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
•Long-term exposure to air pollution is associated with the development of COPD.•Associations persisted at low exposure levels with no evidence for a threshold.•Traffic-related pollutants NO2 and BC ...may be the most relevant.
Air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD), but evidence is sparse and inconsistent.
We examined the association between long-term exposure to low-level air pollution and COPD incidence.
Within the ‘Effects of Low-Level Air Pollution: A Study in Europe’ (ELAPSE) study, we pooled data from three cohorts, from Denmark and Sweden, with information on COPD hospital discharge diagnoses. Hybrid land use regression models were used to estimate annual mean concentrations of particulate matter with a diameter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in 2010 at participants’ baseline residential addresses, which were analysed in relation to COPD incidence using Cox proportional hazards models.
Of 98,058 participants, 4,928 developed COPD during 16.6 years mean follow-up. The adjusted hazard ratios (HRs) and 95% confidence intervals for associations with COPD incidence were 1.17 (1.06, 1.29) per 5 µg/m3 for PM2.5, 1.11 (1.06, 1.16) per 10 µg/m3 for NO2, and 1.11 (1.06, 1.15) per 0.5 10−5m−1 for BC. Associations persisted in subset participants with PM2.5 or NO2 levels below current EU and US limit values and WHO guidelines, with no evidence for a threshold. HRs for NO2 and BC remained unchanged in two-pollutant models with PM2.5, whereas the HR for PM2.5 was attenuated to unity with NO2 or BC.
Long-term exposure to low-level air pollution is associated with the development of COPD, even below current EU and US limit values and possibly WHO guidelines. Traffic-related pollutants NO2 and BC may be the most relevant.
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.
Previous published analyses have focused on the effect of air pollution on asthma and rhinoconjunctivitis throughout early and middle childhood. However, the role of exposure to air pollution in the ...development of childhood and adolescent asthma and rhinoconjunctivitis remains unclear. We aimed to assess the longitudinal associations between exposure to air pollution and development of asthma and rhinoconjunctivitis throughout childhood and adolescence.
We did a population-based birth cohort study of 14 126 participants from four prospective birth cohort studies from Germany, Sweden, and the Netherlands with 14–16 years of follow-up. We linked repeated questionnaire reports of asthma and rhinoconjunctivitis with annual average air pollution concentrations (nitrogen dioxide NO2, particulate matter PM with a diameter of less than 2·5 μm PM2·5, less than 10 μm PM10, and between 2·5 μm and 10 μm PMcoarse, and PM2·5 absorbance indicator of soot) at the participants' home addresses. We analysed longitudinal associations of air pollution exposure at participants' birth addresses and addresses at the time of follow-up with asthma and rhinoconjunctivitis incidence and prevalence in cohort-specific analyses, with subsequent meta-analysis and pooled analyses.
Overall, the risk of incident asthma up to age 14–16 years increased with increasing exposure to NO2 (adjusted meta-analysis odds ratio OR 1·13 per 10 μg/m3 95% CI 1·02–1·25) and PM2·5 absorbance (1·29 per 1 unit 1·00–1·66) at the birth address. A similar, albeit non-significant, trend was shown for PM2·5 and incident asthma (meta-analysis OR 1·25 per 5 μg/m3 95% CI 0·94–1·66). These associations with asthma were more consistent after age 4 years than before that age. There was no indication of an adverse effect of air pollution on rhinoconjunctivitis.
Exposure to air pollution early in life might contribute to the development of asthma throughout childhood and adolescence, particularly after age 4 years, when asthma can be more reliably diagnosed. Reductions in levels of air pollution could help to prevent the development of asthma in children.
The European Union.
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
•We study the association of long-term air pollution exposure with Parkinson’s Disease (PD)•Our study among 271,720 ELAPSE cohort participants noted 381 deaths from PD.•We find that long-term ...exposure to PM2.5, NO2 and BC are associated with risk of PD death.•Associations persist at low levels of pollutant concentration, below current EU standards.•Our study adds strong evidence in support of an association between air pollution and PD.
The link between exposure to ambient air pollution and mortality from cardiorespiratory diseases is well established, while evidence on neurodegenerative disorders including Parkinson’s Disease (PD) remains limited.
We examined the association between long-term exposure to ambient air pollution and PD mortality in seven European cohorts.
Within the project ‘Effects of Low-Level Air Pollution: A Study in Europe’ (ELAPSE), we pooled data from seven cohorts among six European countries. Annual mean residential concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC), and ozone (O3), as well as 8 PM2.5 components (copper, iron, potassium, nickel, sulphur, silicon, vanadium, zinc), for 2010 were estimated using Europe-wide hybrid land use regression models. PD mortality was defined as underlying cause of death being either PD, secondary Parkinsonism, or dementia in PD. We applied Cox proportional hazard models to investigate the associations between air pollution and PD mortality, adjusting for potential confounders.
Of 271,720 cohort participants, 381 died from PD during 19.7 years of follow-up. In single-pollutant analyses, we observed positive associations between PD mortality and PM2.5 (hazard ratio per 5 µg/m3: 1.25; 95% confidence interval: 1.01–1.55), NO2 (1.13; 0.95–1.34 per 10 µg/m3), and BC (1.12; 0.94–1.34 per 0.5 × 10-5m-1), and a negative association with O3 (0.74; 0.58–0.94 per 10 µg/m3). Associations of PM2.5, NO2, and BC with PD mortality were linear without apparent lower thresholds. In two-pollutant models, associations with PM2.5 remained robust when adjusted for NO2 (1.24; 0.95–1.62) or BC (1.28; 0.96–1.71), whereas associations with NO2 or BC attenuated to null. O3 associations remained negative, but no longer statistically significant in models with PM2.5. We detected suggestive positive associations with the potassium component of PM2.5.
Long-term exposure to PM2.5, at levels well below current EU air pollution limit values, may contribute to PD mortality.
•Exposure to PM2.5 was associated with higher risk of lung cancer.•Elevated risks persisted even at levels lower than the EU limit value of 25 µg/m3.•No association between NO2, BC or O3 and lung ...cancer incidence was observed.
Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence.
The “Effects of Low-level Air Pollution: a Study in Europe” (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and ozone (O3) to assign exposure to cohort participants’ residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines.
The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO2, PM2.5, BC and O3 (warm season) were 24.2 µg/m3 (19.5, 29.7), 15.4 µg/m3 (12.8, 17.3), 1.6 10−5m−1 (1.3, 1.8), and 86.6 µg/m3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m3). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m3. We did not observe associations between NO2, BC or O3 and lung cancer incidence.
Long-term ambient PM2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines.
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•This is the first longitudinal study that has not only investigated the impact of air pollution on cognitive impairment but also its progression to dementia, considering various air ...pollutants, and death as competing risk events.•Long-term exposure to ambient air pollution was associated with a significantly elevated risk for cognitive impairment among older adults.•Air pollution almost doubled the risk for dementia incidence among people with cognitive impairment.
Accumulation of evidence has raised concern regarding the harmful effect of air pollution on cognitive function, but results are diverging. We aimed to investigate the longitudinal association of long-term exposure to air pollutants and cognitive impairment and its further progression to dementia in older adults residing in an urban area.
Data were obtained from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). Cognitive impairment, no dementia (CIND) was assessed by a comprehensive neuropsychological battery (scoring ≥1.5 standard deviations below age-specific means in ≥1 cognitive domain). We assessed long-term residential exposure to particulate matters (PM2.5 and PM10) and nitrogen oxides (NOx) with dispersion modeling. The association with CIND was estimated using Cox proportional hazards models with 3-year moving average air pollution exposure. We further estimated the effect of long-term air pollution exposure on the progression of CIND to dementia using Cox proportional hazards models.
Among 1987 cognitively intact participants, 301 individuals developed CIND during the 12-year follow-up. A 1-μg/m3 increment in PM2.5 exposure was associated with a 75% increased risk of incident CIND (HR = 1.75, 95 %CI: 1.54, 1.99). Weaker associations were found for PM10 (HR for 1-μg/m3 = 1.08, 95 %CI: 1.03–1.14) and NOx (HR for 10 μg/m3 = 1.18, 95 %CI: 1.04–1.33). Among those with CIND at baseline (n = 607), 118 participants developed dementia during follow-up. Results also show that exposure to air pollution was a risk factor for the conversion from CIND to dementia (PM2.5: HR for 1-μg/m3 = 1.90, 95 %CI: 1.48–2.43; PM10: HR for 1-μg/m3 = 1.14, 95 %CI: 1.03–1.26; and NOx: HR for 10 μg/m3 = 1.34, 95 %CI: 1.07–1.69).
We found evidence of an association between long-term exposure to ambient air pollutants and incidence of CIND. Of special interest is that air pollution also was a risk factor for the progression from CIND to dementia.
Exposure to air pollution during infancy has been related to lung function decrements in 8-year-old children, but whether the negative effects remain into adolescence is unknown.
To investigate the ...relationship between long-term air pollution exposure and lung function up to age 16 years.
A total of 2,278 children from the Swedish birth cohort BAMSE (Children, Allergy, Milieu, Stockholm, Epidemiological Survey) performed spirometry at age 16 years. Levels of outdoor air pollution from local road traffic were estimated (nitrogen oxides NOx and particulate matter with an aerodynamic diameter of <10 μm PM10) for residential, daycare, and school addresses during the lifetime using dispersion modeling. Associations between exposure in different time windows and spirometry indexes were analyzed by linear regression and mixed effect models.
Exposure to traffic-related air pollution during the first year of life was associated with FEV1 at age 16 years of -15.8 ml (95% confidence interval CI, -33.6 to 2.0 for a 10 μg/m(3) difference in NOx), predominately in males (-30.4 ml; 95% CI, -59.1 to -1.7), and in subjects not exposed to maternal smoking during pregnancy or infancy. Later exposures appeared to have had an additional negative effect. High exposure during the first year of life was also associated with odds ratios for FEV1 and FVC less than the lower limit of normal (LLN) (defined as a z-score < -1.64 SD) of 3.8 (95% CI, 1.3-10.9) and of 4.3 (95% CI, 1.2-15.0), respectively. The results for PM10 were similar to those for NOx.
Exposure to traffic-related air pollution in infancy is negatively associated with FEV1 at age 16 years, leading to increased risk of clinically important deficits.
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