Lung Deposited Surface Area Research Articles

Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment

Currently, methodologies for the identification and apportionment of air pollution sources are not widely applied due to their high cost. We present a new approach, combining mobile measurements from multiple sensors collected from the daily walks of citizen scientists, in a high population density area of Birmingham, UK. The methodology successfully pinpoints the different sources affecting the local air quality in the area using only a handful of measurements. It was found that regional sources of pollution were mostly responsible for the PM2.5 and PM1 concentrations. In contrast, PM10 was mostly associated with local sources. The total particle number and the lung deposited surface area of PM were almost solely associated with traffic, while black carbon was associated with both the sources from the urban background and local traffic. Our analysis showed that while the effect of the hyperlocal sources, such as emissions from construction works or traffic, do not exceed the distance of a couple of hundred meters, they can influence the health of thousands of people in densely populated areas. Thus, using this novel approach we illustrate the limitations of the present measurement network paradigm and offer an alternative and versatile approach to understanding the hyperlocal factors that affect urban air quality. Mobile monitoring by citizen scientists is shown to have huge potential to enhance spatiotemporal resolution of air quality data without the need of extensive and expensive campaigns.

Vehicle emission models alone are not sufficient to understand full impact of change in traffic signal timings

Few studies have considered the real-world impact of changes in traffic signal timings on air pollution and pedestrian exposure with most only drawing their conclusion from vehicle emission models alone. Here, we consider two distinct cycle timings at a junction in London, UK, model the impact using a traffic microsimulation and a NOx emissions model, and compare these results with NOx and other air pollution measurements collected during a two-week field study at the junction.Our models predict that extending the cycle time leads to a 23% decrease in NOx emissions within a 15 m radius of the junction itself. When the wind direction was such that our sensors were downwind of the junction a 21% decrease in traffic and background-adjusted NOx concentrations were seen, suggesting that the intervention was successful. However, when the sensors were upwind of the junction, we observed an increase of 23% in adjusted NOx concentrations. Similar patterns were found for the other pollutants NO2, lung deposited surface area, black carbon and CO2 we measured. This indicates that meteorology was by far the greatest determinant of roadside concentrations during our two-week study period.Looking at pedestrian exposure for pedestrians waiting to cross the road, we found that their NOx exposure increased by 46% as waiting times to cross the road increased and that potential small reductions in air pollution were offset by increases in waiting times on the main road.The study demonstrates the need to go beyond assessing the impact of hyper-local traffic interventions on vehicle emissions. Real-world trials over extended periods are required to evaluate the impact of meteorology and changes to air pollution concentrations and pedestrian exposures.

Commuter exposure to ultrafine particles (UFPs), lung deposited surface area (LDSA), and noise in Ho Chi Minh City, Vietnam

IntroductionTransport is a main contributor to air and noise pollution, particularly in urban areas with the high number of motorized vehicles. Promoting active transport (i.e., cycling and walking) and public transport is an instrumental factor in reducing traffic-related air and noise pollution. However, commuters of these transport modes are often exposed to high air and noise pollution than other commuter groups. MethodsThe levels of exposure to UFP, LDSA, and noise during commuting of different transport modes in Ho Chi Minh City (HCMC) were measured and compared. Then, the dose of inhaled pollutants during commuting was calculated for each commuter group by considering the travel time and minute ventilation. ResultsThe level of exposure to air and noise pollution varies across transport modes. Cyclists and motorcyclists were exposed to the highest level of air and noise pollution. Motorcyclists and cyclists were exposed to the highest UFP number concentrations during their trips, 3.6*105 and 3.1*105 particles/cm3, respectively. Car commuters were exposed to the lowest air and noise pollution level, followed by bus commuters. When considering the minute ventilation and travel time, cyclists inhaled significantly higher levels of air pollution than other commuters, followed by pedestrians, motorcyclists, bus commuters, and car users. As a result, the health impacts of exposure to traffic-related air and noise pollution for active commuters could be greater than other commuter groups. ConclusionsPromoting active and public transport is a promising approach to improving air quality and reducing congestion in HCMC. Using these transport modes also has potential positive health effects on commuters through physical activities. However, active commuters are faced with the highest risk of exposure to high air and noise pollution levels. To promote active transport and public transport in HCMC, measures to reduce the level of exposure to air and noise pollution for these commuter groups are needed.

Investigation into personal exposure to ultrafine particle (UFP) in Phnom Penh, Cambodia: A pilot study

This pilot study aimed to examine personal exposure to ultrafine particles (UFP or particle smaller than 100 nm) in Phnom Penh, Cambodia through the assessment of different transportation modes, travel routes, and the comparison of indoor and outdoor activities. A handheld UFP counter was used to record the UFP size, concentration and as well as Lung Deposited Surface Area (LDSA). GPS data was recorded using a GPS watch and then combined with the UFP data before further analysis with geospatial software (QGIS) along with OpenStreetMap to generate spatial data of personal exposure to UFP. The study revealed significant differences in UFP exposure between motorbike and bus users, with average UFP concentrations of approximately 114,300 and 171,700 particles/cm³ for motorbike users, and 22,600 and 37,200 particles/cm³ for bus users on lines 3 and 4A, respectively. These concentrations exceed WHO guidelines, which consider UFP levels above 20,000 particles/cm³ per hour as high and potentially harmful. The results of our study also revealed varying amounts of exposure on different roadways, with Russian Federation Blvd consistently exhibited the highest concentrations of UFP, especially during periods of peak traffic. Furthermore, indoor environments generally presented lower UFP exposure. Specific activities or events were found to generate temporary increases in particle concentration. It was shown that weekdays had elevated levels of UFP compared to weekends. Regardless of the limitations of the study, especially its small sample size, it provides significant initial observations, highlighting the urgent need for mitigation strategies and further comprehensive studies in the region.

Black carbon and particle lung-deposited surface area in residential wood combustion emissions: Effects of an electrostatic precipitator and photochemical aging

Residential wood combustion (RWC) remains a significant global source of particulate matter (PM) emissions with adverse impacts on regional air quality, climate, and human health. The lung-deposited surface area (LDSA) and equivalent black carbon (eBC) concentrations have emerged as important metrics to assess particulate pollution. In this study we estimated combustion phase-dependent emission factors of LDSA for alveolar, tracheobronchial, and head-airway regions of human lungs and explored the relationships between eBC and LDSA in fresh and photochemically aged RWC emissions. Photochemical aging was simulated in an oxidative flow reactor at OH• exposures equivalent to 1.4 or 3.4 days in the atmosphere. Further, the efficiency of a small-scale electrostatic precipitator (ESP) for reducing LDSA and eBC from the wood stove was determined. For fresh emission eBC correlated extremely well with LDSA, but the correlation decreased after aging. Soot-dominated flaming phase showed the highest eBC dependency of LDSA whereas for ignition and char burning phases non-BC particles contributed strongly the LDSA. Deposition to the alveolar region contributed around 60 % of the total lung-deposition. The ESP was found as an effective method to mitigate particulate mass, LDSA, as well as eBC emissions from wood stoves, as they were reduced on average by 72%, 71%, and 69%, respectively. The reduction efficiencies, however, consistently dropped over the span of an experiment, especially for eBC. Further, the ESP was found to increase the sub-30 nm ultrafine particle number emissions, with implications for LDSA. The results of this study can be used for assessing the contribution of RWC to LDSA concentrations in ambient air.

Assessment of air quality in the Philadelphia, Pennsylvania subway.

Subways are popular and efficient modes of transportation in cities. However, people are exposed to high levels of particulate matter (PM) in subways. Subway air quality in the United States has been investigated in a few cities, but data is lacking on simultaneous measurement of several pollutants, especially ultrafine particles (UFP) and black carbon (BC), in combination with different size fractions of PM. The goals of this study are to assess air quality in a belowground subway and compare it with outdoor ambient levels, to examine temporal variability of PM in the subway, and to analyze the correlation between PM and BC. Particulate matter of varying sizes (PM1, PM2.5, PM10), UFP, and BC were measured using DustTrak, nanoparticle detector, and micro aethalometer, respectively. Measurements were made at the belowground subway platform and the aboveground street level at 15th Street subway station in Philadelphia during summer 2022. Belowground mean PM1, PM2.5, and PM10 were 112.2 ± 61.3 µg/m3, 120 ± 65.5 µg/m3, and 182.1 ± 132 µg/m3, respectively, which were 5.4, 5.7, and 7.6 times higher than the respective aboveground street levels. The UFP lung deposited surface area (LDSA) (59.4 ± 36.2 µm2/cm3) and BC (9.5 ± 5.4 μg/m3) belowground were 1.7 times and 10.7 times higher than the aboveground. The pollutant concentration varied from day-to-day on both the locations. A higher positive correlation was found between the belowground BC and PM2.5 (r = 0.51, p < 0.05) compared to the aboveground (r = 0.16, p < 0.05). This study showed high levels of particulate matter exposure at a belowground subway station in Philadelphia. Particulate matter levels were about 5 to 8 times higher at belowground subway station than the corresponding aboveground street level. Higher levels were also observed for UFP lung deposited surface area (LDSA), while black carbon levels showed the highest concentration at the belowground level by a factor of ten compared to the aboveground level. The study shows the need for air quality management at belowground subways to reduce particulate matter exposure for the commuters.

Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 μm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 μm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100–150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.

High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery.

In this study, we propose a novel long short-term memory (LSTM) neural network model that leverages color features (HSV: hue, saturation, value) extracted from street images to estimate air quality with particulate matter (PM) in four typical European environments: urban, suburban, villages, and the harbor. To evaluate its performance, we utilize concentration data for eight parameters of ambient PM (PM1.0, PM2.5, and PM10, particle number concentration, lung-deposited surface area, equivalent mass concentrations of ultraviolet PM, black carbon, and brown carbon) collected from a mobile monitoring platform during the nonheating season in downtown Augsburg, Germany, along with synchronized street view images. Experimental comparisons were conducted between the LSTM model and other deep learning models (recurrent neural network and gated recurrent unit). The results clearly demonstrate a better performance of the LSTM model compared with other statistically based models. The LSTM-HSV model achieved impressive interpretability rates above 80%, for the eight PM metrics mentioned above, indicating the expected performance of the proposed model. Moreover, the successful application of the LSTM-HSV model in other seasons of Augsburg city and various environments (suburbs, villages, and harbor cities) demonstrates its satisfactory generalization capabilities in both temporal and spatial dimensions. The successful application of the LSTM-HSV model underscores its potential as a versatile tool for the estimation of air pollution after presampling of the studied area, with broad implications for urban planning and public health initiatives.

Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine

Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm2/cm3 and reached maximum peaks of 2140 μm2/cm3 during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm2/cm3 and 1840 μm2/cm3, respectively. At the same time, average PM1 was about 300 μg/ m3 during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m3. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects.

Ultrafine Particles on Electric, Gas, and Diesel Buses in the Mass Transit Buses of Bogotá, Colombia

The concentration of traffic-related air pollutants (TRAPs) inside transport microenvironments takes on relevance today in many megacities with high population density, intense traffic, and prolonged travel times. These conditions can intensify exposure to TRAPs and worsen public health problems. TRAP concentrations in these microenvironments are changing because of the introduction of cleaner technologies. In this study, we compare the ultrafine particles measured inside diesel, gas, and electric buses during their normal operation in Bogotá, Colombia. We used a miniature diffusion size classifier (DiSCmini) to measure ultrafine particle concentrations, average particle size, and the lung-deposited surface area. Our results revealed significantly lower levels of this pollutant inside electric buses. The concentration of ultrafine particles per cubic centimeter is about 41% and 27% lower in electric buses compared to diesel and gas, respectively. The lung-deposited surface area is also lower in electric buses. Nevertheless, the average particle size in electric buses is 10% and 18% smaller compared to diesel and gas, respectively.

Investigating the invisible threat: An exploration of air exchange rates and ultrafine particle dynamics in hospital operating rooms

The air exchange rate (AER), defined as the number of times the air is fully replaced per hour in an indoor space, is an important parameter affecting indoor air quality (IAQ). Hospital operation rooms (ORs), one of the indoor environments with relatively high health risks, should maintain high AERs to quickly remove surgical smoke during surgery. This study aimed to investigate the characteristics and transmission of pollutants in ORs through on-site measurements and numerical simulations. To determine actual AER (AERactual), both exponential decay and concentration variations, resulting in a more precise representation of indoor air pollutant residence time, were considered. Our results revealed that gaseous pollutants exhibited lower AERactual values compared to particulate pollutants due to their higher diffusibility. Notably, the AERactual of total volatile organic compounds (TVOC) was found to be greater than that of carbon dioxide (CO2) due to the higher reactivity of TVOC. Furthermore, this study also investigated the characteristics of surgical smoke particles and observed variations in emission factors (EF) depending on the surgical procedure, ranging from approximately 1010 to 1011 particles per minute. Moreover, coarse particles (>2.5 μm) were primarily influenced by drag forces, resulting in lower AERactual values. Additionally, the AERactual of ultrafine particles (UFPs) was examined, revealing that smaller particles exhibited lower AERactual values, potentially due to stronger Brownian motion. By combining measurement and simulation analysis, the spatial distribution of lung deposited surface area (LDSA) concentrations could be evaluated, providing accurate exposure concentrations for health risk assessments.

Particle lung deposited surface area (LDSAal) size distributions in different urban environments and geographical regions: Towards understanding of the PM2.5 dose–response

Recent studies indicate that monitoring only fine particulate matter (PM2.5) may not be enough to understand and tackle the health risk caused by particulate pollution. Health effects per unit PM2.5 seem to increase in countries with low PM2.5, but also near local pollution sources (e.g., traffic) within cities. The aim of this study is to understand the differences in the characteristics of lung-depositing particles in different geographical regions and urban environments. Particle lung deposited surface area (LDSAal) concentrations and size distributions, along with PM2.5, were compared with ambient measurement data from Finland, Germany, Czechia, Chile, and India, covering traffic sites, residential areas, airports, shipping, and industrial sites. In Finland (low PM2.5), LDSAal size distributions depended significantly on the urban environment and were mainly attributable to ultrafine particles (<100 nm). In Central Europe (moderate PM2.5), LDSAal was also dependent on the urban environment, but furthermore heavily influenced by the regional aerosol. In Chile and India (high PM2.5), LDSAal was mostly contributed by the regional aerosol despite that the measurements were done at busy traffic sites. The results indicate that the characteristics of lung-depositing particles vary significantly both within cities and between geographical regions. In addition, ratio between LDSAal and PM2.5 depended notably on the environment and the country, suggesting that LDSAal exposure per unit PM2.5 may be multiple times higher in areas having low PM2.5 compared to areas with continuously high PM2.5. These findings may partly explain why PM2.5 seems more toxic near local pollution sources and in areas with low PM2.5. Furthermore, performance of a typical sensor based LDSAal measurement is discussed and a new LDSAal2.5 notation indicating deposition region and particle size range is introduced. Overall, the study emphasizes the need for country-specific emission mitigation strategies, and the potential of LDSAal concentration as a health-relevant pollution metric.

Effect of radiation interaction and aerosol processes on ventilation and aerosol concentrations in a real urban neighbourhood in Helsinki

Abstract. Large-eddy simulation (LES) is an optimal tool to examine aerosol particle concentrations in detail within urban neighbourhoods. The concentrations are a complex result of local emissions, meteorology, aerosol processes and local mixing conditions due to thermal and mechanical effects. Despite this, most studies have focused on simplification of the affecting processes such as examining the impact of local mixing in idealised street canyons or treating aerosols as passive scalars. The aim of this study is to include all these processes into LES using the PALM model system and to examine the importance of radiative heating and aerosol processes in simulating local aerosol particle concentrations and different aerosol metrics within a realistic urban neighbourhood in Helsinki under morning rush hour with calm wind conditions. The model outputs are evaluated against mobile laboratory measurements of air temperature and total particle number concentration (Ntot) as well as drone measurements of lung-deposited surface area (LDSA). The inclusion of radiation interaction in LES has a significant impact on simulated near-surface temperatures in our study domain, increasing them on average from 8.6 to 12.4 ∘C. The resulting enhanced ventilation reduces the pedestrian-level (4 m) Ntot by 53 %. The reduction in Ntot due to aerosol processes is smaller, only 18 %. Aerosol processes particularly impact the smallest particle range, whereas radiation interaction is more important in the larger particle range. The inclusion of radiation interaction reduces the bias between the modelled and mobile-laboratory-measured air temperatures from −3.9 to +0.2 ∘C and Ntot from +98 % to −13 %. With both aerosol and radiation interaction on, the underestimation is 16 %, which might be due to overestimation of the ventilation. The results show how inclusion of radiative interaction is particularly important in simulating PM2.5, whereas aerosol processes are more important in simulating LDSA in this calm wind situation.

Particle number, mass, and surface area concentrations inside an underground metalliferous mine in Kazakhstan

Considerable scientific evidence shows that inhalation of diesel exhaust particles is associated with a wide range of adverse health effects. This study examined the concentrations of diesel particles in five stations of the operational phases of an underground mine in Kazakhstan. Real-time monitoring of particulate number concentration (PNC), lung deposited surface area (LDSA) concentrations, PM1, PM2.5, and PM10 concentrations in the mining operational area, and the breathing zone of the loader driver inside the loader cabin was conducted. The results showed that the highest average PNC and LDSA concentrations were 7 × 105 cm−3 and 4 × 103 μm2 cm−3, respectively, and most of the particles were in the sub-100 nm range. The concentrations level in the loader cabin area (LA), and operational area (OA) of mine was similar with respect to PM1 and PM2.5 as a result of the homogenous distribution of the PM inside the mine's operational phase. The major source of the PM1 and PM2.5 was the diesel engine, while the low LA/OA ratio for PM10 in this study suggested the source of the coarse particles was dust resuspension around the loader cabin.

Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe

This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017–2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal patterns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated. The result showed that the peak concentrations of LDSA at UB and TR sites are commonly observed in the morning (06:00–8:00 UTC) and late evening (19:00–22:00 UTC), coinciding with traffic rush hours, biomass burning, and atmospheric stagnation periods. The only LDSA night-time peaks are observed on weekends. Due to the variability of emission sources and meteorology, the seasonal variability of the LDSA concentration revealed significant differences (p = 0.01) between the four seasons at all monitoring sites. Meanwhile, the correlations of LDSA with other pollutant metrics suggested that Aitken and accumulation mode particles play a significant role in the total LDSA concentration. The results also indicated that the main proportion of total LDSA is attributed to the ALV fraction (50 %), followed by the TB (34 %) and HA (16 %). Overall, this study provides valuable information of LDSA as a predictor in epidemiological studies and for the first time presenting total LDSA in a variety of European urban environments.

Comprehensive characterization of wintertime submicron aerosol in a Nordic town influenced by residential wood combustion, traffic and industrial sources

Anthropogenic particulate matter with sizes smaller than 1 μm (PM1) is a concerning air pollutant that can affect human health. In this study, we present PM1 measurements performed in a small town in northern Finland that is exposed to contrasting sources (residential wood burning, traffic, industrial activities). The study was conducted in winter 2021, with a mobile laboratory equipped with sophisticated on-line aerosol instrumentation. The results showed a significant increase in particulate mass and number concentrations from biomass burning in residential areas and town centre due to the high share of residences using biomass burning. Organics and equivalent black carbon (eBC) clearly dominated the PM1 composition during the highest pollution levels, followed by inorganics (sulfate, nitrate and ammonium). PAHs and a few elements (e.g. K, Cl, Zn) were as well higher during evening. A source apportionment confirmed the association between high PM1 values and biomass burning, even though the traffic contribution was also important. PM1 measurements at an industrial area showed an increase in sulfate, organics, eBC, and a few elements (e.g. Cl, Na, Fe), and characteristic size distributions. Simultaneous measurements of lung deposited surface area (LDSA) of particles showed the source-specificity of biomass burning, traffic and industrial emissions on LSDA size distributions. Overall, the results enlighten the impact of relevant pollution sources on Nordic towns air quality during the coldest months and show the importance to also consider the chemical composition, particle numbers, and LDSA in future air quality metrics.

Spatiotemporal Heterogeneity of Lung-Deposited Surface Area in Zurich Switzerland: Lung-Deposited Surface Area as a New Routine Metric for Ambient Particle Monitoring.

Objective: To assess the spatiotemporal heterogeneity of lung-deposited particle surface area concentration (LDSA), while testing the long-term performance of a prototype of low-cost-low-maintenance LDSA sensors. One factor hampering epidemiological studies on fine to ultrafine particles (F-to-UFP) exposure is exposure error due to their high spatiotemporal heterogeneity, not reflected in particle mass. Though LDSA shows consistent associations between F-to-UFP exposure and health effects, LDSA data are limited. Methods: We measured LDSA in a network of ten sensors, including urban, suburban, and rural environments in Zurich, Switzerland. With traffic counts, traffic co-pollutant concentrations, and meteorological parameters, we assessed the drivers of the LDSA observations. Results: LDSA reflected the high spatiotemporal heterogeneity of F-to-UFP. With micrometeorological influences, local sources like road traffic, restaurants, air traffic, and residential combustion drove LDSA. The temporal pattern of LDSA reflected that of the local sources. Conclusion: LDSA may be a viable metric for inexpensively characterizing F-to-UFP exposure. The tested devices generated sound data and may significantly contribute to filling the LDSA exposure data gap, providing grounds for more statistically significant epidemiological studies and regulation of F-to-UFP.

144 Occupational Exposure to Particles Measured by Nanoparticles Tracking Analysis (NTA) in Exhaled Breath Condensate (EBC).

Abstract The production of paints, coatings and construction materials may generate nanoparticles (NP) with potential adverse health effects. We carried out a study aimed at investigating exposure biomarkers reflecting the internal dose of inhaled particles and NP. NTA was used to quantify particle number concentration and size in EBC collected at the end of shift in 81 workers employed in companies producing technological materials. External exposure was characterized in terms of particle number concentration, size and lung deposited surface area (LDSA) in the near operational field by using a set of DiSCmini. Three subgroups were identified: Low-Exposure (LE, n=30), High-Exposure (HE n=35), and non-occupationally exposed (UC, n=16). Comparisons between groups were made using non-parametric analyses. LE and HE workers showed significantly higher number of NP in EBC (p=0.024, p=0.041, respectively) as compared to UC. The HE groups showed higher levels of inflammatory biomarkers, such as IL-1β and IL-10 as compared to LE and UC (p=0.02, p&amp;lt;0.01, respectively). Positive correlations were observed between particle number concentration recorded by DiscMini and NTA data (Rho=0,225, p=0.04) and between the NTA and IL-10 and IL-1β (Rho=0.246, p=0.012; Rho=0.223, p=0.022, respectively). In contrast, LDSA did not correlate with NTA data. These findings provide evidence of the relationship between increasing exposure to NP and the burden of particles in the airways which, in turn, results in sustained inflammation. Given the lack of exposure biomarkers for dusts, these results are promising to investigate the relationship between internal dose of particles and changes in lung pathobiology.

142 Short-Term Exposure to Nanomaterials and Effect Biomarkers at Local and Systemic Levels: dose-Response Modelling Results from the Nanoexplore Project

Abstract We aimed at analyzing dose-response relationship between occupational exposure to a mixture of nanomaterials (NMs) and effect biomarkers measured according to the NanoExplore protocol in exhaled breath condensate (EBC, local pulmonary level)) and urine (systemic level) of 141 workers. Two exposure metrics: particle number concentration (PNC) and lung deposited surface area (LDSA) were monitored using DiSCMini for 2-4 8h-workshifts. Biological samples were collected twice, before and after exposure monitoring. We assumed that current or short-term exposure to NMs is associated in a linear dose-response manner to the effect biomarker concentration. We analyzed each biomarker separately, using the multilevel mixed-effects interval regression models to properly manage the biomarker concentrations the LOD or in the interval between the LOD and the limit of quantification. We used directed acyclic graphs to identify all potential confounders: age, smoking status, medication status, and study center. Center and the participant’s ID were modeled as random effect variables. For IL-10, IL-1β and TNF-α in EBC we observed consistent positive dose-response relationships with both PNC and LDSA. For urinary Total Antioxidant Power the relationship was consistently negative. For MDA, KL-6, hs-CRP or NOTyr in EBC and urinary MDA and 8-Isoprostane, no relationship was found. These results suggest an activation of the innate immune response rather than oxidative stress as the main effect of the mixture of NM investigated. This study offers new insights into the dose-response relationship between exposure to NMs and possible indicators of early and long-term biological alterations in the cohort of recruited workers.

145 Cumulative Occupational Exposure to Nanoparticles, Inflammatory Biomarkers and Respiratory Health in the Nanoexplore Cohort

Abstract Exposure to nanomaterials (NMs) may harm biological systems by altering the redox status and triggering inflammatory processes, which in turn may affect respiratory health. In the NanoExplore cohort of 141 workers, we observed a positive dose-response relationship between the short-term exposure to NMs and biomarkers of inflammation, measured in Exhaled Breath Condensate (EBC), but no relationship with lung function parameters (LFP). This study focuses on the association between cumulative exposure to NMs over the last 10 years and changes in LFP and the potential mediating role of airways inflammation. Individual cumulative exposure was estimated by multiplying the nanoparticle concentration, measured at workers’ workstations by DiSCmini, by the duration of worker’s employment at the corresponding workstation up to last 10 years. In EBC, we measured High-Sensitivity CRP (Hs-CRP), IL-1β, TNF-α, and IL-10 as biomarkers of inflammation. LFP were assessed according to ATS/ERS standards. Their association with cumulative exposure was examined using logistic regression models adjusted for tobacco consumption (in pack-years), among 136 workers with complete data (19-62 yrs., 70% males). We found that per unit increase in 10-year exposure, the risk of observing FEV1 values below the normal limit increases by 0.3% (95%CI: 1.000-1.005, p=0.049) and by 0.8% (95%CI: 1.000-1.015, p=0.039) with the exposure expressed as number of particles and lung deposited surface area, respectively. The ongoing mediation analysis will investigate the potential mediating role of inflammation in the association between cumulative exposure to NMs and lung function