Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen

Exposure to ultrafine particles while bicycling in a residential area near Copenhagen International Airport, Denmark: A repeated measures study

Airports are major sources of ultrafine particles (UFP), raising health concerns among people living in immediate proximity. However, little is known about UFP concentrations in residential areas around airports. In this study, we mapped UFP exposure concentrations in a residential area nearby Copenhagen International Airport (CPH). Particle number concentrations (PNC) were measured using a portable device during 44 bicycling trips on a fixed route of 8.2 km, on weekdays in July and August 2024. The route was located in an area 4 km north of CPH and tracked using GPS. We investigated PNC spatial variation linking measured data to OpenStreetMap. To compare PNC across different times of the day and wind directions, we used Generalized Additive Models (GAM), adjusted for time trends, hourly flights and meteorological variables. We found an overall mean PNC of 7620 pt/cm3 across 44 repeats, with no significant differences between morning and noon trips. Highest means PNC were observed during south wind (11,594 pt/cm3) compared to other wind directions (4189-7069 pt/cm3), showing an increasing gradient of PNC from north to south (∼10,000 to ∼13,000 pt/cm3, respectively) under south wind conditions. We also observed mean PNC of 8151 pt/cm3 across all traffic intersections along the route, with peaks at traffic lights on main roads under south wind, up to 16,442 pt/cm3. Our findings suggest that airports, together with road traffic, are a significant source of UFPs near residential neighbourhoods. The diffusion of UFP is influenced primarily by wind direction with graduation by proximity to the airport.

Comparison of air pollution mortality effect estimates using different long-term exposure assessment modelling methods

INTRODUCTION

Epidemiological studies have used different approaches to assess long-term exposure to ambient air pollution. Little is known about how different exposure models affect health effect estimates in these studies. The aim of this study was to compare air pollution mortality effect estimates in an administrative cohort in the Netherlands based on different exposure assessment methods for black carbon (BC), nitrogen dioxide (NO2), ultrafine particles (UFP), and particulate matter <2.5 μm (PM2.5).

METHODS

Annual average air pollution exposure estimates using eight methods, differing in modelling and monitoring strategy, were applied to a Dutch national cohort of 10.7 million adults aged ≥30 years. Dispersion and land-use regression models based on mobile and fixed-site monitoring were evaluated. Follow-up was from 2013 to 2019. Hazard ratios (HR) for natural and cause-specific mortality were estimated using Cox proportional hazards models.

RESULTS

Exposure estimates from different models were highly correlated. Even though the direction of mortality effect estimates was similar between methods, their magnitude differed substantially, e.g. the HR for BC with natural mortality ranged from 1.01 to 1.09 per increment of 1 μg/m3. No consistent differences in effect estimates were found between deterministic and empirical fixed-site and mobile models. Model predictions over a 10-year period correlated highly and resulted in similar HRs.

DISCUSSION

Different exposure models resulted in similar conclusions about the presence of associations with mortality, but HRs differed up to a ratio of 1.27. Differences in exposure assessment may therefore contribute to the observed heterogeneity of mortality estimates in systematic reviews of epidemiological studies.

Air pollution mapping and variability over five European cities

Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High urban concentrations are due to the proximity of sources such as traffic or residential heating, and to urban density with the presence of buildings that reduce street ventilation. This urban complexity makes fine-scale mapping challenging, even for regulated pollutants such as NO2 and PM2.5. In this study we apply state-of-the-art empirical and deterministic modeling approaches to produce high-resolution (<100 m) pollution maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city mapping capturing intra-urban gradients of concentrations. Depending on the methodology, regulated pollutants (NO2, PM2.5) and/or emerging pollutants (black carbon (BC) and ultrafine particles (UFP characterized here by particulate number concentration PNC)) are considered. For deterministic modelling, different approaches are presented: a multi-scale Eulerian modelling chain down to the street scale with chemistry/aerosol dynamics at all scales, multi-scale hybrid models with Eulerian regional dispersion and Gaussian subgrid dispersion, and a Gaussian-based model. Empirical land use regression models were developed based upon mobile monitoring. To compare the relative performance of the methodologies and to evaluate their performance and limitations, the modelling results are compared to fixed measurement stations. We introduce a standardized metric to quantify spatial and seasonal variability and assess each method's capacity to reproduce fine-scale urban heterogeneity. We also evaluate how data assimilation affects both concentration accuracy and variability representation-particularly relevant for emerging pollutants where measurement data are sparse. We confirm established seasonal and spatial patterns: spatial variability is more pronounced for PNC, NO2 and BC than PM2.5, and concentrations are higher during the winter periods. We also observe reduced spatial variability in winter for PM2. 5 (linked to residential heating) and for BC in cities with significant wood burning emissions. This study adds unique value by evaluating these patterns using fixed measurement stations, and quantifying them across entire urban areas at very fine spatial resolution (<100 m). Furthermore, important methodological strengths and limitations are pointed out, providing practical guidance for the selection and improvement of urban exposure mapping methods, supporting the implementation of the new EU Air Quality Directive.

Examining the social distributions in neighbourhood black carbon and ultrafine particles in Montreal and Toronto, Canada

BACKGROUND

Socioeconomic inequities in outdoor ultrafine particles (UFP) and black carbon (BC) are understudied in Canada, where metropoles like Montreal and Toronto feature distinct sociodemographic diversity and urban characteristics compared to U.S. cities.

METHODS

We collected vulnerability indicators, including social, economic, household composition, and immigration status, at the dissemination area level for Montreal and Toronto using data from the 2006 and 2021 Canadian Census of Population. Areas were classified as disadvantaged, intermediate, or advantaged following K-means clustering analysis. We aggregated and calculated population-weighted average concentrations of BC and UFP, and UFP size at the dissemination area and cluster levels using high-resolution exposure surfaces, derived from year-long mobile monitoring campaigns conducted in each city during 2020-2021. Final exposure surfaces were generated by integrating predictions from land-use regression models and deep convolutional neural network models.

FINDINGS

We observed high within-city variations in aggregated air pollutant levels, with higher outdoor BC and UFP concentrations and smaller UFP sizes in areas near local sources such as major roads, railways, airports, and densely populated regions. Advantaged areas experienced the lowest median UFP concentrations in both Montreal (10,707 pt/cm3) and Toronto (10,988 pt/cm3), as well as the lowest BC concentrations (650 ng/m3) in Montreal. The highest median UFP concentrations were observed in intermediate areas in Montreal (15,709 pt/cm3) and disadvantaged areas in Toronto (12,228 pt/cm3). Conversely, the highest BC concentrations were observed in disadvantaged and intermediate areas in Montreal (805-811 ng/m3), and disadvantaged and advantaged areas in Toronto (1,228-1,252 ng/m3). Notably, high priority areas for the double burden of vulnerability and high BC and UFP concentrations were located near air pollutants local emission sources.

INTERPRETATION

Our findings highlight the importance of prioritizing exposure mitigation for populations residing near local sources and to understand contextual factors influencing inequities across cities and pollutants.

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.

Effect of street trees on local air pollutant concentrations (NO2, BC, UFP, PM2.5) in Rotterdam, the Netherlands

Urban street trees can affect air pollutant concentrations by reducing ventilation rates in polluted street canyons (increasing concentrations), or by providing surface area for deposition (decreasing concentrations). This paper examines these effects in Rotterdam, the Netherlands, using mobile measurements of nitrogen dioxide (NO2), particulate matter (PM), black carbon (BC), and ultrafine particulate matter (UFP). The effect of trees is accounted for in regulatory dispersion models (https://www.cimlk.nl) by the application of an empirically determined tree factor, dependent on the existence and density of the tree canopy, to concentrations due to traffic emissions. Here, we examine the effect of street trees on different pollutants using street-level mobile measurements in a detailed case study (repeated measurements of several neighboring streets) and a larger statistical analysis of measurements across the urban core of Rotterdam. We find that in the summertime, when trees are fully leafed-out, the major short-lived traffic-related pollutants of NO2 and BC have higher concentrations in streets with higher traffic and greater tree cover, while PM2.5 has slightly lower concentrations in streets with higher tree factor. UFP shows a less clear, but decreasing trend with tree factor. In low-traffic streets and in wintertime (fewer leaves on trees) measurements confirm the importance of leaves to pollutant trapping by trees, by finding no enhancement of NO2 and BC with increasing tree cover, rather a slightly decreasing trend in pollutant concentrations with tree factor. Our observations are consistent with the dominant effect of (leafed-out) trees being to trap traffic-emitted pollutants at the surface, but that PM2.5 in street canyons is more often added by transport from outside the street, which can be attenuated by tree cover. Overall, these measurements emphasize that both traffic-emitted and regional sources are important factors that determine air quality in Rotterdam streets, making the effect of street trees different for different pollutants and different seasons.

Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy

Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO2, PM2.5, and PM10, and extracted features from ∼382,000 SVIs at multiple angles (0°, 90°, 180°, 270°) and buffer radii (100-500 m). Additionally, three typical machine learning algorithms were compared with SVI-based land-used regression (LUR) model to explore their performances. Generally, machine learning methods outperform linear LUR, with the ranking: random forest > XGBoost > neural network > LUR. Averaging strategy is an effective method to avoid bias of insufficient feature capture. Therefore, the optimal sampling strategy is to integrating multiple viewing angles at a 100-m buffer, which achieved absolute errors mostly less than 2.5 μg/m3 or ppb. Besides, overexposure, blur, and underexposure led to image misjudgments and incorrect identifications, causing an overestimation of road features and underestimation of human-activity features. These findings enhance understanding and offer valuable support for developing image-based air quality models and other SVI-related research.

Airborne Nanoparticle Concentrations Are Associated with Increased Mortality Risk in Canada's Two Largest Cities

Rationale: Outdoor fine particulate air pollution (particulate matter with an aerodynamic diameter ⩽2.5 μm; PM2.5) contributes to millions of deaths around the world each year, but much less is known about the long-term health impacts of other particulate air pollutants, including ultrafine particles (a.k.a. nanoparticles), which are in the nanometer-size range (<100 nm), widespread in urban environments, and not currently regulated. Objectives: We sought to estimate the associations between long-term exposure to outdoor ultrafine particles and mortality. Methods: Outdoor air pollution levels were linked to the residential addresses of a large, population-based cohort from 2001 to 2016. Associations between long-term exposure to outdoor ultrafine particles and nonaccidental and cause-specific mortality were estimated using Cox proportional hazards models. Measurements and Main Results: An increase in long-term exposure to outdoor ultrafine particles was associated with an increased risk of nonaccidental mortality (hazard ratio = 1.073; 95% confidence interval = 1.061-1.085) and cause-specific mortality, the strongest of which was respiratory mortality (hazard ratio = 1.174; 95% confidence interval = 1.130-1.220). We estimated the mortality burden for outdoor ultrafine particles in Montreal and Toronto, Canada, to be approximately 1,100 additional nonaccidental deaths every year. Furthermore, we observed possible confounding by particle size, which suggests that previous studies may have underestimated or missed important health risks associated with ultrafine particles. Conclusions: As outdoor ultrafine particles are not currently regulated, there is great potential for future regulatory interventions to improve population health by targeting these common outdoor air pollutants.

Spatial and spatiotemporal modelling of intra-urban ultrafine particles: A comparison of linear, nonlinear, regularized, and machine learning methods

BACKGROUND

Machine learning methods are proposed to improve the predictions of ambient air pollution, yet few studies have compared ultrafine particles (UFP) models across a broad range of statistical and machine learning approaches, and only one compared spatiotemporal models. Most reported marginal differences between methods. This limits our ability to draw conclusions about the best methods to model ambient UFPs.

OBJECTIVE

To compare the performance and predictions of statistical and machine learning methods used to model spatial and spatiotemporal ambient UFPs.

METHODS

Daily and annual models were developed from UFP measurements from a year-long mobile monitoring campaign in Quebec City, Canada, combined with 262 geospatial and six meteorological predictors. Various road segment lengths were considered (100/300/500 m) for UFP data aggregation. Four statistical methods included linear, non-linear, and regularized regressions, whereas eight machine learning regressions utilized tree-based, neural networks, support vector, and kernel ridge algorithms. Nested cross-validation was used for model training, hyperparameter tuning and performance evaluation.

RESULTS

Mean annual UFP concentrations was 13,335 particles/cm3. Machine learning outperformed statistical methods in predicting UFPs. Tree-based methods performed best across temporal scales and segment lengths, with XGBoost producing the overall best performing models (annual R2 = 0.78-0.86, RMSE = 2163-2169 particles/cm3; daily R2 = 0.47-0.48, RMSE = 8651-11,422 particles/cm3). With 100 m segments, other annual models performed similarly well, but their prediction surfaces of annual mean UFP concentrations showed signs of overfitting. Spatial aggregation of monitoring data significantly impacted model performance. Longer segments yielded lower RMSE in all daily models and for annual statistical models, but not for annual machine learning models.

CONCLUSIONS

The use of tree-based methods significantly improved spatiotemporal predictions of UFP concentrations, and to a lesser extent annual concentrations. Segment length and hyperparameter tuning had notable impacts on model performance and should be considered in future studies.

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring

Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 μg/m3) and RMSE (5.36 μg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For ultrafine particles (UFP), IDW_Coral's citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

High-resolution urban air pollution mapping

Variation in urban air pollution arises because of complex spatial, temporal, and chemical processes, which profoundly affect population exposure, human health, and environmental justice. This Review highlights insights from two popular in situ measurement methods-mobile monitoring and dense sensor networks-that have distinct but complementary strengths in characterizing the dynamics and impacts of the multidimensional urban air quality system. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. This Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

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.

Mobile monitoring of air pollutants; performance evaluation of a mixed-model land use regression framework in relation to the number of drive days

We used black carbon data from a mobile monitoring campaign in Oakland, USA measuring street segments up to 40 times and compared a data-only, LUR model and mixed-model approach with a long-term average, represented by the average concentration based on 40 drive days on that street segment. The mixed model outperformed the data-only and LUR model estimates, with 80% explained variance after 5 drive days and 90% after 14 drive days. The data-only approach needed 8 and 15 to achieve an explained variance of 80% and 90%, respectively, The LUR model never achieved an explained variance higher than 70%. The mixed model is a scalable approach, as it can be used before all street segments in a domain are measured by developing a LUR model and adds information with increasing repeats per street segment.

Elegant Nematodes Improve Our Understanding of Human Neuronal Diseases, the Role of Pollutants and Strategies of Resilience

The prevalence of neurodegenerative disorders such as Alzheimer's and Parkinson's disease are rising globally. The role of environmental pollution in neurodegeneration is largely unknown. Thus, this perspective advocates exposome research in C. elegans models of human diseases. The models express amyloid proteins such as Aβ, recapitulate the degeneration of specifically vulnerable neurons and allow for correlated neurobehavioral phenotyping throughout the entire life span of the nematode. Neurobehavioral traits like locomotion gaits, rigidity, or cognitive decline are quantifiable and carefully mimic key aspects of the human diseases. Underlying molecular pathways of neurodegeneration are elucidated in pollutant-exposed C. elegans Alzheimer's or Parkinson's models by transcriptomics (RNA-seq), mass spectrometry-based proteomics and omics addressing other biochemical traits. Validation of the identified disease pathways can be achieved by genome-wide association studies in matching human cohorts. A consistent One Health approach includes isolation of nematodes from contaminated sites and their comparative investigation by imaging, neurobehavioral profiling and single worm proteomics. C. elegans models of neurodegenerative diseases are likewise well-suited for high throughput methods that provide a promising strategy to identify resilience pathways of neurosafety and keep up with the number of pollutants, nonchemical exposome factors, and their interactions.