Ambient air pollution is a major environmental cause of morbidity and mortality worldwide. Cities are generally hotspots for air pollution and disease. However, the exact extent of the health effects ...of air pollution at the city level is still largely unknown. We aimed to estimate the proportion of annual preventable deaths due to air pollution in almost 1000 cities in Europe.
We did a quantitative health impact assessment for the year 2015 to estimate the effect of air pollution exposure (PM2·5 and NO2) on natural-cause mortality for adult residents (aged ≥20 years) in 969 cities and 47 greater cities in Europe. We retrieved the cities and greater cities from the Urban Audit 2018 dataset and did the analysis at a 250 m grid cell level for 2015 data based on the global human settlement layer residential population. We estimated the annual premature mortality burden preventable if the WHO recommended values (ie, 10 μg/m3 for PM2·5 and 40 μg/m3 for NO2) were achieved and if air pollution concentrations were reduced to the lowest values measured in 2015 in European cities (ie, 3·7 μg/m3 for PM2·5 and 3·5 μg/m3 for NO2). We clustered and ranked the cities on the basis of population and age-standardised mortality burden associated with air pollution exposure. In addition, we did several uncertainty and sensitivity analyses to test the robustness of our estimates.
Compliance with WHO air pollution guidelines could prevent 51 213 (95% CI 34 036–68 682) deaths per year for PM2·5 exposure and 900 (0–2476) deaths per year for NO2 exposure. The reduction of air pollution to the lowest measured concentrations could prevent 124 729 (83 332–166 535) deaths per year for PM2·5 exposure and 79 435 (0–215 165) deaths per year for NO2 exposure. A great variability in the preventable mortality burden was observed by city, ranging from 0 to 202 deaths per 100 000 population for PM2·5 and from 0 to 73 deaths for NO2 per 100 000 population when the lowest measured concentrations were considered. The highest PM2·5 mortality burden was estimated for cities in the Po Valley (northern Italy), Poland, and Czech Republic. The highest NO2 mortality burden was estimated for large cities and capital cities in western and southern Europe. Sensitivity analyses showed that the results were particularly sensitive to the choice of the exposure response function, but less so to the choice of baseline mortality values and exposure assessment method.
A considerable proportion of premature deaths in European cities could be avoided annually by lowering air pollution concentrations, particularly below WHO guidelines. The mortality burden varied considerably between European cities, indicating where policy actions are more urgently needed to reduce air pollution and achieve sustainable, liveable, and healthy communities. Current guidelines should be revised and air pollution concentrations should be reduced further to achieve greater protection of health in cities.
Spanish Ministry of Science and Innovation, Internal ISGlobal fund.
Ambient air pollution increases the risk of respiratory mortality, but evidence for impacts on lung function and chronic obstructive pulmonary disease (COPD) is less well established. The aim was to ...evaluate whether ambient air pollution is associated with lung function and COPD, and explore potential vulnerability factors.We used UK Biobank data on 303 887 individuals aged 40-69 years, with complete covariate data and valid lung function measures. Cross-sectional analyses examined associations of land use regression-based estimates of particulate matter (particles with a 50% cut-off aerodynamic diameter of 2.5 and 10 µm: PM
and PM
, respectively; and coarse particles with diameter between 2.5 μm and 10 μm: PM
) and nitrogen dioxide (NO
) concentrations with forced expiratory volume in 1 s (FEV
), forced vital capacity (FVC), the FEV
/FVC ratio and COPD (FEV
/FVC <lower limit of normal). Effect modification was investigated for sex, age, obesity, smoking status, household income, asthma status and occupations previously linked to COPD.Higher exposures to each pollutant were significantly associated with lower lung function. A 5 µg·m
increase in PM
concentration was associated with lower FEV
(-83.13 mL, 95% CI -92.50- -73.75 mL) and FVC (-62.62 mL, 95% CI -73.91- -51.32 mL). COPD prevalence was associated with higher concentrations of PM
(OR 1.52, 95% CI 1.42-1.62, per 5 µg·m
), PM
(OR 1.08, 95% CI 1.00-1.16, per 5 µg·m
) and NO
(OR 1.12, 95% CI 1.10-1.14, per 10 µg·m
), but not with PM
Stronger lung function associations were seen for males, individuals from lower income households, and "at-risk" occupations, and higher COPD associations were seen for obese, lower income, and non-asthmatic participants.Ambient air pollution was associated with lower lung function and increased COPD prevalence in this large study.
Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies ...have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM < 10 μm), fine (PM < 2.5 μm, PM2.5) and coarse particles (PM between 2.5 and 10 μm, PM2.5–10) at 1-km2 grid for 2013–2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5–10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5–10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects.
•Estimates of fine and coarse particles at fine spatiotemporal scale are lacking in Italy•We applied a multistage random forest model combining PM data with satellite, land-use and meteorology•We imputed missing satellite AOD data using ensemble atmospheric models•We estimated daily PM10, PM2.5 and PM2.5-10 at a 1-km2 grid over Italy for the years 2013-2015•Our model displayed good CV fitting (R2=0.75 for PM10, R2=0.80 for PM2.5, R2=0.64 for PM2.5-10) and negligible bias
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.
Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate ...long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO2) and ozone (O3) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005–2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM10 (PM<10 microns), PM2.5 (PM<2.5 microns), PM2.5–10 (PM between 2.5 and 10 microns), NO2, and O3 for each squared kilometer of Sweden over the period 2005–2016. Our models were able to describe between 64% (PM10) and 78% (O3) of air pollutant variability in held-out observations, and between 37% (NO2) and 61% (O3) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigations.
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.
Nitrogen dioxide (NO2) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO2 concentrations in Switzerland in a ...multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining ∼58% (R 2 range, 0.56–0.64) of the variation in measured NO2 concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% (R 2 range, 0.70–0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO2 across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO2. The predicted NO2 concentrations will be made available to facilitate health research in Switzerland.
Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term ...assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM10 measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM10 concentrations at 1-km2 grid over Italy, for the years 2006–2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM10=0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM10 levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM10 concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data.
•Estimates of air pollution levels at fine spatiotemporal scale are lacking in Italy•We combined satellite data with land use variables and ground-level PM10 measurements•We estimated daily PM10 concentrations at a 1-km2 grid over Italy, 2006-2012•Our model displayed good cross-validation fitting (R2=0.65) and negligible bias•Spatiotemporal predictions will allow estimation of short and long term health effects of PM10
•Our results confirm that short-term exposures are associated with lung health outcomes.•This study extends our knowledge that lifelong exposure increase the risk of poor lung health.•Lifelong ...exposure to air pollution impact asthma attacks, rhinitis and low lung function.•Lifelong exposure to greenness increased the risk of low lung function in adulthood.
To investigate if air pollution and greenness exposure from birth till adulthood affects adult asthma, rhinitis and lung function. Methods: We analysed data from 3428 participants (mean age 28) in the RHINESSA study in Norway and Sweden. Individual mean annual residential exposures to nitrogen dioxide (NO2), particulate matter (PM10 and PM2.5), black carbon (BC), ozone (O3) and greenness (normalized difference vegetation index (NDVI)) were averaged across susceptibility windows (0–10 years, 10–18 years, lifetime, adulthood (year before study participation)) and analysed in relation to physician diagnosed asthma (ever/allergic/non-allergic), asthma attack last 12 months, current rhinitis and low lung function (lower limit of normal (LLN), z-scores of forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and FEV1/FVC below 1.64). We performed logistic regression for asthma attack, rhinitis and LLN lung function (clustered with family and study centre), and conditional logistic regression with a matched case-control design for ever/allergic/non-allergic asthma. Multivariable models were adjusted for parental asthma and education. Results: Childhood, adolescence and adult exposure to NO2, PM10 and O3 were associated with an increased risk of asthma attacks (ORs between 1.29 and 2.25), but not with physician diagnosed asthma. For rhinitis, adulthood exposures seemed to be most important. Childhood and adolescence exposures to PM2.5 and O3 were associated with lower lung function, in particular FEV1 (range ORs 2.65 to 4.21). No associations between NDVI and asthma or rhinitis were revealed, but increased NDVI was associated with lower FEV1 and FVC in all susceptibility windows (range ORs 1.39 to 1.74). Conclusions: Air pollution exposures in childhood, adolescence and adulthood were associated with increased risk of asthma attacks, rhinitis and low lung function in adulthood. Greenness was not associated with asthma or rhinitis, but was a risk factor for low lung function.
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