Benzo(a)pyrene (BaP) is commonly used as an indicator of the carcinogenic risk from exposure to polycyclic aromatic hydrocarbons (PAHs). The European target for annual average BaP concentration (1.0 ng/m³) is frequently exceeded in Central Europe. Limited monitoring of BaP in Europe means that air quality and health impact estimates rely on modelled data, although model uncertainties can be large. In this study, four chemical transport models (CHIMERE, GLEMOS, MINNI, and SILAM) estimated BaP concentrations across Europe for 2018. All models estimated the highest levels in Central Europe, Northern Italy, and parts of Eastern Europe, while the lowest concentrations were estimated for northern and remote areas. The models accurately reproduced spatial patterns of annual mean BaP concentrations at rural sites, but performance declined in urban areas. On average, all models underestimated BaP in PM10, with CHIMERE underestimating the least. At the few EMEP sites measuring total BaP (gas + particle), SILAM and GLEMOS tended to overestimate, CHIMERE slightly overestimated, and MINNI underestimated concentrations. All models underestimated concentrations at urban sites. Large differences between models were found in the gas/particle partitioning and deposition rates. Comparisons with observed deposition data revealed large discrepancies. The study also assessed the health impacts and costs of BaP exposure. Depending on the model and the uncertainty in the Unit Risk parameter, estimated lung cancer cases ranged from 152 to 825, with associated costs between €320 million and €1.7 billion. Of all European countries, Poland was estimated to have the highest health and economic burden in 2018.

This paper exploits a real-world scenario of significant emission reductions, such as that provided by the COVID-19 pandemic, to assess the responsiveness of an air quality model to real-world emission changes across Europe. The assessment is performed by evaluating the CHIMERE chemical transport model results against observation-based estimates derived from five-year mean concentrations, a Generalised Additive Model, and, for Spain, Gradient Boosting Machine. The impacts estimated by CHIMERE agree quite well with the observation-based estimates, with larger differences in some parts of northern Italy and northern Spain. All methodologies demonstrate generalized and robustly agreed reductions in NO2 concentrations. Regarding O3, the results from the observation-based methodologies concur with the differing impacts estimated by CHIMERE depending on the metric used. Consistent across all methods, the largest O3 reductions were found for metrics influenced by periods with higher solar radiation. Conversely, for the annual mean in areas with high NOx emissions, all methodologies consistently estimate O3 increases, a non-linear response driven by reduced NO-titration that validates the model's ability to capture complex chemical dynamics. By successfully validating CTM responsiveness under real-world emission shifts, this study indicates that these models can be considered useful tools for evaluating future emission reduction scenarios in air quality planning, although considering the uncertainties in areas where local conditions are challenging to model.

This paper aims to provide scientific support for decision-making regarding improvements in air quality by evaluating pollution reduction measures included in the 1st Spanish National Air Pollution Control Programme (SAPCP) at the city level. The study assesses the health impacts of the main air pollutants—particulate matter (PM), nitrogen dioxide (NO2), and ozone (O3)—on the population of Madrid. Two scenarios are analysed for 2030: one consisting of a package of existing measures (EM) and the other including additional measures (AM). First, the impacts on air quality resulting from emission reductions due to these measures are estimated using multiscale air-quality modelling, which provides changes in pollutant concentrations. Next, mortality resulting from exposure is calculated by comparing the scenarios to a reference year. Detailed data on the geographical distribution of the population and health statistics at the district level were utilised. Finally, results are expressed in terms of avoided external costs, measured by the Value of a Life Year (VOLY) and the Value of Statistical Life (VSL). An improvement was noted in the health effects associated with NO2, PM10, and PM2.5, while there was a slight deterioration concerning O3. The most significant benefits identified in the AM scenario indicate a potential reduction of 4643 premature deaths, which is more than double that of the EM scenario. The primary benefits are linked to the decrease in NO2 levels. Regarding social costs, considering the additional measures in package AM, the benefits could amount to approximately €1189 million (VOLY) and €20,574 million (VSL).

This study is the first to investigate volcanic ash (VA) as a soil amendment to mitigate nitrous oxide (N2O) emissions, a potent greenhouse gas mainly produced through nitrification and denitrification processes in agricultural soils. The experiment assessed the effects of VA mixed with soil and combined with mineral (NH4NO3, N) or organic (poultry manure, O) fertilizer on N2O emissions, soil mineral nitrogen (NO3- and NH4+), trace metals (Zn, Cu, Mn), and crop yield in a 4-month pot experiment including treatments with and without VA. Results showed that VA reduced N2O emissions by 55% in mineral fertilizer treatments and 71% in organic fertilizer treatments compared to soils without VA. This reduction was associated with significant changes in nitrogen availability. In mineral fertilizer treatments with VA, soil NO3- concentrations remained high, potentially limiting denitrifier activity, while in organic treatments VA appeared to inhibit nitrogen mineralization. Additionally, VA increased soil concentrations of Zn, Cu, and Mn, which were negatively correlated with N2O emissions, suggesting an influence on microbial processes. Importantly, crop yields were not affected by VA application. Although promising, these preliminary findings highlight the need for further research to optimize application rates and evaluate long-term effects across soil types and management systems.

Recently, air quality has become a major concern for policy makers around the world, which has led to the implementation of mitigation measures. In urban areas, most measures affect the road transport sector, as this is one of the main contributors to air pollution in those areas. Due to the fact that spatial variability of urban air pollution is very heterogeneous, high spatial resolution modelling is necessary. In this context, this study aims to evaluate the air quality impacts of several measures applied to the traffic network around a real urban hot-spot (Plaza Elíptica, Madrid) at high spatial resolution. The methodology used is based on Computational Fluids Dynamics (CFD) modelling, but uses different modelling tools to obtain all the necessary input data. The SUMO microscopic traffic simulator is used to obtain a dataset of traffic flows for each scenario selected in the study. This dataset represents the regular traffic during the week, avoiding the cost of computational time and resources to run simulations for each hour. Emissions are computed for each timestep of the simulations (0.75 s) using the emissions model PHEMLight5 coupled with SUMO. A set of steady-state CFD simulations are previously performed for all wind direction sectors and scenarios, using the previously described emissions. The horizontal spatial resolution of these simulations is of 5x5 m2, which higher resolutions (up to 1x1 m2) near buildings. Relevant meteorological variables are obtained from WRF simulations using the urban parameterization BEP-BEM. These are necessary for both selecting the appropriate CFD simulations from the dataset according to the observed wind direction at each hour and estimating NOx maps from pre-calculated CFD simulations based on the wind speed observed at each hour. Finally, background NOx concentrations are obtained from an urban background air quality monitoring station (AQMS) in Madrid, located 1.6 km NW from the AQMS of Plaza Elíptica. Using this methodology, we have studied four scenarios: Base scenario. (Year 2016) Reorganization of traffic flows by changing traffic directions in some streets. (Year 2019) The initial phase of the implementation of a Low Emissions Zone (ZBE) affecting the most polluting vehicles; with still some reduction of traffic due to the COVID-19 pandemic. (Year 2022) The recovery of traffic after the COVID-19 pandemic. (Year 2023) Results were evaluated for February 2016, 2019, 2022 and 2023 using the observed concentrations at the AQMS in the study area. The impacts of the traffic variations are investigated for different meteorological conditions.

The Mediterranean Basin is recognized as a Biodiversity Hotspot for conservation priorities, mainly due to the high biodiversity of plants. Different environmental protection categories of the territory can help to ensure the long-term preservation of biodiversity and habitats. Among them, National Park is the legal category providing the highest protection for nature areas with remarkable ecological and cultural value. These areas represent valuable locations for long-term monitoring of ecological conservation and ecosystem functioning. The Spanish National Parks Network comprises 16 natural areas, including mountain ranges, volcanic habitats, different types of forest, scrublands and grasslands, and aquatic and marine ecosystems. All the National Parks are part of the European Natura 2000 network. Moreover, 6 of them are part of the LTER-Spain network. By law, National Parks must include a long-term Monitoring and Evaluation Plan for the Network, including ecological, sociological, and functional monitoring programmes. Within the ecological monitoring programme, besides flora, fauna and ecosystem functioning assessments, climate is also considered as one of the main drivers of global change. Tree crown condition is assessed in all the parks with forests following the methodology of ICP-Forest Level I. The next step is to include air quality, since atmospheric pollution is another important driver of global change that can affect ecosystem health and functioning. Particularly tropospheric ozone and atmospheric nitrogen deposition are expected to be exceeding the limit values for vegetation protection. Air quality monitoring networks are mainly focused on the protection of human health, thus some very valuable areas for biodiversity might not be represented by air monitoring stations. In addition, atmospheric deposition is not included at the air quality monitoring stations. Moreover, the extension of some of the National Parks, the complex orography and the different climate, morphology and air pollution sources that influence the National Parks, represent a challenge for managers to design an air quality monitoring program with an efficient use of resources. To address this challenge and provide the information needed to design an air quality network in National Parks, a new project is being developed combining local monitoring with air quality modelling. The spatial representativeness of background rural monitoring stations has been assessed through relating CHIMERE chemistry-transport (v2013) model simulations at 5 x 5 km 2 resolution and measured data at the stations in Spain (including the Canary Islands) for the years 2019-2023 considering NO 2 , SO 2 , O 3 , PM 10 and atmospheric deposition. With this analysis, the suitability of current monitoring stations to represent the air quality in National Parks is being assessed. A risk analysis of the effects on terrestrial ecosystems using CLRTAP critical levels and loads is being performed using the CHIMERE model in the different National Parks. Additionally, a new air quality and deposition monitoring programme has been initiated in 2025 in all the National Parks to measure NO 2 , SO 2 , O 3 and NH 3 using passive samplers and atmospheric wet deposition using bulk collectors connected to ion-exchange resins. Local monitoring data will be used to determine the spatial variability of air quality and deposition within the National Parks and will be compared with modelled data. Effects on aquatic ecosystems are being assessed applying the methodology of CLRTAP ICP-Waters in all the National Parks with suitable locations, including the Canary Islands. Preliminary results will be presented discussing representativeness of current monitoring stations, monitoring techniques available in remote locations and the modelling analyses required.

This paper examines different strategies for reducing air pollution through measures implemented in key sectors. Current environmental and energy policies at the European and Spanish levels are focused on increasing energy efficiency and the penetration of renewable energy sources. In this study, changes in emissions of two major pollutants affecting human health — nitrogen oxides (NOx) and fine particulate matter (PM2.5) — are quantified as a result of implementing a set of planned measures, considering Spain’s 2030 policy targets and using 2021 as the reference year. The measures target sectors that are directly connected to the population: residential buildings and passenger cars. The results indicate that the greatest benefits in terms of emission reductions are achieved through the replacement of combustion-based passenger road transport with electric vehicles, as well as through improvements to building envelopes, particularly once the electricity mix reaches the 2030 renewable energy penetration target.

For air quality management, while numerical tools are mainly evaluated to assess their performances on absolute concentrations, this study assesses the impact of their settings on the robustness of model responses to emission reduction strategies for the main criteria pollutants. The effect of the spatial resolution and chemistry schemes is investigated. We show that whereas the spatial resolution is not a crucial setting (except for NO 2 ), the chemistry scheme has more impact, particularly when assessing hourly values of the absolute potential of concentrations. The analysis of model responses under the various configurations triggered an analysis of the impact of using online models, like WRF-chem or WRF-CHIMERE, which accounts for the impact of aerosol concentrations on meteorology. This study informs the air quality modeling community on what extent some model settings can affect the expected model responses to emission changes. We suggest to not activate online effects when analyzing the effect of an emission reduction strategy to avoid any confusion in the interpretation of results even if an online simulation should represent better the reality.

The sensitivity of air quality model responses to modifications in input data (e.g. emissions, meteorology and boundary conditions) or model configurations is recognized as an important issue for air quality modelling applications in support of air quality plans. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe, https://fairmode.jrc.ec.europa.eu/ ) a dedicated air quality modelling exercise has been designed to address this issue. The main goal was to evaluate the magnitude and variability of air quality model responses when studying emission scenarios/projections by assessing the changes of model output in response to emission changes. This work is based on several air quality models that are used to support model users and developers, and, consequently, policy makers. We present the FAIRMODE exercise and the participating models, and provide an analysis of the variability of O 3 and PM concentrations due to emission reduction scenarios. The key novel feature, in comparison with other exercises, is that emission reduction strategies in the present work are applied and evaluated at urban scale over a large number of cities using new indicators such as the absolute potential, the relative potential and the absolute potency. The results show that there is a larger variability of concentration changes between models, when the emission reduction scenarios are applied, than for their respective baseline absolute concentrations. For ozone, the variability between models of absolute baseline concentrations is below 10%, while the variability of concentration changes (when emissions are similarly perturbed) exceeds, in some instances 100% or higher during episodes. Combined emission reductions are usually more efficient than the sum of single precursor emission reductions both for O 3 and PM. In particular for ozone, model responses, in terms of linearity and additivity, show a clear impact of non-linear chemistry processes. This analysis gives an insight into the impact of model’ sensitivity to emission reductions that may be considered when designing air quality plans and paves the way of more in-depth analysis to disentangle the role of emissions from model formulation for present and future air quality assessments.

Atmospheric aerosols are one of the main factors that contribute to poor air quality. These aerosols are mostly concentrated within the atmospheric boundary layer (ABL) and mixing layer (ML). The ABL extends from ground level to the lowest level of the troposphere directly affected by surface temperature, solar irradiance, the orography and its proximity to coastal areas, causing turbulence in a daily cycle. This turbulence controls the vertical mixing of aerosols and pollutants and their dispersion in the ML. Therefore, proper characterization of these layers is of crucial importance in numerical weather forecasting and climate models; however, their estimation nowadays presents some spatial and temporal limitations. In order to deal with these limitations and to assess the influence of different meteorological conditions on the temporal evolution of the aforementioned layers, the evolution of the ML over Madrid (Spain) has been studied for the year 2020 by means of ceilometer profiles fed into the STRATfinder algorithm. This algorithm is able to give reliable estimates of the height of the ABL (ABLH) and ML (MLH). The results are compared with the ECMWF-IFS model predictions, which is able to compute the MLH under any meteorological condition. Then, the influence of the meteorology in the estimation of MLHs was established by classifying data based on the season and six different prevalent synoptic meteorological situations defined using ground-level pressure fields, as well as by splitting the days into four periods (morning, daytime, evening and nighttime). Our results show that both datasets, the STRATfinder values and the ECMWF-IFS model computations, are very sensitive to the meteorological conditions that play a main role in the MLH temporal evolution. For instance, high solar irradiance and ground radiation cause high turbulence and convection that lead to a well-developed ML. In cases in which the ML is well developed, both methods show similar results, and there are therefore better correlations between them. On the contrary, the results presented here show that the presence of high relative humidity and low temperatures hamper the growth of the ML, causing different errors in both MLH estimations and poor correlations between them. Furthermore, the ECMWF-IFS model has shown a sharp decrease, identified as an artificial behavior from 16:00 UTC, because of the influence of low solar zenith angles and the temporal interpolation. The STRATfinder algorithm also shows a sharp decrease just before the sunset because of the way the algorithm distinguishes between the ML and the residual layer. Thus, this study concludes that the MLH temporal evolution still needs to be characterized using complementary tools, since the methods presented here are strongly affected by the meteorological conditions and do not show enough reliability to work individually. However, ceilometer measurements offer great potential as a correction tool for ABL heights derived from models involved in air pollution dispersion assessments.

This paper aims to assess the impact of individual measures for NOx emission reduction on NO 2 concentrations at very high spatial resolution in an urban district of Madrid City (Spain). A methodology based on a set of Computational Fluid Dynamics simulations for 16 meteorological scenarios combined with the CHIMERE model for background pollution is used to obtain annual NO 2 concentration maps. Two scenarios included in the Spanish National Air Pollution Control Programme are investigated: NOx emission reductions from the installation of more efficient boilers for domestic heating (ECOBOIL) and from the partly substitution of passenger cars with combustion engines by electric cars (EC). This analysis is extended to 9 additional scenarios of more ambitious implementation of electric vehicles in order to determine what the NOx emission reduction required for the annual mean NO 2 concentration EU limit value not being exceeded is. The ECOBOIL scenario has a very weak impact on the NO 2 concentrations. However, the EC scenario implies a more significant reduction of the NO 2 concentrations, but not enough to fully remove NO 2 limit value exceedances in the study area. A small additional (compared with the EC scenario) implementation of electric vehicles seems to fulfil that the spatially averaged NO 2 concentration be lower than the EU limit value, but the area with exceedances is still very large. However, stronger traffic emission reductions (80%) corresponding to the most ambitious scenarios are needed in order to reach that at least 95% of the domain is free of EU limit value exceedances.

Changes in primary emissions due to the COVID-19 lockdowns in Europe for the year 2020 have been estimated by considering fully open-access and near-real-time measured activity data from a wide range of information sources and with simple computational techniques. The estimates consist on a dataset of reduction factors that are both time- and country-dependent and provided for the following source categories: energy industry (power plants), manufacturing industry, road traffic, aviation, shipping and other stationary combustion activities such as residential and commercial-institutional activities. Inspired in other authors' estimates for COVID reductions, the advantage of this methodology is that there is no use of machine learning, making this procedure more accessible to the general scientific community. We have followed a fast methodology that takes advantage of observed relationships between variables (e.g. temperature and energy demand) without needing special algorithms for finding those relationships. The comparison of our estimates with others from other authors indicate a reasonable agreement and pointing out that emissions dropped by a 17% on average in Europe, with large differences between sectors of activities and spatial heterogeneity. The most affected sector was aviation, with a spatial-averaged variation of −63% in emissions since the implementation of first restrictions with respect business-as-usual values. 2020 emission changes with respect to business-as-usual values in countries ranges from a −13% in Norway and Poland to a more than −20% in several Mediterranean countries as well as the United Kingdom. Two main periods of emission reductions have been identified.

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 (PM 2.5 ) and Nitrogen Dioxide (NO 2 ), 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 PM 2.5 concentrations and both FT4 (p<0.001), and FT3 levels ( p <0.001). In multivariate logistic regression, there was a significant association between PM 2.5 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 PM 2.5 (4.86 μg/m 3 ). There was no association between NO 2 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 PM 2.5 in the general population were associated with mild alterations in thyroid function.

Abstract. The Eurodelta-Trends multi-model experiment, aimed to assess the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends. i.e. were there significant trends detected by observations? do the models manage to reproduce observed trends? how close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to PM pollution. An in-depth trend analysis has been performed for PM10 and PM2.5 for the period of 2000–2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the models simulated trends could be regarded as an indicator for modelling uncertainty. The model ensemble simulations indicate overall decreasing trends in PM10 and PM2.5, with reduction by between 2 and 6 μg m−3 m−3 (or between 10 and 30 %) from 2000 to 2010. Compared to PM2.5, relative PM10 trends are weaker due to large inter-annual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is a reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30–40 % over most of Europe, increasing to 50–60 % in northern and eastern parts of EDT domain. Averaged over measurement sites (26 for PM10 and 13 for PM2.5), the mean ensemble simulated trends are −0.24 and −0.22 μg m−3 year−1 for PM10 and PM2.5, which are somewhat weaker than the observed trends of −0.35 and −0.40 μg m−3 year−1, respectively, partly due to models underestimation of PM concentrations. The correspondence is better in relative PM10 and PM2.5 trends, which are −1.7 and −2.0 % year−1 from the model ensemble and −2.1 and −2.9 % year−1 from the observations, respectively. The observations identify significant trends for PM10 at 56 % of the sites and for PM2.5 at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries. The strongest decreasing PM trends and the largest number of sites with significant trends is found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. One important reason for that is the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modeled versus observed PM trends are limited the regions where the sites are located. The analysis reveals a considerable variability of the role of the individual aerosols in PM10 trends across European countries. The multi-model simulations, supported by available observations, point to decreases in SO4−2 concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of NH4+ and NO3− to PM10 decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appears to be a dominant factor in bringing down PM10 in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties and the effect of inter-annual meteorological variability on the trend analysis.

The sensitivity of air quality model responses to emission reductions when input data (e.g. emissions, meteorology and boundary conditions) or the model set-up configurations are changed is recognised as an important issue for the optimisation of model simulations. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe) and, in particular, in its Cross Cutting Task 9 (CT9), a dedicated intercomparison exercise has been designed. The goal is to evaluate the robustness of air quality models when studying emission scenarios/projections and to address the issue of the sensitivity of model responses to emission changes, in particular, to identify, study and possibly reduce discrepancies between models. This will provide robust support to model users and developers, and, consequently, policy makers. The aim of this paper is to present the FAIRMODE CT9 platform exercise, its evaluation tool and the community of models involved and provide a preliminary evaluation on the variability of model responses. The key novel feature (in comparison with other exercises) is that emission reduction strategies are here applied and evaluated at urban scale. Appropriate indicators based on previous studies, such as the absolute potential, the relative potential, the absolute potency are defined. The first results based on emission reductions, show a large variability in the indicator values, more so than for the absolute concentrations. While a variability below 10% between models is observed on Ozone concentrations during episodes, the results on indicators show a variability exceeding 100% and even more looking at the highest values. Also, applying emission reductions on several precursors at the same time seems more beneficial to reduce PM concentrations. Model responses on linearity and additivity show a clear impact of non-linear chemistry processes (particularly for Ozone).

Current European legislation aims to reduce the air pollutants emitted by European countries in the coming years. In this context, this article studies the effects on air quality of the measures considered for 2030 in the Spanish National Air Pollution Control Programme (NAPCP). Three different emission scenarios are investigated: a scenario with the emissions in 2016 and two other scenarios, one with existing measures in the current legislation (WEM2030) and another one considering the additional measures of NAPCP (WAM2030). Previous studies have addressed this issue at a national level, but this study assesses the impact at the street scale in three neighborhoods in Madrid, Spain. NO2 concentrations are modelled at high spatial resolution by means of a methodology based on computational fluid dynamic (CFD) simulations driven by mesoscale meteorological and air quality modelling. Spatial averages of annual mean NO2 concentrations are only estimated to be below 40 µg/m3 in all three neighborhoods for the WAM2030 emission scenarios. However, for two of the three neighborhoods, there are still zones (4–12% of the study areas) where the annual concentration is higher than 40 µg/m3. This highlights the importance of considering microscale simulations to assess the impacts of emission reduction measures on urban air quality.

During the last few decades, European legislation has driven progress in reducing air pollution in Europe through emission mitigation measures. In this paper, we use a chemistry transport model to assess the impact on ambient air quality of the measures considered for 2030 in the for the scenarios with existing (WEM2030) and additional measures (WAM2030). The study estimates a general improvement of air quality for the WAM2030 scenario, with no non-compliant air quality zones for NO2, SO2, and PM indicators. Despite an improvement for O-3, the model still estimates non-compliant areas. For this pollutant, the WAM2030 scenario leads to different impacts depending on the indicator considered. Although the model estimates a reduction in maximum hourly O-3 concentrations, small increases in O-3 concentrations in winter and nighttime in the summer lead to increases in the annual mean in some areas and increases in other indicators (SOMO35 for health impacts and AOT40 for impacts on vegetation) in some urban areas. The results suggest that the lower NOx emissions in the WEM and WAM scenarios lead to less removal of O-3 by NO titration, especially background ozone in winter and both background and locally produced ozone in summer, in areas with high NOx emissions.

This paper aims to provide scientific support for decision-making in the field of improving air quality by evaluating pollution reduction measures included in the current Spanish policy framework of the 1st National Air Pollution Control Programme (NAPCP). First, the health impacts of air quality are estimated by using the concentrations estimated by multiscale air quality modeling and the recommended concentration–response functions (CRF), specifically as a result of exposure to particulate matter (PM), nitrogen dioxide (NO2), and ozone (O3). Second, the associated external costs are calculated by monetization techniques. Two scenarios are analyzed: a package including existing measures (WM2030) and a package with additional measures (WAM2030). Compared with the baseline scenario, an improvement was found in the health effects of NO2, PM10, and PM2.5, while for O3 there was a slight worsening, mainly due to the increase in the O3 metric used (SOMO35), which increases over some urban areas. Despite this, the monetary valuation of the total effects on health as a whole shows external benefits due to the adoption of measures (WM2030), compared with the reference scenario (no measures) of more than € 17.5 billion and, when considering the additional measures (WAM2030), benefits of about € 58.1 billion.

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 (PM 10 ) and < 2.5 μm (PM 2.5 ) 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 PM 10 and PM 2.5 were 20.17 ± 3.91 μg/m 3 and 10.83 ± 2.08 μg/m 3 respectively. During follow-up 282 cases of incident hypertension were recorded. In the fully adjusted model, compared with the lowest quartile of PM 10, the multivariate weighted ORs (95% CIs) for developing hypertension with increasing PM 10 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 PM 2.5 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/m 3 increment in PM 10 and PM 2.5 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.

This paper assesses the health impact, in terms of the reduction of premature deaths associated with changes in air pollutant exposure, resulting from double-aim strategies for reducing emissions of greenhouse gases and air pollutants from the transport sector for the year 2030 in Spain. The impact on air quality of selected measures for reducing emissions from the transport sector (increased penetration of biofuel and electric car use) was assessed by air quality modeling. The estimation of population exposure to NO2, particulate matter (PM) and O3 allows for estimation of associated mortality and external costs in comparison with the baseline scenario with no measures. The results show that the penetration of the electric vehicle provided the largest benefits, even when the emissions due to the additional electricity demand were considered.