Outdoor ultrafine particle concentrations in front of fast food restaurants

Indoor Air Sources of Outdoor Air Pollution: Health Consequences, Policy, and Recommendations: An Official American Thoracic Society Workshop Report

Indoor sources of air pollution worsen indoor and outdoor air quality. Thus, identifying and reducing indoor pollutant sources would decrease both indoor and outdoor air pollution, benefit public health, and help address the climate crisis. As outdoor sources come under regulatory control, unregulated indoor sources become a rising percentage of the problem. This American Thoracic Society workshop was convened in 2022 to evaluate this increasing proportion of indoor contributions to outdoor air quality. The workshop was conducted by physicians and scientists, including atmospheric and aerosol scientists, environmental engineers, toxicologists, epidemiologists, regulatory policy experts, and pediatric and adult pulmonologists. Presentations and discussion sessions were centered on 1) the generation and migration of pollutants from indoors to outdoors, 2) the sources and circumstances representing the greatest threat, and 3) effective remedies to reduce the health burden of indoor sources of air pollution. The scope of the workshop was residential and commercial sources of indoor air pollution in the United States. Topics included wood burning, natural gas, cooking, evaporative volatile organic compounds, source apportionment, and regulatory policy. The workshop concluded that indoor sources of air pollution are significant contributors to outdoor air quality and that source control and filtration are the most effective measures to reduce indoor contributions to outdoor air. Interventions should prioritize environmental justice: Households of lower socioeconomic status have higher concentrations of indoor air pollutants from both indoor and outdoor sources. We identify research priorities, potential health benefits, and mitigation actions to consider (e.g., switching from natural gas to electric stoves and transitioning to scent-free consumer products). The workshop committee emphasizes the benefits of combustion-free homes and businesses and recommends economic, legislative, and education strategies aimed at achieving this goal.

Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area

We performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20-400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM_2.5), black carbon (BC), and nitrogen dioxide (NO₂) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM_2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO₂. Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 μm² cm^-3) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 μm² cm^-3) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO₂) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM_2.5.

Towards healthy school neighbourhoods: A baseline analysis in Greater London

Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5-10 min walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO₂ and PM_2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The neighbourhood of the most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m³ of NO₂, 19.3 µg/m³ of PM_2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The neighbourhood of the least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m³ of NO₂, 15.1 µg/m³ of PM_2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.

Indoor and Outdoor Nanoparticle Concentrations in an Urban Background Area in Northern Sweden: The NanoOffice Study

In recent years, nanoparticles (NPs) have received much attention due to their very small size, high penetration capacity, and high toxicity. In urban environments, combustion-formed nanoparticles (CFNPs) dominate in particle number concentrations (PNCs), and exposure to those particles constitutes a risk to human health. Even though fine particles (<2.5 µm) are regularly monitored, information on NP concentrations, both indoors and outdoors, is still limited. In the NanoOffice study, concentrations of nanoparticles (10–300 nm) were measured both indoors and outdoors with a 5-min time resolution at twelve office buildings in Umeå. Measurements were taken during a one-week period in the heating season and a one-week period in the non-heating season. The measuring equipment SMPS 3938 was used for indoor measurements, and DISCmini was used for outdoor measurements. The NP concentrations were highest in offices close to a bus terminal and lowest in offices near a park. In addition, a temporal effect appeared, usually with higher concentrations of nanoparticles found during daytime in the urban background area, whereas considerably lower nanoparticle concentrations were often present during nighttime. Infiltration of nanoparticles from the outdoor air into the indoor air was also common. However, the indoor/outdoor ratios (I/O ratios) of NPs showed large variations between buildings, seasons, and time periods, with I/O ratios in the range of 0.06 to 0.59. The reasons for high indoor infiltration rates could be NP emissions from adjacent outdoor sources. We could also see particle growth since the indoor NPs were, on average, almost twice as large as the NPs measured outdoors. Despite relatively low concentrations of NPs in the urban background air during nighttime, they could rise to very high daytime concentrations due to local sources, and those particles also infiltrated the indoor air.

Associations between socioeconomic status and ultrafine particulate exposure in the school commute: An environmental inequality study for Toronto, Canada

Ultrafine particulate matter (UFP) air pollution is unevenly distributed across urban environments. Disparities in routine activity patterns, such as the exposure risk we face at work or on the commute, can contribute to chronic exposure-related health outcomes that place excess burdening on vulnerable population groups. In Canada, there is disagreement in the literature on the nature of these exposure-related inequalities, and our understanding of disparities associated with specific activity patterns such as commuting is limited. In the context of UFP specific exposure, these relationships are almost entirely unexplored in the environmental inequality literature. Our study presents an exploratory analysis of UFP exposure patterns in Toronto, Canada. We examined UFP dosage disparities experienced by children during routine school commutes. We estimated single trip dosages that accounted for variation in ambient UFP concentration, route morphology (distance, slope) and their effect on inhalation rate and trip duration. We aggregated these values at the dissemination-area level and collected socioeconomic status descriptors from the 2016 census. Our OLS model showed significant spatial autocorrelation (MI = 0.59, p < 0.001), and we instead applied a spatial error model to account for spatial effects in our dataset. We identified significant associations related to median income (β = -0.087, p < 0.05), government transfer dependence (β = -0.107, p < 0.005), immigration status (β = 0.119, p < 0.001), and education rates (β = -0.059, p < 0.05). Our results diverged from other pollutants in Toronto-based literature and could indicate that UFPs exhibit unique patterns of inequality. Our findings suggest a need to further study UFP dosage from an environmental inequality perspective.

Development and transferability of ultrafine particle land use regression models in London

Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm^-3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016-2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R² ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R² ranging from -18 to 0. MSE-R² was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.

A gradient boost approach for predicting near-road ultrafine particle concentrations using detailed traffic characterization

This study investigates the influence of meteorology, land use, built environment, and traffic characteristics on near-road ultrafine particle (UFP) concentrations. To achieve this objective, minute-level UFP concentrations were measured at various locations along a major arterial road in the Greater Toronto Area (GTA) between February and May 2019. Each location was visited five times, at least once in the morning, mid-day, and afternoon. Each visit lasted for 30 min, resulting in 2.5 h of minute-level data collected at each location. Local traffic information, including vehicle class and turning movements, were processed using computer vision techniques. The number of fast-food restaurants, cafes, trees, traffic signals, and building footprint, were found to have positive impacts on the mean UFP, while distance to the closest major road was negatively associated with UFP. We employed the Extreme Gradient Boosting (XGBoost) method to develop prediction models for UFP concentrations. The Shapley additive explanation (SHAP) measures were used to capture the influence of each feature on model output. The model results demonstrated that minute-level counts of local traffic from different directions had significant impacts on near-road UFP concentrations, model performance was robust under random cross-validation as coefficients of determination (R²) ranged from 0.63 to 0.69, but it revealed weaknesses when data at specific locations were eliminated from the training dataset. This result indicates that proper cross-validation techniques should be developed to better evaluate machine learning models for air quality predictions.

Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

This study develops a set of algorithms to extract built environment features from Google aerial and street view images, reflecting the microcharacteristics of an urban location as well as the different functions of buildings. These features were used to train a Bayesian regularized artificial neural network (BRANN) model to predict near-road air quality based on measurements of ultrafine particles (UFPs) and black carbon (BC) in Toronto, Canada. The resulting models [adjusted R² of 75.87 and 79.10% for UFP and BC and root mean squared error (RMSE) of 21,800 part/cm³ and 1300 ng/m³ for UFP and BC] were compared with similar ANN models developed using the same predictors, but extracted from traditional geographic information system (GIS) databases [adjusted R² of 58.74 and 64.21% for UFP and BC and RMSE values of 23,000 part/cm³ and 1600 ng/m³ for UFP and BC]. The models based on feature extraction exhibited higher predictive power, thus highlighting the greater accuracy of the proposed methods compared to GIS layers that are solely based on aerial images. A comparison with other neural network approaches as well as with a traditional land-use regression model demonstrates the strength of the BRANN model for spatial interpolation of air quality.

The contribution of moped emissions to ultrafine and fine particle concentrations on bike lanes

Although not emitting air pollution themselves, cyclists are exposed to air pollution from motorised traffic. Because of the close proximity of mopeds to cyclists, moped emissions may affect cyclist exposure. However, the quantitative contribution of mopeds to cyclists' air pollution exposures is uncertain. The aim of the research was to quantify the contribution of moped emissions to air pollution concentrations on bike lanes. Measurements of Particle Number Concentrations (PNC),¹ Particulate Matter (PM)² and Black Carbon (BC)³ on bike lanes were performed in September 2016 in four Dutch cities. Passing two- and four-stroke mopeds and other traffic were recorded and distinguished by sound by the trained field worker. One-second PNC, PM and one-minute BC concentrations were measured. Using regression analyses the contribution of passing mopeds to air pollution exposure was analysed. At 18 non-tunnel locations, two-stroke and four-stroke mopeds contributed at average 12,000 and 3000 pt./cm³ PNC per second when passing by, respectively. In a tunnel, this was 92,000 and 12,000 pt./cm³. Two- and four-strokes added 3 to 19% to total PNC at non-tunnel sites and 58% in a tunnel. Four-strokes caused at average 54% of the contribution of moped emissions to total PNC. At non-tunnel sites, the contribution of mopeds to PM was 1.2 and 0.2 μg/m³ for two- and four-strokes, respectively. In a tunnel this was 3.9 and 2.3 μg/m³. Minute-measurements of BC did not show a relation between mopeds passing by and BC. Mopeds caused substantial short-term increases in air pollution levels on bike lanes, especially in a tunnel. Two-stroke mopeds caused higher concentration peaks than four-stroke mopeds. The contribution to total air pollution concentrations of four-stroke mopeds was larger, because of the higher share of four-strokes. Because of the close proximity of mopeds to cyclists, cyclists air pollution exposure can be largely influenced by moped emissions.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling

This study presents land-use regression (LUR) models for submicron particulate matter (PM₁) components from an urban area. Models are presented for mass concentrations of inorganic species (SO₄, NO₃, NH₄), organic aerosol (OA) factors, and total PM₁. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km² area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R² = 0.80, HOA R² = 0.67) and lowest for secondary inorganic species (SO₄ R² = 0.47, NH₄ R² = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM₁ components through a subsampling analysis.

Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentrations

To quantify the fine-scale spatial variations and local source impacts of urban ultrafine particle (UFP) concentrations, we conducted 3-6 weeks of continuous measurements of particle number (a proxy for UFP) and other air pollutant (CO, NO₂, and PM_2.5) concentrations at 32 sites in Pittsburgh, Pennsylvania during the winters of 2017 and 2018. Sites were selected to span a range of urban land use attributes, including urban background, near local and arterial roads, traffic intersections, urban street canyon, near-highway, near large industrial source, and restaurant density. The spatial variations in urban particle number concentrations varied by about a factor of three. Particle number concentrations are 2-3 times more spatially heterogeneous than PM_2.5 mass. The observed order of spatial heterogeneity is UFP > NO₂ > CO > PM_2.5. On average, particle number concentrations near local roads with a cluster of restaurants and near arterial roads are roughly two times higher than the urban background. Particle number concentrations in the urban street canyon, downwind of a major highway, and near large industrial sources are 2-4 times higher than background concentrations. While traffic is known as an important contributor to particle number concentrations, restaurants and industrial emissions also contribute significantly to spatial variations in Pittsburgh. Particle size distribution measurements using a mobile laboratory show that the local spatial variations in particle number concentrations are dictated by concentrations of particles smaller than 50 nm. A large fraction of urban residents (e.g., ~50%) in Pittsburgh live near local sources and are therefore exposed to 50%-300% higher particle number concentrations than urban background location. These locally emitted particles may have greater health effects than background particles.

Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents

Organic aerosol (OA) is a major component of fine particulate matter (PM_2.5) in urban environments. We performed in-motion ambient sampling from a mobile platform with an aerosol mass spectrometer (AMS) to investigate the spatial variability and sources of OA concentrations in Pittsburgh, Pennsylvania, a midsize, largely postindustrial American city. To characterize the relative importance of cooking and traffic sources, we sampled in some of the most populated areas (∼18 km²) in and around Pittsburgh during afternoon rush hour and evening mealtime, including congested highways, major local roads, areas with high densities of restaurants, and urban background locations. We found greatly elevated OA concentrations (10s of μg m^-3) in the vicinity of numerous individual restaurants and commercial districts containing multiple restaurants. The AMS mass spectral information indicates that majority of the high concentration plumes (71%) were from cooking sources. Areas containing both busy roads and restaurants had systematically higher OA concentrations than areas with only busy roads and urban background locations. Elevated OA concentrations were measured hundreds of meters downwind of some restaurants, indicating that these sources can influence air quality on neighborhood scales. Approximately 20% of the population (∼250 000 people) in the Pittsburgh area lives within 200 m of a restaurant; therefore, restaurant emissions are potentially an important source of outdoor PM exposures for this large population.

Each meal matters in the exposome: Biological and community considerations in fast-food-socioeconomic associations

Advances in omics and microbiome technology have transformed the ways in which the biological consequences of life in the 'ecological theatre' can be visualized. Exposome science examines the total accumulated environmental exposures (both detrimental and beneficial) as a means to understand the response of the 'total organism to the total environment' over time. The repetitive stimulation of compensatory physiological responses (immune, cardiovascular, neuroendocrine) in response to stress - including sources of stress highly relevant to socioeconomic disadvantage - may lead to metabolic dysregulation and cellular damage, ultimately influencing behavior and disease. The collective toll of physiological wear and tear, known as allostatic load, is not paid equally throughout developed societies. It is paid in excess by the disadvantaged. In the context of fast-food, human and experimental research demonstrates that the biological response to a single fast-food-style meal - especially as mediated by the microbiome- is a product of the person's total lived experience, including the ability to buffer the fast-food meal-induced promotion of inflammation and oxidative stress. Emerging research indicates that each meal and its nutritional context matters. As we discuss, equal weekly visits to major fast-food outlets by the affluent and deprived do not translate into biological equivalency. Hence, debate concerning reducing fast-food outlets through policy - especially in disadvantaged neighborhoods where they are prevalent - requires a biological context. The fast-food establishment and fast-food meal - as they represent matters of food justice and press upon non-communicable disease risk - are far more than physical structures and collections of carbohydrate, fat, sugar and sodium.

Land Use Regression Models for Ultrafine Particles in Six European Areas

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R² of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R² was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.

Land Use Regression Models for Ultrafine Particles in Six European Areas

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R² of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R² was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.