1. Introduction
Particulate matter (PM) has been recognized as one of the leading environmental health risk factors worldwide, contributing to adverse health effects in life and work environments [1,2]. The negative impact of PM exposure was significant in terms of costs and quality-adjusted life years and it accelerated as the exposure time increased, emphasizing the importance of early interventions [3].
Even if it is known that exposure to atmospheric PM causes severe health problems in humans and significant damage to their respiratory and cardiovascular systems [4], research published is not yet exhaustive regarding the role of air pollution on the geographic spread of the COVID-19 disease both regionally and globally [5]. During the pandemic, it was shown that environmental factors, including PM, may play a significant role in the differential distribution and transmission of SARS-CoV-2 [6,7]. Other evidence from the literature highlighted the important contribution of chronic exposure to PM on the spread and lethality of COVID-19. In particular, PM2.5 and nitrogen dioxide (NO2) appear to be more closely correlated with COVID-19 than PM10 [8]. The review by Zhu et al. [9] provided insights into the detrimental effects of PM on various human health problems including respiratory, circulatory, nervous, and immune systems along with their possible mechanisms of toxicity. Furthermore, the potential effects of short- and long-term exposure to atmospheric pollution on COVID-19 risk and fatality rates were well described in the analysis of the first epidemic wave in Northern Italy [10] and in the Catalan Tarragona Province in Spain [11]. In the United States, Zhou et al. [12] found strong evidence that wildfires amplified the effect of short-term exposure to PM2.5 on COVID-19 cases and deaths, although there was substantial heterogeneity across counties. The study conducted in the Lombardia region (Northern Italy) concluded that the interaction between atmospheric PM and NO2 led to a significant increase in health events linked to the pandemic, with an estimated number of hundreds of infections, additional hospitalizations and intensive care admissions compared to non-urban areas [13].
Other studies reported the important role of indoor air quality in SARS-CoV-2 transmission, highlighting that virus particles may have similar dynamics to other suspended ultrafine particles (UFP, e.g., arising from road traffic, heating or other sources) [14,15,16]. Furthermore, the association between indoor carbon dioxide (CO2) levels and virus spreading has been explored also before the COVID-19 pandemic [17]. In a recent study, Peng and Jimenez [18] described CO2 levels corresponding to a given absolute SARS-CoV-2 infection risk varying by two orders of magnitude for different environments and activities. Other studies report a defined threshold of potential risk of infected droplets/aerosol inhalation fixed at 700 ppm of CO2 in indoor environments [5]. Finally, the improvement of indoor air quality by using ventilation systems has been highlighted as a COVID-19 risk mitigation method [19] as well as social distancing, face masks and hand hygiene and cleaning/disinfection of surfaces [20,21,22,23]. Furthermore, indoor air quality monitoring gained high relevance also to better know the mechanisms of interaction with COVID-19 spreading [24].
Air quality monitoring is conducted using sensors and instrumentation, which face many challenges. Accurate detection of indoor air pollutants and PM requires the selection of appropriate sensors and systems. The utilization of a high number of key performance indicators may increase the reliability of air quality assessment outcomes and associated mitigation strategies [25]. The indoor environment is heterogeneous, with significant variability within space and between different microenvironments and locations [26]. Sensor placement, occupancy, and activity reports, as well as measurements in different microenvironments and locations, can contribute to understanding this variability. Outdoor pollutants can enter the indoor space via the building envelope; however, the measurement of external pollution and environmental conditions and recording details on the building fabric and ventilation conditions can help the apportionment of external contributions [27].
In this framework, the present study would answer the research question of whether mechanical ventilation systems installed as a COVID-19 mitigation measure can be effectively and sustainably used after the pandemic to improve UFP exposure levels, integrated with a continuous low-cost sensor monitoring system.
In this view, the study investigates the role of a mechanical ventilation system equipped with plasma-based filter technology installed inside the dining hall of a university site. An experimental campaign was conducted in May 2021 to mainly assess the impact of such a filtration system on UFP concentration levels, by high frequency (1 Hz) real-time measurements related to the activities carried out in the canteen. PM and other indoor pollutants, such as total volatile organic compounds (TVOC) and CO2, have been simultaneously monitored by low-cost handheld sensors to evaluate overall indoor air quality for the users (workers and students). Finally, considerations about the affordability of low-cost sensors used to monitor indoor PM1 by comparison with high-frequency real-time measurement data of airborne particle concentration are made to evaluate the sustainability of their future applications as continuous UFP and indoor air quality monitoring systems.
2. Materials and Methods
2.1. State of COVID-19 Pandemic Spread at the Time of the Experimental Campaign
Scientific evidence about the transmission ways of the SARS-CoV-2 virus constantly evolved during the pandemic emergency, as shown in the results of studies conducted at the time of the present investigation campaign (first half of May 2021). In 2021, three different pandemic waves were identified in Italy due to the subsequent co-circulation of SARS-CoV-2 variants Beta (B.1.351), Gamma (P.1), Delta (B.1.617) and Omicron (B.1.1.529). On 27th of December 2020, Italy started the vaccination campaign but limitations to inter-regional mobility were enforced since the beginning of January 2021 to contain the second pandemic wave. The re-opening of inter-regional mobility and other lifting measures started on April 26th 2021 and a progressive restart of all activities with safety protocols was obtained, starting from May 19th [28].
The state of knowledge reported that the virus spreads mainly through human-to-human contact, when the infected individual releases respiratory fluids through acts of breathing, speaking, coughing and sneezing or through contact with contaminated surfaces on which the droplets (>5 μm) have settled. The wide dimensional spectrum of respiratory particles (droplets and aerosol) that are transported differently by the airflow may change in size and composition according to the microclimatic conditions of the ambient air (e.g., temperature, relative humidity, etc.) [23]. Evidence that emerged indicated that in addition to the transmission via large droplets and fomites, SARS-CoV-2 was also transmitted via inhalation of smaller aerosol particles [29,30,31,32]. An extensive study regarding the airborne transmission of respiratory viruses was published recently by Wang et al. [33] concluding that the aerosol transmission pathway needs to be reevaluated for all respiratory infectious diseases. In December 2021, the World Health Organization updated the FAQs on how COVID-19 spreads between people, affirming that “another person can then contract the virus when infectious particles that pass through the air are inhaled at short range (this is often called short-range aerosol or short-range airborne transmission) or if infectious particles come into direct contact with the eyes, nose, or mouth (droplet transmission). The virus can also spread in poorly ventilated and/or crowded indoor settings, where people tend to spend longer periods of time. This is because aerosols can remain suspended in the air or travel farther than conversational distance (this is often called long-range aerosol or long-range airborne transmission)”.
No face coverings in indoor environments with poor ventilation and high levels of occupancy clearly increase the risk of transmission [34]. A study of fluid dynamics of SARS-CoV-2 reports that smaller airborne particles may also travel beyond the social distancing limit of 1–2 m [35]. Other numerical simulations show the methods of diffusions of particles in the case of two persons seated at the same table face-to-face with and without face masks [36]. Such evidence suggests the potential risk of COVID-19 infection in the typical setting of eating and drinking in indoor establishments confirmed by many outbreaks reported in food and beverage service activities [37,38]. In this respect, a canteen setting in a university/school may be compared to a restaurant or bar, in which people eat food and drink beverages without face masks.
In general, the “Education” sector reported concerns about high rates of infection from SARS-CoV-2 [39], and a high incidence of COVID-19 cases among workers was reported in different countries [28,40,41,42]. In Italy, during the first period of the pandemic, dining services in schools and universities (as well as in other working activities) were suspended. Governmental protocols for reopening activities provided specific measures for common spaces including canteens, related to: (i) restricting the amount of access, (ii) organizing specific ways for entry and exit, (iii) ensuring 1 m of distance between users, (iv) limiting the time of permanence, (v) providing for regular sanitization, and (vi) guaranteeing continuous ventilation of indoor spaces. In 2022, the Italian Ministry of Health published technical guidelines for ventilation systems to be installed in indoor spaces of universities and schools [43].
2.2. Site Description
This study was conducted within the university canteen of the Scuola Normale Superiore (SNS) of Pisa (Italy) in the period 10–13 May 2021. The places were frequented by students, PhD, postdocs, teachers and staff personnel of SNS for meals on two different shifts (lunch and dinner). The kitchen and canteen service workers have access to the site even before and after the two shifts for preparation and recovering. The service staff also take care of the cleaning and tidying of the rooms before and after each shift, using cleaning products and electrical equipment such as vacuum cleaners and floor cleaners. The dining hall (Figure 1) is located on the mezzanine floor of a building located in the city center (Figure S2), in a restricted traffic area. In the adjacent rooms and on the upper floors there are administrative and management offices. A specific area is used for kitchens where meals are prepared. The food distribution area consists of a counter with exhibitors to which canteen service staff have access on one side and users on the other side, via a self-service path. The dining area is divided into two main zones where the tables are positioned: dining area #1 (Din#1) with windows on the south side, and dining area #2 (Din#2) with windows on the south and east sides of the building. Between the two areas, there is recovery area #3 (Rec#3) dedicated to the storage of the trays used at the end of the meal.
Before the COVID-19 pandemic, the university canteen had about 280–300 people access simultaneously per daily shift, with an average stay time estimated by the managers of 30 min per person. Access to the canteen is via a system of turnstiles that open upon passage through a card reader supplied to authorized users. For the management of COVID-19 risk within its buildings, the SNS administration put in place safety protocols for the mitigation of the risk of contagion for students, its staff and canteen service workers. In addition to the general actions to ensure social distancing, hand hygiene, use of surgical (or superior) masks for staff and students, testing, tracking and promotion of vaccination, specific organizational and structural measures were implemented in the canteen areas, as it represents a high-risk setting due to the number of people who have access and to the activity of consuming meals that takes place there, also generally linked to moments of conversation and socialization without face coverings.
Such specific measures included: Identification of routes for entry and exit in order to reduce the possibility of gathering; Restriction of access to the canteen (occupation was reduced to a maximum of 120 people simultaneously); Instructions to users to stay only for the time necessary to consume the meal (no later than 30 min); Installation of Plexiglas® barriers between the face-to-face seats; Sanitization and cleaning of all surfaces after each shift.
In the areas used for the consumption of meals, the air changes were obtained through a mechanical ventilation system consisting of convectors, equipped with high-efficiency filtering plasma-based technology to reduce the fine fraction of the particulate before it accumulates in the filters of the convector and therefore increase the life of the filters themselves. Such technology is based on an electrostatic filter that collects airborne microparticles by means of electric fields created by applying a high voltage between electrodes. Particles ionized by the electric field are attracted to the surfaces of the collecting electrode. The filter has two stages of operation: particles are first charged by the discharge electrode (positive) by means of the corona effect, and then the charged particles are attracted to the collection surfaces of the plate electrodes of the second stage [44]. Manufacturers declared that the fan coil range can be fitted with the electronic filter, featuring a class D rating (80–90% average efficiency) according to UNI 11254 [45], with similar performances to the initial ones of a traditional mechanical filter featuring a class F9 rating (weighted average efficiency < 95% of 0.4 μm particles) according to EN 779:2012 [46] (updated by EN ISO 16890-1:2016 [47]).
2.3. Measurement Strategy
Measurements were performed at the dining hall in three working days divided into ten homogeneous daily time slots, according to the schedule reported in Table 1. On day 1, electrostatic filters were activated on all the fan coils; on day 2, the filter option was turned off starting from 12 pm. Measurements continued during the night of day 2 until the morning of day 3 at 9 am.
The high-frequency real-time instruments involved in the measurement campaign were: Condensation Particle Counter (CPC mod. 3007, TSI Inc., Shoreview, MN, USA) to measure in real-time the PNC (part/cm3) from 10 nm to 1000 nm, with 1 s time resolution (1 Hz) and accuracy ±20% (total flow 0.7 L/min; detection limits 1 to 100,000 part/cm3). DiSCmini (DM mod. TESTO SE & Co. KGaA, Lenzkirch, Germany), handheld instrument for the measurement of personal PNC in the range 10–700 nm, average diameter (Davg) of diffusion charging and Lung Deposited Surface Area (LDSA) in the range 10–300 nm, based on the model published by the International Commission on Radiological Protection [48], with a lower 1 s time resolution. TygonTM sampling lines 1.5 m in length were used to minimize the particle losses [49]. Three different DMs (DM-UF3, DM-UF4, DM-UF5) were used for simultaneous measurements.
The low-cost sensors included the following devices (further specifications are reported in Table S1): AirWits PM (AW-PM, GeNano Ltd., Espoo, Finland) to measure PM1, PM2.5 and PM10, temperature (°C) and air humidity (%) every 30 min.; detection limits 0–1000 μg/m3, accuracy ±10 μg/m3 (between 0–100 μg/m3) or ±10% (between 100–1000 μg/m3). AirWits CO2 (AW-CO, GeNano Ltd., Espoo, Finland) multipurpose meter, equipped with high-quality sensors to measure CO2, temperature (°C) and air humidity (%) every 30 min; detection limits 0–5000 ppm, accuracy 50 ppm or three percentages from reading 0.2/2%. Two AW-CO devices were used during the campaign, denominated AW-CO1 and AW-CO2. AirWits IAQ (AW-VOC, GeNano Ltd., Espoo, Finland) to measure the amount of TVOCs, indoor air temperature (°C) and air humidity (%) every 30 min; detection limits 0–60,000 ppb; accuracy 15%. Two AW-VOC devices were used, denominated AW-VOC1 and AW-VOC2.
Instruments were placed as shown in Figure 2. The following measurements were taken near the center of dining area #1: PNC, Davg and LDSA with DM-UF5; CO2 with AW-CO1; PM1.0, PM2.5 and PM10 with AW-PM; TVOC with AW-VOC1; air temperature and relative humidity. The following measurements were taken near the center of dining area #2: PNC, Davg and LDSA with DM-UF4; CO2 with AW-CO2; TVOCs with AW-VOC2; air temperature and relative humidity. The following measurements were taken near the center of recovery area #3: PNC, Davg and LDSA with DM-UF3. Outdoor PNC was measured with CPC (sampling point #4) on the south side of the building. It is worth noting that in dining area #1, fan coils recirculated indoor air, while in dining area #2 and recovery area #3, some fan coils were allowed to insert air from the outdoor environment (pale blue arrows in Figure 2). Progressive anonymous data counting of users’ access during the lunch and dinner shifts was allowed by the administrative office of the school. It has been supposed that each user remains inside the dining hall for 30 min, according to the COVID-19 safety protocol established by the University (see Section 2.2).
High-frequency instrument (CPC and DMs) comparisons were performed after and before the measurement campaign in order to align output values, according to a standard methodology [50,51]. Data values of instruments corrected after the comparison are indicated with an asterisk in the following text. DM data were calculated according to Fierz et al. [52].
3. Results and Discussion
3.1. Time Series and Average Data Related to the Activities Inside the Dining Hall
Figure 3 shows real-time high-resolution (1 Hz) measurement data of UFPs and micrometric particles release parameters (PNC, Davg and LDSA) for two days simultaneously conducted in three indoor sampling points (#1, #2 and #3) and one outdoor (#4). The cumulative number of users progressively accessing the canteen for lunch and dinner is also shown on the secondary axis (black line). The mean and standard deviation values of the same parameters per each daily time period (as identified in Table 1) are reported in Table A1 and Table A2.
The PNC increase at all three indoor points (Figure 3a,b) appears strictly related to the users’ access and the dining activities during lunch/dinner time periods (T4 and T8) on both days. Before and after the meals (T3, T5, T7 and T9 periods), preparation and recovery activities were carried out by the canteen personnel (with up to 10 people inside the rooms). Such activities include cooking operations that generate UFPs, the use of cleaning products with possible VOC and secondary organic aerosol generation and the use of vaporization systems that generate water nanoparticles: this exposure scenario for canteen personnel is evident in particular before the lunch and dinner times, with PNC peaks in recovery area #3 higher on day 1 than on day 2. The highest values of indoor airborne submicronic particles associated with the activities inside the canteen are confirmed by average values of PNC during dining times (T4 and T8) compared to the other periods (Table A1). During the night and early morning (T1 and T10 periods) when no people are inside the canteen, PNC indoor values tend to approach the outdoor ones.
Outdoor PNC shows a similar general trend over the two days, with the average value increasing during the day and decreasing in the nighttime. On day 1, an unexpected release of particles until 40,000 part/cm3 is clearly identified after dinner time. On day 2, high concentration peaks until 140,000 part/cm3 before and during lunch time with greater variability than day 1 are reported.
A Davg increase (Figure 3c,d) has been highlighted during the lunch and dinner times, higher on day 2 than on day 1. This growing trend is confirmed during the post lunch and dinner recovery activities. Otherwise, Davg decreases during the preparation activities before lunch and dinner in which smaller particles may be generated by the indoor activities.
As expected, LDSA trends (Figure 3e,f) are correlated to the PNC in each sampling point and time period.
The analysis of PNC time series and average values in different daily time periods (T1–T10) provides evidence that human activities lead to a significant increase in indoor submicrometric particle concentrations.
The comparison between PNC and the related Davg (Dp < 300 nm) values, which shows an increase beyond 30 nm during the activities inside the dining hall and reaches the minimum value of 22 nm (st.dev. 1.53 nm) in T1, shows the major contribution made by the size fractions in the Aitken mode (typically particle size between 30 nm and 100 nm) and accumulation mode (particles size range 110 nm–1 μm) compared to the nucleation one (particle size < 30 nm). By definition, the nucleation mode includes UFP newly formed through source condensation processes or atmospheric chemical reactions. Aitken mode particles are formed from gas-to-particle conversion at ambient temperature, as well as from the condensation of hot vapors during combustion processes. Particles with an aerodynamic diameter greater than 30 nm tend to have longer atmospheric lifetimes, allowing them to grow into the accumulation mode [53]. According to the literature, anthropogenic indoor sources of UFP may include cooking and heating activities related to meal services [54], electrical appliances [55] and emissions from building and furnishings materials [56]. Furthermore, the data analysis confirms that the presence of a human can represent a relevant source of particles [57]. Morawska et al. [58] also described the role of individuals in terms of lifting the particles deposited on the ground in the indoor environment and the transport of particles from the outside by the individuals themselves (i.e., by clothes, shoes, etc.). In other studies, the presence of people in indoor environments has been already associated with particle emissions from clothes abrasion [59] and bioaerosol release (i.e., skin fragments) [60].
In addition to human-related sources, indoor PNC may be dependent on the outdoor concentration and the penetration of the particles from the outside, not only due to the particle transport made by the people but also to the infiltration through the windows and doors (and their possible leakages) and through the mechanical ventilation system, which withdraws the outdoor air. For this reason, UFPs of outdoor air pollutants (e.g., vehicular traffic emissions) may enter and remain suspended in indoor environments [61]. This behavior is confirmed by the analysis of Pearson’s correlations between outdoor and indoor PNC in each sampling point (Table A3). The correlation is positive in the time periods with human activities inside the dining hall (T4, T5 and T8) and during the night (T10). The correlation is moderate and high for dining area #1, which is near outdoor sampling point #4 and has the same orientation; moderate and weak correlations are reported for dining area #2 and recovery area #3, which are farther than dining area #1 from outdoor sampling point #4. It is worth noting that the ventilation system also inlets outdoor air inside the indoor space by fan coils located in dining area #2, which means the outdoor PNC may influence indoor values in that sampling point.
3.2. Two Days Comparison and Plasma-Based Filtration Technology Impact on UFP Concentrations
Box plots in Figure 4 highlight the differences among PNC, Davg and LDSA over two days during each lunch and dinner activity (T4 and T8 according to Table 1) at three different indoor sampling points. In the box plots, the lowest edge of the box indicates the 25th percentile, the line inside the box marks the median and the top of the box indicates the 75th percentile. Whiskers above and below indicate the lowest and highest values.
PNC values for lunchtime (T4) on day 1 results are significantly lower (Wilcoxon test p-value < 0.001) than on day 2 in all three indoor sampling points. Davg average and median values are slightly lower on day 1 than on day 2. The same differences are not confirmed for dinner time (T8). In addition, these findings could be associated with influences from outside in which the higher values and variability of outdoor PNC on day 2 during lunchtime are not repeated at dinner time.
Furthermore, some differences between the daily trend of indoor PNC on day 1 and day 2 could be connected to the effects of the activation of the plasma-based technology filters on the fan coils. On day 1, electrostatic filters were activated, while on day 2, the filter option was turned off from 12 pm until the morning of day 3.
Figure 5 reports the values of γ which is the ratio between PNC (Dp < 700 nm) measured in each sampling point without the activation of the plasma-based technology filters (filters OFF) and the same parameters measured when the filters were used (filters ON):
γ = PNC 30 min avg (filters OFF)/PNC 30 min avg (filters ON)(1)
PNC 1 Hz measurements were averaged over 30 min periods. The zero values in the scale correspond to the missing data.
In all the sampling points, γ had positive values during the dining periods (T4 12–14:30 and T8 19–21.30), reaching the maximum value of 3.2 in dining area #1 during lunchtime (T4), highlighting the contribution of plasma-based filtration technology to reduce PNC (Dp < 700 nm). This effect is also highlighted for dining area #2 (Figure 5b), which also reports γ values more than 1 during the nighttime. A less relevant contribution of the filtration system to reducing PNC is reported for recovery area #3.
3.3. Low-Cost Sensors PM, CO2 and TVOCs Data Analysis
In Figure 6, the comparison between AW-PM low-cost sensor response and DM-UF5 high-resolution (1 Hz) PNC and Davg data (30 min average) is reported. Instrument data are available only for day 2. Both devices were placed at the same sampling point (dining area #1). PNC was measured in the size range 1–700 nm and Davg in the size range of 1–300 nm by DM-UF5. Although the low-cost sensor reported some missing data, the general trend alignment between PNC and PM1 is respected, with both instruments highlighting two peaks in correspondence with the lunch and dinner activities (Figure 6a). Low Davg values (30–40 nm) in Figure 6b correspond to higher PNC compared to PM1, while high Davg values (40–50 nm) reflect lower PNC compared to PM1. This behavior is consistent with the major contribution of smaller particles given to the PNC as compared to the PM1. Otherwise, the contribution in terms of mass of the larger particle sizes is higher than the nanometric ones [62].
An added value of the present study is represented by the successful application of high-resolution real-time measurement techniques for nano and microparticles in occupational settings [63,64]. The findings confirm that the daily trends of PNC (Dp < 700 nm) and PM1 are comparable, but the quantitative correlation is not respected depending on the size of the measured particles. In fact, as the size decreases, the contribution in terms of PNC becomes increasingly significant compared to the contribution in terms of mass concentration. Mass concentration, generally used for determining occupational exposure levels to harmful agents, is currently considered a metric less sensitive for nanoparticles compared to other metrics, such as surface area and PNC [65].
PM1 average concentration measured by the low-cost sensor in the same sampling location and time period (from 1:00 PM to 7:00 AM of the following day) with the activation of plasma-based filtration technology is reduced by a factor of 0.62 (Table 2). PM2.5 average concentrations with and without filter activation are, respectively, 3.94 μg/m3 and 6.59 μg/m3. The same reduction trend (with a low reduction factor of 0.36) allowed by the filters’ activation is shown for PM10 average concentration. As reported in Table S3, the comparison between indoor PM measured values and outdoor data available from the nearest official station of the Regional Environmental Protection Agency (ARPAT) [66] for day 1 and day 2 shows an opposite relation: the daily average values of indoor PM2.5 and PM10 were lower on day 1 (filters ON) than on day 2 (filters OFF), although the respective outdoor daily average PM values were higher on day 1 than on day 2. This seems to further support the effectiveness of plasma-based filtration systems in reducing PM indoor concentration.
By properly evaluating these results, the local conditions of outdoor PNC on day 2 compared to day 1 should be considered, which also have an influence on the indoor PM values, as previously discussed.
PM2.5 measurements obtained by low-cost sensors report average values lower than the limit values for the protection of human health to be attained by 1 January 2030 (not to be exceeded more than 18 times per calendar year) as reported by the recent EU Directive 2024/2881 on ambient air quality and cleaner air for Europe fixed at 25 μg/m3 [67]. The WHO Air Quality Guideline (AQG) states that annual average concentrations of PM2.5 should not exceed 5 µg/m3, while 24 h average exposures should not exceed 15 µg/m3 more than 3–4 days per year [68]. In this view, the use of plasma-based filtration technology could make an effective contribution to reducing PM2.5 below the recommended exposure limits.
A recent cohort study reports that long-term exposure to PM2.5 could be associated with a higher risk of SARS-CoV-2 breakthrough infections and a reduced antibody response to vaccination [13]. In this view, the general reduction of PM2.5 levels in the dining hall really contributed to the mitigation of COVID-19 risk.
Mean concentration values of CO2 (ppm) and total volatile organic compounds (TVOCs, ppb) registered by low-cost sensors during each time period in two sampling positions (Din#1 and Din#2) inside the dining hall are reported in Figure 7. All available data per each time slot according to Table 1 have been averaged.
As expected, an increase in CO2 levels (Figure 7a) corresponds to the presence of people inside the dining hall, with maximum average levels of 837 ppm and 636 ppm measured, respectively, in T4 and T8 (dining area #1). A specific correlation (Pearson R = 0.81) has been highlighted between the number of people inside the dining hall and the CO2 levels, as reported in Figure A2. This correlation was obtained considering the daily average values of CO2 (per 30 min intervals) and the estimated number of people calculated according to the time of access and supposing a maximum stay of half an hour inside the dining hall per person.
As a general recommendation, CO2 concentration values permanently higher than 1000 ppm during the continuous occupation of indoor environments whose main physical and use characteristics are known indicate that external air exchanges and ventilation are insufficient and must be improved. This limit value was still considered adequate for the pandemic period [69]. Therefore, based on the findings of our study, the recommended average concentration limit of 1000 ppm was not overcome during the presence of each person inside the dining hall.
No significant correlation has been observed between CO2 and PM1 in indoor environments under the conditions of the present study (Figure S2).
TVOC indoor levels (Figure 7b) also arise during human activities, with the maximum values registered during the recovery activities (T5 and T9) after the dining periods. This behavior is more evident in dining area #1 compared to the other sampling point. The result is in line with evidence from the literature, which reports that both cleaning activities and occupants are sources of VOC generation in indoor environments [70].
3.4. Study Limitations
The major limitations of the study are related to the measurements carried out in only one session of three days, in the same seasonal climatic conditions, despite the fact the data collected every second (1 Hz) by high-resolution instruments allow significant statistics regarding the daily trends and the specific release events related to the monitored activities. Furthermore, the choice of sampling points was adapted to the needs of the dining hall in order to guarantee the use and normal activities of the occupants, also taking into account the COVID-19 procedure in force at the time of the experimental campaign. In addition, the study set-up had the advantage of making measurements in real exposure conditions, not simulated or performed ad hoc.
Another limitation could be associated with the effectiveness of real-time measurements, which may be influenced by differences between instruments used (i.e., CPC and DM for PNC and among three DMs with different calibration times) regarding principle of operation, size range, upper/lower limits of detection and operating conditions when the measurement was carried out. In order to overcome some of these possible drawbacks, an instruments comparison session was performed in the workplace before the sampling campaign to identify the correction factors and align the PNC, Davg, and LDSA values measured by different devices. The CPC could be used as a reference instrument for PNC comparison according to Asbach et al. [71]. The spherical approximation of particles on which DMs are based may induce further limitations to Davg and LDSA measured levels when the emissions are related to nanoparticles with different morphology. A comparison between three DMs was performed to choose the reference instrument according to Bellagamba et al. [51].
The major limitation related to the effectiveness of low-cost sensors is related to the low-resolution data available (1 measurement every 30 min), which was not enough for describing specific release events with short duration. Furthermore, some missing data (i.e., PM concentrations from 7 am to 1 pm on day 2) were found, probably due to the unstable connection of the sensors’ wireless system with the server and the related software for data collection. In addition, relative humidity and quantity of PM concentrations may have notable impacts on the low-cost sensor data [72]. Finally, there is little information available in the literature regarding the calibration tests of the low-cost sensors used in the present study, so the data collected may have uncertainty due to a lack of proper calibration procedures. Further intercomparison sessions by standardized methods should be recommended [73,74].
4. Conclusions
In the present study, the role of a mechanical ventilation system installed inside the dining hall of a university site and the effectiveness of the introduction of UFP filtration systems have been evaluated as a measure for improving air quality in indoor environments at specific risk from SARS-CoV-2 contamination (such as the dining hall) and in which other personal protective measures (as face masks) were not used during the consumption of meals.
An experimental campaign was conducted in May 2021 to mainly assess the impact of the filtration system on UFP concentration levels using high-frequency (1 Hz) real-time measurements related to the activities carried out in the canteen. Other indoor pollutants such as PM, TVOCs and CO2 were monitored by low-cost sensors to evaluate the indoor air quality for the users (workers and students) as a possible indicator of COVID-19 risk mitigation.
In general, the measurements carried out allowed us to conclude that the opportunity that arose from COVID-19 mitigation measures to use plasma-based filtration systems showed a reduction in PNC and PM concentrations in the tested conditions of use, proving to be effective in improving airborne submicrometric particles exposure, in particular during the hours of users’ stay. This study demonstrates that a mechanical ventilation system equipped with plasma-based filter technology could also be effectively used after the pandemic at the university site to improve indoor air quality and UFP exposure levels as a general indoor air quality improvement measure for occupational safety and health. It will also have an impact in terms of health outcomes and costs associated with the ambient fine particle exposure that may increase the sustainability of the proposed solution.
This study confirms the importance of high-resolution monitoring techniques to evaluate UFP concentrations and dynamics in indoor environments.
Although with the described limitations, low-cost sensors showed moderate reliability compared to the high-resolution instruments in the monitoring of PM at low-resolution, in particular for describing macroscopic trends and significant gradients. They can also produce data useful to evaluate when other indicators of indoor air quality such as average CO2 and TVOC concentrations may overcome the recommended limit values. Further studies to improve data collection in that direction are recommended.
In conclusion, based on the lessons learned by COVID-19 risk mitigation measures the improvement of low-cost sensors used to monitor indoor PM and other parameters relevant to the occupants’ health could be a promising sustainable application as a continuous indoor monitoring system to improve indoor air quality levels. In this view, future investigation and comparison with reference instruments are needed to overcome general biases of low-cost sensor measurements and improve the quality of their data.
Conceptualization, F.B. and P.P.; methodology, F.B., R.F., F.T. and P.P.; formal analysis, F.B., R.F., F.T. and P.P.; investigation, F.B. and P.P.; resources, P.P.; data curation, F.B., R.F., F.T. and P.P.; writing—original draft preparation, F.B., R.F., F.T. and P.P.; writing—review and editing, F.B., R.F., F.T., S.I. and P.P.; visualization, F.B., R.F., F.T. and P.P.; supervision, F.B, S.I. and P.P.; project administration, F.B., S.I. and P.P. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.
The authors are grateful to Dr. Giuliana Buresti of INAIL Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, for her expert support in statistical data analysis.
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
The following abbreviations are used in this manuscript:
| AirWits CO2 | AW-CO |
| AirWits IAQ | AW-VOC |
| AirWits PM | AW-PM |
| AQG | WHO Air Quality Guidelines |
| ARPAT | Agenzia Regionale per la Protezione Ambientale della Toscana |
| CO2 | Carbon Dioxide |
| CPC | Condensation Particle Counter |
| Davg | Average Diameter |
| DM | DiSCmini |
| EU | European Union |
| ILO | International Labour Organization |
| INAIL | Italian Workers’ Compensation Authority |
| ISS | Italian Health Institute |
| ISO | International Standard Organization |
| LDSA | Lung Deposited Surface Area |
| NO2 | Nitrogen Dioxide |
| OECD | Organization for Economic Cooperation and Development |
| OSH | Occupational Safety and Health |
| PM | Particulate Matter |
| PNC | Particle Number Concentration |
| SNS | Scuola Normale Superiore |
| TVOCs | Total Volatile Organic Compounds |
| WHO | World Health Organization |
| UFPs | Ultrafine Particles |
| UNI | Ente Italiano di Normazione |
| US CDC | United States Center for Diseases Control |
| US EPA | United States Environmental Protection Agency |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 1 SNS university canteen pictures of different zones: (a) dining area #1; (b) dining area #2; (c) recovery area #3.
Figure 2 SNS University canteen floor plan with schematic layout of instrument positions: (a) dining area #1; (b) recovery area #3; (c) dining area #2.
Figure 3 Daily time series of PNC (a,b), Davg (c,d) and LDSA (e,f) on day 1 and day 2 for each sampling point: dining area #1 (violet); dining area #2 (green); recovery area #3 (red). Cumulative number of people accessing the dining hall for meals is reported on the secondary axis (black line).
Figure 4 Box plots of PNC (a,b) and Davg (c,d) and LDSA (e,f) during lunch (T4) and dinner (T8) time slots on day 1 and day 2. The lowest edge of each box indicates the 25th percentile, the line inside the box marks the median and the top of the box indicates the 75th percentile. Whiskers indicate the lowest and highest values.
Figure 5 Ratio of γ between high resolution daily PNC (<700 nm) 30 min average with particulate filters OFF and high-resolution PNC (<700 nm) 30 min average with particulate filters ON per each sampling point: dining area #1 (a); dining area #2 (b); recovery area #3 (c).
Figure 6 Comparisons between daily high-resolution PNC (<700 nm) measured by DM-UF5 and PM1 measured by low-cost sensor GeNano on secondary axis (a); simultaneous high-resolution Davg (<300 nm) (b). Data (30 min average) for dining area #1—day 2. DM-UF5 data values corrected after the instruments comparison are indicated with an asterisk (see
Figure 7 Daily average CO2 (a) and TVOCs (b) in dining area #1 (Din#1) and dining area #2 (Din#2) sampling positions in different time periods (T1–T10). Standard deviation bars are reported per each value.
Daily time periods of measurements and related activities in the dining all.
| No. | Period | Time Slot | Occupancy | Ventilation | Activities |
|---|---|---|---|---|---|
| T1 | Night | 0 am–6 am | None | Doors/windows closed; forced ventilation ON | None |
| T2 | Morning | 6 am–10 am | None | Doors/windows closed; forced ventilation ON | None |
| T3 | Pre-Lunch | 10 am–12 pm | Canteen personnel | Doors/windows occasionally open; forced ventilation ON | Cleaning/washing and cooking preparation activities |
| T4 | Lunch | 12 pm–2:30 pm | Canteen personnel and students | Doors/windows frequently open; forced ventilation ON | Lunch activities |
| T5 | Post-Lunch | 2:30 pm–4 pm | Canteen personnel | Doors/windows occasionally open; forced ventilation ON | Cleaning/washing and recovery activities |
| T6 | Afternoon | 4 pm–5:30 pm | None | Doors/windows closed; forced ventilation ON | None |
| T7 | Pre-Dinner | 5:30 pm–7 pm | Canteen personnel | Doors/windows occasionally open; forced ventilation ON | Cleaning/washing and cooking preparation activities |
| T8 | Dinner | 7 pm–9:30 pm | Canteen personnel and students | Doors/windows frequently open; forced ventilation ON | Dinner activities |
| T9 | Post-Dinner | 9:30 pm–11 pm | Canteen personnel | Doors/windows occasionally open; forced ventilation ON | Cleaning/washing and recovery activities |
| T10 | Night | 11 pm–0 am | None | Doors/windows closed; forced ventilation ON | None |
Average PM values measured by the low-cost sensor from 1:00 PM to 7:00 AM in dining area #2, with (Filters ON) and without (Filters OFF) plasma-based technology activation.
| Filters | PM1 (μg/m3) | PM2.5 (μg/m3) | PM10 (μg/m3) |
|---|---|---|---|
| ON | 1.23 | 3.94 | 6.35 |
| OFF | 3.23 | 6.59 | 9.91 |
Supplementary Materials
The following supporting information can be downloaded at:
Appendix A
Day 1 average PNC, Davg and LDSA per each time period (T1–T10) and in each sampling point (dining area #1, dining area #2, recovery area #3 and outdoor #4).
| Day 1 | PNC (part/cm3) | Davg (nm) | LDSA (μm/cm2) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Din#1 | Din#2 | Rec#3 | Out#4 | Din#1 | Din#2 | Rec#3 | Din#1 | Din#2 | Rec#3 | ||
| T1 | avg | ||||||||||
| 12 am–6 am | st.dev. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| T2 | avg | ||||||||||
| 6 am–10 am | st.dev. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| T3 | avg | 3186 | 21,135 | 25,589 | 5753 | 59.67 | 33.72 | 36.21 | 4.87 | 41.14 | 47.97 |
| 10 am–12 pm | st.dev. | 409 | 15,439 | 20,143 | 1755 | 9,26 | 7.77 | 8.18 | 0.67 | 22.10 | 24.06 |
| T4 | avg | 7456 | 8359 | 12,233 | 12,112 | 32.69 | 25.03 | 28.72 | 9.12 | 11.34 | 19.72 |
| 12 pm–2:30 pm | st.dev. | 3170 | 3173 | 5008 | 4363 | 7.84 | 4.17 | 3.98 | 4.07 | 5.76 | 6.91 |
| T5 | avg | 10,751 | 6347 | 11,433 | 14,724 | 29.85 | 27.42 | 30.79 | 13.87 | 8.65 | 20.32 |
| 2:30 pm–4 pm | st.dev. | 4620 | 2356 | 4332 | 4420 | 3.10 | 2.05 | 2.60 | 6.13 | 4.36 | 6.73 |
| T6 | avg | 3649 | 3246 | 5233 | 11,396 | 38.49 | 37.05 | 29.66 | 3.97 | 3.82 | 8.29 |
| 4 pm–5:30 pm | st.dev. | 325 | 152 | 545 | 3445 | 1.87 | 2.24 | 1.90 | 0.56 | 0.14 | 0.64 |
| T7 | avg | 7677 | 12,332 | 26,778 | 8886 | 34.22 | 31.27 | 31.56 | 11.37 | 23.16 | 52.92 |
| 5:30 pm–7 pm | st.dev. | 4167 | 9134 | 28,537 | 4743 | 5.13 | 5.90 | 7.83 | 9.28 | 20.83 | 61.31 |
| T8 | avg | 24,706 | 25,093 | 30,060 | 8855 | 35.23 | 38.53 | 37.31 | 41.47 | 75.11 | 70.80 |
| 7 pm–9:30 pm | st.dev. | 18,388 | 21,345 | 16,881 | 3735 | 7.06 | 12.10 | 7.89 | 29.32 | 63.06 | 40.74 |
| T9 | avg | 11,981 | 22,791 | 20,328 | 15,908 | 47.23 | 38.01 | 45.46 | 30.66 | 64.74 | 61.38 |
| 9:30 pm–11 pm | st.dev. | 4671 | 9241 | 5506 | 6628 | 4.31 | 4.86 | 3.52 | 13.31 | 27.83 | 19.65 |
| T10 | avg | 5475 | 7331 | 8017 | 10,111 | 42.52 | 30.31 | 38.79 | 9.43 | 12.43 | 18.73 |
| 11 pm–12 am | st.dev. | 907 | 1720 | 1745 | 791 | 1.39 | 1.17 | 1.35 | 2.43 | 4.29 | 5.15 |
Day 2 average PNC, Davg and LDSA per each time period (T1–T10) and in each sampling point (dining area #1, dining area #2, recovery area #3 and outdoor #4).
| Day 2 | PNC (part/cm3) | Davg (nm) | LDSA (mm/cm2) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Din#1 | Din#2 | Rec#3 | Out#4 | Din#1 | Din#2 | Rec#3 | Din#1 | Din#2 | Rec#3 | ||
| T1 | avg | 8897 | 3642 | 9184 | 9035 | 24.53 | 22.59 | 22.00 | 8.95 | 2.55 | 11.21 |
| 12 am–6 am | st.dev. | 1435 | 448 | 1410 | 3750 | 1.55 | 3.36 | 1.53 | 1.95 | 0.44 | 2.11 |
| T2 | avg | 5806 | 2978 | 5650 | 5093 | 24.52 | 24.73 | 21.81 | 5.14 | 1.79 | 6.57 |
| 6 am–10 am | st.dev. | 859 | 431 | 871 | 1397 | 2.23 | 3.96 | 2.28 | 1.60 | 0.86 | 1.76 |
| T3 | avg | 10,230 | 11,047 | 11,607 | 7206 | 32.08 | 29.17 | 29.44 | 13.59 | 17.32 | 18.40 |
| 10 am–12 pm | st.dev. | 8810 | 13,705 | 11,651 | 6499 | 3.75 | 3.94 | 3.35 | 12.71 | 23.65 | 17.90 |
| T4 | avg | 24,142 | 21,060 | 21,033 | 12,796 | 33.23 | 31.92 | 31.32 | 48.02 | 53.09 | 44.78 |
| 12 pm–2:30 pm | st.dev. | 9520 | 14,377 | 9883 | 12,189 | 5.80 | 6.34 | 5.69 | 21.27 | 28.35 | 20.03 |
| T5 | avg | 23,808 | 17,497 | 22,202 | 8242 | 40.34 | 43.40 | 39.88 | 53.69 | 57.84 | 56.60 |
| 2:30 pm–4 pm | st.dev. | 9657 | 6522 | 8878 | 1889 | 1.95 | 1.82 | 1.61 | 22.82 | 25.39 | 22.76 |
| T6 | avg | 8779 | 6653 | 8105 | 6285 | 34.66 | 33.31 | 33.24 | 14.12 | 12.44 | 15.89 |
| 4 pm–5:30 pm | st.dev. | 1221 | 1086 | 1007 | 3400 | 2.65 | 2.18 | 2.79 | 3.83 | 3.98 | 3.64 |
| T7 | avg | 8450 | 11,325 | 9727 | 4948 | 39.29 | 35.40 | 37.83 | 16.74 | 29.90 | 24.64 |
| 5:30 pm–7 pm | st.dev. | 5488 | 10,969 | 8384 | 2980 | 5.02 | 4.70 | 5.64 | 17.56 | 40.16 | 27.96 |
| T8 | avg | 21,358 | 20,565 | 23,774 | 8521 | 41.74 | 34.20 | 37.65 | 49.13 | 46.68 | 53.44 |
| 7 pm–9:30 pm | st.dev. | 9785 | 10,324 | 14,177 | 5460 | 6.55 | 5.75 | 5.77 | 25.28 | 21.22 | 25.10 |
| T9 | avg | 11,683 | 10,654 | 11,756 | 5323 | 48.31 | 40.82 | 45.72 | 30.89 | 29.66 | 34.01 |
| 9:30 pm–11 pm | st.dev. | 3978 | 4177 | 4180 | 922 | 4.10 | 0.85 | 2.23 | 13.90 | 14.33 | 12.00 |
| T10 | avg | 5990 | 5649 | 5599 | 6046 | 45.78 | 39.92 | 44.13 | 11.88 | 12.15 | 14.31 |
| 11 pm–12 am | st.dev. | 357 | 286 | 483 | 861 | 1.24 | 0.71 | 1.17 | 0.98 | 0.94 | 1.38 |
The Pearson product-moment correlation coefficient R is a dimensionless index that ranges from −1.0 to 1.0 (included) and reflects the extent of a linear relationship between two data sets (X, Y). An absolute value of exactly 1 implies that a linear equation describes the relationship between X and Y perfectly, with all data points lying on a line. The correlation sign is determined by the regression slope: a value of +1 implies that all data points lie on a line for which Y increases as X increases, and vice versa for −1. A value of 0 implies that there is no linear dependency between the variables.
More generally, it is worth noting that the correlation coefficient is positive if Xi and Yi tend to be simultaneously greater than, or simultaneously less than, their respective means. The correlation coefficient is negative (anti-correlation) if Xi and Yi tend to lie on opposite sides of their respective means. Moreover, the stronger is either tendency, the larger is the absolute value of the correlation coefficient.
Pearson product moment correlation coefficient R between indoor and outdoor PNC on day 1, day 2 and day 3, per each sampling point and time slot.
| Day 1 | Day 2 | Day 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| #1 | #2 | #3 | #1 | #2 | #3 | #1 | #2 | #3 | |
| T1 | - | - | - | −0.0955 | 0.3388 | −0.0212 | 0.7079 | 0.5263 | 0.4402 |
| T2 | - | - | - | −0.2379 | −0.1295 | 0.1904 | - | - | - |
| T3 | 0.1523 | −0.1604 | −0.2393 | −0.0009 | 0.0081 | −0.0314 | - | - | - |
| T4 | 0.7583 | 0.5952 | 0.6043 | 0.4494 | 0.2438 | 0.3794 | - | - | - |
| T5 | 0.4042 | 0.6501 | 0.5907 | 0.6509 | 0.6670 | 0.6793 | - | - | - |
| T6 | 0.0725 | −0.1095 | 0.1106 | 0.4881 | 0.5428 | 0.4658 | - | - | - |
| T7 | −0.5402 | −0.1111 | −0.1876 | −0.1431 | −0.1135 | −0.1149 | - | - | - |
| T8 | 0.6076 | −0.0363 | 0.4222 | 0.0190 | 0.0857 | 0.0738 | - | - | - |
| T9 | −0.5115 | −0.5948 | −0.7251 | −0.0873 | −0.0914 | −0.0979 | - | - | - |
| T10 | 0.4881 | 0.5861 | 0.5235 | 0.6232 | 0.7511 | 0.7027 | - | - | - |
Figure A1 Time series of average CO2 Din#1 (a), TVOCs Din#1 (b), CO2 Din#2 (c) TVOCs Din#2 (d) on day 1 and day 2.
Figure A2 Scatter plot of N. of persons and CO2 daily average values inside the dining hall.
1. Lelieveld, J.; Evans, J.S.; Fnais, M.; Giannadaki, D.; Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature; 2015; 525, pp. 367-371. [DOI: https://dx.doi.org/10.1038/nature15371] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26381985]
2. Ohlwein, S.; Kappeler, R.; Kutlar Joss, M.; Kunzli, N.; Hoffmann, B. Health effects of ultrafine particles: A systematic literature review update of epidemiological evidence. Int. J. Public. Health; 2019; 64, pp. 547-559. [DOI: https://dx.doi.org/10.1007/s00038-019-01202-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30790006]
3. Choi, G.; Kim, Y.; Shin, G.; Bae, S. Projecting Lifetime Health Outcomes and Costs Associated with the Ambient Fine Particulate Matter Exposure among Adult Women in Korea. Int. J. Environ. Res. Public Health; 2022; 19, 2494. [DOI: https://dx.doi.org/10.3390/ijerph19052494]
4. World Health Organization (WHO). Ambient (Outdoor) Air Pollution. 2021; Available online: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (accessed on 28 March 2025).
5. Piscitelli, P.; Miani, A.; Setti, L.; De Gennaro, G.; Rodo, X.; Artinano, B.; Vara, E.; Rancan, L.; Arias, J.; Passarini, F.
6. Di Biagio, K.; Baldini, M.; Dolcini, J.; Serafini, P.; Sarti, D.; Dorillo, I.; Ranzi, A.; Settimo, G.; Bartolacci, S.; Simeoni, T.V.
7. Hassan, M.A.; Mehmood, T.; Lodhi, E.; Bilal, M.; Dar, A.A.; Liu, J. Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly. Int. J. Environ. Res. Public. Health; 2022; 19, 13540. [DOI: https://dx.doi.org/10.3390/ijerph192013540]
8. Copat, C.; Cristaldi, A.; Fiore, M.; Grasso, A.; Zuccarello, P.; Signorelli, S.S.; Conti, G.O.; Ferrante, M. The role of air pollution (PM and NO2) in COVID-19 spread and lethality: A systematic review. Environ. Res.; 2020; 191, 110129. [DOI: https://dx.doi.org/10.1016/j.envres.2020.110129] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32853663]
9. Zhu, Y.J.; Xie, J.G.; Huang, F.M.; Cao, L.Q. Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Sci. Total Environ.; 2020; 727, 138704. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.138704]
10. Ho, C.C.; Hung, S.C.E.; Ho, W.C. Effects of short- and long-term exposure to atmospheric pollution on COVID-19 risk and fatality: Analysis of the first epidemic wave in northern Italy. Environ. Res.; 2021; 199, 111293. [DOI: https://dx.doi.org/10.1016/j.envres.2021.111293]
11. Marquès, M.; Domingo, J.L. Contamination of inert surfaces by SARS-CoV-2: Persistence, stability and infectivity. A review. Environ. Res.; 2021; 193, 110559. [DOI: https://dx.doi.org/10.1016/j.envres.2020.110559]
12. Zhou, X.D.; Josey, K.; Kamareddine, L.; Caine, M.C.; Liu, T.J.; Mickley, L.J.; Cooper, M.; Dominici, F. Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States. Sci. Adv.; 2021; 7, eabi8789. [DOI: https://dx.doi.org/10.1126/sciadv.abi8789] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34389545]
13. Veronesi, G.; Gianfagna, F.; Karachaliou, M.; Guasti, L.; Kogevinas, M.; Ferrario, M.M. Association between long-term exposure to air pollutants with breakthrough SARS-CoV-2 infections and antibody responses among COVID-19 vaccinated older adults in Northern Italy. Environ. Res.; 2025; 265, 120450. [DOI: https://dx.doi.org/10.1016/j.envres.2024.120450] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39603588]
14. Bourdrel, T.; Annesi-Maesano, I.; Alahmad, B.; Maesano, C.N.; Bind, M.A. The impact of outdoor air pollution on COVID-19: A review of evidence from animal, and human studies. Eur. Respir. Rev.; 2021; 30, 200242. [DOI: https://dx.doi.org/10.1183/16000617.0242-2020]
15. D’Alessandro, D.; Gola, M.; Appolloni, L.; Dettori, M.; Fara, G.M.; Rebecchi, A.; Settimo, G.; Capolongo, S. COVID-19 and Living space challenge. Well-being and Public Health recommendations for a healthy, safe, and sustainable housing. Acta Biomed.; 2020; 91, pp. 61-75. [DOI: https://dx.doi.org/10.23750/abm.v91i9-S.10115]
16. Brunekreef, B.; Downward, G.; Forastiere, F.; Gehring, U.; Heederik, D.J.J.; Hoek, G.; Koopmans, M.P.G.; Smit, L.A.M.; Vermeulen, R.C.H. Air pollution and COVID-19. Including Elements of Air Pollution in Rural Areas, Indoor Air Pollution and Vulnerability and Resilience Aspects of Our Society Against Respiratory Disease, Social Inequality Stemming from Air Pollution; European Parliament: Luxembourg, 2021.
17. Rudnick, S.N.; Milton, D.K. Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor Air; 2003; 13, pp. 237-245. [DOI: https://dx.doi.org/10.1034/j.1600-0668.2003.00189.x]
18. Peng, Z.; Jimenez, J.L. Exhaled CO as a COVID-19 Infection Risk Proxy for Different Indoor Environments and Activities. Environ. Sci. Technol. Lett.; 2021; 8, pp. 392-397. [DOI: https://dx.doi.org/10.1021/acs.estlett.1c00183]
19. Braggion, A.; Dugerdil, A.; Wilson, O.; Hovagemyan, F.; Flahault, A. Indoor Air Quality and COVID-19: A Scoping Review. Public Health Rev.; 2023; 44, 1605803. [DOI: https://dx.doi.org/10.3389/phrs.2023.1605803]
20. International Labour Organization (ILO)World Health Organization (WHO). Preventing and Mitigating COVID-19 at Work; Geneva. 2021; Available online: https://iris.who.int/bitstream/handle/10665/341328/WHO-2019-nCoV-Workplace-actions-Policy-brief-2021.1-eng.pdf?sequence=1 (accessed on 28 March 2025).
21. U.S. Environmental Protection Agency (US EPA). Indoor Air and Coronavirus (COVID-19). 2022; Available online: https://www.epa.gov/indoor-air-quality-iaq/indoor-air-and-coronavirus-covid-19#:~:text=Although%20improvements%20to%20ventilation%20and,include%20physical%20distancing%2C%20wearing%20face (accessed on 28 March 2025).
22. U.S. Center for Disease Control (CDC). Scientific Brief: SARS-CoV-2 Transmission; National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases: Atlanta, GA, USA, 2021; Available online: https://www.ncbi.nlm.nih.gov/books/NBK570442/ (accessed on 28 March 2025).
23. Italian Health Institute (ISS). Ad Interim Provisions to Prevent and Manage the Indoor Environment in Relation to the Transmission of the Infection by the SARS-CoV-2 Virus. Updating Rapporto ISS COVID-19 n. 5/2020 Rev. 2. Version of April 18, 2021 [Italian Version]; ISS Working Group Environment and Indoor Air Quality: Rome, Italy, 2021; 21p.
24. Morawska, L.; Li, Y.; Salthammer, T. Lessons from the COVID-19 pandemic for ventilation and indoor air quality. Science; 2024; 385, pp. 396-401. [DOI: https://dx.doi.org/10.1126/science.adp2241] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39052782]
25. Tariq, H.; Touati, F.; Crescini, D.; Ben Mnaouer, A. State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration and Evaluation for Respiration-Associated Diseases: A Systematic Review. Atmosphere; 2024; 15, 471. [DOI: https://dx.doi.org/10.3390/atmos15040471]
26. Boccuni, F.; Ferrante, R.; Tombolini, F.; Iavicoli, S.; Pelliccioni, A. Relationship between Indoor High Frequency Size Distribution of Ultrafine Particles and Their Metrics in a University Site. Sustainability; 2021; 13, 5504. [DOI: https://dx.doi.org/10.3390/su13105504]
27. Higgins, C.; Kumar, P.; Morawska, L. Indoor air quality monitoring and source apportionment using low-cost sensors. Environ. Res. Commun; 2024; 6, 012001. [DOI: https://dx.doi.org/10.1088/2515-7620/ad1cad]
28. Boccuni, F.; Rondinone, B.M.; Buresti, G.; Brusco, A.; Bucciarelli, A.; D’Amario, S.; Persechino, B.; Iavicoli, S.; Marinaccio, A. Covid-19 Occupational Risk Incidence and Working Sectors Involved During the Pandemic in Italy. Saf. Health Work; 2023; 14, pp. 398-405. [DOI: https://dx.doi.org/10.1016/j.shaw.2023.10.011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38187213]
29. Villanueva, F.; Notario, A.; Cabañas, B.; Martín, P.; Salgado, S.; Gabriel, M.F. Assessment of CO2 and aerosol (PM2.5, PM10, UFP) concentrations during the reopening of schools in the COVID-19 pandemic: The case of a metropolitan area in Central-Southern Spain. Environ. Res.; 2021; 197, 111092. [DOI: https://dx.doi.org/10.1016/j.envres.2021.111092]
30. Allen, J.G.; Marr, L.C. Recognizing and controlling airborne transmission of SARS-CoV-2 in indoor environments. Indoor Air; 2020; 30, pp. 557-558. [DOI: https://dx.doi.org/10.1111/ina.12697] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32557915]
31. Miller, S.L.; Nazaroff, W.W.; Jimenez, J.L.; Boerstra, A.; Buonanno, G.; Dancer, S.J.; Kurnitski, J.; Marr, L.C.; Morawska, L.; Noakes, C. Transmission of SARS-CoV-2 by inhalation of respiratory aerosol in the Skagit Valley Chorale superspreading event. Indoor Air; 2021; 31, pp. 314-323. [DOI: https://dx.doi.org/10.1111/ina.12751]
32. Morawska, L.; Tang, J.W.; Bahnfleth, W.; Bluyssen, P.M.; Boerstra, A.; Buonanno, G.; Cao, J.; Dancer, S.; Floto, A.; Franchimon, F.
33. Wang, C.C.; Prather, K.A.; Sznitman, J.; Jimenez, J.L.; Lakdawala, S.S.; Tufekci, Z.; Marr, L.C. Airborne transmission of respiratory viruses. Science; 2021; 373, eabd9149. [DOI: https://dx.doi.org/10.1126/science.abd9149]
34. Jones, N.R.; Qureshi, Z.U.; Temple, R.J.; Larwood, J.P.J.; Greenhalgh, T.; Bourouiba, L. Two metres or one: What is the evidence for physical distancing in covid-19?. BMJ Br. Med. J.; 2020; 370, m3223. [DOI: https://dx.doi.org/10.1136/bmj.m3223]
35. Rosti, M.E.; Olivieri, S.; Cavaiola, M.; Seminara, A.; Mazzino, A. Fluid dynamics of COVID-19 airborne infection suggests urgent data for a scientific design of social distancing. Sci. Rep.; 2020; 10, 22426. [DOI: https://dx.doi.org/10.1038/s41598-020-80078-7]
36. Cravero, C.; Marsano, D. Simulation of COVID-19 indoor emissions from coughing and breathing with air conditioning and mask protection effects. Indoor Built Environ.; 2022; 31, pp. 1242-1261. [DOI: https://dx.doi.org/10.1177/1420326X211039546]
37. Li, Y.; Qian, H.; Hang, J.; Chen, X.; Cheng, P.; Ling, H.; Wang, S.; Liang, P.; Li, J.; Xiao, S.
38. Brandal, L.T.; MacDonald, E.; Veneti, L.; Ravlo, T.; Lange, H.; Naseer, U.; Feruglio, S.; Bragstad, K.; Hungnes, O.; Odeskaug, L.E.
39. Iavicoli, S.; Boccuni, F.; Buresti, G.; Gagliardi, D.; Persechino, B.; Valenti, A.; Rondinone, B.M. Risk assessment at work and prevention strategies on COVID-19 in Italy. PLoS ONE; 2021; 16, e0248874. [DOI: https://dx.doi.org/10.1371/journal.pone.0248874]
40. Magnusson, K.; Nygard, K.; Methi, F.; Vold, L.; Telle, K. Occupational risk of COVID-19 in the first versus second epidemic wave in Norway, 2020. Euro Surveill; 2021; 26, 2001875. [DOI: https://dx.doi.org/10.2807/1560-7917.ES.2021.26.40.2001875]
41. Marinaccio, A.; Boccuni, F.; Rondinone, B.M.; Brusco, A.; D’Amario, S.; Iavicoli, S. Occupational factors in the COVID-19 pandemic in Italy: Compensation claims applications support establishing an occupational surveillance system. Occup. Environ. Med.; 2020; 77, pp. 818-821. [DOI: https://dx.doi.org/10.1136/oemed-2020-106844] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32967988]
42. Verbeeck, J.; Vandersmissen, G.; Peeters, J.; Klamer, S.; Hancart, S.; Lernout, T.; Dewatripont, M.; Godderis, L.; Molenberghs, G. Confirmed COVID-19 Cases per Economic Activity during Autumn Wave in Belgium. Int. J. Environ. Res. Public Health; 2021; 18, 12489. [DOI: https://dx.doi.org/10.3390/ijerph182312489]
43. Italian Ministry of Health. Linee Guida Sulle Specifiche Tecniche in Merito All’adozione di Dispositivi Mobili di Purificazione e Impianti Fissi di Aerazione e Agli Standard Minimi di Qualità Dell’aria Negli Ambienti Scolastici e in Quelli Confinati Degli Stessi Edifici. D.P.R. 26 Luglio 2022 [Italian Version]. (GU Serie Generale n.180 del 03-08-2022). 2022; Available online: https://www.gazzettaufficiale.it/atto/serie_generale/caricaDettaglioAtto/originario?atto.dataPubblicazioneGazzetta=2022-08-03&atto.codiceRedazionale=22A04476&elenco30giorni=false (accessed on 28 March 2025).
44. Dottori, M.; Fava, G.; Ruello, M.L. Bacteria removal and viability attenuation by means of an electrostatic barrier. Indoor Built Environ.; 2004; 13, pp. 309-314. [DOI: https://dx.doi.org/10.1177/1420326X04044158]
45. Ente Italiano Di Normazione (UNI). UNI 11254:2007 Filtri per Aria Elettrostatici Attivi per la Ventilazione Generale—Determinazione Della Prestazione di Filtrazione [Italian Version]; UNI: Milano, Italy, 2007.
46.
47.
48. International Committee for Radiological Protection (ICRP). Human Respiratory Tract Model for Radiological Protection; ICRP: Ottawa, ON, Canada, 1994.
49. Asbach, C.; Kaminski, H.; Lamboy, Y.; Schneiderwind, U.; Fierz, M.; Todea, A.M. Silicone sampling tubes can cause drastic artifacts in measurements with aerosol instrumentation based on unipolar diffusion charging. Aerosol Sci. Technol.; 2016; 50, pp. 1375-1384. [DOI: https://dx.doi.org/10.1080/02786826.2016.1241858]
50. Boccuni, F.; Ferrante, R.; Tombolini, F.; Natale, C.; Gordiani, A.; Sabella, S.; Iavicoli, S. Occupational exposure to graphene and silica nanoparticles. Part I: Workplace measurements and samplings. Nanotoxicology; 2020; 14, pp. 1280-1300. [DOI: https://dx.doi.org/10.1080/17435390.2020.1834634]
51. Bellagamba, I.; Boccuni, F.; Ferrante, R.; Tombolini, F.; Natale, C.; Marra, F.; Sarto, M.S.; Iavicoli, S. Occupational Exposure during the Production and the Spray Deposition of Graphene Nanoplatelets-Based Polymeric Coatings. Nanomaterials; 2023; 13, 1378. [DOI: https://dx.doi.org/10.3390/nano13081378]
52. Fierz, M.; Houle, C.; Steigmeier, P.; Burtscher, H. Design, Calibration, and Field Performance of a Miniature Diffusion Size Classifier. Aerosol Sci. Technol.; 2011; 45, pp. 1-10. [DOI: https://dx.doi.org/10.1080/02786826.2010.516283]
53. Oberdörster, G. Pulmonary effects of inhaled ultrafine particles. Int Arch. Occup. Environ. Health; 2000; 74, pp. 1-8. [DOI: https://dx.doi.org/10.1007/s004200000185]
54. Afshari, A.; Matson, U.; Ekberg, L.E. Characterization of indoor sources of fine and ultrafine particles: A study conducted in a full-scale chamber. Indoor Air; 2005; 15, pp. 141-150. [DOI: https://dx.doi.org/10.1111/j.1600-0668.2005.00332.x]
55. Wallace, L.A.; Ott, W.R.; Weschler, C.J. Ultrafine particles from electric appliances and cooking pans: Experiments suggesting desorption/nucleation of sorbed organics as the primary source. Indoor Air; 2015; 25, pp. 536-546. [DOI: https://dx.doi.org/10.1111/ina.12163]
56. D’Amico, A.; Pini, A.; Zazzini, S.; D’Alessandro, D.; Leuzzi, G.; Currà, E. Modelling VOC Emissions from Building Materials for Healthy Building Design. Sustainability; 2021; 13, 184. [DOI: https://dx.doi.org/10.3390/su13010184]
57. Di Menno di Bucchianico, A.D.; Cusano, M.; Gaddi, R.; Gaeta, A.; Leone, G.; Boccuni, F.; Ferrante, R.; Pelliccioni, A.; Cattani, G. Indoor and Outdoor Particle Number Concentration in the Sapienza University Campus of Rome. Sustainability; 2021; 13, 9126. [DOI: https://dx.doi.org/10.3390/su13169126]
58. Morawska, L.; Ayoko, G.A.; Bae, G.N.; Buonanno, G.; Chao, C.Y.H.; Clifford, S.; Fu, S.C.; Hänninen, O.; He, C.; Isaxon, C.
59. Licina, D.; Morrison, G.C.; Bekö, G.; Weschler, C.J.; Nazaroff, W.W. Clothing-Mediated Exposures to Chemicals and Particles. Environ. Sci. Technol.; 2019; 53, pp. 5559-5575. [DOI: https://dx.doi.org/10.1021/acs.est.9b00272]
60. Marcovecchio, F.; Perrino, C. Bioaerosol Contribution to Atmospheric Particulate Matter in Indoor University Environments. Sustainability; 2021; 13, 1149. [DOI: https://dx.doi.org/10.3390/su13031149]
61. Chen, C.; Yao, M.Y.; Luo, X.; Zhu, Y.L.; Liu, Z.Y.; Zhuo, H.C.; Zhao, B. Outdoor-to-indoor transport of ultrafine particles: Measurement and model development of infiltration factor. Environ. Pollut.; 2020; 267, 115402. [DOI: https://dx.doi.org/10.1016/j.envpol.2020.115402]
62. Natale, C.; Tombolini, F.; Ferrante, R.; Sebastiani, F.; Gordiani, A.; Manigrasso, M.; Del Rio Castillo, A.E.; Bonaccorso, F.; Sabella, S.; Boccuni, F. Scaling up the graphene production from R&D to the pilot plant stage: Implications for workers’ exposure to airborne nano-objects. NanoImpact; 2025; 38, 100555. [DOI: https://dx.doi.org/10.1016/j.impact.2025.100555] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/40118342]
63. Organization for Economic Cooperation and Development (OECD). Harmonized Tiered Approach to Measure and Assess the Potential Exposure to Airborne Emissions of Engineered Nano-Objects and Their Agglomerates and Aggregates at Workplaces. ENV/JM/MONO (2015) 19. 2015; Available online: http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2015)19&doclanguage=en (accessed on 28 March 2025).
64. McCormick, S.; Niang, M.; Dahm, M.M. Occupational Exposures to Engineered Nanomaterials: A Review of Workplace Exposure Assessment Methods. Curr. Environ. Health Rep.; 2021; 8, pp. 223-234. [DOI: https://dx.doi.org/10.1007/s40572-021-00316-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34101152]
65. Zou, H.; Zhang, Q.W.; Xing, M.L.; Gao, X.J.; Zhou, L.F.; Tollerud, D.J.; Tang, S.C.; Zhang, M.B. Relationships between number, surface area, and mass concentrations of different nanoparticles in workplaces. Environ. Sci. Process. Impacts; 2015; 17, pp. 1470-1481. [DOI: https://dx.doi.org/10.1039/C5EM00172B] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26166442]
66. Agenzia Regionale Per La Protezione Ambientale Della Toscana. Archivio Storico dei Dati Orari; 2021 [Italian Version]. Available online: https://www.arpat.toscana.it/temi-ambientali/aria/qualita-aria/archivio_dati_orari (accessed on 5 May 2025).
67. European Union (EU). Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on Ambient Air Quality and Cleaner Air for Europe. 2024; Available online: https://eur-lex.europa.eu/eli/dir/2024/2881/oj/eng (accessed on 28 March 2025).
68. World Health Organization (WHO). WHO Global Air Quality Guidelines. 2021; Available online: https://www.who.int/news-room/questions-and-answers/item/who-global-air-quality-guidelines (accessed on 28 March 2025).
69. Italian Health Institute (ISS). Interim Technical Note. CO2 Monitoring for Prevention and Management in indoor Environments in Relation to the Transmission of SARS-CoV-2 Virus Infection. 2022; Available online: https://www.iss.it/documents/20126/6703853/NT_co2_DEF.pdf/ (accessed on 28 March 2025).
70. Bhoonah, R.; Mendez, M.; Maury-Micolier, A. Human health impacts and indoor chemical reactions of VOCs from cleaning products and occupants. Atmos. Environ.; 2024; 338, 120846. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2024.120846]
71. Asbach, C.; Kaminski, H.; Von Barany, D.; Kuhlbusch, T.A.J.; Monz, C.; Dziurowitz, N.; Pelzer, J.; Vossen, K.; Berlin, K.; Dietrich, S.
72. Kim, D.; Shin, D.; Hwang, J. Calibration of Low-cost Sensors for Measurement of Indoor Particulate Matter Concentrations via Laboratory/Field Evaluation. Aerosol Air Qual. Res.; 2023; 23, 230097. [DOI: https://dx.doi.org/10.4209/aaqr.230097]
73. Karagulian, F.; Gerboles, M.; Barbiere, M.; Kotsev, A.; Lagler, F.; Borowiak, A. Review of Sensors for Air Quality Monitoring; Publications Office of the European Union: Luxembourg, 2019.
74. Giordano, M.R.; Malings, C.; Pandis, S.N.; Presto, A.A.; McNeill, V.F.; Westervelt, D.M.; Beekmann, M.; Subramanian, R. From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors. J. Aerosol Sci.; 2021; 158, 105833. [DOI: https://dx.doi.org/10.1016/j.jaerosci.2021.105833]
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; Ferrante Riccardo 1 ; Tombolini Francesca 1 ; Iavicoli Sergio 2 ; Pingue Pasqualantonio 3
1 Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers’ Compensation Authority, Via Fontana Candida 1, I-00078 Rome, Italy; [email protected] (R.F.); [email protected] (F.T.)
2 Directorate General for Communication and European and International Relations, Italian Ministry of Health, Lungotevere Ripa 1, I-00153 Rome, Italy; [email protected]
3 NEST Laboratory, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Piazza San Silvestro 12, I-56127 Pisa, Italy; [email protected]