reducing the risk of infection. In situations where the air flow rate was 3 ACH, we found that the highest number of inhaled virus copies could range from 237 to 1186. However, with a flow rate of 30 ACH, the highest number was reduced to 153 to 509. The study also showed that wearing surgical masks decreased the highest number of inhaled virus copies to 74 to 155.

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM, PM) from January 2020 to September 2021. Measurements of PM and PM particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM and PM were reduced by 9-10 μg/m below typical peak levels experienced in recent years, mean daily PM and PM concentrations were only ∼1 μg/m lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM and PM levels to within WHO health-based guidelines and to achieve compliance with PM targets developed under the Environment Act 2021.

Online learning has drawn much more attention since the outbreak of COVID-19. Most related studies have focused on online platform design and instructional design. However, the physical environment where online learning is conducted (e.g., students' homes) is rarely studied. To understand the thermal conditions in students' online learning environment and its impact on students' thermal comfort and their performance during online learning, an experiment, including both objective measurement and subjective assessment, was conducted in a student's apartment. Thirty university students participated in this experiment, and they were randomly assigned into six groups (three thermal conditions (i.e., control, cold, and hot) × two-course durations). Both environmental parameters (i.e., air temperature, radiant temperature, air velocity, etc.) and physiological parameters (i.e. skin temperatures) were measured at the same time. Besides, students' thermal sensation, acceptance, and learning performance were self-evaluated and collected through questionnaires. Results showed that participants' thermal sensation was positively correlated with their mean skin temperature (MST) and the operative temperature (T) in the apartment (MST: ρ = 0.94, p < 0.001; T: ρ = 0.91, p < 0.001), yet no significant relation with their personal characteristics was observed in the current study, which might be caused by the small sample size. Moreover, inverted U-shape relationships were identified between participants' perceived performance and their thermal sensation/MST/T. When students felt slightly cool (TSV = -0.3), they thought they could reach their best performance. This study revealed the impacts of the thermal environment on students' online learning performance, more performance tasks could be conducted in the future to examine the impacts in more detail.

Alternative care facilities (ACFs) based on large-space public buildings were widely used early at the start of the coronavirus disease 2019 (COVID-19) pandemic. However, studies have shown that the indoor spatial environment of ACFs can significantly induce mental health problems among users. Thus, this study hypothesizes that improving the visual environment in the interiors of large-space ACFs may reduce mental health problems among users. To verify this hypothesis, this study used critical analysis to screen the influencing factors and used analytic hierarchy process analysis to determine the weights. Particularly, the analyses were based on ACF research in Wuhan and questionnaire surveys of patients with experience using ACFs. Subsequently, virtual reality experiments were conducted to measure physiological indicators and subjective questionnaire collection based on the orthogonal experimental design of the four screened visual environment components. The results revealed the following related to large-space ACFs: 1) Lifestyle support was the most dominant patient requirement and preference for the visual environment. 2) The visual environment can influence the participants' efficiency of psychological stress relief, emotional regulation, and subjective perception. 3) Different design characteristics of the four visual environment components were causally related to restorative effects. To the best of our knowledge, this is the first study analyzing patients' preferences and psychological needs for the visual environment of large-space ACFs and combining subjective and objective measures to investigate the restorative effects of the visual environment. Improving the quality of the visual environment in large-space ACFs presents an effective intervention for alleviating the psychological problems of admitted patients.

The recent outbreak of COVID-19 has threatened public health. Owing to the relatively sealed environment and poor ventilation in elevator cabins, passengers are at risk of respiratory tract infection. However, the distribution and dispersion of droplet aerosols in elevator cabins remain unclear. This study investigated the transmission of droplet aerosols exhaled by a source patient under three ventilation modes. Droplet aerosols produced by nose breathing and mouth coughing were resolved using computational fluid dynamics (CFD) simulations. We adopted the verified renormalization group (RNG) - turbulence model to simulate the flow field and the Lagrangian method to track the droplet aerosols. In addition, the influence of the ventilation mode on droplet transmission was evaluated. The results showed that droplet aerosols gathered in the elevator cabin and were difficult to discharge under the mixed and displacement ventilation modes with specific initial conditions. The inhalation proportion of droplet aerosols for air curtain was 0.016%, which was significantly lower than that for mixed ventilation (0.049%) and displacement ventilation (0.071%). The air curtain confined the transmission of droplet aerosols with the minimum ratios of inhalation, deposition, and suspension and is thus recommended to reduce the exposure risk.

Personal cloud, termed as the difference in air pollutant concentrations between breathing zone and room sites, represents the bias in approximating personal inhalation exposure that is linked to accuracy of health risk assessment. This study performed a two-week field experiment in a naturally ventilated office during the COVID-19 pandemic to assess occupants' exposure to common air pollutants and to determine factors contributing to the personal cloud effect. During occupied periods, indoor average concentrations of endotoxin (0.09 EU/m), TVOC (231 μg/m), CO (630 ppm), and PM (14 μg/m) were below the recommended limits, except for formaldehyde (58 μg/m). Personal exposure concentrations, however, were significantly different from, and mostly higher than, concentrations measured at room stationary sampling sites. Although three participants shared the same office, their personal air pollution clouds were mutually distinct. The mean personal cloud magnitude ranged within 0-0.05 EU/m, 35-192 μg/m, 32-120 ppm, and 4-9 μg/m for endotoxin, TVOC, CO, and PM, respectively, and was independent from room concentrations. The use of hand sanitizer was strongly associated with an elevated personal cloud of endotoxin and alcohol-based VOCs. Reduced occupancy density in the office resulted in more pronounced personal CO clouds. The representativeness of room stationary sampling for capturing dynamic personal exposures was as low as 28% and 5% for CO and PM, respectively. The findings of our study highlight the necessity of considering the personal cloud effect when assessing personal exposure in offices.

During the SARS-CoV-2 (COVID-19) pandemic, most citizens were cooperative towards the face-masking policy; however, undeniably, face masking has increased complaints of thermal discomfort to varying degrees and resulted in potential health hazards during summer. Thus, a thermal comfort survey was conducted under tree-shaded areas generally preferred by pedestrians to explore the thermal response of face-masked pedestrians. Thirty-two subjects, with and without masks, participated in walking experiments, and their thermal parameters and physiological indicators were recorded; moreover, the subjects were asked to fill in subjective questionnaires. The results showed that although tree shades significantly reduced the average radiant temperature, dampness in the mask may cause some discomfort symptoms, among which intense sweating (54.55%) and tachycardia (42.18%) accounted for the largest proportion. Based on thermal indices, it could be concluded that face-masking does not significantly affect the thermal comfort of subjects walking in shaded areas. Notably, a 30-min walk in tree-shaded areas with face masking does not adversely affect human health or quality of life. Thus, the present assessment of the thermal safety of humans in shaded environments provides reference data for determining thermal comfort levels during outdoor walking with face masking.

The COVID-19 pandemic has prompted huge efforts to further the scientific knowledge of indoor ventilation and its relationship to airborne infection risk. Exhaled infectious aerosols are spread and inhaled as a result of room airflow characteristics. Many calculation methods and assertions on risk assume 'well-mixed' flow conditions. However, ventilation in buildings is complex and often not showing well-mixed conditions. Ventilation guidance is typically based on the provision of generic minimum ventilation flow rates for a given space, irrespective of the effectiveness in the delivery of the supply air. Furthermore, the airflow might be heavily affected by the season, the HVAC ventilation, or the opening of windows, which would potentially generate draughts and non-uniform conditions. As a result, fresh air concentration would be variable depending upon a susceptible receptor's position in a room and, therefore, associated airborne infection risk. A computational fluid dynamics (CFD) and dynamic thermal modelling (DTM) framework is proposed to assess the influence of internal airflow characteristics on airborne infection risk. A simple metric is proposed, the hourly airborne infection rate (HAI) which can easily help designers to stress-test the ventilation within a building under several conditions. A case study is presented, and the results clearly demonstrate the importance of understanding detailed indoor airflow characteristics and associated concentration patterns in order to provide detailed design guidance, e.g. occupancy, supply air diffusers and furniture layouts, to reduce airborne infection risk.

Coronavirus disease (COVID-19), which emerged in 2019, has induced worldwide chaos. The main cause of COVID-19 mass infection indoors is the spread of virus-containing droplets via indoor airflow, which is affected by air conditioners and purifiers. Here, ten experimental cases were established to analyze how use of air purifiers affects the spread of virus-containing droplets. The experiments were conducted in a school classroom with an air conditioner in summer. In the droplet dispersion experiment, paraffin oil was used as the droplet substance. Two main scenarios were simulated: (1) an infected student was seated in the back of the classroom; and (2) the teacher, standing in the front of the classroom, was infected. The results were expressed using two parameters: peak concentration and loss rate, which reflect the degree of direct and indirect infection (airborne infection), respectively. The air purifier induced a peak concentration decrease of 42% or an increase of 278%, depending on its location in the classroom. Conversely, when the air purifier was operated in the high mode (flow rate = 500 CMH; cubic meters per hour), the loss rate showed that the amount of droplet nuclei only decreased by 39% and the droplet amount decreased by 22%. Thus, the airborne infection degree can be significantly reduced. Finally, the use of air purifiers in the summer may be helpful in preventing group infections by reducing the loss rate and peak concentration if the air purifier is placed in a strategic location, according to the airflow of the corresponding room.

According to the World Health Organization (https://covid19.who.int/), more than 651 million people have been infected by COVID-19, and more than 6.6 million of them have died. COVID-19 has spread to almost every country in the world because of air travel. Cases of COVID-19 transmission from an index patient to fellow passengers in commercial airplanes have been widely reported. This investigation used computational fluid dynamics (CFD) to simulate airflow and COVID-19 virus (SARS-CoV-2) transport in a variety of airliner cabins. The cabins studied were economy-class with 2-2, 3-3, 2-3-2, and 3-3-3 seat configurations, respectively. The CFD results were validated by using experimental data from a seven-row cabin mockup with a 3-3 seat configuration. This study used the Wells-Riley model to estimate the probability of infection with SARS-CoV-2. The results show that CFD can predict airflow and virus transmission with acceptable accuracy. With an assumed flight time of 4 h, the infection probability was almost the same among the different cabins, except that the 3-3-3 configuration had a lower risk because of its airflow pattern. Flying time was the most important parameter for causing the infection, while cabin type also played a role. Without mask wearing by the passengers and the index patient, the infection probability could be 8% for a 10-h, long-haul flight, such as a twin-aisle air cabin with 3-3-3 seat configuration.

Vegetation plays an important role in biosphere-atmosphere exchange, including emission of biogenic volatile organic compounds (BVOCs) that influence the formation of secondary pollutants. Gaps exist in our knowledge of BVOC emissions from succulent plants, which are often selected for urban greening on building roofs and walls. In this study, we characterize the CO uptake and BVOC emission of eight succulents and one moss using proton transfer reaction - time of flight - mass spectrometry in controlled laboratory experiments. CO uptake ranged 0 to 0.16 μmol [g DW (leaf dry weight)] s and net BVOC emission ranges -0.10 to 3.11 μg [g DW] h. Specific BVOCs emitted or removed varied across plants studied; methanol was the dominant BVOC emitted, and acetaldehyde had the largest removal. Isoprene and monoterpene emissions of studied plants were generally low compared to other urban trees and shrubs, ranging 0 to 0.092 μg [g DW] h and 0 to 0.44 μg [g DW] h, respectively. Calculated ozone formation potentials (OFP) of the succulents and moss range 4×10 - 4×10 g O [g DW] d. Results of this study can inform selection of plants used in urban greening. For example, on a per leaf mass basis, and have OFP lower than many plants presently classified as low OFP and may be promising candidates for greening in urban areas with ozone exceedances.

Obtaining visual comfort, satisfaction and well-being in residential interiors are now becoming more important, especially in times of extreme events such as the COVID-19 pandemic. It also became important to collect users' evaluations and their own solutions for residential lighting in order to improve the current lighting conditions. For this aim, with a group of international and inter-disciplinary researchers, a comprehensive study was conducted. This study is the last part of a three-stage investigation aimed at increasing our knowledge of the current lighting conditions in residential areas during and after the COVID-19 pandemic. For the current study, an online survey and in-depth interviews were conducted between June and August 2022 in Poland, Turkey, Sweden, and the U.K., with 520 participants. As results of this study show, a correlation was found between daylight satisfaction and its sufficiency. Similar correlations were found between artificial lighting satisfaction, its sufficiency, and its uniformity. The differences between seasons were detected for being very satisfied with daylight quality. Also, the correlation between satisfaction with daylighting and the ratio of windows showed difference among seasons. Stronger correlations between satisfaction with artificial lighting, its sufficiency and uniformity were found in summerterm according to winter-term results. Correlations between artificial lighting brightness - CRI and uniformity weakened in summer-term. Results from open-ended questions and in-depth interviews showed, removing the shading device and augmenting the characteristics of artificial lighting were the mostly done adjustments during the COVID- 19 pandemic. The most prominent theme is visual comfort according to the in-depth interview responses.'

The pandemic of COVID-19 and its transmission ability raise much attention to ventilation design as indoor-transmission outstrips outdoor-transmission. Impinging jet ventilation (IJV) systems might be promising to ventilate densely occupied large spaces due to their high jet momentum. However, their performances in densely occupied spaces have rarely been explored. This study proposes a modified IJV system and evaluates its performance numerically in a densely occupied classroom mockup. A new assessment formula is also proposed to evaluate the nonuniformity of target species CO. The infector is assumed as the manikin with the lowest tracer gas concentration in the head region. The main results include: a) Indoor air quality (IAQ) in the classroom is improved significantly compared with a mixing ventilation system, i.e., averaged CO in the occupied zone (OZ) is reduced from 1287 ppm to 1078 ppm, the OZ-averaged mean age of air is reduced from 439 s to 177 s; b) The mean infection probability is reduced from 0.047% to 0.027% with an infector, and from 0.035% to 0.024% with another infector; c) Cooling coil load is reduced by around 21.0%; d) Overall evaluation indices meet the requirements for comfortable environments, i.e., the temperature difference between head and ankle is within 3 °C and the OZ-averaged predictive mean vote is in the range of -0.5 - 0.5; e) Thermal comfort level and uniformity are decreased, e.g., overcooling near diffuser at ankle level. Summarily, the target system effectively improves IAQ, reduces exhaled-contaminant concentration in head regions, and saves energy as well.

Escalating demands of assessing airborne disease infection risks had been awakened from ongoing pandemics. An inhalation index linked to biomedical characteristics of pathogens (e.g. for coronavirus delta variant) was proposed to quantify human uptake dose. A modified Wells-Riley risk-assessment framework was then developed with enhanced capability of integrating biological and spatiotemporal features of infectious pathogens into assessment. The instantaneous transport characteristics of pathogens were traced by Eulerian-Lagrangian method. Droplets released via speaking and coughing in a conference room with three ventilation strategies were studied to assess occupants' infection risks using this framework. Outcomes revealed that speaking droplets could travel with less distance (0.5 m) than coughing droplets (1 m) due to the frequent interaction between speaking flow and thermal plume. Quantified analysis of inhalation index revealed a higher inhalation possibility of droplets with nuclei size smaller than , and this cut-off size was found sensitive to ventilation. With only 60-second exposure, occupants in the near-field of host started to have considerable infection risks (approximately 20%). This risk was found minimising over distance exponentially. This modified framework demonstrated the systematic analysis of airborne transmission, from quantifying particle inhalation possibility, targeting specific disease's , to ultimate evaluation of infection risks.

Asthma and allergy symptoms vary seasonally due to exposure to environmental sources of allergen, including fungi. However, we need an improved understanding of seasonal influence on fungal exposures in the indoor environment. We hypothesized that concentrations of total fungi and allergenic species in vacuumed dust vary significantly by season.