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.
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.
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.
Epidemiological studies investigating the relationship between air temperature or heat and health, still, by and large, rely on either information from the nearest weather station or on coarse ...gridded temperature predictions, thereby ignoring small‐scale intra‐urban variations. Recent methodological advances show promise in achieving high spatiotemporal temperature predictions, thus improving the characterization of spatial variations in temperature and decreasing bias in health studies. Here, we applied a two‐stage approach using random forest to (a) impute missing moderate resolution imaging spectroradiometer (MODIS) land surface temperature at a 1 × 1 km resolution and (b) to use the gap‐filled MODIS data to explain spatiotemporal variation in the measured ground‐based air temperature data at a 100 × 100 m resolution across Switzerland using a range of predictor variables, including meteorological parameters, normalized difference vegetation index, impervious surface and altitude. Models presented here managed to capture temporal and spatial variations in air temperature in Switzerland from 2003 to 2018 at a fine spatial resolution of 100 × 100 m. Stage 1 models achieved an overall R2 of 0.98 and a root mean squared error (RMSE) of 1.49°C (independent validation), and the stage 2 model performed well for all years with R2 and RMSE ranging from 0.94 to 0.99 and 1.05 to 1.86°C, respectively. We were also able to capture the urban heat island effect and some typical weather phenomena caused by Switzerland's complex topography, like the foehn effect and inversion conditions. The resulting daily temperature surfaces for 2003–2018 will facilitate ongoing epidemiological research investigating the health effects of heat.
Existing temperature models are too coarse to reproduce small scale temperature variability over complex topography or cities. New temperature models were developed to improve the spatiotemporal resolution, capturing small scale phenomena like the urban heat island effect. The models predict daily values for mean, maximum and minimum temperature across Switzerland from 2003 to 2018 at a fine spatial resolution of 100 × 100 m.
Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and ...consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016–2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50–0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24–0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays’. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35–0.70; PM2.5 hourly R2 = 0.01–0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.
•Hourly LUR models for NO2 and PM2.5 are developed with SLR and RF.•Models' performance and structure differ between hours, seasons and weekday type.•Hourly LUR models outperformed temporal adjustment of annual surfaces.
The paper investigates the dependences between levels of severity of road traffic accidents, accounting at the same time for spatial and temporal correlations. The study analyses road traffic ...accidents data at ward level in England over the period 2005–2013. We include in our model multivariate spatially structured and unstructured effects to capture the dependences between severities, within a Bayesian hierarchical formulation. We also include a temporal component to capture the time effects and we carry out an extensive model comparison. The results show important associations in both spatially structured and unstructured effects between severities, and a downward temporal trend is observed for low and high levels of severity. Maps of posterior accident rates indicate elevated risk within big cities for accidents of low severity and in suburban areas in the north and on the southern coast of England for accidents of high severity. The posterior probability of extreme rates is used to suggest the presence of hot spots in a public health perspective.
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
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.
Poor housing conditions, such as poor building materials and weak structures as well as high levels of indoor air pollution, are important risk factors for a broad range of diseases, including acute ...respiratory infections (ARI). In mining areas, research on the determinants of respiratory health predominantly focuses on exposures to outdoor air pollutants deriving from mining operations. However, mining projects also influence the socioeconomic status of households, which, in turn, affect housing quality and individual behaviors and, thus, housing quality and levels of indoor air pollution. In this study, we aimed to determine how proximity to an industrial mining project impacts housing quality, sources of indoor air pollution, and prevalence of ARI.
We merged data from 131 Demographic and Health Surveys (DHS) with georeferenced data on mining projects in sub-Saharan Africa (SSA) to determine associations between housing quality, indoor air pollution sources, and child respiratory health. Spatial differences in selected indicators were explored using descriptive cross-sectional analyses. Furthermore, we applied a quasi-experimental difference-in-differences (DiD) approach using generalized linear mixed-effects models to compare temporal changes in household and child health indicators at different operational phases of mining projects and as a function of distance to mines.
For cross-sectional analyses, data of 183,466 households and 141,384 children from 27 countries in SSA were used, while 41,648 households and 34,406 children from 23 SSA countries were included in the DiD analyses. The increase in the share of houses being built from finished building materials after mine opening was more than 4-fold higher (odds ratio (OR): 4.32, 95% confidence interval (CI): 2.98–6.24) in close proximity to mining sites (i.e., ≤ 10 km) compared to areas further away (i.e., 10–50 km). However, these benefits were not equally distributed across socioeconomic strata, with considerably weaker effects observed among poorer households. Increases in indoor tobacco smoking rates in close proximity to operating mines were twice as high as in comparison areas (OR: 2.06, 95% CI: 1.15–3.68). The cross-sectional analyses revealed that traditional cooking fuels (e.g., charcoal, dung, and wood) were less frequently used (OR: 0.27, 95% CI: 0.23–0.31) in areas located in close proximity to mines than in comparison areas. Overall, no statistically significant association between mining operations and the prevalence of symptoms related to ARI in children under the age of 5 years was observed (OR: 0.78, 95% CI: 0.29–2.07).
Mines impact known risk factors for ARI through diverse pathways. The absence of significant changes in ARI symptoms among children is likely the result of counteracting effects between improvements in housing infrastructure and increased exposures to air pollutants from outdoor sources and tobacco smoking. For mining projects to unfold their full potential for community development, we recommend that impact assessments move beyond the mere appraisal of mining-related pollution emissions and try to include a more comprehensive set of pathways through which mines can affect ARI in exposed communities.
•Data from 183,466 households in mining sites in 27 African countries were analyzed.•Housing infrastructures improve in mining areas, but poorer households benefit less.•Tobacco smoking within the households increase after mine opening.•Households in proximity to mining sites have better access to clean cooking fuels.•No changes were observed in acute respiratory infections among children.
An association between long-term exposure to fine particulate matter (PM2.5) and lung cancer has been established in previous studies. PM2.5 is a complex mixture of chemical components from various ...sources and little is known about whether certain components contribute specifically to the associated lung cancer risk. The present study builds on recent findings from the “Effects of Low-level Air Pollution: A Study in Europe” (ELAPSE) collaboration and addresses the potential association between specific elemental components of PM2.5 and lung cancer incidence.
We pooled seven cohorts from across Europe and assigned exposure estimates for eight components of PM2.5 representing non-tail pipe emissions (copper (Cu), iron (Fe), and zinc (Zn)), long-range transport (sulfur (S)), oil burning/industry emissions (nickel (Ni), vanadium (V)), crustal material (silicon (Si)), and biomass burning (potassium (K)) to cohort participants’ baseline residential address based on 100 m by 100 m grids from newly developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates. 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).
The pooled study population comprised 306,550 individuals with 3916 incident lung cancer events during 5,541,672 person-years of follow-up. We observed a positive association between exposure to all eight components and lung cancer incidence, with adjusted HRs of 1.10 (95% CI 1.05, 1.16) per 50 ng/m3 PM2.5 K, 1.09 (95% CI 1.02, 1.15) per 1 ng/m3 PM2.5 Ni, 1.22 (95% CI 1.11, 1.35) per 200 ng/m3 PM2.5 S, and 1.07 (95% CI 1.02, 1.12) per 200 ng/m3 PM2.5 V. Effect estimates were largely unaffected by adjustment for nitrogen dioxide (NO2). After adjustment for PM2.5 mass, effect estimates of K, Ni, S, and V were slightly attenuated, whereas effect estimates of Cu, Si, Fe, and Zn became null or negative.
Our results point towards an increased risk of lung cancer in connection with sources of combustion particles from oil and biomass burning and secondary inorganic aerosols rather than non-exhaust traffic emissions. Specific limit values or guidelines targeting these specific PM2.5 components may prove helpful in future lung cancer prevention strategies.
•Exposure to PM2.5 is associated with a higher risk of lung cancer.•PM2.5 is a complex mixture of components from various sources.•We observed positive associations between all components and lung cancer.•Combustion particles and secondary inorganic aerosols may be of special importance.
PDF
