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

Unveiling the Impact of Wildfires on Nanoparticle Characteristics and Exposure Disparities through Mobile and Fixed-Site Monitoring in Toronto, Canada

This study investigates the impacts of wildfires on nanoparticle characteristics and exposure disparities in Toronto, integrating data from a large-scale mobile monitoring campaign and fixed-site measurements during the unprecedented 2023 wildfire season. Our results reveal changes in particle characteristics during wildfire days, with particle number concentrations decreasing by 60% and particle diameter increasing by 30% compared to nonwildfire days. Moreover, the median lung deposited surface area (LDSA) levels rose by 31% during wildfire events. We employed gradient boosting models to estimate near-road LDSA levels on both wildfire and nonwildfire days. The LDSA ratio (wildfire/nonwildfire) exceeded 2.0 in certain areas along highways and in downtown Toronto. Furthermore, our findings show that marginalized communities faced greater LDSA increases than less marginalized ones. Under wildfire conditions, the LDSA ratio difference between the most and least marginalized groups was 16% for recent immigrants and visible minorities and 7% for seniors and children, both statistically significant. This study delivers critical insights into the spatiotemporal variations of nanoparticle characteristics during wildfire and nonwildfire periods, demonstrating the substantial health risks posed by increased LDSA levels and the inequitable distribution of these risks among Toronto's diverse population.

Importance of sub-23 nm particles in traffic environments: Particle number emission factors and extrathoracic deposition doses

New research suggests that exposure to ultrafine particles (UFPs; particle diameter dp < 100 nm) is particularly harmful to brain health. One pathway into the body is via deposition in the respiratory system, where the smallest UFPs deposit efficiently in human extrathoracic airways. Traffic is a major source of these particles, yet sub-23 nm (dp < 23 nm) particles are currently unregulated in engine emission testing worldwide, including the stringent requirements of the European Union, nor are there requirements for ambient monitoring. In this study, we report size-resolved particle number emission factors (EFs) for traffic and estimates of extrathoracic dose rates of sub-23 nm particles. The EFs and dose rates are based on measurements conducted in different urban environments, including roads, tunnels, an airport, and a riverside, in two Central European cities (Düsseldorf and Prague) from March to April 2022. A key difference between the cities is that Düsseldorf has a low-emission zone in its central area and a newer vehicle fleet compared to Prague. Overall, traffic-influenced sites had large EFs for sub-23 nm particles. In the highway and tunnel environments, EFs of particles with dp > 2.5 nm were between 2 and 18 times greater than the EFs of particles with dp > 23 nm. Near the airport, the EF of particles with dp > 23 nm was already high, being 2-9 times higher than in other environments. The number concentrations of sub-23 nm particles varied significantly within the studied cities, and dose rates (measured in billions of particles per hour) differed by up to a factor of ten or more depending on the location.

Urban emissions of fine and ultrafine particulate matter in Los Angeles: Sources and variations in lung-deposited surface area

Airborne particulate matter (PM) in urban environments poses significant health risks by penetrating the respiratory system, with concern over lung-deposited surface area (LDSA) as an indicator of particle exposure. This study aimed to investigate the diurnal trends and sources of LDSA, particle number concentration (PNC), elemental carbon (EC), and organic carbon (OC) concentrations in Los Angeles across different seasons to provide a comprehensive understanding of the contributions from primary and secondary sources of ultrafine particles (UFPs). Hourly measurements of PNC and LDSA were conducted using the DiSCmini and Scanning Mobility Particle Sizer (SMPS), while OC and EC concentrations were measured using the Sunset Lab EC/OC Monitor. The results showed distinct diurnal trends in PNC and EC, with peaks occurring in the early morning and evening, which were consistent with periods of increased traffic volume. During warmer periods, a midday increase in PNC was observed, attributed to photochemical reactions. In contrast, a nighttime peak during colder months suggested the formation of secondary aerosols through aqueous-phase chemistry. Additionally, the DiSCmini consistently reported higher LDSA values than SMPS, indicating the presence of irregularly shaped UFPs, particularly during periods of heavy traffic flow. Positive Matrix Factorization (PMF) analysis identified three primary sources. Factor 1 (photochemically influenced processes), driven by secondary organic aerosol formation during warmer periods, contributed to 19% of LDSA. Factor 2, in which primarily traffic influenced emissions were the dominant contributor, accounting for 70% of LDSA and associated with high loadings of OC (61%), EC (78%), and NOx (94%). Factor 3 (aqueous phase secondary process influenced emissions) during colder months, accounted for 11% of LDSA. Both Factor 1 and 3 sources exhibited comparable contributions of OC4 (52% and 48%, respectively), underscoring their roles in secondary aerosol formation. These findings emphasize the need to address both primary and secondary emissions to mitigate health risks associated with UFP exposure.

Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends

Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, making it challenging to separate these effects. This study utilized valuable long-term particle number and size distribution (PNSD) data from 2018 to 2023 to develop a tree-based machine learning model enhanced with an interpretable component, incorporating temporal markers to characterize background or time series residuals. Our results demonstrated that, differing from PM2.5, which is significantly shaped by planetary boundary layer height, wind speed plays a crucial role in determining the particle number concentration (PNC), showing strong regional specificity. Furthermore, we systematically identified and analyzed anthropogenically influenced periodic trends. Notably, while Aitken mode observations are initially linked to traffic-related peaks, both Aitken and nucleation modes contribute to concentration peaks during rush hour periods on short-term impacts after deweather adjustment. Pollutant baseline concentrations are largely driven by human activities, with meteorological factors modulating their variability, and the secondary formation of UFPs is likely reflected in temporal residuals. This study provides a flexible framework for isolating meteorological effects, allowing more accurate assessment of anthropogenic impacts and targeted management strategies for UFP and PNC.

Exploring ultrafine particle emission characteristics from in-use light-duty diesel trucks in China using a portable measurement system

Diesel vehicle exhaust is one of the major contributors to ultrafine particles (UFPs) in urban areas in China. However, there is still a lack of knowledge about UFPs emission characteristics from current in-use diesel vehicles. This study has carried out an on-road test of 10 in-use Light-duty Diesel Trucks (LDDTs) with different emission control standards in China using a self-established portable measurement system based on the Electronic Low-pressure Impactor (ELPI) and characterized the ultrafine particle number (PN) concentration, particle size distribution and metal element contents. The results revealed a significant reduction of 93.37% in the average PN0.1 emission factor of LDDTs from China III to China VI. Notably, LDDTs compliant with the China VI vehicle emission control standard exhibited the lowest PN0.1 and PM0.1 emission factors, measuring 4.991 × 1014 #/km and 0.627 g/km, respectively. By taking into account emissions under real driving conditions, we found that the PN emission rates grow with the increase of the Vehicle Specific Power (VSP). The cold-start phase had higher PN emissions than the hot-start phase, with 8590, 1890, 477, and 22 times higher than those of the ambient air (1.18 × 105 #/cm3), respectively. The installation of Diesel Particulate Filter (DPF) can decrease UFPs by more than 99.8%, while the PN emission factor during the DPF regeneration stage (1.85 × 1016 #/km) increased by 5 orders of magnitude that of the DPF normal works (7.51 × 1011 #/km). Metal element contents analysis shows that Fe, Ca, Al and Mg are the dominant elements in UFPs of LDDT exhaust gas, but the element of Ni is slightly increasing in a China VI, possibly due to the new automotive engine exhaust manifolds being made of Ni instead of cast iron for the purpose of having more high-temperature resistance. Our study demonstrates the importance of monitoring and routine maintenance of exhaust after-treatment systems.

Source apportionment of ultrafine particles in urban Europe

There is a body of evidence that ultrafine particles (UFP, those with diameters ≤ 100 nm) might have significant impacts on health. Accordingly, identifying sources of UFP is essential to develop abatement policies. This study focuses on urban Europe, and aims at identifying sources and quantifying their contributions to particle number size distribution (PNSD) using receptor modelling (Positive Matrix Factorization, PMF), and evaluating long-term trends of these source contributions using the non-parametric Theil-Sen's method. Datasets evaluated include 14 urban background (UB), 5 traffic (TR), 4 suburban background (SUB), and 1 regional background (RB) sites, covering 18 European and 1 USA cities, over the period, when available, from 2009 to 2019. Ten factors were identified (4 road traffic factors, photonucleation, urban background, domestic heating, 2 regional factors and long-distance transport), with road traffic being the primary contributor at all UB and TR sites (56-95 %), and photonucleation being also significant in many cities. The trends analyses showed a notable decrease in traffic-related UFP ambient concentrations, with statistically significant decreasing trends for the total traffic-related factors of -5.40 and -2.15 % yr-1 for the TR and UB sites, respectively. This abatement is most probably due to the implementation of European emissions standards, particularly after the introduction of diesel particle filters (DPFs) in 2011. However, DPFs do not retain nucleated particles generated during the dilution of diesel exhaust semi-volatile organic compounds (SVOCs). Trends in photonucleation were more diverse, influenced by a reduction in the condensation sink potential facilitating new particle formation (NPF) or by a decrease in the emissions of UFP precursors. The decrease of primary PM emissions and precursors of UFP also contributed to the reduction of urban and regional background sources.

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.

Atmospheric new particle formation identifier using longitudinal global particle number size distribution data

Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

PM10-bound trace elements in pan-European urban atmosphere

Although many studies have discussed the impact of Europe's air quality, very limited research focused on the detailed phenomenology of ambient trace elements (TEs) in PM10 in urban atmosphere. This study compiled long-term (2013-2022) measurements of speciation of ambient urban PM10 from 55 sites of 7 countries (Switzerland, Spain, France, Greece, Italy, Portugal, UK), aiming to elucidate the phenomenology of 20 TEs in PM10 in urban Europe. The monitoring sites comprised urban background (UB, n = 26), traffic (TR, n = 10), industrial (IN, n = 5), suburban background (SUB, n = 7), and rural background (RB, n = 7) types. The sampling campaigns were conducted using standardized protocols to ensure data comparability. In each country, PM10 samples were collected over a fixed period using high-volume air samplers. The analysis encompassed the spatio-temporal distribution of TEs, and relationships between TEs at each site. Results indicated an annual average for the sum of 20 TEs of 90 ± 65 ng/m3, with TR and IN sites exhibiting the highest concentrations (130 ± 66 and 131 ± 80 ng/m3, respectively). Seasonal variability in TEs concentrations, influenced by emission sources and meteorology, revealed significant differences (p < 0.05) across all monitoring sites. Estimation of TE concentrations highlighted distinct ratios between non-carcinogenic and carcinogenic metals, with Zn (40 ± 49 ng/m3), Ti (21 ± 29 ng/m3), and Cu (23 ± 35 ng/m3) dominating non-carcinogenic TEs, while Cr (5 ± 7 ng/m3), and Ni (2 ± 6 ng/m3) were prominent among carcinogenic ones. Correlations between TEs across diverse locations and seasons varied, in agreement with differences in emission sources and meteorological conditions. This study provides valuable insights into TEs in pan-European urban atmosphere, contributing to a comprehensive dataset for future environmental protection policies.

Chemically Resolved Respiratory Deposition of Ultrafine Particles Characterized by Number Concentration in the Urban Atmosphere

Ultrafine particles (UFPs) dominate the atmospheric particles in number concentration, impacting human health and climate change. However, existing studies primarily rely on mass-based approaches, leading to a restricted understanding of the number-based and chemically resolved health effects of atmospheric UFPs. In this study, we utilized a high-mass-resolution single-particle aerosol mass spectrometer to investigate the online chemical composition and number size distribution of ultrafine, fine, and coarse particles during the summertime in urban Shenzhen, China. Human respiratory deposition dose assessments of particles with varying chemical compositions were further conducted by a respiratory deposition model. The results showed that during our observation, particles containing elemental carbon (EC) were the dominant components in UFPs (0.05-0.1 μm). Compared to fine and coarse particles, UFPs can deposit more deeply into the respiratory tract with a daily dose of ∼2.08 ± 0.67 billion particles. Among the deposited UFPs, EC-cluster particles constituted ∼85.7% in number fraction, accounting for a daily number dose of ∼1.78 billion particles, which poses a greater impact on human health. Simultaneously, we found discrepancies in the chemically resolved particle depositions among number-, surface area-, and mass-based approaches, emphasizing the importance of an appropriate metric for particle health-risk evaluation.

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.

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.