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The last decade of air pollution epidemiology and the challenges of quantitative risk assessment

Environmental Health volume 23, Article number: 98 (2024) Cite this article

Abstract

Epidemiologic research and quantitative risk assessment play a crucial role in transferring fundamental scientific knowledge to policymakers so they can take action to reduce the burden of ambient air pollution. This commentary addresses several challenges in quantitative risk assessment of air pollution that require close attention. The background to this discussion provides a summary of and conclusions from the epidemiological evidence on ambient air pollution and health outcomes accumulated since the 1990s. We focus on identifying relevant exposure-health outcome pairs, the associated concentration-response functions to be applied in a risk assessment, and several caveats in their application. We propose a structured and comprehensive framework for assessing the evidence levels associated with each exposure-health outcome pair within a health impact assessment context. Specific issues regarding the use of global or regional concentration-response functions, their shape, and the range of applicability are discussed.

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Introduction

It is widely acknowledged that air pollution is one of the most significant environmental risks to health globally [76]. The updated World Health Organization (WHO) air quality guidelines (AQGs), published in September 2021, underscore the urgent need for immediate action to combat the adverse effects of pollutants such as fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) on public health and the environment [85]. Modern quantitative health risk assessment (HRA) plays a crucial role in transferring fundamental scientific knowledge to policymakers, empowering them to take action to reduce the burden of air pollution, and evaluating the health benefits of interventions. Rigaud et al.‘s recent contribution to this Journal (2024) provides an extensive overview of HRA methods and offers excellent examples. This commentary addresses some of the challenges in quantitative risk assessment of ambient air pollution that require close attention, especially considering the lessons learnt from the recently completed WHO project Estimating the Morbidity from Air Pollution and its Economic Costs (EMAPEC) [26]. A summary of the epidemiological evidence on air pollution and health outcomes accumulated over the last decades provides a background to this discussion.

Early epidemiological studies on short and long-term exposure

During the early stages of research in environmental epidemiology, the identification of an association between short (on the scale of hours to days) and long-term exposure to air pollution (on the scale of months to years) and various adverse health outcomes, particularly the impact on mortality, laid the foundation for quantifying the attributable burden of pollution. Short-term observational studies began gaining prominence in the late 1980s and early 1990s [6, 67, 68]. These studies were driven by growing concerns about air quality and its impact on public health, particularly in urban areas with significant air pollution levels. Throughout the 1990s, these short-term studies expanded, particularly with the use of time-series analyses, which examined daily fluctuations in air pollution levels and corresponding health outcomes, such as hospital admissions and mortality, in several cities [39]. These studies provided compelling evidence of the acute health effects of air pollution. Regarding long-term exposure, a seminal air pollution research paper published in 1993, the ‘Six Cities’ cohort study in the United States [21], demonstrated a link between mortality and long-term exposure to PM2.5. This discovery gained further support in 1995 after the analysis of the health data from the American Cancer Society (ACS), a cohort of approximately half a million residents from across the United States [61], which revealed an almost linear increase in mortality risk across the entire range of observed PM2.5 annual mean concentrations (from 10 to 30 µg/m³) in urban background areas. A subsequent 2002 analysis of cardiovascular and lung cancer mortality with exposure to PM2.5 [61] of the same ACS cohort triggered research into the biological mechanisms responsible for these effects. Note that the statistical methodology used in the ACS study was advanced and considered several individual potential confounders. The potential spatial autocorrelation due to missing or mismeasured risk factors spatially correlated with air pollution was addressed in the statistical model by incorporating a spatially-varying random-effects component to correct for autocorrelation, a practice that has been in common use in subsequent years. This overall evidence led to the formulation of the first global air quality guidelines by WHO in 2005 [84].

The first decade of the 21st century witnessed a convergence of epidemiological studies, controlled human exposure trials, and animal experiments. Epidemiological studies in humans (both on short- and long-term exposures) have provided refined estimates of environmental risks, with evidence suggesting that associations between PM and health outcomes are present even at ambient concentrations below the WHO air quality guideline levels. Mechanistic studies in animals and humans have provided a framework for understanding the pathways by which air pollution exposure may predispose individuals to health effects. This interdisciplinary approach deepened the understanding of the cardiovascular effects of PM exposure and confirmed the causality of PM2.5 effects [5]. Results from cohort studies during this period further strengthened the epidemiological evidence of the effects of PM2.5 and NO2 on all-cause (in many cases non-accidental or natural mortality) and cause-specific mortality, and the development (incidence) of selected cardiovascular and respiratory diseases. Evidence from European cohorts, such as the collaborative ESCAPE study [1], confirmed earlier findings reported in North American studies. In addition, epidemiological studies began using administrative databases, including census and mortality registries, which enabled large-scale data pooling of millions of individuals for a period extending a decade or longer, as exemplified by the observational study of the 2.1 million people from Canada [16] and 1.3 million residents of Rome [10]. Exposure to ultrafine particles has been gaining attention because of the potential translocation from the lungs to the circulatory system and the possible effects on the heart [35, 55].

The last decade of air pollution epidemiology

Several studies based on “administrative cohorts” have been published in the last decade [7, 11, 17, 19, 25]. Cohorts based on administrative data have several advantages over “traditional cohorts” in investigating air pollution risks, namely their size, including millions of people, their representativeness of the general population, including different demographic and socioeconomic groups, enhancing the generalizability of findings, data collected over many years that allow for an extended follow-up. These advantages make administrative cohorts a valuable resource for large-scale, population-based research. However, the lack of detailed individual-level data on lifestyle factors is a drawback. This limitation has been partially overcome using area-based indicators to represent individual data, for example, area-based socioeconomic status (SES) as a surrogate for individual SES. In addition, several cohorts have used “indirect adjustment” for individual confounders that employs an ancillary database that matches the demographic characteristics of the original cohort and contains information on both the individual factors (that are missing in the original cohort) and air pollution exposure data [24, 73].

While most initial cohort studies were conducted in North America and Europe, areas in which populations were exposed to low or medium levels of PM2.5 concentration (< 30 µg/m3), the last decade has seen a notable expansion of epidemiological studies in regions with higher ambient air pollution levels, particularly in China and Korea [43, 46], where observed PM exposures were typically 2 to 4 times higher than in previous studies. The newer studies accounted for various confounders that may influence mortality, such as diet, lifestyle, and climate. A meta-analysis published in 2020, specifically designed to support the work in updating the WHO AQGs, considered the findings from over 100 cohort studies that examined the link between PM10 or PM2.5 and all-cause and cause-specific premature mortality [12]. This latter meta-analysis has been recently updated to consider several studies published in the last few years [57].

The literature on air pollution and its impact on health has witnessed significant methodological advancements over the past decade. Firstly, the modelling of air pollution exposure has undergone substantial enhancements. Early evaluations at the population level relied primarily on fixed monitors, which, while providing benchmark standards, were limited in assessing small-scale exposure variability due to geographical constraints. To address this issue, recent air pollution exposure assessments have incorporated various indicator variables to measure fine-scale exposures, including land-use regression variables to better capture a subject’s proximity to roads and industrial facilities, as well as using remote sensed satellite measurements with appropriate downscaling, and results of chemical transport model (CTM) estimates of PM2.5 and NO2 concentrations [18, 20, 30, 80]. These enhancements have broadened the geographical coverage of exposures, especially in areas lacking physical monitoring stations, to provide highly spatially resolved concentration maps in both urban and rural settings, with a typical resolution scale of one kilometre, or less (100 m) in some cases. This spatial precision allows for addressing issues related to community exposure disparities and pinpointing local pollution hotspots. A recent application of these models in the US over the past decade revealed an increase in racial and ethnic relative disparities in PM2.5-related mortality and NO2-related pediatric asthma despite overall declines in public health effects associated with these pollutants [40].

Secondly, notable systemic consequences of air pollution have been identified, leading to additional adverse health outcomes. While respiratory and cardiovascular conditions have traditionally been linked to air pollution, recent studies suggest potential associations with neurological conditions (such as Parkinson’s and Alzheimer’s disease), diabetes, various types of cancer (in addition to lung cancer, which was studied long before), neurobehavioral development issues in children, mental disorders, and perinatal health [75]. Ongoing research explores the mechanistic pathways connecting environmental exposure to these health outcomes, highlighting the potential role of air pollution-induced inflammatory responses and the persistent generation of reactive oxygen radicals [53]. Moreover, investigations into the intake of ultrafine particles and their passage across biological barriers, including the circulatory system, blood-brain barrier, and placental barrier, have spurred additional insights. Ultrafine particles can reach all organs of the body, and potentially accumulate at sites of disease [66]. Epidemiological research on ultrafine particles is complicated because of the difficulties in exposure assessment, but increasing evidence in both North America and Europe suggests important health impacts [56].

Most recently, three large-scale studies funded by the Health Effects Institute (HEI) have investigated the health effects of low-level air pollution exposure in Canada, the United States, and Europe. These studies are the “Mortality Air Pollution Associations in Low Exposure Environments” (MAPLE) Canadian study by Brauer et al. [3], the USA Medicare study by Dominici et al. [23], and the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) by Brunekreef et al. [7]. These studies included millions of participants, employed advanced exposure assessment techniques, and used comprehensive statistical analyses with innovative approaches. All three studies reported statistically significant positive associations between long-term PM2.5 exposure and mortality risks in nationally representative administrative cohorts. The hazard ratios (HR) and 95% confidence intervals (CI) associated with an increase in PM2.5 exposure of 5 µg/m³ and mortality from natural causes were as follows: 1.041 (95%CI 1.036, 1.047) in the Canadian Census Health and Environment Cohorts (CanCHEC) in MAPLE; 1.032 (95%CI 1.029, 1.036) in the USA Medicare cohort; and 1.053 (95%CI 1.021, 1.085) in the six ELAPSE administrative cohorts. Note that the USA Medicare cohort used the all-cause mortality (i.e. including accidental deaths) in the cohort older than 65 years old. The pooled analysis of the three studies provided by Chen et al. [11] suggests an increasing risk starting from the lowest observed exposure level (3.7 µg/m3). The ELAPSE study in Europe, using pooled data from several traditional cohorts [74], also explored several morbidity outcomes, including myocardial infarction and stroke [88], asthma in adults [51], COPD [50], and lung cancer [37].

The insights derived from these “low-level studies” [2] have informed the WHO in its work on the 2021 AQGs and played a crucial role in shaping recent regulations on PM2.5 in both the USA and Europe. Notably, the US Environmental Protection Agency’s decision to lower the National Ambient Air Quality Standards (NAAQS) for fine particulate matter air pollution (PM2.5) in urban background areas from 12 to 9 µg/m3 in 2024Footnote 1, and the European Parliament’s decision to approve the revision of the Ambient Air Quality Directive to reduce the annual limit value for PM2.5 from 25 µg/m3 to 10 µg/m3 in 2030 Footnote 2 are outcomes directly influenced by the information provided by these studies.

Global burden of Disease (GBD) studies

The first in a series of GBD studies was published in 1993, and detailed the state of global health for eight world regions as of 1990 [54]. The comparative risk assessment covered 106 illnesses and ten risk factors. The study of morbidity and mortality was an effort to characterise the burden of disease using a standardised approach. The risk factors included behavioural, occupational, metabolic, and environmental factors, including air pollution.

In 2007, a new study (GBD 2010) was funded by the Bill & Melinda Gates Foundation (BMGF) [48]. An important innovation of the air pollution health impact assessment methodology in GBD 2010 was the introduction of the integrated exposure-response functions (IERs) for cause-specific mortality. Since PM2.5 levels in many regions of the world exceed the levels observed in epidemiological studies, the IERs combined population exposure to various sources of combustion particulates, such as outdoor and indoor air pollution, environmental tobacco smoke (second-hand smoke), and active smoking. The health outcomes included diseases of the cardiovascular and respiratory systems, and lung cancer. The IERs, which have changed considerably over the years [58], have a non-linear shape, and they increase monotonically with concentration, starting from a “counterfactual concentration”, also known as the theoretical minimum risk exposure level (TMREL), which represents the lowest exposure across the available epidemiological studies with the lowest credible level for the existence of a health burden. In principle, the TMREL is uncertain, and might vary by location and demographic characteristics. Since 2015, the GBD study has used a TMREL for PM2.5 based on a uniform distribution over the range of 2.4 to 5.9 µg/m³.

Since GBD 2010, several revisions of the GBD have been published, with the most recent update in 2024 and identified as GBD 2021 [27]. Each iteration has contributed to methodological innovations and the development of more robust techniques for exposure assessment, data synthesis, analysis, and interpretation. Furthermore, with each iteration of GBD, the level of detail of the results has improved, including added diseases and risk factors, geographical stratification, and specification of health effects by age. The latest GBD 2021 study provides results (e.g. attributable deaths and disability-adjusted life years (DALYs)) for 88 risk factors across 204 countries and 811 subnational areas for the historical period between 1990 and 2021. Forecasted estimates for 2050 are also available [28]. The current calculations for PM are based on an updated risk function, estimated in an analysis of epidemiological studies of the effect of particulate air pollution from outdoor and indoor sources, with no inputs from studies on environmental tobacco smoke or active smoking. According to the GBD 2021 study, the global mortality attributed to PM2.5 ambient air pollution in 2021 was 4.7 2 (95% Uncertainty Interval: 3.48; 5.80) million deaths, including 4.51 million deaths among the population over 25 years (97 deaths per 100,000 in the population 25+, or 7.9% of natural deaths). Air pollution has increased its importance compared to other leading risk factors like high systolic blood pressure, smoking, and high fasting plasma glucose. Among all risk factors, particulate matter pollution (indoor plus outdoor exposure) contributed the largest share of global DALYs (approx. 8%), and the second largest share of global deaths (approx. 12%) in 2021. (https://vizhub.healthdata.org/gbd-compare/)

The benefits of quantitative risk assessment

Briggs [4] focused on the evolution from risk assessment of single exposures to the more complex need to predict “the health-related impacts of policies and other interventions that affect the environment in ways that take account of the complexities, interdependencies and uncertainties of the real world” in an integrated Health Impact Assessment (HIA). Therefore, risk assessment is traditionally focused on single exposures or events while HIA takes a broad concept of both the environment and health [86]. Rigaud et al. [64] emphasize that there is a terminology issue, and the term “quantitative risk assessment” represents a systematic approach that uses the same methodology to evaluate and present the potential health impacts of single exposures, projects, policies, or programs with the aim of protecting public health. For this reason, we will use the term quantitative risk assessment or health impact assessment interchangeably. Quantitative risk assessment has been crucial in formulating air pollution guidelines and regulatory criteria to safeguard human health. Recent initiatives by regulatory bodies like the US Environmental Protection Agency and the European Parliament indicated above have underscored the significance of quantitative risk assessment in shaping appropriate regulatory measures. In both the USA and EU, the CRFs derived from large epidemiological studies are used to estimate the health impacts of air pollution. In setting air quality standards, a cost-benefit analysis is conducted where benefits (e.g. lives saved) are monetized using value of statistical life (VSL) and compared to the costs of achieving lower air pollution levels through regulations. Undoubtedly, the total number of deaths attributable to exposure in a population speaks more clearly than relative risks or hazard ratios, providing an indication of the actual number of cases that a population has to deal with because of the exposure. The number of cases/deaths serves as a cornerstone for understanding the risks associated with exposure to major air pollutants. It is the basis for an economic assessment to identify the cost efficiency of the proposed interventions, and to guide the implementation of measures to safeguard public well-being. Rigaud et al. [64] provide valuable references for historical context and methodological insights, drawing from works such as those by Harris-Roxas et al. [32] and Briggs [4], as well as resources like the WHO document “Health risk assessment of air pollution – general principles” [86].

Approaches like the Global Burden of Disease (GBD) [27] and those adopted by the European Environment Agency (EEA) [71] have contributed significantly to this field. For the specific situation of Europe, Khomenko et al. [41, 42] conducted studies estimating the proportion of annual deaths due to air pollution in numerous cities across Europe. Their work also evaluates spatial and sector-specific emission contributions to ambient air pollution, and assesses the effects of source-specific reductions in pollutants on mortality in European cities.

Navigating new challenges in quantitative risk assessment

The basic approach in a Health Impact Assessment (HIA) is well-established [87]. It uses exposure data (either measured or modelled pollutant concentrations), baseline health data (e.g. mortality/morbidity data from registers), and concentration-response functions (CRF) from epidemiological studies to quantify the health effects in terms of premature death and/or morbidity. Rigaud et al. [64] have underscored several important challenges that we will specifically address in this discussion, e.g., issues about identifying pollution/health outcome pairs, the associated concentration-response functions, and their application in an HIA. Other pertinent issues will be reserved for future deliberations, such as estimates of vulnerable populations, exposure data, baseline morbidity and mortality data, methodological uncertainties, and counterfactual values.

Traditionally, air pollution HIA have focused on all-cause (non-accidental or natural mortality) and cause-specific mortality based on CRFs derived from systematic literature reviews and meta-analyses, which synthesize the epidemiological evidence on the health effects of air pollution. While the most appropriate CRFs for the relationship between long-term exposure to PM2.5/NO2 and mortality are currently under discussion [36], the ongoing work in the revision of the WHO-HRAPIE project (Health risks of air pollution in Europe) [34] is actively addressing this aspect by carrying out updated meta-analyses on CRFs [56]. Earlier, morbidity outcomes reflecting the multiplicity of air pollutant effects on various organs have often been overlooked [75], leading to an underestimation of the full burden of air pollution. When considering societal costs, chronic conditions such as chronic obstructive pulmonary disease (COPD) or dementia place a significant burden on social welfare and healthcare systems. The EMAPEC project coordinated by WHO has proposed CRFs for the incidence of various diseases related to long-term exposure to PM2.5 and NO2 [26]. The selection was made after reviewing the empirical epidemiological evidence and screening for acceptable systematic review/meta-analysis quality to propose appropriate CRFs.

The most recent studies, based on very large populations and advanced air pollution modelling methods, go beyond using a mass concentration of PM2.5 as a health risk determinant and explore the associations of selected PM2.5 chemical components with mortality [31, 83]. Confirmation of these associations, including the relevant CRFs, in further studies, especially from regions with different pollution levels and sources, would create new opportunities for risk assessment, allowing better impact assessment of source- (and component-) specific PM2.5 reduction.

Ongoing research is anticipated to yield further proposals for pollutant/outcome pairs in the future. This multiplicity of potential CRFs makes Rigaud’s open question regarding “proposing a formal approach to the quantitative handling of the level of evidence regarding each exposure-health outcome pair” very relevant and timely. Based on insights gained in EMAPEC, we address it in a proposed framework illustrated in Fig. 1. This framework aims to provide a structured and comprehensive approach to assessing the evidence levels associated with each exposure-health outcome pair in an HIA or cost-benefit analysis (CBA) context.

Fig. 1
figure 1

Schematic representation of the steps in choosing the appropriate concentration-response function (CRF)

Full size image
  1. 1.

    Is causality a necessary condition?

Conducting an HIA requires a causality determination of the health effects associated with the specific exposure. It would be difficult to recommend a particular risk assessment function if the qualitative relationship between exposure and disease has not passed the hazard identification steps, which consider the entirety of human, toxicological and mechanistic evidence. In other words, we must be certain that a specific exposure is causally related to a particular outcome before quantifying the impact. In the EMAPEC project, the decision on hazard identification was taken based on the determination from the US Environmental Protection Agency’s Integrated Science Assessment (EPA ISA) for NO2 [77] and PM2.5 [78]. Only pollutant/outcome pairs categorised as “Causal” or “Likely to be causal” according to the US EPA ISA methodology [59] were considered (this is schematically summarised in the left part of Fig. 1. The sole exception was for the relationship between PM2.5 and type II diabetes, which was not considered as “Causal” or “Likely to be causal” in the US EPA ISA 2019, but its inclusion in the list of relevant health outcomes was supported, in our view, by independent new epidemiological and toxicological evidence [46, 47].

It should be acknowledged that relying solely on the US EPA ISA is a conservative approach, especially if the assessment is outdated (as in the case of NO2, which was last reviewed in 2016; in this specific case, insufficient adjustment for other pollutants was a major reason for giving a low causality rating for this pollutant). Choosing causality as a prerequisite, especially when a causality assessment has not already been done, might lead to a potential underestimation of the health burden or impact. We aim to avoid neglecting emerging hazards due to insufficient data for a comprehensive impact assessment, as Rigaud et al. [63, 64] highlighted, we do not want to “leave emerging hazards by the roadside”.

A possible solution is to compile two lists of CRFs to be used in an HIA, one that contains the CRFs for which the causality link has been well established (Core list) and one that includes CRFs for which the causality assessment has yet to be established, but the emerging evidence of an association is reasonably strong to consider the exposure-outcome pair in a sensitivity analyses (Non-Core list) (Fig. 1). The right part of Fig. 1 illustrates the situation when there is a causality evaluation less than “Likely to be causal” (e.g. “Suggestive”), including situations where the causality evaluation has not been done. In such cases, the decision to proceed is influenced by the information gathered from epidemiological studies, and the supporting evidence from toxicological and mechanistic studies. While the relationship between PM2.5 and diabetes is well-supported in the published literature [62, 63], for other illnesses reaching a decision could be more challenging as it would require careful consideration and justification for inclusion by experts using multiple sources of information. A guiding principle in this respect is a critical evaluation of the quality of the available epidemiologic studies and the information they provide. Following the general framework for the design and analysis of aetiologic studies [52], the likelihood of finding evidence of causality in air pollution research is higher when more scientifically rigorous decisions are taken [22]. An in-depth evaluation of design choices, such as the exposure levels being compared, appropriate comparison group, and care to control confounding, should be used to determine the evidence of causation. Triangulation can be useful as it involves considering different studies that reach or do not reach the same conclusion about the risk association, but that are potentially affected by different biases (or the same bias to different extents). In other words, we can justify the need to proceed with the assessment if the epidemiological studies are of good design, are robust to covariate adjustment, and the cross-study results are consistent. The presence of information from toxicology and the mechanism of action is important, but it should not be a prerequisite. For instance, in the IARC strategy [65], “sufficient” evidence from human observational investigations (e.g. epidemiological studies) is enough in categorising a substance as being carcinogenic to humans (Group 1).

Pollutant/outcome pairs from this part of the evaluation enter the “Non-Core” list rather than the “Core” list and might be applied in a sensitivity analysis. The “Non-Core” category may include pollutant/outcome pairs with varying levels of evidence supporting causality, allowing for a nuanced approach to assessing health effects. Further, a pollutant/outcome pair could migrate from the “Non-Core” list to the “Core” list once a formal level of evidence of causality has been established based, for example, on the US EPA or other authoritative agencies evaluation. In the EMAPEC work, a Core CRF list (list A) and a Non-Core CRF list (List B+) were suggested. For PM2.5, the Core list (List A) included incidence of asthma in children, COPD, ischemic heart disease events, stroke, hypertension, and lung cancer. For NO2, a Core list (list A) was provided for the incidence of asthma in children, asthma in adults, and acute respiratory infections in children. Three outcomes (diabetes, dementia, and autism spectrum disorders) in relation to PM2.5 were added to the list for sensitivity analysis (Non-Core, list B+).

  1. 2.

    Quality of Systematic Reviews and Meta-analyses providing CRF

To provide a reliable CRF, it is imperative to gather evidence from human studies, which is typically achieved through systematic reviews (SRs) and meta-analyses. Situations may arise where no SRs exist or existing reviews on a given health effect are outdated. In such cases, the only viable solution is to initiate a new SR, carefully considering the required human and time resources to do the work. Luckily, recent SRs are often readily available, and two primary options exist:

  • New SR based on a comprehensive selection of individual studies from all available SRs. One option is to select all individual studies from all available SRs and then use this collective experience to perform a new SR. This approach leverages the groundwork laid by previous SRs, streamlining the search, scrutiny, and selection of primary studies. However, the subsequent work would involve conducting a new SR to synthesise the entirety of the collected information.

  • Quality evaluation and selection of SRs. Alternatively, as undertaken in the EMAPEC project, one can assess the quality of all recently available SRs, and then choose those with the highest quality for further consideration. A meticulous evaluation of systematic reviews is crucial for extracting reliable CRFs. Based on the EMAPEC experience, this was achieved by considering various aspects, including literature search, inclusion criteria, data extraction, statistical analysis, and bias assessment [26, 69]).

In both options, verifying the evidence included in the SRs based on original papers is advisable. The experience from EMAPEC shows that mistakes (e.g. the inclusion of studies that do not fit the review scope, or the use of incorrect risk estimates in the meta-analysis) or simply selective choices (e.g. a fully co-pollutant adjusted estimate versus a non-co-pollutant adjusted estimate, or an estimate in a subgroup versus an estimate in the full population) may occur even in SRs that are deemed to be of overall good quality.

Drawing lessons from previous experiences, such as the need to consider new outcomes in the HEI traffic review [33] or the approach taken in EMAPEC when multiple SRs were available, can significantly improve the assessment process.

  1. 3.

    Confidence in the CRF

To ascertain confidence in the findings derived from selected systematic reviews and meta-analyses, which serve as sources for the recommended CRFs, a comprehensive evaluation of various aspects concerning the robustness of the results should be taken into consideration [26]:

  • Number of studies and size of the cohorts: Evaluate the number of studies (and their size, e.g. the size of the various cohort studies) used in the systematic reviews and meta-analyses. A larger database often contributes to more robust and reliable findings, providing a broader foundation for drawing meaningful conclusions.

  • Geographic coverage: Assess the geographic diversity of the studies included in the systematic reviews. A wide-ranging representation of diverse populations and regions enhances the generalisability and applicability of the CRFs, contributing to their reliability across different geographical settings.

  • Weight of studies: Consider the relative weight of individual studies within the systematic reviews. Higher-quality studies with robust methodologies and larger sample sizes should have more influence in the selection of the CRFs, ensuring that the recommendations are anchored in strong evidence. At the same time, it is desirable that the evidence should come from various studies and regions, rather than a limited number of studies.

  • Precision of effect estimates: Scrutinise the precision of effect estimates provided by the systematic reviews. Precise estimates, indicated by narrow confidence intervals, signify a higher degree of certainty in the observed relationships and contribute to increased confidence in the recommended CRFs.

  • Unexplained heterogeneity: Evaluate the level of heterogenicity across the studies, as lower unexplained heterogenicity provides greater confidence in the results. The presence of factors that could explain heterogeneity among the included studies should be considered. A practical approach is to evaluate whether there is heterogenicity across regions [12]. Heterogeneity in the direction of associations (both positive and negative associations) across studies should be considered a factor limiting the confidence in the CRF more than when heterogeneity is in the magnitude of the effects (different effect estimates but in the same direction). Addressing and understanding sources of heterogeneity contribute to the robustness of the CRFs, ensuring that the recommended functions are not unduly influenced by the variability of external factors.

By systematically evaluating these aspects, one can gain valuable insights into the reliability and robustness of the concentration-response functions derived from the systematic reviews and meta-analyses.

A different approach to assess the quality of the epidemiological evidence has been applied in GBD 2021, namely, based on Zheng et al. [90]; a “Burden of proof risk function (BPRF)” method was developed to understand, evaluate and summarise evidence of risk across different risk-outcome pairs. Five outcomes associated with particulate air pollution (Ischemic Heart Disease, Diabetes, Stroke, Lung cancer, Lower Respiratory Infection) were assigned 3 stars (indicating moderate evidence of association), while COPD was rated 4 stars (indicating strong evidence of association).

  1. 4.

    Global versus regional CRF

The choice between using a global (based on the ensemble of studies from across the world) or regional CRF (based on studies in a particular continent, country, or location) to estimate the effects of air pollution will depend on several factors, including the nature of the assessment, the specific policy questions, and the intended application. There are various advantages to using global CRFs. (1) they are derived from pooled data covering multiple regions and populations, thus providing a broader perspective of the overall relationship by integrating susceptibility across world populations. (2) The uncertainty around the global estimate could be narrower than the one based on fewer regional studies. (3) A single global CRF can be applied to estimate health impacts in various locations without requiring region-specific CRFs (although region-specific baseline health data would still be necessary). (4) It is suitable for generating overarching policies and general recommendations for widespread application. The last reason is the most compelling because policies for large areas are not intended to account for local specificity. For instance, the Global monitoring of the Sustainable Development Goal (SDG) indicator 3.9.1 (mortality attributed to air pollution) that was established by the United Nations deliberation [79] applies global functions to estimate the attributable proportions [84]. Overall, the inclusive nature of global CRF assumes consistent biological linkages between exposure and health effects across different populations and locations and may facilitate a more comprehensive integration of the available evidence.

On the other hand, regional CRFs could better reflect such factors as population demographics (age distribution, disease profile, health status and access to health care coverage), socioeconomic status, personal behavioural risk factors, and local environmental conditions (source contribution and pollution composition) that may influence the relationship between pollutant exposure and health outcomes. For instance, using specific European CRFs for EU regulations could be suggested as it would increase confidence in the calculated disease burden. Regional CRFs are valuable for conducting HIAs tailored to specific communities or regions, providing confidence to local communities in the estimates of the health effects of air pollution [60]. The argument for using location-specific CRFs is strongest if there is a dominant local source (e.g. a local refinery, or power plant), a particular local PM composition, and also specific population characteristics (for example, the ageing European population compared to much younger populations in other regions of the world).

The regional CRF could be used in an ad hoc analysis to show the particularities of the impact under specific conditions. Another possibility is to construct a CRF that combines the information from the global evidence with that coming from regional studies, such as a weighted mean of the regional value and the pooled estimate e.g. the shrunken estimate approach proposed in Le Tertre et al. [44]. Of course, the use of a regional CRF is possible only if studies covering a relevant population exists. For many regions, this is still not the case, so the use of the evidence gathered in other parts of the world remains the only choice for the HIA.

  1. 5.

    Shape of the CRF

The shape of the CRF should be carefully considered. Understanding how the health risk changes with varying levels of exposure (especially in the low and high concentration range) is crucial in a health impact assessment. Most health impact assessments have employed linear concentration-response functions. These relationships assign the same percentage increase in health risk for the same change in PM2.5 over the entire ambient air pollution concentration range observed in the epidemiological studies. For example, the assessment conducted by the European Environment Agency [71] assumed a linear concentration-response function for PM2.5 and natural mortality. However, recent epidemiologic evidence has suggested that the CRF shape at low PM2.5 concentrations may be supralinear, meaning the change in health risk per unit change in concentration is larger at lower exposures.

In a close examination of the available studies on PM2.5 and mortality, Vodonos et al. [81] showed that the CRF slope decreased at higher concentrations. Worth noting that the PM2.5 contrast range was limited to < 30 µg/m3, except for three studies in Asia. The maximum average PM2.5 concentration was 43.7 µg/m3 (a study in China). They found that a specific non-linear function approximated the data well, with a larger slope at lower concentration levels, and provided a parametric estimate that fit better than a linear or logarithmic term for average PM2.5. In the Global Exposure Mortality Model (GEMM) developed by Burnett et al. [9], the authors proposed a non-linear CRF for PM2.5 and mortality (calculated as the sum of noncommunicable diseases and lower respiratory infections in the adult population), based on published health risk data from 41 cohorts in 16 countries. They observed that the hazard ratio predictions increased at low PM2.5 concentrations, indicating a supralinear association at lower exposures, meanwhile, a gradually decreasing health risk was predicted at higher concentrations [8, 9]. The estimated European burden of mortality attributable to PM2.5 has increased considerably because of the use of this new model in a health impact assessment [45]. Sigsgaard and Hoffmann [70] have underlined that the use of the global linear CRF underlying the 2021 WHO Air Quality Guidelines [12] resulted in a disease burden of 275,000 premature deaths attributable to PM2.5 exposure in Europe in 2020 (Soares et al. [71]), but when applying estimates from the supralinear concentration-response functions the resulting disease burden in Europe from PM2.5 and NO2 would be much larger (i.e., by ∼40% for PM2.5 and ∼110% for NO2). Weichenthal et al. [82] indicated that considering a supralinear CRF for outdoor PM2.5 and mortality at the low end of the exposure distribution results in more than 1.5 million additional attributable deaths each year globally.

The most recent evidence from the combined analysis of the “low-level” studies supported by the Health Effects Institute (HEI) should be considered. Chen et al. [11] reported that the shape of the CRFs differed substantially across the various cohorts. For the ELAPSE study in Europe [72], only two cohorts (Norway and Denmark) contributed to the shape of the CRF at PM2.5 levels below 10 µg/m3, and only Norway for levels below 7 µg/m3. The cohorts from Norway and Canada [3] reported a supra-linear shape up to 7 µg/m3. Such a supra-linear pattern has not been observed in the USA Medicare study [23], which reported a sub-linear pattern below 7 µg/m3, and then a steep slope from 7 to 9 µg/m3. Overall, the preliminary conclusion is that the evidence regarding the shape of the CRF at low concentrations is not clear and regional considerations could greatly affect the results.

Since there are uncertainties on the form of the relationship and how to accurately quantify the disease burden from air pollution in areas with low (or high) exposure, it is preferable to consider, as COMEAP indicated in 2022 [15], that the evidence is not sufficient to recommend any change from the assumption of a linear CRF for long-term exposure to PM2.5, at least for concentration levels observed in Europe and North America. With (expected) growth of the epidemiological evidence on the shape of the CRFs at various exposure levels (in particular, in the higher concentrations), better supported decisions on the selection of CRF shape for HIA in a particular population should be possible.

The different shapes of the CRFs and the non-linearities could depend on several factors, including population characteristics, exposure assessment, spatial variation in the composition of total PM2.5 mass, and measured and unmeasured confounders. For instance, Boogaard et al. [2] indicated that different population characteristics in rural areas of Canada with lower levels of air pollution might have contributed to the results for that country. In a simulation study, Glasgow et al. [29] found that the relationship between PM2.5 and mortality could falsely appear to be supralinear when the fraction of the mass that is toxic is higher in areas with lower total PM2.5 mass as compared to areas with higher total PM2.5 mass. Despite the intensive research already conducted, the scientific question of whether some PM mixture components are more toxic than others is still a priority in research [2].

  1. 6.

    Applicability of the CRF

Several key assumptions are inherent in all the applications of health impact assessment of air pollution, including two important aspects, namely the generalisability of the CRFs to different age ranges and their applicability to various concentration levels. It is usually assumed that the CRF derived in epidemiology studies conducted in a few countries applies globally despite differences in pollution mix, ethnicity, lifestyle factors, socioeconomic status, temperature, health status, and access to medical care. However, as different populations’ susceptibility to the effects of air pollution is possible, it is at least important to restrict the age range of application of the CRFs to the same age range considered in the specific epidemiologic studies that have generated the CRF. This is why EMAPEC [26] provided specific age ranges for applying the CRFs in an HIA.

Another important consideration is the concentration range pertinent to the selected CRF. It is advisable to check the range of pollutant concentrations investigated in the original epidemiological studies and apply the CRF only over that range [26]. The range of mean concentrations in source studies indicates the range of mean exposures for which the uncertainty of the risk assessment is minimised. Figure 2 shows the risk coefficients of the association between PM2.5 and the incidence of dementia (upper panel) and autism spectrum disorder (ASD) (lower panel) by ranges of concentrations in the various studies that have been evaluated in EMAPEC. For dementia, the bulk of the studies considered in the systematic review by Cheng et al. [13] cover the range from less than 5 µg/m3 to 17 µg/m3, with two studies extending the range to 25 µg/m3, while only one study has been conducted for larger concentrations. For ASD, the bulk of the studies reviewed by Lin et al. [48] lie in the 5–30 µg/m3 concentration range, with only one study exceeding this interval. Furthermore, the PM2.5 contrast intervals (range of concentrations being compared) in most of the individual studies for both diseases was around 10 µg/m3. Therefore, in EMAPEC, it was recommended to apply these CRFs in an HIA within the concentration ranges 5–25 µg/m3 for dementia and 5–30 µg/m3 for ASD, but for changes in PM2.5 concentration less than 10 µg/m3.

Fig. 2
figure 2

Risk coefficients of the association between PM2.5 exposure and risk of dementia incidence (upper panel) and Autism Spectrum Disorder (ASD) incidence (lower panel) by ranges of concentrations in the various studies included in the systematic reviews by Cheng et al. [13] and Lin et al. [49]

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  1. 7.

    Risk of Double Counting

Understanding the interplay between various CRFs and their respective health predictions is necessary to ensure accurate risk assessments. There are two possible reasons for double counting: when different pollutants cause the same health outcome (as in the well-known case of PM2.5 and NO2) and across different CRFs when one outcome predicts another outcome.

Note that an attempt to separate the effects of individual pollutants would be pertinent information in assessing the cost of mitigation, as the level of emissions of different pollutants is source-dependent (although interventions that mitigate NOx emission, also affect PM emissions, and vice versa). A potential resolution to the double counting dilemma concerning PM2.5 and NO2 in relation to mortality has been thoroughly deliberated by the UK Committee on the Medical Effects of Air Pollutants [14]. A majority of the Committee agreed that when adding the health effects of NO2 to the unadjusted PM2.5 “it was plausible that the effects on mortality attributable to NO2 itself lay within the range of 25–55% of the unadjusted coefficient; and that, with suitable strong caveats, this could be used as a guide for policy assessment”.

An alternative approach is to consider the Cumulative Risk framework, initially introduced by Crouse et al. [17], which expanded previous work published by Jerrett et al. [38]. This approach considers the cumulative effects of exposure to various pollutants in the same model in a specific epidemiological study, providing a more comprehensive understanding of the overall health risks associated with air pollution exposure. The cumulative risk estimate assumes additive effects of combined pollutant exposures on the outcome and represents the relative hazard for 1-unit increases in each of the pollutants in the mix compared with no increase in any of the exposures. In the Crouse et al. [17] analysis of the CanCHEC study in Canada, for example, the single pollutant effect on mortality for PM2.5 was 1.035 (95%CI 1.029, 1.041) for 5.0 µg/m3, the single pollutant effect for NO2 was 1.052 (1.045, 1.059) for 8.1 ppb (i.e. 15.2 µg/m3), and the cumulative risk estimate (for the same increments of the pollutants, i.e. 5 µg/m3 for PM2.5 and 8.1 ppb for NO2) was 1.070 (1.062, 1.078). This example shows that there is only a small overlap of the effects as the cumulative effect (1.070) is only slightly smaller than the sum of the single pollutant effects (1.035 and 1.052). A systematic review and meta-analysis of several epidemiological studies that provide results from single pollutant models as well as from two-pollutant models could better inform the debate about the overlap, double counting, and decision-making process.

There are numerous scenarios in which a particular disease or disorder acts as an antecedent, or risk factor, for another health condition. Figure 3 illustrates the interconnection between various health outcomes commonly suggested for health impact assessments, drawing from robust evidence in established medical literature. However, a crucial question arises regarding whether these associations might lead to double counting of the health impact. For instance, when predicting the influence of air pollution on the incidence of specific disorders like diabetes and stroke, the independent counting of diseases is generally not problematic. However, if we shift our focus to prevalence instead of incidence, considering that an individual can simultaneously have diabetes and be a survivor of a stroke episode, or if we aim to evaluate the economic costs associated with these health outcomes, a more cautious approach is warranted. This necessitates careful consideration of the interplay between different health conditions and their implications for health impact assessment and economic evaluation. Emerging studies on the role of air pollution in the transition between various stages of the same disease or related diseases include, for example, Zhang et al. [89] and Zou et al. [91].

Fig. 3
figure 3

Schematic representation of the links between various air pollution-related outcomes (TIA, transient ischaemic attack)

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Conclusion

Quantitative risk assessment requires a robust framework for choosing the relevant CRFs and their application to the population at risk. The evolving landscape of air pollution epidemiology underscores the urgency of science-based support to actions addressing global risks of air pollution through robust research, advanced modelling, and comprehensive risk assessments. Rigaud et al.‘s [64] contribution and the studies discussed in this review collectively contribute to the evolving risk assessment methodology. Addressing challenges requires a comprehensive and adaptive approach, drawing on lessons from past experiences, incorporating the latest research findings, and fostering collaboration between different disciplines involved in the health impact assessment. Finally, a robust and reliable quantitative risk assessment is critical in supporting a cost-benefit analysis of measures to mitigate air pollution.

Data availability

No datasets were generated or analysed during the current study.

Notes

  1. https://www.epa.gov/pm-pollution/national-ambient-air-quality-standards-naaqs-pm.

  2. https://www.europarl.europa.eu/legislative-train/theme-a-european-green-deal/file-revision-of-eu-ambient-air-quality-legislation.

References

  1. Beelen R, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, Wolf K, Samoli E, Fischer P, Nieuwenhuijsen M, Vineis P, Xun WW, Katsouyanni K, Dimakopoulou K, Oudin A, Forsberg B, Modig L, Havulinna AS, Lanki T, Turunen A, Oftedal B, Nystad W, Nafstad P, De Faire U, Pedersen NL, Östenson CG, Fratiglioni L, Penell J, Korek M, Pershagen G, Eriksen KT, Overvad K, Ellermann T, Eeftens M, Peeters PH, Meliefste K, Wang M, Bueno-de-Mesquita B, Sugiri D, Krämer U, Heinrich J, de Hoogh K, Key T, Peters A, Hampel R, Concin H, Nagel G, Ineichen A, Schaffner E, Probst-Hensch N, Künzli N, Schindler C, Schikowski T, Adam M, Phuleria H, Vilier A, Clavel-Chapelon F, Declercq C, Grioni S, Krogh V, Tsai MY, Ricceri F, Sacerdote C, Galassi C, Migliore E, Ranzi A, Cesaroni G, Badaloni C, Forastiere F, Tamayo I, Amiano P, Dorronsoro M, Katsoulis M, Trichopoulou A, Brunekreef B, Hoek G. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 2014;383(9919):785–95. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(13)62158-3. Epub 2013 Dec 9. PMID: 24332274.

    Article  CAS  Google Scholar 

  2. Boogaard H, Crouse DL, Tanner E, Mantus E, van Erp AM, Vedal S, Samet J. Assessing adverse Health effects of Long-Term exposure to low levels of Ambient Air Pollution: the HEI experience and what’s Next? Environ Sci Technol. 2024;58(29):12767–83. https://doiorg.publicaciones.saludcastillayleon.es/10.1021/acs.est.3c09745. Epub 2024 Jul 11. PMID: 38991107; PMCID: PMC11270999.

    Article  CAS  Google Scholar 

  3. Brauer M, Brook JR, Christidis T, Chu Y, Crouse DL, Erickson A, et al. Mortality-air Pollution associations in low exposure environments (MAPLE): phase 2. Research Report 212. Boston, MA: Health Effects Institute; 2022.

    Google Scholar 

  4. Briggs DJ. A framework for integrated environmental health impact assessment of systemic risks. Environ Health. 2008;7:61.

    Article  Google Scholar 

  5. Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr, Whitsel L, Kaufman JD, American Heart Association Council on Epidemiology and Prevention. Council on the kidney in Cardiovascular Disease, and Council on Nutrition, physical activity and metabolism. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation. 2010;121(21):2331–78. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/CIR.0b013e3181dbece1. Epub 2010 May 10. PMID: 20458016.

    Article  CAS  Google Scholar 

  6. Brunekreef B, Dockery DW, Krzyzanowski M. Epidemiologic studies on short-term effects of low levels of major ambient air pollution components. Environ Health Perspect. 1995;103(2):3–13. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/ehp.95103s23. PMID: 7614944; PMCID: PMC1518847.

    Article  CAS  Google Scholar 

  7. Brunekreef B, Strak M, Chen J, Andersen ZJ, Atkinson R, Bauwelinck M, et al. Mortality and Morbidity effects of Long-Term exposure to low-level PM2.5, Black Carbon, NO2, and O3: an analysis of European cohorts. Boston, MA: Health Effects Institute: Research Report; 2021.

    Google Scholar 

  8. Burnett R, Cohen A. Relative risk functions for estimating excess mortality attributable to Outdoor PM2.5 Air Pollution: evolution and state-of‐the‐art. Atmosphere. 2020;11:589. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/atmos11060589.

    Article  CAS  Google Scholar 

  9. Burnett R, Chen H, Szyszkowicz M, Fann N, Hubbell B, Pope CA 3rd, Apte JS, Brauer M, Cohen A, Weichenthal S, Coggins J, Di Q, Brunekreef B, Frostad J, Lim SS, Kan H, Walker KD, Thurston GD, Hayes RB, Lim CC, Turner MC, Jerrett M, Krewski D, Gapstur SM, Diver WR, Ostro B, Goldberg D, Crouse DL, Martin RV, Peters P, Pinault L, Tjepkema M, van Donkelaar A, Villeneuve PJ, Miller AB, Yin P, Zhou M, Wang L, Janssen NAH, Marra M, Atkinson RW, Tsang H, Quoc Thach T, Cannon JB, Allen RT, Hart JE, Laden F, Cesaroni G, Forastiere F, Weinmayr G, Jaensch A, Nagel G, Concin H, Spadaro JV. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc Natl Acad Sci U S A. 2018;115(38):9592–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1073/pnas.1803222115. Epub 2018 Sep 4. PMID: 30181279; PMCID: PMC6156628.

    Article  CAS  Google Scholar 

  10. Cesaroni G, Badaloni C, Gariazzo C, Stafoggia M, Sozzi R, Davoli M, Forastiere F. Long-term exposure to Urban Air Pollution and Mortality in a cohort of more than a million adults in Rome. Environ Health Perspect. 2013;121:324–31. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/ehp.1205862.

    Article  CAS  Google Scholar 

  11. Chen J, Hoek G. Long-term exposure to PM and all-cause and cause-specific mortality: a systematic review and meta-analysis. Environ Int. 2020;143:105974. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2020.105974. PMID: 32703584.

    Article  CAS  Google Scholar 

  12. Chen J, Braun D, Christidis T, Cork M, Rodopoulou S, Samoli E, Stafoggia M, Wolf K, Wu X, Yuchi W, Andersen ZJ, Atkinson R, Bauwelinck M, de Hoogh K, Janssen NAH, Katsouyanni K, Klompmaker JO, Kristoffersen DT, Lim YH, Oftedal B, Strak M, Vienneau D, Zhang J, Burnett RT, Hoek G, Dominici F, Brauer M, Brunekreef B. Long-Term Exposure to Low-Level PM2.5 and Mortality: Investigation of Heterogeneity by Harmonizing Analyses in Large Cohort Studies in Canada, United States, and Europe. Environ Health Perspect. 2023;131(12):127003. doi: 10.1289/EHP12141. Epub 2023 Dec 1. PMID: 38039140; PMCID: PMC10691665.

  13. Cheng S, Jin Y, Dou Y, et al. Long-term particulate matter 2.5 exposure and dementia: a systematic review and meta-analysis. Public Health. 2022;212:33–41. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.puhe.2022.08.006.

    Article  CAS  Google Scholar 

  14. Committee on the Medical Effects of Air Pollutants (COMEAP). Associations of long-term average concentrations of nitrogen dioxide with mortality. A report by the Committee on the Medical Effects of Air Pollutants, GOV.UK (www.gov.uk) 2018.

  15. Committee on the Medical Effects of Air Pollutants (COMEAP). Particulate air pollution: quantifying effects on mortality – appendix B: Summary of COMEAP views on the studies in populations with low-level exposures and the shape of the concentration-response curve. A report by the Committee on the Medical Effects of Air Pollutants, GOV.UK (www.gov.uk), 2022.

  16. Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, Khan S, Atari DO, Jerrett M, Pope CA, Brauer M, Brook JR, Martin RV, Stieb D, Burnett RT. Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a Canadian national-level cohort study. Environ Health Perspect. 2012;120(5):708–14. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/ehp.1104049. Epub 2012 Feb 7. PMID: 22313724; PMCID: PMC3346774.

    Article  Google Scholar 

  17. Crouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA 3rd, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. Ambient PM2.5, O₃, and NO₂ exposures and associations with Mortality over 16 years of Follow-Up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect. 2015;123(11):1180–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/ehp.1409276. Epub 2015 Nov 1. PMID: 26528712; PMCID: PMC4629747.

    Article  CAS  Google Scholar 

  18. de Hoogh K, Gulliver J, Donkelaar AV, Martin RV, Marshall JD, Bechle MJ, Cesaroni G, Pradas MC, Dedele A, Eeftens M, Forsberg B, Galassi C, Heinrich J, Hoffmann B, Jacquemin B, Katsouyanni K, Korek M, Künzli N, Lindley SJ, Lepeule J, Meleux F, de Nazelle A, Nieuwenhuijsen M, Nystad W, Raaschou-Nielsen O, Peters A, Peuch VH, Rouil L, Udvardy O, Slama R, Stempfelet M, Stephanou EG, Tsai MY, Yli-Tuomi T, Weinmayr G, Brunekreef B, Vienneau D, Hoek G. Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data. Environ Res. 2016;151:1–10. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2016.07.005. Epub 2016 Jul 20. PMID: 27447442.

    Article  CAS  Google Scholar 

  19. Di Q, Wang Y, Zanobetti A, Wang Y, Koutrakis P, Choirat C, Dominici F, Schwartz JD. Air Pollution and Mortality in the Medicare Population. N Engl J Med. 2017;376(26):2513–22. https://doiorg.publicaciones.saludcastillayleon.es/10.1056/NEJMoa1702747. PMID: 28657878; PMCID: PMC5766848.

    Article  CAS  Google Scholar 

  20. Diao M, Holloway T, Choi S, O’Neill SM, Al-Hamdan MZ, Van Donkelaar A, Martin RV, Jin X, Fiore AM, Henze DK, Lacey F, Kinney PL, Freedman F, Larkin NK, Zou Y, Kelly JT, Vaidyanathan A. Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models. J Air Waste Manag Assoc. 2019;69(12):1391–414. Epub 2019 Oct 15. PMID: 31526242; PMCID: PMC7072999.

    Article  CAS  Google Scholar 

  21. Dockery DW, Pope CA 3rd, Xu X, Spengler JD, Ware JH, Fay ME, Ferris BG Jr, Speizer FE. An association between air pollution and mortality in six US cities. N Engl J Med. 1993;329(24):1753-9. https://doiorg.publicaciones.saludcastillayleon.es/10.1056/NEJM199312093292401. PMID: 8179653.

  22. Dominici F, Zigler C. Best practices for gauging evidence of causality in Air Pollution Epidemiology. Am J Epidemiol. 2017;186(12):1303–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/aje/kwx307. PMID: 29020141; PMCID: PMC5860368.

    Article  Google Scholar 

  23. Dominici F, Zanobetti A, Schwartz J, Braun D, Sabath B, Wu X. (2022). Assessing adverse health effects of long-term exposure to low levels of ambient air pollution: implementation of causal inference methods. Research Report Health Effects Institute, 2022(211), 1–56. PMID: 36193708.

  24. Erickson AC, Brauer M, Christidis T, Pinault L, Crouse DL, van Donkelaar A, Weichenthal S, Pappin A, Tjepkema M, Martin RV, Brook JR, Hystad P, Burnett RT. Evaluation of a method to indirectly adjust for unmeasured covariates in the association between fine particulate matter and mortality. Environ Res. 2019;175:108–116. doi: 10.1016/j.envres.2019.05.010. Epub 2019 May 11. PMID: 31108354.

  25. Fischer PH, Marra M, Ameling CB, Hoek G, Beelen R, de Hoogh K, Breugelmans O, Kruize H, Janssen NA, Houthuijs D. Air Pollution and Mortality in seven million adults: the Dutch Environmental Longitudinal Study (DUELS). Environ Health Perspect. 2015;123(7):697–704. Epub 2015 Mar 11. PMID: 25760672; PMCID: PMC4492265.

    Article  CAS  Google Scholar 

  26. Forastiere F, Spadaro JV, Ancona C, Jovanovic Andersen Z, Cozzi I, Gumy S, Loncar D, Mudu P, Medina S, Perez Velasco R, Walton H, Zhang J, Krzyzanowski M. (2024). Choices of morbidity outcomes and concentration-response functions for Health Risk Assessment of long-term exposure to air pollution. Environmental Epidemiology, 8(4):p e314, August 2024. DOI: 10.1097/EE9.0000000000000314.

  27. GBD 2021 Forecasting Collaborators. Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2204–2256. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(24)00685-8. PMID: 38762325.

  28. GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2162–2203. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(24)00933-4. PMID: 38762324.

  29. Glasgow G, Ramkrishnan B, Smith AE. Model misspecification, measurement error, and apparent supralinearity in the concentration-response relationship between PM2.5 and mortality. PLoS ONE. 2024;19(5):e0303640. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0303640. PMID: 38781233; PMCID: PMC11115258.

    Article  CAS  Google Scholar 

  30. Hammer MS, van Donkelaar A, Li C, Lyapustin A, Sayer AM, Hsu NC, Levy RC, Garay MJ, Kalashnikova OV, Kahn RA, Brauer M, Apte JS, Henze DK, Zhang L, Zhang Q, Ford B, Pierce JR, Martin RV. Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ Sci Technol. 2020;54(13):7879–90. https://doiorg.publicaciones.saludcastillayleon.es/10.1021/acs.est.0c01764. Epub 2020 Jun 17. PMID: 32491847.

    Article  CAS  Google Scholar 

  31. Hao H, Wang Y, Zhu Q, Zhang H, Rosenberg A, Schwartz J, Amini H, van Donkelaar A, Martin R, Liu P, Weber R, Russel A, Yitshak-Sade M, Chang H, Shi L. National Cohort Study of Long-Term exposure to PM2.5 components and mortality in Medicare American older adults. Environ Sci Technol. 2023;57(17):6835–43. https://doiorg.publicaciones.saludcastillayleon.es/10.1021/acs.est.2c07064. Epub 2023 Apr 19. PMID: 37074132; PMCID: PMC10157884.

    Article  CAS  Google Scholar 

  32. Harris-Roxas B, Viliani F, Bond A, Cave B, Divall M, Furu P, Harris P, Soeberg M, Wernham A, Winkler M. Health impact assessment: the state of the art. Impact Assess Proj Apprais. 2012;30(1):43–52.

    Article  Google Scholar 

  33. HEI (Health Effects Institute) Panel. 2022. Systematic Review and Meta-Analysis of Selected Health Effects of Long-Term Exposure to Traffic-Related Air Pollution. Special Report 23. Boston, MA, US: Health Effects Institute. https://www.healtheffects.org/publication/systematic-review-and-meta-analysis-selected-health-effects-long-term-exposure-traffic

  34. Héroux ME, Anderson HR, Atkinson R, Brunekreef B, Cohen A, Forastiere F, Hurley F, Katsouyanni K, Krewski D, Krzyzanowski M, Künzli N, Mills I, Querol X, Ostro B, Walton H. Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project. Int J Public Health. 2015;60(5):619–27. Epub 2015 May 30. PMID: 26024815; PMCID: PMC4480843.

    Article  Google Scholar 

  35. Hoek G, Boogaard H, Knol A, de Hartog J, Slottje P, Ayres JG, Borm P, Brunekreef B, Donaldson K, Forastiere F, Holgate S, Kreyling WG, Nemery B, Pekkanen J, Stone V, Wichmann HE, van der Sluijs J. Concentration response functions for ultrafine particles and all-cause mortality and hospital admissions: results of a European expert panel elicitation. Environ Sci Technol. 2010;44(1):476–82. https://doiorg.publicaciones.saludcastillayleon.es/10.1021/es9021393.

  36. Hoffmann B, Brunekreef B, Andersen ZJ, Forastiere F, Boogaard H. Benefits of future clean air policies in Europe: Proposed analyses of the mortality impacts of PM2.5 and NO2. Environ Epidemiol. 2022;6(5):e221. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/EE9.0000000000000221. 

  37. Hvidtfeldt UA, Severi G, Andersen ZJ, Atkinson R, Bauwelinck M, Bellander T, Boutron-Ruault MC, Brandt J, Brunekreef B, Cesaroni G, Chen J, Concin H, Forastiere F, van Gils CH, Gulliver J, Hertel O, Hoek G, Hoffmann B, de Hoogh K, Janssen N, Jöckel KH, Jørgensen JT, Katsouyanni K, Ketzel M, Klompmaker JO, Krog NH, Lang A, Leander K, Liu S, Ljungman PLS, Magnusson PKE, Mehta AJ, Nagel G, Oftedal B, Pershagen G, Peter RS, Peters A, Renzi M, Rizzuto D, Rodopoulou S, Samoli E, Schwarze PE, Sigsgaard T, Simonsen MK, Stafoggia M, Strak M, Vienneau D, Weinmayr G, Wolf K, Raaschou-Nielsen O, Fecht D. Long-term low-level ambient air pollution exposure and risk of lung cancer - A pooled analysis of 7 European cohorts. Environ Int. 2021;146:106249. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2020.106249. Epub 2020 Nov 13. PMID: 33197787.

    Article  CAS  Google Scholar 

  38. Jerrett M, Burnett RT, Beckerman BS, Turner MC, Krewski D, Thurston G, et al. Spatial analysis of air pollution and mortality in California. Am J Respir Crit Care Med. 2013;188(5):593–9.

    Article  Google Scholar 

  39. Katsouyanni K, Touloumi G, Spix C, Schwartz J, Balducci F, Medina S, Rossi G, Wojtyniak B, Sunyer J, Bacharova L, Schouten JP, Ponka A, Anderson HR. Short-term effects of ambient sulphur dioxide and particulate matter on mortality in 12 European cities: results from time series data from the APHEA project. Air Pollution and Health: a European Approach. BMJ. 1997;314(7095):1658–63. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmj.314.7095.1658. PMID: 9180068; PMCID: PMC2126873.

    Article  CAS  Google Scholar 

  40. Kerr GH, van Donkelaar A, Martin RV, Brauer M, Bukart K, Wozniak S, Goldberg DL, Anenberg SC. Increasing racial and ethnic disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States. Environ Health Perspect. 2024;132(3):37002. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/EHP11900. Epub 2024 Mar 6. PMID: 38445892; PMCID: PMC10916678.

    Article  CAS  Google Scholar 

  41. Khomenko S, Cirach M, Pereira-Barboza E, Mueller N, Barrera-Gómez J, Rojas-Rueda D, de Hoogh K, Hoek G, Nieuwenhuijsen M. Premature mortality due to air pollution in European cities: a health impact assessment. Lancet Planet Health. 2021;5(3):e121–34. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2542-5196(20)30272-2. Epub 2021 Jan 19. PMID: 33482109.

    Article  Google Scholar 

  42. Khomenko S, Pisoni E, Thunis P, Bessagnet B, Cirach M, Iungman T, Barboza EP, Khreis H, Mueller N, Tonne C, de Hoogh K, Hoek G, Chowdhury S, Lelieveld J, Nieuwenhuijsen M. Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment. Lancet Public Health. 2023;8(7):e546–58. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2468-2667(23)00106-8. PMID: 37393093.

    Article  Google Scholar 

  43. Kim H, Byun G, Choi Y, Kim S, Kim S-Y, Lee J-T. Effects of long-term exposure to air pollution on all-cause mortality and cause-specific mortality in seven major cities of South Korea: Korean national health and nutritional examination surveys with mortality follow-up. Environ Res. 2021;192:110290. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2020.110290.

    Article  CAS  Google Scholar 

  44. Le Tertre A, Schwartz J, Touloumi G. Empirical Bayes and adjusted estimates approach to estimating the relation of mortality to exposure of PM(10). Risk Anal. 2005;25(3):711–8.

    Article  Google Scholar 

  45. Lelieveld J, Klingmüller K, Pozzer A, Pöschl U, Fnais M, Daiber A, Münzel T. Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions. Eur Heart J. 2019;40(20):1590–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/eurheartj/ehz135. PMID: 30860255; PMCID: PMC6528157.

    Article  CAS  Google Scholar 

  46. Li T, Zhang Y, Wang J, Xu D, Yin Z, Chen H, Lv Y, Luo J, Zeng Y, Liu Y, Kinney PL, Shi X. All-cause mortality risk associated with long-term exposure to ambient PM2·5 in China: a cohort study. Lancet Public Health. 2018;3:e470–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2468-2667(18)30144-0.

    Article  Google Scholar 

  47. Liang D, Li Z, Vlaanderen J, Tang Z, et al. A state-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: current progress, Analytical challenges, and recommendations for future direction. Environ Health Perspect. 2023;131(5):56002. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/EHP11851. Epub 2023 May 16. PMID: 37192319; PMCID: PMC10187974.

    Article  CAS  Google Scholar 

  48. Lim S, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease study 2010. Lancet. 2012;380:2224–60. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(12)61766-8.

    Article  Google Scholar 

  49. Lin LZ, Zhan XL, Jin CY, et al. The epidemiological evidence linking exposure to ambient particulate matter with neurodevelopmental disorders: a systematic review and meta-analysis. Environ Res. 2022;209:112876. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2022.112876.

    Article  CAS  Google Scholar 

  50. Liu S, Jørgensen JT, Ljungman P, Pershagen G, Bellander T, Leander K, Magnusson PKE, Rizzuto D, Hvidtfeldt UA, Raaschou-Nielsen O, Wolf K, Hoffmann B, Brunekreef B, Strak, Chen J, Mehta A, Atkinson RW, Bauwelinck M, Varraso R, Boutron-Ruault M-C, Brandt J, Cesaroni G, Forastiere F, Fecht D, Gulliver J, Hertel J, de Hoogh K, Janssen NAH, Katsouyanni K, Ketzel M, Klompmaker JO, Nagel G, Oftedal B, Peters A, Tjønneland A, Rodopoulou SP, Samoli E, Bekkevold T, Sigsgaard T, Stafoggia M, Vienneau D, Weinmayr G, Hoek G, Andersen ZJ. Long-term exposure to low-level air pollution and incidence of chronic obstructive pulmonary disease: The ELAPSE project. Environment International 146 (2021). (Liu 2021b).

  51. Liu S, Jørgensen JT, Ljungman P, Pershagen G, Bellander T, Leander K, Magnusson PKE, Rizzuto D, Hvidtfeldt UA, Raaschou-Nielsen O, Wolf K, Hoffmann B, Brunekreef B, Strak M, Chen J, Mehta A, Atkinson RW, Bauwelinck M, Varraso R, Boutron-Ruault MC, Brandt J, Cesaroni G, Forastiere F, Fecht D, Gulliver J, Hertel O, de Hoogh K, Janssen NAH, Katsouyanni K, Ketzel M, Klompmaker JO, Nagel G, Oftedal B, Peters A, Tjønneland A, Rodopoulou SP, Samoli E, Kristoffersen DT, Sigsgaard T, Stafoggia M, Vienneau D, Weinmayr G, Hoek G, Andersen ZJ. Long-term exposure to low-level air pollution and incidence of asthma: the ELAPSE project. Eur Respir J. 2021;57(6):2003099. https://doiorg.publicaciones.saludcastillayleon.es/10.1183/13993003.03099-2020. (Liu et al., 2021a).

    Article  CAS  Google Scholar 

  52. Maldonado G, Greenland S. Estimating causal effects. Int J Epidemiol. 2002;31(2):422-9. PMID: 11980807.

  53. Mudway IS, Kelly FJ, Holgate ST. Oxidative stress in air pollution research. Free Radic Biol Med. 2020;151:2–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.freeradbiomed.2020.04.031. Epub 2020 May 1. PMID: 32360613; PMCID: PMC7252129.

    Article  CAS  Google Scholar 

  54. Murray CJL, Lopez AD, editors. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in 1990 and projected to 2020. Cambridge: Harvard University Press; 1996.

    Google Scholar 

  55. Nemmar A, Hoet PH, Vanquickenborne B, Dinsdale D, Thomeer M, Hoylaerts MF, Vanbilloen H, Mortelmans L, Nemery B. Passage of inhaled particles into the blood circulation in humans. Circulation. 2002;105(4):411–4. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/hc0402.104118. PMID: 11815420.

    Article  CAS  Google Scholar 

  56. Ohlwein S, Kappeler R, Kutlar Joss M, Künzli N, Hoffmann B. Health effects of ultrafine particles: a systematic literature review update of epidemiological evidence. Int J Public Health. 2019;64(4):547–59. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00038-019-01202-7. Epub 2019 Feb 21. PMID: 30790006.

    Article  Google Scholar 

  57. Orellano P, Kasdagli MI, Pérez Velasco R, Samoli E. Long-term exposure to Particulate Matter and Mortality: an update of the WHO Global Air Quality Guidelines Systematic Review and Meta-analysis. Int J Public Health. 2024;69:1607683. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/ijph.2024.1607683. PMID: 39399882; PMCID: PMC11466858.

    Article  Google Scholar 

  58. Ostro B, Spadaro JV, Gumy S, Mudu P, Awe Y, Forastiere F, Peters A. Assessing the recent estimates of the global burden of disease for ambient air pollution: Methodological changes and implications for low- and middle-income countries. Environ Res. 2018;166:713–725. doi: 10.1016/j.envres.2018.03.001. Epub 2018 Jun 5. PMID: 29880237.

  59. Owens EO, Patel MM, Kirrane E, Long TC, Brown J, Cote I, Ross MA, Dutton SJ. Regul Toxicol Pharmacol. 2017;88:332–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.yrtph.2017.05.014. Epub 2017 May 17. PMID: 28526659; PMCID: PMC6613194. Framework for assessing causality of air pollution-related health effects for reviews of the National Ambient Air Quality Standards.

  60. Pascal M, de Crouy, Chanel P, Wagner V, Corso M, Tillier C, Bentayeb M, Blanchard M, Cochet A, Pascal L, Host S, Goria S, Le Tertre A, Chatignoux E, Ung A, Beaudeau P, Medina S. The mortality impacts of fine particles in France. Sci Total Environ. 2016;571:416–25.

    Article  CAS  Google Scholar 

  61. Pope AC 3rd, Thun MJ, Namboodiri MM, et al. Particulate air pollution as a predictor of mortality in a prospective study of US adults. Am J Respir Crit Care Med. 1995;151:669–74.

    Article  Google Scholar 

  62. Pope CA 3rd, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA. 2002;287(9):1132–41. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jama.287.9.1132. PMID: 11879110; PMCID: PMC4037163.

    Article  CAS  Google Scholar 

  63. Rajagopalan S, Brook RD, Salerno PRVO, Bourges-Sevenier B, Landrigan P, Nieuwenhuijsen MJ, Munzel T, Deo SV, Al-Kindi S. Air pollution exposure and cardiometabolic risk. Lancet Diabetes Endocrinol. 2024;12(3):196–208. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2213-8587(23)00361-3. Epub 2024 Feb 1. PMID: 38310921.

    Article  CAS  Google Scholar 

  64. Rigaud M, Buekers J, Bessems J, Basagaña X, Mathy S, Nieuwenhuijsen M, Slama R. The methodology of quantitative risk assessment studies. Environ Health. 2024;23(1):13. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-023-01039-x. PMID: 38281011; PMCID: PMC10821313.

    Article  Google Scholar 

  65. Samet JM, Chiu WA, Cogliano V, Jinot J, Kriebel D, Lunn RM, Beland FA, Bero L, Browne P, Fritschi L, Kanno J, Lachenmeier DW, Lan Q, Lasfargues G, Le Curieux F, Peters S, Shubat P, Sone H, White MC, Williamson J, Yakubovskaya M, Siemiatycki J, White PA, Guyton KZ, Schubauer-Berigan MK, Hall AL, Grosse Y, Bouvard V, Benbrahim-Tallaa L, El Ghissassi F, Lauby-Secretan B, Armstrong B, Saracci R, Zavadil J, Straif K, Wild CP. The IARC Monographs: Updated Procedures for Modern and Transparent Evidence Synthesis in Cancer Hazard Identification. J Natl Cancer Inst. 2020;112(1):30-7. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/jnci/djz169.

  66. Schraufnagel DE. The health effects of ultrafine particles. Exp Mol Med. 2020;52(3):311–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s12276-020-0403-3. Epub 2020 Mar 17. PMID: 32203102; PMCID: PMC7156741.

    Article  CAS  Google Scholar 

  67. Schwartz J. Particulate air pollution and daily mortality in Detroit. Environ Res. 1991;56(2):204 – 13. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s0013-9351(05)80009-x. PMID: 1769365.

  68. Schwartz J. Air pollution and daily mortality: a review and meta analysis. Environ Res. 1994;64(1):36–52. https://doiorg.publicaciones.saludcastillayleon.es/10.1006/enrs.1994.1005. PMID: 8287841.

  69. Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmj.j4008. PMID: 28935701; PMCID: PMC5833365.

    Article  Google Scholar 

  70. Sigsgaard T, Hoffmann B. Assessing the health burden from air pollution. Science. 2024;384(6691):33–34. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/science.abo3801. Epub 2024 Apr 4. PMID: 38574150.

  71. Soares J, González Ortiz A, Gsella A, Horálek J, Plass D, Kienzler S. Health risk assessment of air pollution and the impact of the new WHO guidelines. European Environment Agency, ETC-HE Report 2022/10, 2022.

  72. Stafoggia M, Oftedal B, Chen J, Rodopoulou S, Renzi M, Atkinson RW, Bauwelinck M, Klompmaker JO, Mehta A, Vienneau D, Andersen ZJ, Bellander T, Brandt J, Cesaroni G, de Hoogh K, Fecht D, Gulliver J, Hertel O, Hoffmann B, Hvidtfeldt UA, Jöckel KH, Jørgensen JT, Katsouyanni K, Ketzel M, Kristoffersen DT, Lager A, Leander K, Liu S, Ljungman PLS, Nagel G, Pershagen G, Peters A, Raaschou-Nielsen O, Rizzuto D, Schramm S, Schwarze PE, Severi G, Sigsgaard T, Strak M, van der Schouw YT, Verschuren M, Weinmayr G, Wolf K, Zitt E, Samoli E, Forastiere F, Brunekreef B, Hoek G, Janssen NAH. Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project. Lancet Planet Health. 2022;6(1):e9-e18. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2542-5196(21)00277-1. PMID: 34998464.

  73. Strak M, Janssen N, Beelen R, Schmitz O, Karssenberg D, Houthuijs D, van den Brink C, Dijst M, Brunekreef B, Hoek G. Associations between lifestyle and air pollution exposure: potential for confounding in large administrative data cohorts. Environ Res. 2017;156:364–73. Epub 2017 Apr 10. PMID: 28395240.

    Article  CAS  Google Scholar 

  74. Strak M, Weinmayr G, Rodopoulou S, Chen J, de Hoogh K, Andersen ZJ, Atkinson R, Bauwelinck M, Bekkevold T, Bellander T, Boutron-Ruault MC, Brandt J, Cesaroni G, Concin H, Fecht D, Forastiere F, Gulliver J, Hertel O, Hoffmann B, Hvidtfeldt UA, Janssen NAH, Jöckel KH, Jørgensen JT, Ketzel M, Klompmaker JO, Lager A, Leander K, Liu S, Ljungman P, Magnusson PKE, Mehta AJ, Nagel G, Oftedal B, Pershagen G, Peters A, Raaschou-Nielsen O, Renzi M, Rizzuto D, van der Schouw YT, Schramm S, Severi G, Sigsgaard T, Sørensen M, Stafoggia M, Tjønneland A, Verschuren WMM, Vienneau D, Wolf K, Katsouyanni K, Brunekreef B, Hoek G, Samoli E. Long term exposure to low level air pollution and mortality in eight European cohorts within the ELAPSE project: pooled analysis. BMJ. 2021;374:n1904. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmj.n1904. PMID: 34470785; PMCID: PMC8409282.

  75. Thurston GD, Kipen H, Annesi-Maesano I, Balmes J, Brook RD, Cromar K, De Matteis S, Forastiere F, Forsberg B, Frampton MW, Grigg J, Heederik D, Kelly FJ, Kuenzli N, Laumbach R, Peters A, Rajagopalan ST, Rich D, Ritz B, Samet JM, Sandstrom T, Sigsgaard T, Sunyer J, Brunekreef B. A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution? An analytical framework. Eur Respir J. 2017;49(1):1600419. https://doiorg.publicaciones.saludcastillayleon.es/10.1183/13993003.00419-2016. PMID: 28077473; PMCID: PMC5751718.

    Article  CAS  Google Scholar 

  76. UN Environment Assembly. (2024). Resolution adopted by the United Nations Environment Assembly on 1 March 2024: 6/10. Promoting regional cooperation on air pollution to improve air quality globally. https://www.unep.org/environmentassembly/unea6/outcomes

  77. US EPA. (2016). Integrated Science Assessment (ISA) for oxides of nitrogen – health criteria (final report, January 2016). Washington (DC): United States Environmental Protection Agency.

  78. US EPA. Integrated science assessment for particulate matter (Final Report, 2019). EPA/600/R-19/188. US: Environment Protection Agency, Research Triangle Park; 2019.

  79. UN General Assembly, Transforming our world: the 2030 Agenda for Sustainable Development. A/ RES/70/1. 2015. https://sdgs.un.org/2030agenda.

  80. van Donkelaar A, Martin RV, Brauer M, Boys BL. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ Health Perspect. 2015;123(2):135–43. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/ehp.1408646.

  81. Vodonos A, Awad YA, Schwartz J. The concentration-response between long-term PM2.5 exposure and mortality; a meta-regression approach. Environ Res. 2018;166:677–89. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2018.06.021. Epub 2018 Aug 1. PMID: 30077140.

    Article  CAS  Google Scholar 

  82. Weichenthal S, Pinault L, Christidis T, Burnett RT, Brook JR, Chu Y, Crouse DL, Erickson AC, Hystad P, Li C, Martin RV, Meng J, Pappin AJ, Tjepkema M, van Donkelaar A, Weagle CL, Brauer M. How low can you go? Air pollution affects mortality at very low levels. Sci Adv. 2022;8(39):eabo3381. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/sciadv.abo3381. Epub 2022 Sep 28. PMID: 36170354; PMCID: PMC9519036.

    Article  Google Scholar 

  83. Weichenthal S, Christidis T, Olaniyan T, van Donkelaar A, Martin R, Tjepkema M, Burnett RT, Brauer M. Epidemiological studies likely need to consider PM2.5 composition even if total outdoor PM2.5 mass concentration is the exposure of interest. Environ Epidemiol. 2024;8(4):e317. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/EE9.0000000000000317. PMID: 39022188; PMCID: PMC11254114.

    Article  Google Scholar 

  84. WHO. Sustainable development goal indicator 3.9.1: mortality attributed to air pollution. Geneva: World Health Organization; 2024. 3.0 IGO.

    Google Scholar 

  85. WHO. (2021). WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization 2021. https://apps.who.int/iris/handle/10665/345329

  86. WHO. (2006) Air quality guidelines. Global update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. World Health Organization 2006.

  87. WHO Regional Office for Europe. Health risk assessment of air pollution – general principles. Copenhagen: WHO Regional Office for Europe; 2016.

    Google Scholar 

  88. Wolf K, Hoffmann B, Andersen ZJ, Atkinson RW, Bauwelinck M, Bellander T, Brandt J, Brunekreef B, Cesaroni G, Chen J, de Faire U, de Hoogh K, Fecht D, Forastiere F, Gulliver J, Hertel O, Hvidtfeldt UA, Janssen NAH, Jørgensen JT, Katsouyanni K, Ketzel M, Klompmaker JO, Lager A, Liu S, MacDonald CJ, Magnusson PKE, Mehta AJ, Nagel G, Oftedal B, Pedersen NL, Pershagen G, Raaschou-Nielsen O, Renzi M, Rizzuto D, Rodopoulou S, Samoli E, van der Schouw YT, Schramm S, Schwarze P, Sigsgaard T, Sørensen M, Stafoggia M, Strak M, Tjønneland A, Verschuren WMM, Vienneau D, Weinmayr G, Hoek G, Peters A, Ljungman PLS. Long-term exposure to low-level ambient air pollution and incidence of stroke and coronary heart disease: a pooled analysis of six European cohorts within the ELAPSE project. Lancet Planet Health. 2021;5(9):e620-e632. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2542-5196(21)00195-9. PMID: 34508683.

  89. Zhang S, Qian ZM, Chen L, Zhao X, Cai M, Wang C, Zou H, Wu Y, Zhang Z, Li H, Lin H. Exposure to Air Pollution during Pre-Hypertension and Subsequent Hypertension, Cardiovascular Disease, and Death: A Trajectory Analysis of the UK Biobank Cohort. Environ Health Perspect. 2023;131(1):17008. doi: 10.1289/EHP10967. Epub 2023 Jan 25. Erratum in: Environ Health Perspect. 2023;131(2):29001. PMID: 36696106; PMCID: PMC9875843.

  90. Zheng P, Afshin A, Biryukov S, Bisignano C, Brauer M, Bryazka D, Burkart K, Cercy KM, Cornaby L, Dai X, Dirac MA, Estep K, Fay KA, Feldman R, Ferrari AJ, Gakidou E, Gil GF, Griswold M, Hay SI, He J, Irvine CMS, Kassebaum NJ, LeGrand KE, Lescinsky H, Lim SS, Lo J, Mullany EC, Ong KL, Rao PC, Razo C, Reitsma MB, Roth GA, Santomauro DF, Sorensen RJD, Srinivasan V, Stanaway JD, Vollset SE, Vos T, Wang N, Welgan CA, Wozniak SS, Aravkin AY, Murray CJL. The Burden of Proof studies: assessing the evidence of risk. Nat Med. 2022;28(10):2038–44. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41591-022-01973-2. Epub 2022 Oct 10. PMID: 36216935; PMCID: PMC9556298.

    Article  CAS  Google Scholar 

  91. Zou H, Zhang S, Cai M, Qian ZM, Zhang Z, Chen L, Wang X, Arnold LD, Howard SW, Li H, Lin H. Ambient air pollution associated with incidence and progression trajectory of cardiometabolic diseases: a multi-state analysis of a prospective cohort. Sci Total Environ. 2023;862:160803. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.scitotenv.2022.160803. Epub 2022 Dec 7. PMID: 36493826.

    Article  CAS  Google Scholar 

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Acknowledgements

We acknowledge the scientific contribution of all the members of VALESOR (VALUATION OF ENVIRONMENTAL STRESSORS), an EU-funded project (https://valesor.eu/) under HORIZON-HLTH-2022-ENVHLTH-04-01.

Funding

The work has been partially funded in the context of the EU project “VALUATION OF ENVIRONMENTAL STRESSORS, VALESOR” Project number: 101095611.

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Authors and Affiliations

  1. National Research Council, IFT, Palermo, Italy

    Francesco Forastiere

  2. Environmental Research Group, Imperial College, London, UK

    Francesco Forastiere & Michal Krzyzanowski

  3. Unit of Sustainable Health, Umea University, Umea, Sweden

    Hans Orru

  4. Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia

    Hans Orru

  5. Spadaro Environmental Research Consultants (SERC), Philadelphia, PA, USA

    Joseph V. Spadaro

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  1. Francesco Forastiere
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FF wrote the first draft of the main text, while HO, MK, and JVS provided noteworthy comments and substantial editing of the manuscript. All authors reviewed and approved the last version of the manuscript.

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Correspondence to Francesco Forastiere.

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Forastiere, F., Orru, H., Krzyzanowski, M. et al. The last decade of air pollution epidemiology and the challenges of quantitative risk assessment. Environ Health 23, 98 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-024-01136-5

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-024-01136-5

Keywords

Environmental Health

ISSN: 1476-069X