Long-term exposure to ultrafine particles and natural and cause-specific mortality

In-situ investigation of real-world emissions from 111 measurements on solid fuel household boilers

Over the years, emissions from significant stationary and mobile sources have significantly reduced, while solid fuel household combustion appliances have substantially contributed to air pollution. Growing evidence links residential solid fuel combustion to air pollution, prompting extensive studies on emissions. Field-based emission measurements provide more accurate characterisation of real-world emissions. A mobile emissions laboratory and measurement procedure was constructed to measure emissions from domestic boilers in the field, in residential settings, during real-world operation by homeowners using typical fuels. Emissions of total suspended particulate matter (TSP), organic gaseous compounds (OGC), carbon monoxide (CO) and overall thermal efficiency during three-hour operating tests were measured. Data were collected from 111 measurements of boilers, including overfire, downdraft, gasification, and automatic boilers that combust wood pellets, wood chips, wood logs, and coals, covering different brands, constructions, and emissions certification standards. The results show that the investigated newer boilers had lower emissions of CO, OGC, and TSP (approximately half) compared to older boiler technology, resulting in a substantial improvement. The emissions from most of the new boilers were substantially higher (about five times) than their strict applicable limit values due to improper operation. In many cases, fuel size, composition, wood moisture content, boiler installation and maintenance, operating procedure, and operator technical knowledge were considered substandard, with some observations of excessive wear and tampering. The findings confirm the importance of real-world local heating emissions measurements and suggest that relatively fast, inexpensive, and substantial emissions reductions may be achieved by improving the maintenance and education of operators.

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.

Associations between long-term exposure to ultrafine particles and type 2 diabetes: A large-scale, multicenter study in China

Few studies have examined the associations between long-term exposure to ultrafine particles (UFP) and type 2 diabetes (T2DM). This study aimed to investigate the impact of long-term UFP exposure on diabetes prevalence and stages, as well as glycemic markers, using data from a large multi-center cohort collected from 2017 to 2021. The health outcomes assessed included diabetes prevalence and stages (normoglycemia, prediabetes, and diabetes), as well as glycemic markers, i.e., fasting blood glucose (FPG) and glycated hemoglobin (HbA1c). The three-year average UFP concentration prior to baseline was used as the long-term UFP exposure level. This cross-sectional study included 93,990 participants, with a diabetes prevalence of 10.97 %. An interquartile range increase in UFP was significantly associated with diabetes prevalence and stages, with ORs of 1.20 (95 % CI: 1.14, 1.26) and 1.11 (95 % CI: 1.07, 1.44), respectively. Specifically, for comparison between normoglycemia and prediabetes, and between prediabetes and diabetes, the corresponding ORs were 1.01 (95 % CI: 0.96, 1.04) and 1.24 (95 % CI: 1.17, 1.31), respectively. UFP exposure was also significantly associated with elevated levels of FPG and HbA1c. These findings suggest that long-term UFP exposure may be a potential risk factor for diabetes with larger risks in the prediabetes population.

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.

Variability in mobility-based air pollution exposure assessment: Effects of GPS tracking duration and temporal resolution of air pollution maps

Mobility-based exposure assessment of air pollution has been proposed as a potentially more valid approach than home-based assessments. However, methodological uncertainties in operationalizing mobility-based assessment may still increase inaccuracies in estimating exposures. It remains unclear whether using short-term mobility data and yearly average air pollution concentrations is reliable for estimating personal air pollution exposure. This study aimed to assess variability in exposure estimates modeled by short- and long-term global positioning system (GPS) data and air pollution maps with yearly and monthly temporal resolutions. We tracked 428 participants for a short period (14 days) with a GPS device and for a long period (several months) with a smartphone application. Exposure estimates of nitrogen dioxide, ozone, and fine particulate matter (PM10 and PM2.5) were computed based on GPS data, air pollution maps, and temporal and indoor/outdoor adjustments. The concordance correlation coefficient (CCC) indicated excellent agreement (0.85-0.99) between exposure estimates based on short- and long-term GPS data from smartphones but ranged from moderate to excellent (0.57-0.99) when comparing exposure estimates based on data from different devices. Agreement between yearly and monthly map-based estimates was poor to moderate without temporal adjustment (CCC: 0-0.63) but excellent after temporal adjustment (CCC: 0.92-1.0). The findings suggest that using short-term (i.e., 7 or 14 days) GPS data and yearly average air pollution concentrations in mobility-based assessments can well represent long-term mobility and yearly averages for determining long-term exposures. However, GPS data collected via dedicated devices and smartphones may identify distinct indoor/outdoor patterns, affecting the indoor/outdoor adjustments of exposure estimates. Additionally, careful selection of using yearly or monthly maps is advised for assessing exposures within specific short periods.

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.

Occupational exposure to ultrafine particles and lung cancer in a population exposed to asbestos

BACKGROUND

Ultrafine particles are present in ever greater quantities in the workplace and only one epidemiological study to date has found an association with the occurrence of lung cancer.

OBJECTIVE

To investigate the effect of occupational exposure to ultrafine particles on the risk of lung cancer.

METHODS

The ARDCO is a surveillance program involving retiree workers who had been exposed to asbestos during their working life. Exposure to ultrafine particles over the complete lifetime occupational history was assessed using the French job exposure matrix MATPUF. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CI).

RESULTS

There was no association between exposure to ultrafine particles and lung cancer after adjustment for smoking and exposure to asbestos and crystalline silica.

CONCLUSION

The findings do not indicate increased risks of lung cancer for UFP after adjustment for level of exposure to asbestos, crystalline silica, and smoking status.

CLINICAL TRIAL NUMBER

Not applicable.

Mapping Source-Specific Air Pollution Exposures Using Positive Matrix Factorization Applied to Multipollutant Mobile Monitoring in Seattle, WA

Mobile monitoring strategies are increasingly used to provide fine spatial estimates of multiple air pollutant concentrations. This study demonstrates a novel approach using positive matrix factorization (PMF) applied to multipollutant mobile monitoring data to assess source-specific air pollution exposures and to estimate associated emission factors. Data were collected from one-year mobile monitoring, with an average of 26 repeated measures of size-resolved particle number counts (PNC), PM2.5, BC, NO2, and CO2 at 309 sites in Seattle from 2019 to 2020. PMF was used to characterize underlying source-related factors. The sources associated with these six factors included emissions from aviation, diesel trucks, gasoline/hybrid vehicles, oil combustion, wood combustion, and accumulation mode aerosols. Fuel-based emission factors for three transportation-related sources were also estimated. This study reveals that PNC of ultrafine particles with size <18, 18-42, and 42-178 nm was dominated by features associated with aircraft, diesel trucks, and both oil and wood combustion. Gasoline and hybrid vehicles contributed the most to CO2 and NO2 concentrations. This approach can also be extended to other metropolitan areas, enhancing the exposure assessment in epidemiology studies.

Spatial and temporal variation of façade-level particle number concentrations using portable monitors in Copenhagen, Denmark

Ultrafine particles (UFP), commonly expressed as particle number concentrations (PNC), have been associated with harm to human health yet are currently not regulated or routinely monitored in many places. This has limited the potential for studies of health effects of long-term exposure to UFP. The present study aims to understand the spatial and temporal variation in façade-level UFP exposures in Copenhagen, Denmark. We measured PNC at the façades of 27 residences across the city for approximately 72 h each in two campaigns and continuously at an urban background reference site for twelve consecutive months, using portable monitors (miniature diffusion size classifiers [DiSCminis]). We estimated annual means at the residential sites based on temporal adjustment using reference site data. Furthermore, we co-located the DiSCminis at a regulatory monitoring station on three occasions and compared daily means from our reference site to those from seven fixed-site monitoring stations throughout the city. Annual mean PNC at the reference site was 4715 (SD of hourly mean: 3001) pt/cm3, while annual means at 27 residences were slightly higher with a mean of 5201 pt/cm3 (SD: 807), ranging between 3735 and 6588 pt/cm3. The two individual adjusted campaign-specific means at 27 residential sites were weakly correlated (Spearman's correlation 0.11) and had an intra-class correlation coefficient of 0.06 (95%-confidence interval: -0.18, 0.28). Daily PNC at the reference site was highly correlated (R = 0.64-0.84) with PNC monitored at seven fixed-site stations throughout the city. We observed a seasonal trend at the reference site with the highest PNC in spring. Our measurement campaign revealed that façade-level PNC at residences in Copenhagen in 2021-2022 was relatively low with small spatial variability. The large variability in time suggests possibly longer and more frequent measurement campaigns to obtain more stable annual averages. Our study illustrates the challenges of UFP long-term exposure assessment.

Differential effects of air pollution on ischemic stroke and ischemic heart disease by ethnicity in a nationwide cohort in the Netherlands

BACKGROUND

Air pollution is a major risk factor for cardiovascular diseases and contributes to health disparities, particularly among minority ethnic groups, who often face higher exposure levels. Knowledge on whether the effect of air pollution on cardiovascular diseases differs between ethnic groups is crucial for identifying mechanisms underlying health disparities, ultimately informing targeted public health strategies and interventions. We explored differences in associations between air pollution and ischemic stroke and ischemic heart disease (IHD) for the six largest ethnic groups in the Netherlands.

METHODS

This nationwide analysis (2014-2019), linked residential-address concentrations of NO2 and PM2.5 to individual-level hospital and mortality data. To evaluate incident ischemic stroke, we created a cohort of residents ≥30 years and free of ischemic stroke at baseline and for incident IHD we created a cohort free of IHD. We performed Cox proportional hazard survival analyses in each cohort with 2014 average concentrations of PM2.5 or NO2 as determinants, stratified by ethnicity (Dutch, German, Indonesian, Surinamese, Moroccan, Turkish) and adjusted for age, sex, socioeconomic indicators and region.

RESULTS

Both cohorts included > 9.5 million people. During follow-up, 127,673 (1.3%) developed ischemic stroke and 156,517 (1.6%) developed IHD. For ischemic stroke, the p-values for the interaction between air pollution and ethnicity were 0.057 for NO2 and 0.055 for PM2.5. The HR of 1 IQR increase (6.42 µg/m3) of NO2 for ischemic stroke was lowest for Moroccans (0.92 [0.84-1.02], p-value = 0.032 difference with Dutch) and highest for Turks (1.09 [1.00-1.18], p-value = 0.157 difference with Dutch). PM2.5 results were similar. For IHD, higher exposure was unexpectedly associated with lower incidence. The p-values for the interaction with ethnicity were 1.75*10- 5 for NO2 and 1.06*10- 3 for PM2.5. The HRs for IHD were lowest for Turks (NO2: 0.88 [0.83-0.92], p-value = 2.0*10- 4 difference with Dutch, PM2.5: 0.86 [0.82-0.91], p-value = 1.3*10- 4 difference with Dutch) and highest for Surinamese (NO2: 1.02 [0.97-1.07], p-value = 0.014 difference with Dutch) and Dutch (PM2.5: 0.96 [0.94-0.98]).

CONCLUSIONS

Associations between air pollutants and ischemic stroke or IHD differ notably between ethnic groups in the Netherlands. Policies to reduce air pollution and prevent ischemic stroke should target populations vulnerable to air pollution with a high cardiovascular disease risk.

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.

Long-Term Exposure to Nitrogen Dioxide and Ozone and Mortality: Update of the WHO Air Quality Guidelines Systematic Review and Meta-Analysis

Objectives

We performed a systematic review and meta-analysis on long-term exposure to nitrogen dioxide (NO2) and ozone (O3) with mortality, to expand evidence that informed 2021 the WHO Air Quality Guidelines and guide the Health Risks of Air Pollution in Europe project.

Methods

We included cohorts investigating NO2 and O3 mortality from all-causes, respiratory diseases, chronic obstructive pulmonary disease (COPD), acute lower respiratory infections (ALRI); and NO2 mortality from circulatory, ischemic heart, cerebrovascular diseases and lung cancer. We pooled estimates by random-effects models and investigated heterogeneity. We assessed the certainty of the evidence using the Grading of Recommendations Assessment Development approach and Evaluation (GRADE).

Results

We selected 83 studies for NO2 and 26 for O3 for the meta-analysis. NO2 was associated with all outcomes, except for cerebrovascular mortality. O3 was associated with respiratory mortality following annual exposure. There was high heterogeneity, partly explained by region and pollutant levels. Certainty was high for NO2 with COPD and ALRI, and annual O3 with respiratory mortality.

Conclusion

An increasing body of evidence, with new results from countrywide areas and the Western Pacific, supports certainty, including new outcomes.

Air pollution exposure and mortality from neurodegenerative diseases in the Netherlands: A population-based cohort study

BACKGROUND

Long-term exposure to ambient air pollution has been linked with all-cause mortality and cardiovascular and respiratory diseases. Suggestive associations between ambient air pollutants and neurodegeneration have also been reported, but due to the small effect and relatively rare outcomes evidence is yet inconclusive. Our aim was to investigate the associations between long-term air pollution exposure and mortality from neurodegenerative diseases.

METHODS

A Dutch national cohort of 10.8 million adults aged ≥30 years was followed from 2013 until 2019. Annual average concentrations of air pollutants (ultra-fine particles (UFP), nitrogen dioxide (NO2), fine particles (PM2.5 and PM10) and elemental carbon (EC)) were estimated at the home address at baseline, using land-use regression models. The outcome variables were mortality due to amyotrophic lateral sclerosis (ALS), Parkinson's disease, non-vascular dementia, Alzheimer's disease, and multiple sclerosis (MS). Hazard ratios (HR) were estimated using Cox models, adjusting for individual and area-level socio-economic status covariates.

RESULTS

We had a follow-up of 71 million person-years. The adjusted HRs for non-vascular dementia were significantly increased for NO2 (1.03; 95% confidence interval (CI) 1.02-1.05) and PM2.5 (1.02; 95%CI 1.01-1.03) per interquartile range (IQR; 6.52 and 1.47 μg/m3, respectively). The association with PM2.5 was also positive for ALS (1.02; 95%CI 0.97-1.07). These associations remained positive in sensitivity analyses and two-pollutant models. UFP was not associated with any outcome. No association with air pollution was found for Parkinson's disease and MS. Inverse associations were found for Alzheimer's disease.

CONCLUSION

Our findings, using a cohort of more than 10 million people, provide further support for associations between long-term exposure to air pollutants (PM2.5 and particularly NO2) and mortality of non-vascular dementia. No associations were found for Parkinson and MS and an inverse association was observed for Alzheimer's disease.

Predicting within-city spatiotemporal variations in daily median outdoor ultrafine particle number concentrations and size in Montreal and Toronto, Canada

Background

Epidemiological evidence suggests that long-term exposure to outdoor ultrafine particles (UFPs, <0.1 μm) may have important human health impacts. However, less is known about the acute health impacts of these pollutants as few models are available to estimate daily within-city spatiotemporal variations in outdoor UFPs.

Methods

Several machine learning approaches (i.e., generalized additive models, random forest models, and extreme gradient boosting) were used to predict daily spatiotemporal variations in outdoor UFPs (number concentration and size) across Montreal and Toronto, Canada using a large database of mobile monitoring measurements. Separate models were developed for each city and all models were evaluated using a 10-fold cross-validation procedure.

Results

In total, our models were based on measurements from 12,705 road segments in Montreal and 10,929 road segments in Toronto. Daily median outdoor UFP number concentrations varied substantially across both cities with 1st-99th percentiles ranging from 1389 to 181,672 in Montreal and 2472 to 118,544 in Toronto. Outdoor UFP size tended to be smaller in Montreal (mean [SD]: 34 nm [15]) than in Toronto (mean [SD]: 44 nm [25]). Extreme gradient boosting models performed best and explained the majority of spatiotemporal variations in outdoor UFP number concentrations (Montreal, R 2: 0.727; Toronto, R 2: 0.723) and UFP size (Montreal, R 2: 0.823; Toronto, R 2: 0.898) with slopes close to one and intercepts close to zero for relationships between measured and predicted values.

Conclusion

These new models will be applied in future epidemiological studies examining the acute health impacts of outdoor UFPs in Canada's two largest cities.

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.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Association between Airport Ultrafine Particles and Lung Cancer Risk: The Multiethnic Cohort Study

BACKGROUND

Ultrafine particles (UFP) are unregulated air pollutants abundant in aviation exhaust. Emerging evidence suggests that UFPs may impact lung health due to their high surface area-to-mass ratio and deep penetration into airways. This study aimed to assess long-term exposure to airport-related UFPs and lung cancer incidence in a multiethnic population in Los Angeles County.

METHODS

Within the California Multiethnic Cohort, we examined the association between long-term exposure to airport-related UFPs and lung cancer incidence. Multivariable Cox proportional hazards regression models were used to estimate the effect of UFP exposure on lung cancer incidence. Subgroup analyses by demographics, histology and smoking status were conducted.

RESULTS

Airport-related UFP exposure was not associated with lung cancer risk [per one IGR HR, 1.01; 95% confidence interval (CI), 0.97-1.05] overall and across race/ethnicity. A suggestive positive association was observed between a one IQR increase in UFP exposure and lung squamous cell carcinoma (SCC) risk (HR, 1.08; 95% CI, 1.00-1.17) with a Phet for histology = 0.05. Positive associations were observed in 5-year lag analysis for SCC (HR, 1.12; 95% CI, CI, 1.02-1.22) and large cell carcinoma risk (HR, 1.23; 95% CI, 1.01-1.49) with a Phet for histology = 0.01.

CONCLUSIONS

This large prospective cohort analysis suggests a potential association between airport-related UFP exposure and specific lung histologies. The findings align with research indicating that UFPs found in aviation exhaust may induce inflammatory and oxidative injury leading to SCC.

IMPACT

These results highlight the potential role of airport-related UFP exposure in the development of lung SCC.

Quantifying the contributions of road and air traffic to ambient ultrafine particles in two urban communities

Traffic-related activities are widely acknowledged as a primary source of urban ambient ultrafine particles (UFPs). However, a notable gap exists in quantifying the contributions of road and air traffic to size-resolved and total UFPs in urban areas. This study aims to delineate and quantify the traffic's contributions to size-resolved and total UFPs in two urban communities. To achieve this, stationary sampling was conducted at near-road and near-airport communities in Seattle, Washington State, to monitor UFP number concentrations during 2018-2020. Comprehensive correlation analyses among all variables were performed. Furthermore, a fully adjusted generalized additive model, incorporating meteorological factors, was developed to quantify the contributions of road and air traffic to size-resolved and total UFPs. The study found that vehicle emissions accounted for 29% of total UFPs at the near-road site and 13% at the near-airport site. Aircraft emissions contributed 14% of total UFPs at the near-airport site. Notably, aircraft predominantly emitted UFP sizes below 20 nm, while vehicles mainly emitted UFP sizes below 50 nm. These findings reveal the variability in road and air traffic contributions to UFPs in distinct areas. Our study emphasizes the pivotal role of traffic layout in shaping urban UFP exposure.

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.

Influence of anthropogenic activities on metals, sugars and PAHs in PM10 in the city of Fez, Morocco: Implications on air quality

Particulate matter (PM) is an important component in the atmosphere, affecting air quality, health, radiation balance, and global climate. To assess regional air quality in the city of Fez, an intensive field campaign was carried out in the autumn of 2019 in the Middle Atlas region of Morocco. Aerosol sampling was performed simultaneously at two urban sites in the city of Fez: (1) Fez University (FU), a sub-urban site, and (2) Fez Parc (FP), an urban site located in the city center of Fez, using PM10 collectors. Various laboratory analyses were carried out, including PM mass, trace metals, inorganic ions, OC/EC, sugar compounds, and PAHs. The results indicate that the PM10 mass (61 ng m-3) was comparable at both sites, with a 37-107 ng m-3 range. Most of the 19 investigated PAHs at the FU site (10.2 ± 6.2 ng m-3) were low-molecular-weight PAHs, while the most abundant PAHs at the FP site (6.9 ± 3.8 ng m-3) were mainly higher-molecular-weight PAHs. A diagnostic ratio analysis at both sites indicated that PAHs originated from fossil fuel combustion and traffic emissions from diesel engines, with Ant/(Ant + Phe) and Flu/(Flu + Pyr) ratios averaging 0.22 and 0.84, respectively. PMF analysis identified traffic emissions as a major source (30%), with secondary inorganic aerosols (20%) and biomass burning (14%). Polar plots highlight the dominance of local anthropogenic activities in PM pollution, with vehicular emissions, industrial activities, and biomass burning. This study shows that local sources and combustion processes significantly contribute to PM10 sources in Morocco, providing insights into air pollution mitigation in North Africa.

Los Angeles Basin's air quality transformation: a long-term investigation on the impacts of PM regulations on the trends of ultrafine particles and co-pollutants

This study investigates the long-term trends of ambient ultrafine particles (UFPs) and associated airborne pollutants in the Los Angeles Basin from 2007 to 2022, focusing on the indirect effects of regulations on UFP levels. The particle number concentration (PNC) of UFPs was compiled from previous studies in the area, and associated co-pollutant data, including nitrogen oxides (NOx), carbon monoxide (CO), elemental carbon (EC), organic carbon (OC), and ozone (O3), were obtained from the chemical speciation network (CSN) database. Over the study period, a general decrease was noted in the PNC of UFPs, NOx, EC, and OC, except for CO, the concentration trends of which did not exhibit a consistent pattern. UFPs, NOx, EC, and OC were positively correlated, while O3 had a negative correlation, especially with NOx. Our analysis discerned two distinct subperiods in pollutant trends: 2007-2015 and 2016-2022. For example, there was an overall decrease in the PNC of UFPs at an annual rate of -850.09 particles/cm3/year. This rate was more pronounced during the first sub-period (2007-2015) at -1814.9 particles/cm3/year and then slowed to -227.21 particles/cm3/year in the second sub-period (2016-2023). The first sub-period (2007-2015) significantly influenced pollutant level changes, exhibiting more pronounced and statistically significant changes than the second sub-period (2016-2022). Since 2016, almost all primary pollutants have stabilized, indicating a reduced impact of current regulations, and emphasizing the need for stricter standards. In addition, the study included an analysis of Vehicle Miles Traveled (VMT) trends from 2007 to 2022 within the Los Angeles Basin. Despite the general increase in VMT, current regulations and cleaner technologies seem to have successfully mitigated the potential increase in increase in PNC. Overall, while a decline in UFPs and co-pollutant levels was observed, the apparent stabilization of these levels underscores the need for more stringent regulatory measures and advanced emission standards.

Outdoor Ultrafine Particulate Matter and Risk of Lung Cancer in Southern California

Rationale: Particulate matter ⩽2.5 μm in aerodynamic diameter (PM2.5) is an established cause of lung cancer, but the association with ultrafine particulate matter (UFP; aerodynamic diameter < 0.1 μm) is unclear. Objectives: To investigate the association between UFP and lung cancer overall and by histologic subtype. Methods: The Los Angeles Ultrafines Study includes 45,012 participants aged ⩾50 years in southern California at enrollment (1995-1996) followed through 2017 for incident lung cancer (n = 1,770). We estimated historical residential ambient UFP number concentrations via land use regression and back extrapolation using PM2.5. In Cox proportional hazards models adjusted for smoking and other confounders, we estimated associations between 10-year lagged UFP (per 10,000 particles/cm3 and quartiles) and lung cancer overall and by major histologic subtype (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma). We also evaluated relationships by smoking status, birth cohort, and historical duration at the residence. Measurements and Main Results: UFP was modestly associated with lung cancer risk overall (hazard ratio [HR], 1.03 [95% confidence interval (CI), 0.99-1.08]). For adenocarcinoma, we observed a positive trend among men; risk was increased in the highest exposure quartile versus the lowest (HR, 1.39 [95% CI, 1.05-1.85]; P for trend = 0.01) and was also increased in continuous models (HR per 10,000 particles/cm3, 1.09 [95% CI, 1.00-1.18]), but no increased risk was apparent among women (P for interaction = 0.03). Adenocarcinoma risk was elevated among men born between 1925 and 1930 (HR, 1.13 [95% CI, 1.02-1.26] per 10,000) but not for other birth cohorts, and was suggestive for men with ⩾10 years of residential duration (HR, 1.11 [95% CI, 0.98-1.26]). We found no consistent associations for women or other histologic subtypes. Conclusions: UFP exposure was modestly associated with lung cancer overall, with stronger associations observed for adenocarcinoma of the lung.

Diverse Metabolic Effects of Cooking Oil Fume from Four Edible Oils on Human BEAS-2B Cells: Implications for Health Guidelines

The 2021 WHO guidelines stress the importance of measuring ultrafine particles using particle number concentration (PNC) for health assessments. However, commonly used particle metrics such as aerodynamic diameter and number concentrations do not fully capture the diverse chemical makeup of complex particles. To address this issue, our study used high-throughput mass spectrometry to analyze the properties of cooking oil fumes (COFs) in real time and evaluate their impact on BEAS-2B cell metabolism. Results showed insignificant differences in COF number size distributions between soybean oil and olive oil (peak concentrations of 5.20 × 105/cm3), as well as between corn oil and peanut oil (peak concentrations of 4.35 × 105/cm3). Despite the similar major chemical components among the four COFs, variations in metabolic damage were observed, indicating that the relatively small amount of chemical components of COFs can also influence particle behavior within the respiratory system, thereby impacting biological responses. Additionally, interactions between accompanying gaseous COFs and particles may alter their chemical composition through various mechanisms, introducing additional chemicals and modifying existing proportions. Hence, the chemical composition and gaseous components of COFs hold equal importance to the particle number concentration (PNC) when assessing their impact on human health. The absence of these considerations in the current guidelines underscores a research gap. It is imperative to acknowledge that for a more comprehensive approach to safeguarding public health, guidelines must be regularly updated to reflect new scientific findings and robust epidemiological evidence.

Exposure to phenols reduces melanogenesis in B16F10 cells and zebrafish

Phenols, ubiquitous environmental contaminants found in water, soil, and air, pose risks to organisms even at minimal concentrations, and many are classified as hazardous pollutants. Skin pigmentation is a natural shield against ultraviolet-induced DNA damage and oxidative stress, pivotal in reducing skin cancer incidences. Studies on B16F10 melanoma cells and zebrafish offer valuable insights into potential therapeutic avenues for melanoma in the context of phenol exposure. Upon phenol treatment, there was a marked decrease in melanin content and melanogenesis-associated protein expression, such as tyrosinase and the microphthalmia-associated transcription factor (MITF) in these melanoma cells. Additionally, phenols led to diminished p38 phosphorylation, amplified extracellular signal-regulated kinase (ERK) phosphorylation, and curtailed melanin expression in zebrafish. These observations underscore the detrimental impact of phenols on melanogenesis and propose a mechanism of action centered on the ERK/p38 signaling pathway. Consequently, our data spotlight the adverse effects of phenols on melanogenesis."

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models

BACKGROUND

Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.

OBJECTIVE

This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto.

METHODS

We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models.

RESULTS

In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1-2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses.

CONCLUSION

Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.