Associations of long-term exposure to air pollution and greenness with incidence of chronic obstructive pulmonary disease in Northern Europe: The Life-GAP project.

BACKGROUND: Prolonged exposure to air pollution has been linked to adverse respiratory health, yet the evidence concerning its association with chronic obstructive pulmonary disease (COPD) is inconsistent. The evidence of a greenness effect on chronic respiratory diseases is limited. OBJECTIVE: This study aimed to investigate the association between long-term exposure to particulate matter (PM2.5 and PM10), black carbon (BC), nitrogen dioxide (NO2), ozone (O3) and greenness (as measured by the normalized difference vegetation index - NDVI) and incidence of self-reported chronic bronchitis or COPD (CB/COPD). METHODS: We analyzed data from 5355 adults from 7 centers participating in the Respiratory Health in Northern Europe (RHINE) study. Mean exposures to air pollution and greenness were assessed at available residential addresses in 1990, 2000 and 2010 using air dispersion models and satellite data, respectively. Poisson regression with log person-time as an offset was employed to analyze the association between air pollution, greenness, and CB/COPD incidence, adjusting for confounders. RESULTS: Overall, there were 328 incident cases of CB/COPD during 2010-2023. Despite wide statistical uncertainty, we found a trend for a positive association between NO2 exposure and CB/COPD incidence, with incidence rate ratios (IRRs) per 10 µg/m³ difference ranging between 1.13 (95% CI: 0.90-1.41) in 1990 and 1.18 (95% CI: 0.96-1.45) in 2000. O3 showed a tendency for inverse association with CB/COPD incidence (IRR from 0.84 (95% CI: 0.66-1.07) in 2000 to 0.88 (95% CI: 0.69-1.14) in 2010. No consistent association was found between PM, BC and greenness with CB/COPD incidence across different exposure time windows. CONCLUSION: Consistent with prior research, our study suggests that individuals exposed to higher concentrations of NO2 may face an elevated risk of developing COPD, although evidence remains inconclusive. Greenness was not associated with CB/COPD incidence, while O3 showed a tendency for an inverse association with the outcome.

Impact of anthropogenic emission control in reducing future PM2.5 concentrations and the related oxidative potential across different regions of China.

Affected by both future anthropogenic emissions and climate change, future prediction of PM2.5 and its Oxidative Potential (OP) distribution is a significant challenge, especially in developing countries like China. To overcome this challenge, we estimated historical and future PM2.5 concentrations and associated OP using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. Considering different future socio-economic pathways and emission scenario assumptions, we quantified how the contribution from various anthropogenic emission sectors will change under these scenarios. Results show that compared to the CESM_SSP2-4.5_CLE scenario (based on moderate radiative forcing and Current Legislation Emission), the CESM_SSP1-2.6_MFR scenario (based on sustainability development and Maximum Feasible Reductions) is projected to yield greater environmental and health benefits in the future. Under the CESM_SSP1-2.6_MFR scenario, annual average PM2.5 concentrations (OP) are expected to decrease to 30 (0.8 nmolmin-1m-3) in almost all regions by 2030, which will be 65 % (67 %) lower than that in 2010. From a long-term perspective, it is anticipated that OP in the Fen-Wei Plain region will experience the maximum reduction (82.6 %) from 2010 to 2049. Largely benefiting from the effective control of PM2.5 in the region, it has decreased by 82.1 %. Crucially, once emission reduction measures reach a certain level (in 2040), further reductions become less significant. This study also emphasized the significant role of secondary aerosol formation and biomass-burning sources in influencing OP during both historical and future periods. In different scenarios, the reduction range of OP from 2010 to 2049 is estimated to be between 71 % and 85 % by controlling precursor emissions involved in secondary aerosol formation and emissions from biomass burning. Results indicate that strengthening the control of anthropogenic emissions in various regions are key to achieving air quality targets and safeguarding human health in the future.

Pregnancy outcomes as related to in utero exposure to air pollution and greenness: The Life-GAP Project.

Background: Lower birth weight and preterm birth may increase the risk of adverse health outcomes later in life. We examined whether maternal exposure to air pollution and greenness during pregnancy is associated with offspring birth weight and preterm birth. Methods: We analyzed data on 4286 singleton births from 2358 mothers from Respiratory Health in Northern Europe, a prospective questionnaire-based cohort study (1990-2010). Mixed-effects regression models with random intercepts for mothers and centers were used to estimate the association of exposures to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), black carbon (BC), and greenness (Normalized Difference Vegetation Index in 300m-buffers [NDVI300m]) with birth outcomes, adjusting for potential confounders. Results: Median (interquartile range [IQR]) exposures to PM2.5, PM10, NO2, O3, BC, and NDVI300m during pregnancy were 8.4(5.0) µg/m3, 14.4(8.3) µg/m3, 14.0(11.0) µg/m3, 54.7(10.2) µg/m3, 0.47(0.41) µg/m3, and 0.31(0.20), respectively. IQR increases in air pollution exposures during pregnancy were associated with decreased birth weight and the strongest association was seen for PM2.5 (-49g; 95% confidence interval [CI] = -83, -16). However, O3 showed an opposite association. IQR increase in NDVI300m was associated with an increase in birth weight of 25 g (95% CI = 7, 44). Preterm birth was not associated with the exposures. Conclusion: Increased greenness and decreased air pollution may contribute to healthier pregnancies and improve overall health in the next generation. This emphasizes the need to adopt policies that target the reduction of air pollution emissions and exposure of the population.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019.

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.