Particle exposure and inhaled dose while commuting in Lisbon

Air pollution exposure in active versus passive travel modes across five continents: A Bayesian random-effects meta-analysis

Epidemiological studies on health effects of air pollution usually estimate exposure at the residential address. However, ignoring daily mobility patterns may lead to biased exposure estimates, as documented in previous exposure studies. To improve the reliable integration of exposure related to mobility patterns into epidemiological studies, we conducted a systematic review of studies across all continents that measured air pollution concentrations in various modes of transport using portable sensors. To compare personal exposure across different transport modes, specifically active versus motorized modes, we estimated pairwise exposure ratios using a Bayesian random-effects meta-analysis. Overall, we included measurements of six air pollutants (black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10, PM2.5) and ultrafine particles (UFP)) for seven modes of transport (i.e., walking, cycling, bus, car, motorcycle, overground, underground) from 52 published studies. Compared to active modes, users of motorized modes were consistently the most exposed to gaseous pollutants (CO and NO2). Cycling and walking were the most exposed to UFP compared to other modes. Active vs passive mode contrasts were mostly inconsistent for other particle metrics. Compared to active modes, bus users were consistently more exposed to PM10 and PM2.5, while car users, on average, were less exposed than pedestrians. Rail modes experienced both some lower exposures (compared to cyclists for PM10 and pedestrians for UFP) and higher exposures (compared to cyclist for PM2.5 and BC). Ratios calculated for motorcycles should be considered carefully due to the small number of studies, mostly conducted in Asia. Computing exposure ratios overcomes the heterogeneity in pollutant levels that may exist between continents and countries. However, formulating ratios on a global scale remains challenging owing to the disparities in available data between countries.

Exposure to airborne particulate matter during commuting using portable sensors: Effects of transport modes in a French metropolis study case

Outdoor exposure to particulate matter (PM2.5 and PM10) in urban areas can vary considerably depending on the mode of transport. This study aims to quantify this difference in exposure during daily travel, by carrying out a micro-sensor measurement campaign. The pollutant exposure was assessed simultaneously over predefined routes in order to allow comparison between different transport modes having the same starting and ending points. During the six-week measurement campaign, the average reference values for PM background concentrations were 13.72 and 17.92μg/m3 for the PM2.5 and PM10, respectively. The results revealed that the mode with the highest exposure to PM2.5 adjusted to background concentration (PM2.5Norm) was the bus (1.65) followed by metro (1.51), walking (1.33), tramway (1.31), car (1.09) and finally the bike (1.06). For PM10Norm, the tramway had the highest exposure (1.86), followed by walking (1.68), metro (1.65), bus (1.61), bike (1.43) and finally the car (1.39). The level of urbanization around the route and the presence of preferential lanes for public transportation influenced the concentration to which commuters were exposed. For the active modes (bike and walking), we observed frequent variations in concentrations during the trip, characterized by punctual peaks in concentration, depending on the local characteristics of road traffic and urban morphology. Fluctuations in particulate matter inside public transport vehicles were partly explained by the opening and closing of doors during stops, as well as the passenger flows, influencing the re-suspension of particles. The car was one of the least exposed modes overall, with the lowest concentration variability, although these concentrations can vary greatly depending on the ventilation parameters used. These results encourage measures to move the most exposed users away from road traffic, by developing a network of lanes entirely dedicated to cycling and walking, particularly in densely populated areas, as well as encouraging the renewal of motorized vehicles to use less polluting fuels with efficient ventilation systems.

Advancing air quality monitoring: A low-cost sensor network in motion - Part I

In urban areas, high levels of air pollution pose significant risks to human health, emphasising the need for detailed air quality (AQ) monitoring. However, traditional AQ monitoring relies on the data from Reference Monitoring Stations, which are sparsely distributed and provide only hourly or daily data, failing to capture the spatial and temporal variability of air pollutant concentrations. Addressing this challenge, we introduce in this article the ExpoLIS system, an all-weather mobile AQ monitoring system that integrates various AQ low-cost sensors (LCSs), providing high spatio-temporal resolution data. This study demonstrates that the inclusion of an extended sampling device may mitigate the effect of the meteorological parameters and other disturbances on readings. At the same time, it did not reduce the quality of the data, both in static conditions and in motion, as we were able to maintain a certain level of agreement between the LCSs. In conclusion, the ExpoLIS system proves its versatility by enabling the collection of large quantities of accurate data, allowing a deeper understanding of the AQ dynamics in urban environments.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

Childhood exposure to outdoor air pollution in different microenvironments and cognitive and fine motor function in children from six European cohorts

BACKGROUND

Exposure to air pollution during childhood has been linked with adverse effects on cognitive development and motor function. However, limited research has been done on the associations of air pollution exposure in different microenvironments such as home, school, or while commuting with these outcomes.

OBJECTIVE

To analyze the association between childhood air pollution exposure in different microenvironments and cognitive and fine motor function from six European birth cohorts.

METHODS

We included 1301 children from six European birth cohorts aged 6-11 years from the HELIX project. Average outdoor air pollutants concentrations (NO2, PM2.5) were estimated using land use regression models for different microenvironments (home, school, and commute), for 1-year before the outcome assessment. Attentional function, cognitive flexibility, non-verbal intelligence, and fine motor function were assessed using the Attention Network Test, Trail Making Test A and B, Raven Colored Progressive Matrices test, and the Finger Tapping test, respectively. Adjusted linear regressions models were run to determine the association between each air pollutant from each microenvironment on each outcome.

RESULTS

In pooled analysis we observed high correlation (rs = 0.9) between air pollution exposures levels at home and school. However, the cohort-by-cohort analysis revealed correlations ranging from low to moderate. Air pollution exposure levels while commuting were higher than at home or school. Exposure to air pollution in the different microenvironments was not associated with working memory, attentional function, non-verbal intelligence, and fine motor function. Results remained consistently null in random-effects meta-analysis.

CONCLUSIONS

No association was observed between outdoor air pollution exposure in different microenvironments (home, school, commute) and cognitive and fine motor function in children from six European birth cohorts. Future research should include a more detailed exposure assessment, considering personal measurements and time spent in different microenvironments.

Health damage assessment of commuters and staff in the metro system based on field monitoring-A case study of Nanjing

Introduction

The metro has emerged as a major mode of transportation. A significant number of commuters and staff in the metro system are exposed to air pollutants because of its shielded environment, and substantial health damage requires quantitative assessment. Previous studies have focused on comparing the health impacts among different transportation modes, overlooking the specific population characteristics and pollutant distribution in metro systems.

Methods

To make improvements, this study implements field monitoring of the metro's air environment utilizing specialized instruments and develops a health damage assessment model. The model quantifies health damage of two main groups (commuters and staff) in metro systems at three different areas (station halls, platforms, and metro cabins) due to particulate matter 10 and benzene series pollution.

Conclusion

A case study of Nanjing Metro Line 3 was conducted to demonstrate the applicability of the model. Health damage at different metro stations was analyzed, and the health damage of commuters and staff was assessed and compared. This study contributes to enhancing research on health damage in the metro systems by providing a reference for mitigation measures and guiding health subsidy policies.

Reduced commuter exposure to PM2.5 and PAHs in response to improved emission standards in bus rapid transit systems in Mexico

We evaluated impacts of progressive technological updates to bus rapid transit (BRT) systems on in-cabin concentrations of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5), and the various polyaromatic hydrocarbons (PAHs) to which commuters were exposed. PM2.5 samples were collected and real-time concentrations measured from October 2017 to March 2020 inside cabins of BRT buses equipped with Euro IV, V and VI diesel emission standards in the Mexico City Metropolitan Area (MCMA). For effective comparison, similar samplings and measurements were carried out on trains in the MCMA underground (MCU) system. Peak in-cabin PM2.5 concentrations decreased significantly (p < 0.05) by 35% from Euro IV to Euro V buses, and by 80% from Euro IV to Euro VI buses. PM2.5 concentrations inside Euro VI buses were significantly lower (p < 0.05) than in Euro IV and Euro V buses and in underground trains. The in-cabin excess (ICE) of PM2.5 relative to ambient concentrations was significantly (p < 0.05) higher for Euro IV than for Euro V buses during morning the traffic peak, and consistently higher than for Euro VI buses. Indeed, ICEs calculated for Euro VI buses were always lower than those for electricity-powered underground trains. The frequency of hotspots decreased from Euro IV to Euro VI buses due to the combined effect of low emissions and closed, air-conditioned cabins. Concentrations of total PAHs including carcinogenic species also decreased from Euro IV to Euro V buses and were below limits of detection aboard Euro VI buses. This work shows that in real-life conditions, advanced diesel technologies and cabin design significantly reduce commuters' exposure to PM2.5 and to toxic PAH compounds.

Job Exposure Matrix, a Solution for Retrospective Assessment of Particle Exposure in a Subway Network and Their Long-Term Effects

INTRODUCTION

Health effects after long-term exposure to subway particulate matter (PM) remain unknown due to the lack of individual PM exposure data. This study aimed to apply the job exposure matrix (JEM) approach to retrospectively assess occupational exposure to PM in the Parisian subway.

METHODS

Job, the line and sector of the transport network, as well as calendar period were four JEM dimensions. For each combination of these dimensions, we generated statistical models to estimate the annual average PM10 concentration using data from an exhaustive inventory of the PM measurement campaigns conducted between 2004 and 2020 in the Parisian subway and historical data from the Parisian air pollution monitoring network. The resulting JEM and its exposure estimates were critically examined by experts using the uncertainty analysis framework.

RESULTS

The resulting JEM allows for the assignment of the estimated annual PM10 concentration to three types of professionals working in the subway: locomotive operators, station agents, and security guards. The estimates' precision and validity depend on the amount and quality of PM10 measurement data used in the job-, line-, and sector-specific models. Models using large amounts of personal exposure measurement data produced rather robust exposure estimates compared to models with lacunary data (i.e., in security guards). The analysis of uncertainty around the exposure estimates allows for the identification of the sources of uncertainty and parameters to be addressed in the future in order to refine and/or improve the JEM.

CONCLUSIONS

The JEM approach seems relevant for the retrospective exposure assessment of subway workers. When applied to available data on PM10, it allows for the estimation of this exposure in locomotive operators and station agents with an acceptable validity. Conversely, for security guards, the current estimates have insufficient validity to recommend their use in an epidemiological study. Therefore, the current JEM should be considered as a valid prototype, which shall be further improved using more robust measurements for some jobs. This JEM can also be further refined by considering additional exposure determinants.

The characteristics of particulate matter in different subway station environmental control systems

Particulate matter (PM2.5, PM10) in urban subway stations can significantly impact passengers' health. The particle concentration in subway stations is influenced by many factors. However, few existing studies have explored the impact of environmental control systems in-depth, especially under different outdoor pollution conditions. To address this research gap, this study focused on measuring and comparing the characteristics of PM2.5 and PM10 at subway stations with three control systems (open, closed, and screen door) under varying pollution conditions in Beijing. Particle concentrations from platforms, carriages, and outdoors were monitored and analyzed using statistical methods. The results showed that the particle concentration in the closed system was generally 20-40 μg/m3 higher than that in the screen system at the platform, which might be attributed to the piston wind, as the air from the tunnel with a lot of dirt. The pollution in the carriage was more severe for the open system than that of the screen system. The PM2.5/PM10 ratio in the carriage was 91%, 90%, and 83.84% for the closed, open, and screen systems, respectively. This indicates that the screen door could reduce the particle concentration in the platform to 10%-50%. The particle concentration varied among subway stations with different environmental control systems, suggesting that the prevention and control strategies for particulate matter pollution should be different for stations with different systems.

An investigation of the PM2.5 concentrations and cumulative inhaled dose during subway commutes in Changchun, China

Air quality in subway systems is crucial as it affects the health of passengers and staff. Although most tests of PM2.5 concentrations in subway stations have taken place in public areas, PM2.5 is less understood in workplaces. Few studies have estimated the cumulative inhaled dose of passengers based on real-time changes in PM2.5 concentrations as they commute. To clarify the above issues, this study first measured PM2.5 concentrations in four subway stations in Changchun, China, where measuring points included five workrooms. Then, passengers' exposure to PM2.5 during the whole subway commute (20-30 min) was measured and segmented inhalation was calculated. The results showed that PM2.5 concentration in public places ranged from 50 to 180 μg/m3, and was strongly correlated with outdoors. While the PM2.5 average concentration in workplaces was 60 µg/m3, and it was less affected by outdoor PM2.5 concentration. Passenger's cumulative inhalations in single commuting were about 42 μg and 100 μg when the outdoor PM2.5 concentrations were 20-30 μg/m3 and 120-180 μg/m3, respectively. The PM2.5 inhalation in carriages accounted for the largest proportion of the entire commuting, about 25-40%, because of the longer exposure time and higher PM2.5 concentrations. It is recommended to improve the tightness of the carriage and filter the fresh air to improve the air quality inside. The average daily PM2.5 inhaled by staff was 513.53 μg, which was 5-12 times higher than that of passengers. Installing air purification devices in workplaces and reminding staff to take personal protection can positively protect their health.

Assessment of particulate matter inhalation during the trip process with the considerations of exercise load

A Particulate Matter (PM) inhalation model considering exercise load is established to evaluate the impact of PM on residents' travel health. The study chooses PM detectors to collect PM concentrations at the various transportation space, including walking, bicycle, bus, taxi, and subway. A multiple linear regression model revised by road greening is utilized to study the influence factors that have a potential impact on the PM concentration. The air inhalation model with the consideration of exercise load can be acquired by connecting the heart rate (HR) and individual characteristics. The PM_2.5 and PM₁₀ inhalation for a complete trip of traveler can be estimated using the proposed model based on air inhalation per time unit, travel time, and PM concentration. The analysis results using the experimental data in Xi'an indicate that PM concentrations in taxi carriage, bus carriage, and subway carriage are significantly different from those obtained from environmental monitoring stations. However, the difference is not significant in the locations of sidewalk, non-motorized lane, taxi station, bus station, subway concourse, and subway platform. PM concentration and humidity in background environment have a positive influence on the increase of PM concentration in transportation environment, while temperature and wind speed are negative. The mean values of air inhalation per time unit for male and female using each mode are in the range of 9.6-26.8 L/min and 9.8-27.8 L/min, respectively. Exposure time in non-motorized transportation has a large effect on PM inhalation of travelers, walking connections and waiting in motorized transportation are the main contributing states to PM inhalation of travelers. The results of the study can be used to predict travelers' PM inhalation in completed trips, and provide recommendations for travelers to choose a healthier mode.

A Low-Cost Sensor System Installed in Buses to Monitor Air Quality in Cities

Air pollution is an important source of morbidity and mortality. It is essential to understand to what levels of air pollution citizens are exposed, especially in urban areas. Low-cost sensors are an easy-to-use option to obtain real-time air quality (AQ) data, provided that they go through specific quality control procedures. This paper evaluates the reliability of the ExpoLIS system. This system is composed of sensor nodes installed in buses, and a Health Optimal Routing Service App to inform the commuters about their exposure, dose, and the transport's emissions. A sensor node, including a particulate matter (PM) sensor (Alphasense OPC-N3), was evaluated in laboratory conditions and at an AQ monitoring station. In laboratory conditions (approximately constant temperature and humidity conditions), the PM sensor obtained excellent correlations (R²≈1) against the reference equipment. At the monitoring station, the OPC-N3 showed considerable data dispersion. After several corrections based on the k-Köhler theory and Multiple Regression Analysis, the deviation was reduced and the correlation with the reference improved. Finally, the ExpoLIS system was installed, leading to the production of AQ maps with high spatial and temporal resolution, and to the demonstration of the Health Optimal Routing Service App as a valuable tool.

Source apportionment of children daily exposure to particulate matter

The present study aims to investigate the sources of particulate pollution in indoor and outdoor environments, with focus on determining their contribution to the exposure of children to airborne particulate matter (PM). To this end, parallel indoor and outdoor measurements were carried out for a selection of 40 homes and 5 schools between September 2017 and October 2018. PM2.5 and PM2.5-10 samples were collected during five days in each microenvironment (ME) and analysed by X-Ray Fluorescence (XRF), for the determination of elements, and by a thermal-optical technique, for the measurement of organic and elemental carbon. The source apportionment analysis of the PM composition data, by means of the receptor model SoFi (Source Finder) 8 Pro, resulted in the identification of nine sources: exhaust and non-exhaust emissions from traffic, secondary particles, heavy oil combustion, industry, sea salt, soil, city dust, and an indoor source characterized by high levels of organic carbon. Integrated daily exposure to PM2.5 was on average 21 μg/m³. The organic matter, resulting from cleaning, cooking, smoking and biological material, was the major source contributing by 31% to the PM2.5 exposure. The source city dust, which was highly influenced by the resuspension of dust in classrooms, was the second main source (26%), followed by traffic (24%). The major sources affecting the integrated exposure to PM10, which was on average 33 μg/m³, were the city dust (39%), indoor organics (24%) and traffic (16%). This study provides important information for the design of measures to reduce the exposure of children to PM.

Sixteen-Year Monitoring of Particulate Matter Exposure in the Parisian Subway: Data Inventory and Compilation in a Database

The regularly reported associations between particulate matter (PM) exposure, and morbidity and mortality due to respiratory, cardiovascular, cancer, and metabolic diseases have led to the reduction in recommended outdoor PM10 and PM2.5 exposure limits. However, indoor PM10 and PM2.5 concentrations in subway systems in many cities are often higher than outdoor concentrations. The effects of these exposures on subway workers and passengers are not well known, mainly because of the challenges in exposure assessment and the lack of longitudinal studies combining comprehensive exposure and health surveillance. To fulfill this gap, we made an inventory of the PM measurement campaigns conducted in the Parisian subway since 2004. We identified 5856 PM2.5 and 18,148 PM10 results from both personal and stationary air sample measurements that we centralized in a database along with contextual information of each measurement. This database has extensive coverage of the subway network and will enable descriptive and analytical studies of indoor PM exposure in the Parisian subway and its potential effects on human health.

Inequalities in occupational exposures among people using popular commute modes

Several recent studies have looked into the differences in air qualities inside popular commute modes. The impact of daily commuting patterns and work-related trips on inhalation doses, however, are not investigated. The purpose of this study is to quantify the variation in air pollutants within popular commute modes in Mumbai, India, and to estimate the variation in exposure as a result of occupational or work-related trips across different sub-groups. Real-time pollutants, both gaseous and particulate matters (PM), were measured on a pre-defined route during rush and non-rush hours on buses, cars, auto-rickshaws, sub-urban trains, and motorbikes through several trips (N = 98). Household surveys were conducted to estimate the exposures of different occupational subgroups (cab-driver, auto-rickshaw drivers, delivery persons) and people commuting to their offices daily. Participants (N = 800) from various socioeconomic backgrounds in the city were asked about their job categories, work-activity patterns, and work-related commute trips. Mass concentrations of particles in different size ranges (PM₁, PM_2.5, and PM₁₀) were substantially higher (p < 0.05) inside auto-rickshaws (44.6 μg/m³, 84.7 μg/m³, and 138.3 μg/m³) compared to other modes. Inside cars, gaseous pollutants such as carbon monoxide (CO) and total volatile organic compounds (TVOC) were significantly higher (p < 0.05). Although both gaseous and particulate concentrations were lower (p < 0.05) inside buses, bus-commuters were found to be highly exposed to the pollutants due to the extended trip time (∼1.2 times longer than other modes) and driving conditions. Office commuters inhale a large fraction of their daily doses (25-30%) during their work-related travel. Occupational sub-groups, on the other hand, inhale ∼90% of the pollutants during their work. In a day, an auto-rickshaw driver inhales 10-15% more (p < 0.05) pollutants than cab driver or delivery personnel. Therefore, this study highlights the inequalities in occupational exposure as a combined effect of in-cabin air qualities and commute patterns due to occupational obligations.

Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM_2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM_2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM_2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM_2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM_2.5 ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R² of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R² of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM_2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

Exposure of Malaysian Children to Air Pollutants over the School Day

Children are sensitive to air pollution and spend long hours in and around their schools, so the school day has an important impact on their overall exposure. This study of Kuala Lumpur, Selangor and its surroundings assesses exposure to PM2.5 and NO2, from travel, play and study over a typical school day. Most Malaysian children in urban areas are driven to school, so they probably experience peak NO2 concentrations in the drop-off and pick-up zones. Cyclists are likely to receive the greatest school travel exposure during their commute, but typically, the largest cumulative exposure occurs in classrooms through the long school day. Indoor concentrations tend to be high, as classrooms are well ventilated with ambient air. Exposure to PM2.5 is relatively evenly spread across Selangor, but NO2 exposure tends to be higher in areas with a high population density and heavy traffic. Despite this, ambient PM2.5 may be more critical and exceed guidelines as it is a particular problem during periods of widespread biomass burning. A thoughtful adjustment to school approach roads, design of playgrounds and building layout and maintenance may help minimise exposure.

Analysis of spatial factors, time-activity and infiltration on outdoor generated PM2.5 exposures of school children in five European cities

Atmospheric particles are a major environmental health risk. Assessments of air pollution related health burden are often based on outdoor concentrations estimated at residential locations, ignoring spatial mobility, time-activity patterns, and indoor exposures. The aim of this work is to quantify impacts of these factors on outdoor-originated fine particle exposures of school children. We apply nested WRF-CAMx modelling of PM_2.5 concentrations, gridded population, and school location data. Infiltration and enrichment factors were collected and applied to Athens, Kuopio, Lisbon, Porto, and Treviso. Exposures of school children were calculated for residential and school outdoor and indoor, other indoor, and traffic microenvironments. Combined with time-activity patterns six exposure models were created. Model complexity was increased incrementally starting from residential and school outdoor exposures. Even though levels in traffic and outdoors were considerably higher, 80-84% of the exposure to outdoor particles occurred in indoor environments. The simplest and also commonly used approach of using residential outdoor concentrations as population exposure descriptor (model 1), led on average to 26% higher estimates (15.7 μg/m³) compared with the most complex model (# 6) including home and school outdoor and indoor, other indoor and traffic microenvironments (12.5 μg/m³). These results emphasize the importance of including spatial mobility, time-activity and infiltration to reduce bias in exposure estimates.

Airborne particles in city bus: concentrations, sources and simulated pulmonary solubility

PM10 and PM2.5 concentrations in Ljubljana city bus were monitored during entire shift, and individual particles were morphologically and chemically characterised in order to determine PM concentration variability, particle sources, solubility in simulated pulmonary environment and effects on human health. PM measurements revealed high mean PM10 (82.8 μg/m³) and PM2.5 (47 μg/m³), which were highest and most variable during rush hours with fluid traffic and lowest during traffic jams with standing vehicles. Individual particle analysis showed that airborne particles were dominated by metal-bearing phases, particularly small-sized (Cr,Mn,Zn)-bearing Fe-oxyhydroxides and Al-/Fe-Al-oxides, large (Fe,Cr,Ni)- and (Cu,Zn,Ni)-alloys, and small-sized Sb-sulphide and Ba-sulphate. Non-metallic phases were represented by (Ca,Mg)-carbonates, Al-silicates, Na-chloride and Ca-sulphate. Comparison with possible source materials (vehicle exhaust emissions, brake disc dust and road sediment) showed that primary sources of these metal-bearing phases were wear of brake discs, brake pads and tyres, and also wear of engine components and catalytic converters. Most non-metallic phases originated from resuspension of road sediment, containing road sanding materials, but also from emissions of burned fuel and lubricating oil (Ca-sulphate). Assessment of effects on human health indicated that mean PM concentrations, which significantly exceeded daily limit values, increased mortality (by 2-3%) and morbidity (by 7-8%) risk for bus drivers. Simplified PHREEQC calculations of airborne metal-bearing phase solubility in aqueous solutions simulating pulmonary environment showed that metallic Fe, Ba-sulphate, Sb-sulphide and Al-oxide, partly also Cu-bearing metal alloys, were soluble under reducing and oxidising conditions, but released metals were removed from solution by precipitation of stable secondary metal-bearing phases.

Airborne particles and microorganisms in a dental clinic: Variability of indoor concentrations, impact of dental procedures, and personal exposure during everyday practice

This study presents for the first time comprehensive measurements of the particle number size distribution (10 nm to 10 μm) together with next-generation sequencing analysis of airborne bacteria inside a dental clinic. A substantial enrichment of the indoor environment with new particles in all size classes was identified by both activities to background and indoor/outdoor (I/O) ratios. Grinding and drilling were the principal dental activities to produce new particles in the air, closely followed by polishing. Illumina MiSeq sequencing of 16S rRNA of bioaerosol collected indoors revealed the presence of 86 bacterial genera, 26 of them previously characterized as potential human pathogens. Bacterial species richness and concentration determined both by qPCR, and culture-dependent analysis were significantly higher in the treatment room. Bacterial load of the treatment room impacted in the nearby waiting room where no dental procedures took place. I/O ratio of bacterial concentration in the treatment room followed the fluctuation of I/O ratio of airborne particles in the biology-relevant size classes of 1-2.5, 2.5-5, and 5-10 μm. Exposure analysis revealed increased inhaled number of particles and microorganisms during dental procedures. These findings provide a detailed insight on airborne particles of both biotic and abiotic origin in a dental clinic.

Estimation of the Inhaled Dose of Pollutants in Different Micro-Environments: A Systematic Review of the Literature

Recently, the need to assess personal exposure in different micro-environments has been highlighted. Further, estimating the inhaled dose of pollutants is considerably one of the most interesting parameters to be explored to complete the fundamental information obtained through exposure assessment, especially if associated with a dose-response approach. To analyze the main results obtained from the studies related to the estimation of the inhaled dose of pollutants in different micro-environments (environments in which an individual spends a part of his day), and to identify the influence of different parameters on it, a systematic review of the literature was performed. The principal outcomes from the considered studies outlined that (i) exposure concentration and residence time are among the most important parameters to be evaluated in the estimation of the inhaled dose, especially in transport environments. Further, (ii) the pulmonary ventilation rate can be of particular interest during active commuting because of its increase, which increases the inhalation of pollutants. From a methodological point of view, the advent of increasingly miniaturized, portable and low-cost technologies could favor these kinds of studies, both for the measurement of atmospheric pollutants and the real-time evaluation of physiological parameters used for estimation of the inhaled dose. The main results of this review also show some knowledge gaps. In particular, numerous studies have been conducted for the evaluation (in terms of personal exposure and estimation of the inhaled dose) of different PM fractions: other airborne pollutants, although harmful to human health, are less represented in studies of this type: for this reason, future studies should be conducted, also considering other air pollutants, not neglecting the assessment of exposure to PM. Moreover, many studies have been conducted indoors, where the population spends most of their daily time. However, it has been highlighted how particular environments, even if characterized by a shorter residence time, can contribute significantly to the dose of inhaled pollutants. These environments are, therefore, of particular importance and should be better evaluated in future studies, as well as occupational environments, where the work results in a high pulmonary ventilation rate. The attention of future studies should also be focused on these categories of subjects and occupational studies.

Factors affecting the exposure to physicochemical and microbiological pollutants in vehicle cabins while commuting in Lisbon

Commuters are exposed to a variety of physicochemical and microbiological pollutants that can lead to adverse health effects. This study aims to evaluate the indoor air quality (IAQ) in cars, buses and trains in Lisbon, to estimate inhaled doses while commuting and to evaluate the impacts of cleaning and ventilation on the IAQ. Particulate matter with diameter lower than 1, 2.5 and 10 μm (PM₁, PM_2.5 and PM₁₀), black carbon (BC), carbon monoxide (CO), carbon dioxide (CO₂) volatile organic compounds (VOCs), formaldehyde (CH₂O) and total airborne bacteria and fungi were measured and bacterial isolates were identified. Results showed that the type of ventilation is the main factor affecting the IAQ in vehicle cabins. Under the fan off condition, the concentration of BC was lower, but the concentration of gases such as CO₂, CO and VOC tended to accumulate rapidly. When the ventilation was used, the coarse particles were filtered originating the decrease of indoor concentrations. Commuters travelling in trains received the lowest dose for all chemical pollutants, except VOC, mainly because railways are further away from the direct vehicular emissions. Commuters travelling in cars without ventilation received the highest inhaled dose for almost all pollutants despite having the lowest travel duration. Airborne microbiota was highly affected by the occupancy of the vehicles and therefore, the fungi and bacterial loads were higher in trains and buses. Most of the isolated species were human associated bacteria and some of the most abundant species have been linked to respiratory tract infections.

Chemical characterisation of particulate matter in urban transport modes

Traffic is a main source of air pollutants in urban areas and consequently daily peak exposures tend to occur during commuting. Personal exposure to particulate matter (PM) was monitored while cycling and travelling by bus, car and metro along an assigned route in Lisbon (Portugal), focusing on PM2.5 and PM10 (PM with aerodynamic diameter <2.5 and 10 µm, respectively) mass concentrations and their chemical composition. In vehicles, the indoor-outdoor interplay was also evaluated. The PM2.5 mean concentrations were 28 ± 5, 31 ± 9, 34 ± 9 and 38 ± 21 µg/m³ for bus, bicycle, car and metro modes, respectively. Black carbon concentrations when travelling by car were 1.4 to 2.0 times higher than in the other transport modes due to the closer proximity to exhaust emissions. There are marked differences in PM chemical composition depending on transport mode. In particular, Fe was the most abundant component of metro PM, derived from abrasion of rail-wheel-brake interfaces. Enhanced concentrations of Zn and Cu in cars and buses were related with brake and tyre wear particles, which can penetrate into the vehicles. In the motorised transport modes, Fe, Zn, Cu, Ni and K were correlated, evidencing their common traffic-related source. On average, the highest inhaled dose of PM2.5 was observed while cycling (55 µg), and the lowest in car travels (17 µg). Cyclists inhaled higher doses of PM2.5 due to both higher inhalation rates and longer journey times, with a clear enrichment in mineral elements. The presented results evidence the importance of considering the transport mode in exposure assessment studies.

Influence of methodology on the estimation of the particle surface area dose received by a population in all-day activities

In everyday life, people are exposed to different concentrations of airborne particles depending on the microenvironment where they perform their different activities. Such exposure can lead to high sub-micron particle doses. The received dose depends on particle concentration to which people are exposed (typically expressed in terms of number or surface area), time spent in each activity or microenvironment (time activity pattern) and amount of air inhaled (inhalation rate). To estimate an actual value of the received dose, all these parameters should be measured under real-life conditions; in fact, the concentrations should be measured on a personal scale (i.e. through a direct exposure assessment), whereas time activity patterns and inhalation rates specific to the activity performed should be considered. The difficulties in obtaining direct measurements of these parameters usually lead to adopt time activity patterns and inhalation rates already available in scientific literature for typical populations, and local outdoor particle concentrations measured with fixed monitoring stations and extrapolated for all the other microenvironments. To overcome these limitations, we propose a full-field method for estimating the received dose of a population sample, in which all the parameters (concentration levels, time activity patterns and inhalation rates) are measured under real-life conditions (also including the inhalation rates, that were evaluated on the basis of the measured heart rates). Specifically, 34 volunteers were continuously monitored for seven days and the data of sub-micron particle concentrations, activities performed, and inhalation rates were recorded. The received dose was calculated with the proposed method and compared with those obtained from different simplified methodologies that consider typical data of particle concentrations, time activity patterns and inhalation rates obtained from literature. The results show that, depending on the methodology used, the differences in the received daily dose can be significant, with a general underestimation of the most simplified method.

Ambient particulate matter source apportionment using receptor modelling in European and Central Asia urban areas

This work presents the results of a PM2.5 source apportionment study conducted in urban background sites from 16 European and Asian countries. For some Eastern Europe and Central Asia cities this was the first time that quantitative information on pollution source contributions to ambient particulate matter (PM) has been performed. More than 2200 filters were sampled and analyzed by X-Ray Fluorescence (XRF), Particle-Induced X-Ray Emission (PIXE), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentrations of chemical elements in fine particles. Samples were also analyzed for the contents of black carbon, elemental carbon, organic carbon, and water-soluble ions. The Positive Matrix Factorization receptor model (EPA PMF 5.0) was used to characterize similarities and heterogeneities in PM2.5 sources and respective contributions in the cities that the number of collected samples exceeded 75. At the end source apportionment was performed in 11 out of the 16 participating cities. Nine major sources were identified to have contributed to PM2.5: biomass burning, secondary sulfates, traffic, fuel oil combustion, industry, coal combustion, soil, salt and "other sources". From the averages of sources contributions, considering 11 cities 16% of PM2.5 was attributed to biomass burning, 15% to secondary sulfates, 13% to traffic, 12% to soil, 8.0% to fuel oil combustion, 5.5% to coal combustion, 1.9% to salt, 0.8% to industry emissions, 5.1% to "other sources" and 23% to unaccounted mass. Characteristic seasonal patterns were identified for each PM2.5 source. Biomass burning in all cities, coal combustion in Krakow/POL, and oil combustion in Belgrade/SRB and Banja Luka/BIH increased in Winter due to the impact of domestic heating, whereas in most cities secondary sulfates reached higher levels in Summer as a consequence of the enhanced photochemical activity. During high pollution days the largest sources of fine particles were biomass burning, traffic and secondary sulfates.

Estimation of the Inhaled Dose of Airborne Pollutants during Commuting: Case Study and Application for the General Population

During rush hours, commuters are exposed to high concentrations and peaks of traffic-related air pollutants. The aims of this study were therefore to extend the inhaled dose estimation outcomes from a previous work investigating the inhaled dose of a typical commuter in the city of Milan, Italy, and to extend these results to a wider population. The estimation of the dose of pollutants inhaled by commuters and deposited within the respiratory tract could be useful to help commuters in choosing the modes of transport with the lowest exposure and to increase their awareness regarding this topic. In addition, these results could provide useful information to policy makers, for the creation/improvement of a mobility that takes these results into account. The principal result outcomes from the first part of the project (case study on a typical commuter in the city of Milan) show that during the winter period, the maximum deposited mass values were estimated in the “Other” environments and in “Underground”. During the summer period, the maximum values were estimated in the “Other” and “Walking (high-traffic conditions)” environments. For both summer and winter, the lowest values were estimated in the “Car” and “Walking (low-traffic conditions)” environments. Regarding the second part of the study (the extension of the results to the general population of commuters in the city of Milan), the main results show that the period of permanence in a given micro-environment (ME) has an important influence on the inhaled dose, as well as the pulmonary ventilation rate. In addition to these results, it is of primary importance to report how the inhaled dose of pollutants can be strongly influenced by the time spent in a particular environment, as well as the subject’s pulmonary ventilation rate and pollutant exposure levels. For these reasons, the evaluation of these parameters (pulmonary ventilation rate and permanence time, in addition to the exposure concentration levels) for estimating the inhaled dose is of particular relevance.

Passive Exposure to Pollutants from a New Generation of Cigarettes in Real Life Scenarios

The use of electronic cigarettes (e-cigarettes) and heat-not-burn tobacco (HNBT), as popular nicotine delivery systems (NDS), has increased among adult demographics. This study aims to assess the effects on indoor air quality of traditional tobacco cigarettes (TCs) and new smoking alternatives, to determine the differences between their potential impacts on human health. Measurements of particulate matter (PM₁, PM_2.5 and PM₁₀), black carbon, carbon monoxide (CO) and carbon dioxide (CO₂) were performed in two real life scenarios, in the home and in the car. The results indicated that the particle emissions from the different NDS devices were significantly different. In the home and car, the use of TCs resulted in higher PM₁₀ and ultrafine particle concentrations than when e-cigarettes were smoked, while the lowest concentrations were associated with HNBT. As black carbon and CO are released by combustion processes, the concentrations of these two pollutants were significantly lower for e-cigarettes and HNBT because no combustion occurs when they are smoked. CO₂ showed no increase directly associated with the NDS but a trend linked to a higher respiration rate connected with smoking. The results showed that although the levels of pollutants emitted by e-cigarettes and HNBT are substantially lower compared to those from TCs, the new smoking devices are still a source of indoor air pollutants.