Exposure to air particulate matter has been linked with hypertension and blood pressure levels. The metabolic risks of air pollution could vary according to the specific characteristics of each area, and has not been sufficiently evaluated in Spain. We analyzed 1103 individuals, participants in a Spanish nationwide population based cohort study (di@bet.es), who were free of hypertension at baseline (2008–2010) and completed a follow-up exam of the cohort (2016–2017). Cohort participants were assigned air pollution concentrations for particulate matter < 10 μm (PM10) and < 2.5 μm (PM2.5) during follow-up (2008–2016) obtained through modeling combined with measurements taken at air quality stations (CHIMERE chemistry-transport model). Mean and SD concentrations of PM10 and PM2.5 were 20.17 ± 3.91 μg/m3 and 10.83 ± 2.08 μg/m3 respectively. During follow-up 282 cases of incident hypertension were recorded. In the fully adjusted model, compared with the lowest quartile of PM10, the multivariate weighted ORs (95% CIs) for developing hypertension with increasing PM10 exposures were 0.82 (0.59–1.14), 1.28 (0.93–1.78) and 1.45 (1.05–2.01) in quartile 2, 3 and 4 respectively (p for a trend of 0.003). The corresponding weighted ORs according to PM2.5 exposures were 0.80 (0.57–1.13), 1.11 (0.80–1.53) and 1.48 (1.09–2.00) (p for trend 0.004). For each 5-μg/m3 increment in PM10 and PM2.5 concentrations, the odds for incident hypertension increased 1.22 (1.06–1.41) p = 0.007 and 1.39 (1.07–1.81) p = 0.02 respectively. In conclusion, our study contributes to assessing the impact of particulate pollution on the incidence of hypertension in Spain, reinforcing the need for improving air quality as much as possible in order to decrease the risk of cardiometabolic disease in the population.

Background Recent reports have suggested that air pollution may impact thyroid function, although the evidence is still scarce and inconclusive. In this study we evaluated the association of exposure to air pollutants to thyroid function parameters in a nationwide sample representative of the adult population of Spain. Methods The Di@bet.es study is a national, cross-sectional, population-based survey which was conducted in 2008-2010 using a random cluster sampling of the Spanish population. The present analyses included 3859 individuals, without a previous thyroid disease diagnosis, and with negative thyroid peroxidase antibodies (TPO Abs) and thyroid-stimulating hormone (TSH) levels of 0.1-20 mIU/L. Participants were assigned air pollution concentrations for particulate matter <2.5μm (PM2.5) and Nitrogen Dioxide (NO2), corresponding to the health examination year, obtained by means of modeling combined with measurements taken at air quality stations (CHIMERE chemistry-transport model). TSH, free thyroxine (FT4), free triiodothyronine (FT3) and TPO Abs concentrations were analyzed using an electrochemiluminescence immunoassay (Modular Analytics E170 Roche). Results In multivariate linear regression models, there was a highly significant negative correlation between PM2.5 concentrations and both FT4 (p<0.001), and FT3 levels (p<0.001). In multivariate logistic regression, there was a significant association between PM2.5 concentrations and the odds of presenting high TSH [OR 1.24 (1.01-1.52) p=0.043], lower FT4 [OR 1.25 (1.02-1.54) p=0.032] and low FT3 levels [1.48 (1.19-1.84) p=<0.001] per each IQR increase in PM2.5 (4.86 μg/m3). There was no association between NO2 concentrations and thyroid hormone levels. No significant heterogeneity was seen in the results between groups of men, pre-menopausal and post-menopausal women. Conclusions Exposures to PM2.5 in the general population were associated with mild alterations in thyroid function.

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Abstract. Here, the capability of the chemical weather forecasting model CHIMERE (version 2017r4) to reproduce surface ozone, particulate matter and nitrogen dioxide concentrations in complex terrain is investigated for the period from June 21 to August 21, 2018. The study area is the northwestern Iberian Peninsula, where both coastal and mountain climates can be found in direct vicinity and a large fraction of the land area is covered by forests. Driven by lateral boundary conditions from the ECMWF Composition Integrated Forecast System, anthropogenic emissions from two commonly used top-down inventories and meteorological data from the Weather Research and Forecasting Model, CHIMERE's performance with respect to observations is tested with a range of sensitivity experiments. We assess the effects of 1) an increase in horizontal resolution, 2) an increase in vertical resolution, 3) the use of distinct model chemistries and 4) the use of distinct anthropogenic emissions inventories, downscaling techniques and landuse databases. In comparsion with the older HTAP emission inventory downscaled with basic options, the updated and sophistically downscaled EMEP inventory only leads to partial model improvements and so does the computationally costly horizontal resolution increase. Model performance changes caused by the choice of distinct chemical mechanisms are not systematic either and rather depend on the considered anthropgenic emission configuration and pollutant. Albeit the results are thus heterogeneous in general terms, the model's response to a vertical resolution increase confined to the lower to middle troposphere is homogeneous in the sense of improving virtually all verification aspects. We conclude that, as long as the aforementioned top-down emission inventories are used, it is generally not necessary to use a horizontal model mesh much finer than the native grid of the inventories. A relatively coarse horizontal mesh combined with 20 model layers between 999 and 500 hPa is sufficient to yield balanced results. The chemical mechanism should be chosen as a function of the intended application.