Nanoparticle emissions from road vehicles have been studied extensively in the recent past due to their dominant contribution towards the total airborne particle number concentrations (PNCs) found in ...the urban atmospheric environment. In view of upcoming tighter vehicle emission standards and adoption of cleaner fuels in many parts of the world, the contribution to urban nanoparticles from non-vehicle exhaust sources (NES) may become more pronounced in future. As of now, only limited information exists on nanoparticle emissions from NES through the discretely published studies. This article presents critically synthesised information in a consolidated manner on 11 NES (i.e. road–tyre interaction, construction and demolition, aircraft, ships, municipal waste incineration, power plants, domestic biomass burning, forest fires, cigarette smoking, cooking, and secondary formation). Source characteristics and formation mechanisms of nanoparticles emitted from each NES are firstly discussed, followed by their emission strengths, airborne concentrations and physicochemical characteristics. Direct comparisons of the strengths of NES are not straightforward but an attempt has been made to discuss their importance relative to the most prominent source (i.e. road vehicles) of urban nanoparticles. Some interesting comparisons emerged such as 1 kg of fast and slow wood burning produces nearly the same number of particles as for each km driven by a heavy duty vehicle (HDV) and a light duty vehicle, respectively. About 1 min of cooking on gas can produce the similar particle numbers generated by ∼10 min of cigarette smoking or 1 m travel by a HDV. Apportioning the contribution of numerous sources from the bulk measured airborne PNCs is essential for determining their relative importance. Receptor modelling methods for estimation of source emission contributions are discussed. A further section evaluates the likely exposure risks, health and regulatory implications associated with each NES. It is concluded that much research is needed to provide adequate quantification of all nanoparticle sources, and to establish the relative toxicity of nanosize particles from each.
► Nanoparticle emissions from 11 non-vehicle exhaust sources (NES) are reviewed. ► Limited information exists but encouraging progress made to characterise NES. ► No air quality regulations exist currently to control nanoparticle exposure. ► Physicochemical characterisation and exposure to NES derived nanoparticles needed. ► Relative toxicity and contribution of NES produced nanoparticles unknown.
Air pollutants such as NO2 and PM2.5 have consistently been linked to mortality, but only few previous studies have addressed associations with long-term exposure to black carbon (BC) and ozone (O3).
...We investigated the association between PM2.5, PM10, BC, NO2, and O3 and mortality in a Danish cohort of 49,564 individuals who were followed up from enrollment in 1993–1997 through 2015. Residential address history from 1979 onwards was combined with air pollution exposure obtained by the state-of-the-art, validated, THOR/AirGIS air pollution modelling system, and information on residential traffic noise exposure, lifestyle and socio-demography.
We observed higher risks of all-cause as well as cardiovascular disease (CVD) mortality with higher long-term exposure to PM2.5, PM10, BC, and NO2. For PM2.5 and CVD mortality, a hazard ratio (HR) of 1.29 (95% CI: 1.13–1.47) per 5 μg/m3 was observed, and correspondingly HRs of 1.16 (95% CI: 1.05–1.27) and 1.11 (95% CI: 1.04–1.17) were observed for BC (per 1 μg/m3) and NO2 (per 10 μg/m3), respectively. Adjustment for noise gave slightly lower estimates for the air pollutants and CVD mortality. Inverse relationships were observed for O3. None of the investigated air pollutants were related to risk of respiratory mortality. Stratified analyses suggested that the elevated risks of CVD and all-cause mortality in relation to long-term PM, NO2 and BC exposure were restricted to males.
This study supports a role of PM, BC, and NO2 in all-cause and CVD mortality independent of road traffic noise exposure.
•Higher exposure to PM2.5, PM10, NO2 and black carbon was associated with mortality.•Associations of air pollutants and CVD mortality were independent of noise exposure.•O3 exposure was not associated with increased mortality risk.
In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable ...exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe.
We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013).
The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.
We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
•Robust PM2.5, NO2, BC and O3 hybrid LUR models at a 100x100 m resolution for Western Europe were developed•Models included large scale SAT and CTM estimates and fine scale traffic and land use and were further improved with kriging•Models were robust in time at European scale, becoming less robust at smaller spatial scales.
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary ...linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.
This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.
For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58–0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48–0.57; EV R2 0.39–0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.
Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.
•Multiple statistical algorithms with very different assumptions were compared.•Despite the difference in modeling frameworks, predictions among the models exhibit generally good agreement.•The use of an external evaluation dataset strengthens evaluation by cross-validation.
•Primary carbonaceous particle and SOA exposure was associated with mortality.•Primary carbonaceous particle and SIA exposure was associated with CVD mortality.•Sea salt exposure was not associated ...with increased mortality risk.
Studies on health effects of long-term exposure to specific PM2.5 constituents are few. Previous studies have reported an association between black carbon (BC) exposure and cardiovascular diseases (CVD) and a few studies have found an association between sulfate exposure and mortality. These studies, however, relied mainly on exposure data from centrally located air-monitoring stations, which is a crude approximation of personal exposure.
We focused on specific chemical constituents of PM2.5, i.e. elemental and primary organic carbonaceous particles (BC/OC), sea salt, secondary inorganic aerosols (SIA, i.e. NO3–, NH4+, and SO42-), and secondary organic aerosols (SOA), in relation to all-cause, CVD and respiratory disease mortality.
We followed a Danish cohort of 49,564 individuals from enrollment in 1993–1997 through 2015. We combined residential address history from 1979 onwards with mean annual air pollution concentrations obtained by the AirGIS air pollution modelling system, lifestyle information from baseline questionnaires and socio-demography obtained by register linkage.
During 895,897 person-years of follow-up, 10,193 deaths from all causes occurred – of which 2319 were CVD-related and 870 were related to respiratory disease. The 15-year time-weighted average concentrations of PM2.5, BC/OC, sea salt, SIA and SOA were 13.8, 2.8, 3.4, 4.9, and 0.3 µg/m3, respectively. For all-cause mortality, a higher risk was observed with higher exposure to PM2.5, BC/OC and SOA with adjusted hazard ratios of 1.03 (95% confidence intervals: 1.01, 1.05), 1.06 (1.03, 1.09), and 1.08 (1.03, 1.13) per interquartile range, respectively. The associations for BC/OC and SOA remained after adjustment for PM2.5 in two-pollutant models. For CVD mortality, we observed elevated risks with higher exposure to PM2.5, BC/OC and SIA. The results showed no clear relationship between sea salt and mortality.
In this study, we observed a relationship between long-term exposure to PM2.5, BC/OC, and SOA and all-cause mortality and between PM2.5, BC/OC, and SIA and CVD mortality.
•Air pollution, traffic noise and lack of green space have been associated with diabetes in analyses mainly focusing on one or two environmental factors at a time.•We aimed to investigate if air ...pollution, road traffic noise and green space are independent risk factors of type 2 diabetes.•In a multi-pollutant analysis, ultrafine particles, NO2, noise at both most and least exposed façade and two proxies of lack of green space were all associated with higher risk of type 2 diabetes.•The cumulative risk estimate of the multi-pollutant analysis was much higher than the risk estimate of any single pollutant.
Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective.
We estimated 5-year residential mean exposure to fine particles (PM2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO2) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates.
In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO2 remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO2, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149).
Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.
It has been suggested that air pollution may increase the risk of type 2 diabetes but data on particulate matter with diameter <2.5μm (PM2.5) are inconsistent. We examined the association between ...long-term exposure to PM2.5 and diabetes incidence.
We used the Danish Nurse Cohort with 28,731 female nurses who at recruitment in 1993 or 1999 reported information on diabetes prevalence and risk factors, and obtained data on incidence of diabetes from National Diabetes Register until 2013. We estimated annual mean concentrations of PM2.5, particulate matter with diameter <10μm (PM10), nitrogen oxides (NOx) and nitrogen dioxide (NO2) at their residence since 1990 using a dispersion model and examined the association between the 5-year running mean of pollutants and diabetes incidence using a time-varying Cox regression.
Of 24,174 nurses 1137 (4.7%) developed diabetes. We detected a significant positive association between PM2.5 and diabetes incidence (hazard ratio; 95% confidence interval: 1.11; 1.02–1.22 per interquartile range of 3.1μg/m3), and weaker associations for PM10 (1.06; 0.98–1.14 per 2.8μg/m3), NO2 (1.05; 0.99–1.12 per 7.5μg/m3), and NOx (1.01; 0.98–1.05 per 10.2μg/m3) in fully adjusted models. Associations with PM2.5 persisted in two-pollutant models. Associations with PM2.5 were significantly enhanced in never smokers (1.24; 1.09–1.42), and augmented in obese (1.25; 1.06–1.47) and subjects with myocardial infarction (1.32; 0.86–2.02), but without significant interaction.
Fine particulate matter may the most relevant pollutant for diabetes development among women, and non-smokers, obese women, and heart disease patients may be most susceptible.
•Evidence on association of PM2.5 with diabetes is inconsistent.•We linked residential PM2.5 to diabetes incidence in Danish Nurse Cohort.•We found 39% (4–86%) increased risk of diabetes per 10μg/m3 increase in PM2.5.•PM2.5 may be the most relevant pollutant for diabetes development.•Non-smokers, obese, and heart disease patients may be most susceptible.
•Air pollution during pregnancy associated with telomere length (TL) in newborns.•Second trimester air pollution positively associated with umbilical cord blood TL.•Third trimester air pollution ...inversely associated with umbilical cord blood TL.•Air pollution at home and work show similar association with umbilical cord blood TL.•No association between air pollution and TL in placenta or maternal blood.
Telomere length (TL) is a biomarker of biological aging that may be affected by prenatal exposure to air pollution. The aim of this study was to assess the association between prenatal exposure to air pollution and TL in maternal blood cells (leukocytes), placenta and umbilical cord blood cells, sampled immediately after birth in 296 Danish mother-child pairs from a birth cohort. Exposure data was obtained using the high-resolution and spatial–temporal air pollution modeling system DEHM-UBM-AirGIS for PM2.5, PM10, SO2, NH4+, black carbon (BC), organic carbon (OC), CO, O3, NO2, and NOx at residential and occupational addresses of the participating women for the full duration of the pregnancy. The association between prenatal exposure to air pollutants and TL was investigated using distributed lag models. There were significant and positive associations between TL in umbilical cord blood cells and prenatal exposure to BC, OC, NO2, NOx, CO, and O3 during the second trimester. TL in umbilical cord blood was significantly and inversely associated with prenatal exposure to PM2.5, BC, OC, SO2, NH4+, CO and NO2 during the third trimester. There were similar inverse associations between TL from umbilical cord blood cells and air pollution exposure at the residential and occupational addresses. There were weaker or no associations between air pollution exposure and TL in placenta tissue and maternal blood cells. In conclusion, both the second and third trimesters of pregnancy are shown to be sensitive windows of exposure to air pollution affecting fetal TL.
Atrial fibrillation is the most common sustained arrhythmia and is associated with cardiovascular morbidity and mortality. The few studies conducted on short-term effects of air pollution on episodes ...of atrial fibrillation indicate a positive association, though not consistently.
The aim of this study was to evaluate the long-term impact of traffic-related air pollution on incidence of atrial fibrillation in the general population.
In the Danish Diet, Cancer, and Health cohort of 57,053 people 50-64 years old at enrollment in 1993-1997, we identified 2,700 cases of first-ever hospital admission for atrial fibrillation from enrollment to end of follow-up in 2011. For all cohort members, exposure to traffic-related air pollution assessed as nitrogen dioxide (NO
) and nitrogen oxides (NO
) was estimated at all present and past residential addresses from 1984 to 2011 using a validated dispersion model. We used Cox proportional hazard model to estimate associations between long-term residential exposure to NO
and NO
and risk of atrial fibrillation, after adjusting for lifestyle and socioeconomic position.
A 10 μg/m
higher 10-year time-weighted mean exposure to NO
preceding diagnosis was associated with an 8% higher risk of atrial fibrillation incidence rate ratio: 1.08; 95% confidence interval (CI): 1.01, 1.14 in adjusted analysis. Though weaker, similar results were obtained for long-term residential exposure to NO
. We found no clear tendencies regarding effect modification of the association between NO
and atrial fibrillation by sex, smoking, hypertension or myocardial infarction.
We found long-term residential traffic-related air pollution to be associated with higher risk of atrial fibrillation. Accordingly, the present findings lend further support to the demand for abatement of air pollution. Citation: Monrad M, Sajadieh A, Christensen JS, Ketzel M, Raaschou-Nielsen O, Tjønneland A, Overvad K, Loft S, Sørensen M. 2017. Long-term exposure to traffic-related air pollution and risk of incident atrial fibrillation: a cohort study. Environ Health Perspect 125:422-427; http://dx.doi.org/10.1289/EHP392.
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•Mixed-effect model predictions showed hyperlocal variation of air pollution in urban settings.•First time that UFP map has been created with measurements on all street ...segments.•Hotspot and ratio analyses revealed differences in spatial variation between BC, NO2 and UFP.•May helps policymakers by zooming into the areas of interest and adapt urban topography.•May helps epidemiologists to differentiate between the health effects of pollutants.
Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 – March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables.
We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2.
This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).
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