Commentary: A road map for future data-driven urban planning and environmental health research.

Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with better knowledge. Better understanding through harmonising and analysing data in large numbers of cities is essential to identifying the most effective means of disease prevention and understanding context dependencies important for policy.

GCoM datasets: a collection of climate and energy action plans with mitigation, adaptation and energy access commitments.

This paper presents a collection of datasets holding information on the energy and climate action plans of 6,850 municipalities, taking part in the transnational initiative of the Global Covenant of Mayors (GCoM). This collection includes commitments for reducing net GHG emissions by at least 20% by 2020, 55% by 2030 and becoming climate neutral by 2050. The signatories commit to addressing any of the three pillars of the initiative, namely climate change mitigation, adaptation and energy access. Following two previous releases, the third release of the GCoM collection is introduced, with closing date September 2022. The datasets include information on the action plans and monitoring reports as they are self-reported by signatories, undergoing a quality-harnessing procedure before publication. Additionally, an external comparison is developed with the Emissions Database for Global Atmospheric Research (EDGAR v7), controlling for comparable sources and activity sectors, ensuring the usability of the GCoM datasets for relevant research on local policies and their effects on reducing the impact of climate change.

Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review.

BACKGROUND: As the world becomes increasingly urbanised, there is recognition that public and planetary health relies upon a ubiquitous transition to sustainable cities. Disentanglement of the complex pathways of urban design, environmental exposures, and health, and the magnitude of these associations, remains a challenge. A state-of-the-art account of large-scale urban health studies is required to shape future research priorities and equity- and evidence-informed policies. OBJECTIVES: The purpose of this review was to synthesise evidence from large-scale urban studies focused on the interaction between urban form, transport, environmental exposures, and health. This review sought to determine common methodologies applied, limitations, and future opportunities for improved research practice. METHODS: Based on a literature search, 2958 articles were reviewed that covered three themes of: urban form; urban environmental health; and urban indicators. Studies were prioritised for inclusion that analysed at least 90 cities to ensure broad geographic representation and generalisability. Of the initially identified studies, following expert consultation and exclusion criteria, 66 were included. RESULTS: The complexity of the urban ecosystem on health was evidenced from the context dependent effects of urban form variables on environmental exposures and health. Compact city designs were generally advantageous for reducing harmful environmental exposure and promoting health, with some exceptions. Methodological heterogeneity was indicative of key urban research challenges; notable limitations included exposure and health data at varied spatial scales and resolutions, limited availability of local-level sociodemographic data, and the lack of consensus on robust methodologies that encompass best research practice. CONCLUSION: Future urban environmental health research for evidence-informed urban planning and policies requires a multi-faceted approach. Advances in geospatial and AI-driven techniques and urban indicators offer promising developments; however, there remains a wider call for increased data availability at local-levels, transparent and robust methodologies of large-scale urban studies, and greater exploration of urban health vulnerabilities and inequities.

Relationship between Environmental Conditions and Utilisation of Community-Based Mental Health Care: A Comparative Study before and during the COVID-19 Pandemic in Italy.

(1) Background: Lower socioeconomic status increases psychiatric service use, exacerbated during the COVID-19 pandemic by environmental stressors like air pollution and limited green spaces. This study aims to assess the influence of sociodemographic and environmental factors on mental health service utilisation. (2) Methods: This retrospective study uses an administrative database focusing on community mental health services in Northeast Italy. Spatial and temporal analyses were used to address space-time dependencies. (3) Results: Findings showed that sociodemographic factors like living in rented apartments and lower education levels predicted higher mental health service use. Environmental factors, such as elevated NO2 levels and, before the pandemic, lower solar radiation and tree cover, correlated with increased service utilisation. COVID-19 reduced most of the pre-existing differences associated with these factors across census blocks with a different composition of sociodemographic and environmental factors. (4) Conclusions: These findings contribute to a better understanding of the impact of the environment on public mental health.

Impact of air quality model settings for the evaluation of emission reduction strategies to curb air pollution.

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 NO2), 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.

Reducing biomass burning is key to decrease PM2.5 exposure in European cities.

Throughout the world, ambient fine particulate matter (PM2.5) is the environmental factor that poses the greatest risk to health and most European citizens continue to be exposed to PM2.5 levels well above World Health Organization guidelines. Here we present a comprehensive PM2.5 modelling-based source allocation assessment in 708 urban areas in Europe. The results show that urban cores, together with their commuting zones, contribute an average of 22% to urban PM2.5 concentrations levels. The residential sector is the highest source sector in 56% of cities. Its average contribution to PM2.5 formation is 27%, with a cluster of cities in Northern Italy and Eastern Europe contributing to more than 50%. Industry, agriculture and road transport show average contributions of 18%, 17% and 14%, respectively. Most emissions from residential sectors are anthropogenic primary PM2.5 which includes a condensable fraction. Furthermore, anthropogenic primary PM2.5 represents the precursor with the highest contribution in most cities (72%), contributing an average of 35% to urban PM2.5 levels. Emissions of anthropogenic primary PM2.5 by the residential sector are almost entirely (with exceptions of few countries) due to biomass burning. These results suggest that the residential sector should be a key target of any policy to improve air quality and that climate policies promoting biomass as a climate-neutral fuel could have a detrimental effect on air quality. A more integrated approach to climate and air quality policy design is desirable.

Addressing the different paces of climate and air quality combustion emissions across the world.

Greenhouse gases (GHG) and air pollutants (AP) share several anthropic sources but evolve differently in time across the various regions of the globe. Fossil and biological fuel combustion is by far the single process producing the highest amounts of both types of compounds. We have analyzed the paces of change of both GHG and AP emissions across the world and in some selected highly emitting regions using purposely designed indicators. We have observed that, overall, combustion processes are generally producing a lower amount of pollutants per unit of GHG emitted in 2018 than in 1970, with the noticeable exception of ammonia emissions in transport. Nevertheless, comparing countries at different development levels, evidence of possible further improvement clearly emerges, depending on the technological evolution of the most important emitting sectors and on the implementation of appropriate control measures and policies.

Prenatal exposure to environmental air pollution and psychosocial stress jointly contribute to the epigenetic regulation of the serotonin transporter gene in newborns.

Antenatal exposures to maternal stress and to particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) have been independently associated with developmental outcomes in early infancy and beyond. Knowledge about their joint impact, biological mechanisms of their effects and timing-effects, is still limited. Both PM2.5 and maternal stress exposure during pregnancy might result in altered patterns of DNA methylation in specific stress-related genes, such as the serotonin transporter gene (SLC6A4 DNAm), that might, in turn, influence infant development across several domains, including bio-behavioral, cognitive and socio-emotional domains. Here, we investigated the independent and interactive influence of variations in antenatal exposures to maternal pandemic-related stress (PRS) and PM2.5 on SLC6A4 DNAm levels in newborns. Mother-infant dyads (N = 307) were enrolled at delivery during the COVID-19 pandemic. Infants' methylation status was assessed in 13 CpG sites within the SLC6A4 gene's region (chr17:28562750-28562958) in buccal cells at birth and women retrospectively report on PRS. PM2.5 exposure throughout the entire gestation and at each gestational trimester was estimated using a spatiotemporal model based on residential address. Among several potentially confounding socio-demographic and health-related factors, infant's sex was significantly associated with infants' SLC6A4 DNAm levels, thus hierarchical regression models were adjusted for infant's sex. Higher levels of SLC6A4 DNAm at 6 CpG sites were found in newborns born to mothers reporting higher levels of antenatal PRS and greater PM2.5 exposure across gestation, while adjusting for infant's sex. These effects were especially evident when exposure to elevated PM2.5 occurred during the second trimester of pregnancy. Several important brain processes (e.g., synaptogenesis and myelination) occur during mid-pregnancy, potentially making the second trimester a sensitive time window for the effects of stress-related exposures. Understanding the interplay between environmental and individual-level stressors has important implications for the improvement of mother-infant health during and after the pandemic.

Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment.

BACKGROUND: Ambient air pollution is a major risk to health and wellbeing in European cities. We aimed to estimate spatial and sector-specific contributions of emissions to ambient air pollution and evaluate the effects of source-specific reductions in pollutants on mortality in European cities to support targeted source-specific actions to address air pollution and promote population health. METHODS: We conducted a health impact assessment of data from 2015 for 857 European cities to estimate source contributions to annual PM2·5 and NO2 concentrations using the Screening for High Emission Reduction Potentials for Air quality tool. We evaluated contributions from transport, industry, energy, residential, agriculture, shipping, and aviation, other, natural, and external sources. For each city and sector, three spatial levels were considered: contributions from the same city, the rest of the country, and transboundary. Mortality effects were estimated for adult populations (ie, ≥20 years) following standard comparative risk assessment methods to calculate the annual mortality preventable on spatial and sector-specific reductions in PM2·5 and NO2. FINDINGS: We observed strong variability in spatial and sectoral contributions among European cities. For PM2·5, the main contributors to mortality were the residential (mean contribution of 22·7% [SD 10·2]) and agricultural (18·0% [7·7]) sectors, followed by industry (13·8% [6·0]), transport (13·5% [5·8]), energy (10·0% [6·4]), and shipping (5·5% [5·7]). For NO2, the main contributor to mortality was transport (48·5% [SD 15·2]), with additional contributions from industry (15·0% [10·8]), energy (14·7% [12·9]), residential (10·3% [5·0]), and shipping (9·7% [12·7]). The mean city contribution to its own air pollution mortality was 13·5% (SD 9·9) for PM2·5 and 34·4% (19·6) for NO2, and contribution increased among cities of largest area (22·3% [12·2] for PM2·5 and 52·2% [19·4] for NO2) and among European capitals (29·9% [12·5] for PM2·5 and 62·7% [14·7] for NO2). INTERPRETATION: We estimated source-specific air pollution health effects at the city level. Our results show strong variability, emphasising the need for local policies and coordinated actions that consider city-level specificities in source contributions. FUNDING: Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making 2023-2026 Horizon Europe project.

The impact of PM2.5, PM10 and NO2 on Covid-19 severity in a sample of patients with multiple sclerosis: A case-control study.

BACKGROUND: Many studies investigated the association between air pollution and Covid-19 severity but the only study focusing on patients with Multiple Sclerosis (MS) exclusively evaluated exposure to PM2.5. We aim to study, in a sample of MS patients, the impact of long-term exposure to PM2.5, PM10 and NO2 on Covid-19 severity, described as occurrence of pneumonia. METHODS: A 1:2 ratio case-control study was designed, differentiating cases and controls based on Covid-19 pneumonia. Associations between pollutants and outcome were studied using logistic regression. Weighted quantile sum (WQS) logistic regression was used to identify the individual contribution of each pollutant within the mixture; Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression was performed to confirm the variable selection from WQS. All the analyses were adjusted for confounders selected a priori. RESULTS: Of the 615 eligible patients, 491 patients provided detailed place of exposure and were included in the principal analysis. Higher concentrations of air pollutants were associated with increased odds of developing Covid-19 pneumonia (PM2.5: 3rd vs 1st tercile OR(95% CI)=2.26(1.29;3.96); PM10: 3rd vs 1st tercile OR(95% CI)=2.12(1.22;3.68); NO2: 3rd vs 1st tercile OR(95% CI)=2.12(1.21;3.69)). Pollutants were highly correlated with each other; WQS index was associated to an increased risk of pneumonia (ß=0.44; p-value=0.004) and the main contributors to this association were NO2 (41%) and PM2.5 (34%). Consistently, Lasso method selected PM2.5 and NO2. CONCLUSIONS: Higher long-term exposure to PM2.5, PM10 and NO2 increased the odds of Covid-19 pneumonia among MS patients and the most dangerous pollutants were NO2 and PM2.5.

Design and implementation of a new module to evaluate the cost of air pollutant abatement measures.

Integrated Assessment Model provides a useful framework for evaluating different aspects of air quality policies, spanning from abatement measures to emissions, concentrations, health impacts and costs. These models are then useful to provide a holistic view of the impacts of policies, so that ex-ante one can evaluate how various policies will impact air concentrations, health benefits and implementation costs. Among these Integrated Assessment Models, SHERPA (Screening for High Emission Potentials on Air) has been recently used to evaluate the impact of policies, covering all aspects from measures to health, but without being able to provide the dimension related to abatement measures costs. In this paper we fill this gap, developing a module able to associate to a SHERPA scenario its related implementation costs. This paper describes how this module has been developed and provide a concrete application of this tool. Results of this module can be useful to provide a full cost-benefit analysis of alternative policies based on technological changes, covering both internal costs (costs of abatement measures) and external costs (related to human health impacts of air quality).

Emissions of Carbonaceous Particulate Matter and Ultrafine Particles from Vehicles-A Scientific Review in a Cross-Cutting Context of Air Pollution and Climate Change.

Airborne particulate matter (PM) is a pollutant of concern not only because of its adverse effects on human health but also on visibility and the radiative budget of the atmosphere. PM can be considered as a sum of solid/liquid species covering a wide range of particle sizes with diverse chemical composition. Organic aerosols may be emitted (primary organic aerosols, POA), or formed in the atmosphere following reaction of volatile organic compounds (secondary organic aerosols, SOA), but some of these compounds may partition between the gas and aerosol phases depending upon ambient conditions. This review focuses on carbonaceous PM and gaseous precursors emitted by road traffic, including ultrafine particles (UFP) and polycyclic aromatic hydrocarbons (PAHs) that are clearly linked to the evolution and formation of carbonaceous species. Clearly, the solid fraction of PM has been reduced during the last two decades, with the implementation of after-treatment systems abating approximately 99% of primary solid particle mass concentrations. However, the role of brown carbon and its radiative effect on climate and the generation of ultrafine particles by nucleation of organic vapour during the dilution of the exhaust remain unclear phenomena and will need further investigation. The increasing role of gasoline vehicles on carbonaceous particle emissions and formation is also highlighted, particularly through the chemical and thermodynamic evolution of organic gases and their propensity to produce particles. The remaining carbon-containing particles from brakes, tyres and road wear will still be a problem even in a future of full electrification of the vehicle fleet. Some key conclusions and recommendations are also proposed to support the decision makers in view of the next regulations on vehicle emissions worldwide.

A spatiotemporal analysis of NO 2 concentrations during the Italian 2020 COVID-19 lockdown.

When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify-in space and time-the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV-2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatiotemporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factor, as well as the spatial and temporal correlation existing in the data. In particular, we focus here on the 2019/2020 relative change in nitrogen dioxide (NO 2 ) concentrations in the north of Italy, for the period of March and April during which the lockdown measure was in force. We found that during March and April 2020 most of the studied area is characterized by negative relative changes (median values around - 25%), with the exception of the first week of March and the fourth week of April (median values around 5%). As these changes cannot be attributed to a weather effect, it is likely that they are a byproduct of the lockdown measures. There are two aspects of our research that are equally interesting. First, we provide a unique statistical perspective for calculating the relative change in the NO 2 by jointly modeling pollutant concentrations time series. Second, as an output we provide a collection of weekly continuous maps, describing the spatial pattern of the NO 2 2019/2020 relative changes.

The effect of air pollution on COVID-19 severity in a sample of patients with multiple sclerosis.

BACKGROUND AND PURPOSE: Some studies have shown that air pollution, often assessed by thin particulate matter with diameter below 2.5 µg/m3 (PM2.5), may contribute to severe COVID-19 courses, as well as play a role in the onset and evolution of multiple sclerosis (MS). However, the impact of air pollution on COVID-19 has never been explored specifically amongst patients with MS (PwMS). This retrospective observational study aims to explore associations between PM2.5 and COVID-19 severity amongst PwMS. METHODS: Data were retrieved from an Italian web-based platform (MuSC-19) which includes PwMS with COVID-19. PM2.5 2016-2018 average concentrations were provided by the Copernicus Atmospheric Monitoring Service. Italian patients inserted in the platform from 15 January 2020 to 9 April 2021 with a COVID-19 positive test were included. Ordered logistic regression models were used to study associations between PM2.5 and COVID-19 severity. RESULTS: In all, 1087 patients, of whom 13% required hospitalization and 2% were admitted to an intensive care unit or died, were included. Based on the multivariate analysis, higher concentrations of PM2.5 increased the risk of worse COVID-19 course (odds ratio 1.90; p = 0.009). CONCLUSIONS: Even if several other factors explain the unfavourable course of COVID-19 in PwMS, the role of air pollutants must be considered and further investigated.

Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley.

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-linear model) have been assessed: temporal parametric trend components were included together with some random effects that allowed the accounting of spatial structure of the region. The Bayesian approach allowed the borrowing information effect, including simpler model results in the more complex setting. To compare these models, Watanabe-Akaike Information Criteria (WAIC) and Leave One Out Information Criteria (LOOIC) were applied. In the modelling phase, the relationship between the disease risk and pollutants exposure (PM2.5) accounting for the urbanisation level of each geographical unit showed a strong significant effect of the pollutant exposure (OR = 1.075 and posterior probability, or PP, >0.999, equivalent to p < 0.001). A high-risk cluster of Cardiovascular mortality in the Lomellina subareas in the studied window was identified.

PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach.

Some environmental factors are associated with an increased risk of multiple sclerosis (MS). Air pollution could be a main one. This study was conducted to investigate the association of particulate matter 2.5 (PM2.5) concentrations with MS prevalence in the province of Pavia, Italy. The overall MS prevalence in the province of Pavia is 169.4 per 100,000 inhabitants. Spatial ground-level PM2.5 gridded data were analysed, by municipality, for the period 2010-2016. Municipalities were grouped by tertiles according to PM2.5 concentration. Ecological regression and Bayesian statistics were used to analyse the association between PM2.5 concentrations, degree of urbanization, deprivation index and MS risk. MS risk was higher among persons living in areas with an average winter PM2.5 concentration above the European annual limit value (25 µg/m3). The Bayesian map revealed sizeable MS high-risk clusters. The study found a relationship between low MS risk and lower PM2.5 levels, strengthening the suggestion that air pollution may be one of the environmental risk factors for MS.

Impacts of a climate change initiative on air pollutant emissions: Insights from the Covenant of Mayors.

The Covenant of Mayors (CoM) is a successful European initiative which encourages local authorities to be proactive in fighting climate change. Recently, it expanded to cover adaptation and energy access/poverty and became a global initiative. In this study we investigate an additional perspective: synergies and trade-offs between climate and air quality. Signatories pledge to reduce their Greenhouse gas (GHG) emissions and voluntarily report their emissions, energy consumption and the measures that they carry out to reach their goals. We develop a methodology to estimate air pollutant emissions corresponding to CO2 emissions CoM signatories report, using information they already submit and national estimates of air pollutant emission factors. The methodology is applied to over 1600 signatories in Europe, representing over 80 million inhabitants. Results show that, in general, signatories are reducing both types of emissions. However, there are also cases where emissions increase. We explore the reasons behind these changes and highlight the role of technological improvement. This work calls for an increased coherence between climate and air quality plans at the local scale and provides a first step and a tool to support signatories, even the smallest ones, to move in this direction.

Electric light commercial vehicles: Are they the sleeping giant of electromobility?

Transport emissions need to be drastically decreased in order to put Europe on a path towards a long-term climate neutrality. Commercial transport, and especially last mile delivery is expected to grow because of the rise of e-commerce. In this frame, electric light commercial vehicles (eLCVs) can be a promising low-emission solution. Literature holistically analysing the potential of eLCVs as well as related support policies is sparse. This paper attempts to close this research gap. To this aim, the total cost of ownership (TCO) comparisons for eLCVs and benchmark vehicles are performed and support measures that target the improvement of the eLCV TCO are analysed. Various eLCV deployment scenarios until 2030 are explored and their impact on carbon dioxide (CO2) and other pollutant emissions as well as pollutant concentrations are calculated. It is found that while in several European Union (EU) countries eLCVs are already cost competitive, because of fiscal support, some remaining market barriers need to be overcome to pave the way to mass market deployment of eLCVs. High penetration of eLCVs alone can lead to a reduction of total transport CO2 emissions by more than 3% by 2030. For pollutant emissions, such as nitrogen oxide (NOx) and particulate matter (PM), the reduction would be equal or even higher. In the case of PM, this can translate to reductions in concentrations by nearly 2% in several urban areas by 2030. Carefully designed support policies could help to ensure that the potential of eLCVs as a low-emission alternative is fully leveraged in the EU.

Urban pollution in the Danube and Western Balkans regions: The impact of major PM2.5 sources.

The SHERPA tool was used to assess the major pollution sources and the geographical areas impacting on the PM2.5 of the main cities in the Danube and Western Balkans regions. The activity sectors influencing most the PM2.5 levels in the study area are energy production (22%), agriculture (19%), residential combustion (16%) and road transport (7%). The energy production in inefficient coal-fuelled power plants was identified as one of main source of PM2.5 in the Western Balkans. As for the geographical origin of PM2.5, the transboundary pollution is confirmed as the main origin of PM2.5 (44%) in the investigated cities, while the city own emissions and the national sources outside the concerned city impact on average 22% and 15%, respectively. An association was observed between the long-range transport and the impact of agriculture and energy production, while both local urban emissions and long-range transport were associated with the residential sector. A special attention is given in this study to biomass, a renewable source, which use is often promoted in the frame of climate and energy policies. Nevertheless, the combustion of biomass in inefficient small appliances has considerable particulate matter emissions and therefore this type of practice impacts negatively on air quality. Considering that biomass is traditionally used in South-East Europe as fuel for residential heating, the interpretation of the model results was supported with the estimation of biomass burning contributions to PM2.5 obtained with receptor models and data on biomass fuel consumption from the literature. The analysis of the contributions from biomass burning derived from receptor models suggests that biomass burning is the dominant source within the residential heating sector in the studied area and that the emissions from this source are likely underestimated. This study concludes that more effort is needed to improve the estimations of biomass burning emissions and that policies to improve air quality in the cities should involve a geographic context wider than the city level.

Multi-level policies for air quality: implications of national and sub-national emission reductions on population exposure.

Poor air quality and related health impacts are still an issue in many cities and regions worldwide. Integrated assessment models (IAMs) can support the design of measures to reduce the emissions of precursors affecting air pollution. In this study, we apply the SHERPA (screening for high emission reduction potentials for air quality) model to compare spatial and sectoral emission reductions, given country-scale emission targets. Different approaches are tested: (a) country "uniform" emission reductions, (b) emission reductions targeting urban areas, (c) emission reductions targeting preferential sectors. As a case study, we apply the approaches to the implementation of the National Emission Ceiling Directive. Results are evaluated in terms of the reduction in average population exposure to PM2.5 overall in a country and in its main cities. Results indicate that the reduction of population exposure to PM2.5 highly depends on the way emission reductions are implemented. This work also shows the usefulness of the SHERPA model to support national authorities implementing national emission reduction targets while, at the same time, addressing their local air quality issues.