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Simulation models can be valuable tools in supporting development of air pollution policy. However, exploration of future scenarios depends on reliable and robust modelling to provide confidence in outcomes which cannot be tested against measurements. Here we focus on the UK Integrated Assessment Model, a fast reduced-form model with a purpose to support policy development with modelling of multiple alternative future scenarios, and the EMEP4UK model which is a complex Eulerian Atmospheric Chemistry Transport Model requiring significant computing resources. The EMEP4UK model has been used to model selected core scenarios to compare with UKIAM, and to investigate sensitivity studies such as the interannual variability in response to meteorological differences between years. This model intercomparison addresses total PM 2.5 , primary PM 2.5 and Secondary Inorganic Aerosol concentrations for a baseline of 2018 and selected scenarios for projections to 2040. This work has confirmed the robustness of the UK Integrated Assessment Model for assessing alternative futures through a direct comparison with EMEP4UK. Both models have shown good agreement with measurements, and EMEP4UK shows an ability to replicate past trends. These comparisons highlight how a combination of reduced-form modelling (UKIAM) and complex chemical transport modelling (EMEP4UK) can be effectively used in support of air pollution policy development, informing understanding of projected futures in the context of emerging evidence and uncertainties., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Emerging research suggests exposure to high levels of air pollution at critical points in the life-course is detrimental to brain health, including cognitive decline and dementia. Social determinants play a significant role, including socio-economic deprivation, environmental factors and heightened health and social inequalities. Policies have been proposed more generally, but their benefits for brain health have yet to be fully explored., Objective and Methods: Over the course of two years, we worked as a consortium of 20+ academics in a participatory and consensus method to develop the first policy agenda for mitigating air pollution's impact on brain health and dementia, including an umbrella review and engaging 11 stakeholder organisations., Results: We identified three policy domains and 14 priority areas. Research and Funding included: (1) embracing a complexities of place approach that (2) highlights vulnerable populations; (3) details the impact of ambient PM 2.5 on brain health, including current and historical high-resolution exposure models; (4) emphasises the importance of indoor air pollution; (5) catalogues the multiple pathways to disease for brain health and dementia, including those most at risk; (6) embraces a life course perspective; and (7) radically rethinks funding. Education and Awareness included: (8) making this unrecognised public health issue known; (9) developing educational products; (10) attaching air pollution and brain health to existing strategies and campaigns; and (11) providing publicly available monitoring, assessment and screening tools. Policy Evaluation included: (12) conducting complex systems evaluation; (13) engaging in co-production; and (14) evaluating air quality policies for their brain health benefits., Conclusion: Given the pressing issues of brain health, dementia and air pollution, setting a policy agenda is crucial. Policy needs to be matched by scientific evidence and appropriate guidelines, including bespoke strategies to optimise impact and mitigate unintended consequences. The agenda provided here is the first step toward such a plan., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2022. Published by Elsevier Inc. All rights reserved.)

Background: Exposure to air pollution is associated with a range of diseases. Biomarkers derived from DNA methylation (DNAm) offer potential mechanistic insights into human health differences, connecting disease pathogenesis and biological ageing. However, little is known about sensitive periods during the life course where air pollution might have a stronger impact on DNAm, or whether effects accumulate over time. We examined associations between air pollution exposure across the life course and DNAm-based markers of biological ageing., Methods: Data were derived from the Scotland-based Lothian Birth Cohort 1936. Participants' residential history was linked to annual levels of fine particle (PM 2.5 ), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) around 1935, 1950, 1970, 1980, 1990, and 2001; pollutant concentrations were estimated using the EMEP4UK atmospheric chemistry transport model. Blood samples were obtained between ages of 70 and 80 years, and Horvath DNAmAge, Hannum DNAmAge, DNAmPhenoAge, DNAmGrimAge, and DNAm telomere length (DNAmTL) were computed. We applied the structured life-course modelling approach: least angle regression identified best-fit life-course models for a composite measure of air pollution (air quality index [AQI]), and mixed-effects regression estimated selected models for AQI and single pollutants., Results: We included 525 individuals with 1782 observations. In the total sample, increased air pollution around 1970 was associated with higher epigenetic age (AQI: b = 0.322 year, 95 %CI: 0.088, 0.555) measured with Horvath DNAmAge in late adulthood. We found shorter DNAmTL among males with higher air pollution around 1980 (AQI: b = -0.015 kilobase, 95 %CI: -0.027, -0.004) and among females with higher exposure around 1935 (AQI: b = -0.017 kilobase, 95 %CI: -0.028, -0.006). Findings were more consistent for the pollutants PM 2.5 , SO 2 and NO 2 ., Discussion: We tested the life-course relationship between air pollution and DNAm-based biomarkers. Air pollution around birth and in young-to-middle adulthood is linked to accelerated epigenetic ageing and telomere-associated ageing in later life., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Air pollution has been consistently linked with dementia and cognitive decline. However, it is unclear whether risk is accumulated through long-term exposure or whether there are sensitive/critical periods. A key barrier to clarifying this relationship is the dearth of historical air pollution data., Objective: To demonstrate the feasibility of modelling historical air pollution data and using them in epidemiologicalmodels., Methods: Using the EMEP4UK atmospheric chemistry transport model, we modelled historical fine particulate matter (PM2.5) concentrations for the years 1935, 1950, 1970, 1980, and 1990 and combined these with contemporary modelled data from 2001 to estimate life course exposure in 572 participants in the Lothian Birth Cohort 1936 with lifetime residential history recorded. Linear regression and latent growth models were constructed using cognitive ability (IQ) measured by the Moray House Test at the ages of 11, 70, 76, and 79 years to explore the effects of historical air pollution exposure. Covariates included sex, IQ at age 11 years, social class, and smoking., Results: Higher air pollution modelled for 1935 (when participants would have been in utero) was associated with worse change in IQ from age 11-70 years (β = -0.006, SE = 0.002, p = 0.03) but not cognitive trajectories from age 70-79 years (p > 0.05). There was no support for other critical/sensitive periods of exposure or an accumulation of risk (all p > 0.05)., Conclusion: The life course paradigm is essential in understanding cognitive decline and this is the first study to examine life course air pollution exposure in relation to cognitive health.

The potential to capture additional air pollutants by introducing more vegetation or changing existing short vegetation to woodland on first sight provides an attractive route for lowering urban pollution. Here, an atmospheric chemistry and transport model was run with a range of landcover scenarios to quantify pollutant removal by the existing total UK vegetation as well as the UK urban vegetation and to quantify the effect of large-scale urban tree planting on urban air pollution. UK vegetation as a whole reduces area (population)-weighted concentrations significantly, by 10% (9%) for PM 2.5 , 30% (22%) for SO 2 , 24% (19%) for NH 3 and 15% (13%) for O 3 , compared with a desert scenario. By contrast, urban vegetation reduces average urban PM 2.5 by only approximately 1%. Even large-scale conversion of half of existing open urban greenspace to forest would lower urban PM 2.5 by only another 1%, suggesting that the effect on air quality needs to be considered in the context of the wider benefits of urban tree planting, e.g. on physical and mental health. The net benefits of UK vegetation for NO 2 are small, and urban tree planting is even forecast to increase urban NO 2 and NO x concentrations, due to the chemical interaction with changes in BVOC emissions and O 3 , but the details depend on tree species selection. By extrapolation, green infrastructure projects focusing on non-greenspace (roadside trees, green walls, roof-top gardens) would have to be implemented at very large scales to match this effect. Downscaling of the results to micro-interventions solely aimed at pollutant removal suggests that their impact is too limited for their cost-benefit analysis to compare favourably with emission abatement measures. Urban vegetation planting is less effective for lowering pollution than measures to reduce emissions at source. The results highlight interactions that cannot be captured if benefits are quantified via deposition models using prescribed concentrations, and emission damage costs. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Purpose of Review: The literature on air pollution and brain health is rapidly expanding and it is a universal finding that greater exposure to air pollution is associated with worse outcomes, whatever the measure of brain health used (clinical dementia, neuroimaging correlates, or cognitive impairment). However, there are a number of important questions which the studies currently published are not able to answer: when in the life course does exposure to air pollution most have the most impact?; which pollutant(s) or components are most important?; and since dementia describes a heterogeneous group of conditions, which is most affected by exposure to air pollution?, Recent Findings: We briefly review and discuss the nine articles which have been published so far in 2018, so recently that they were not included in the four review articles also published this year. We highlight the variation in estimates of air pollution used but the consistency in deriving them from residential address (with or without some knowledge of an individual's previous home locations)., Summary: We are now at the stage when the research agenda needs to be agreed and we believe these three questions should be the focus of future research.

Traditional approaches of quantifying population-level exposure to air pollution assume that concentrations of air pollutants at the residential address of the study population are representative for overall exposure. This introduces potential bias in the quantification of human health effects. Our study combines new UK Census data comprising information on workday population densities, with high spatio-temporal resolution air pollution concentration fields from the WRF-EMEP4UK atmospheric chemistry transport model, to derive more realistic estimates of population exposure to NO 2 , PM 2.5 and O 3 . We explicitly allocated workday exposures for weekdays between 8:00 am and 6:00 pm. Our analyses covered all of the UK at 1 km spatial resolution. Taking workday location into account had the most pronounced impact on potential exposure to NO 2 , with an estimated 0.3 μg m -3 (equivalent to 2%) increase in population-weighted annual exposure to NO 2 across the whole UK population. Population-weighted exposure to PM 2.5 and O 3 increased and decreased by 0.3%, respectively, reflecting the different atmospheric processes contributing to the spatio-temporal distributions of these pollutants. We also illustrate how our modelling approach can be utilised to quantify individual-level exposure variations due to modelled time-activity patterns for a number of virtual individuals living and working in different locations in three example cities. Changes in annual-mean estimates of NO 2 exposure for these individuals were considerably higher than for the total UK population average when including their workday location. Conducting model-based evaluations as described here may contribute to improving representativeness in studies that use small, portable, automatic sensors to estimate personal exposure to air pollution., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Urban air quality in China has been declining substantially in recent years due to severe haze episodes. The reduction of sulfur dioxide (SO 2 ) and nitrogen oxide (NO x ) emissions since 2013 does not yet appear to yield substantial benefits for haze mitigation. As the reductions of those key precursors to secondary aerosol formation appears not to sufficient, other crucial factors need to be considered for the design of effective air pollution control strategies. Here we argue that ammonia (NH 3 ) plays a - so far - underestimated role in the formation of secondary inorganic aerosols, a main component of urban fine particulate matter (PM 2.5 ) concentrations in China. By analyzing in situ concentration data observed in major cities alongside gridded emission data obtained from remote sensing and inventories, we find that emissions of NH 3 have a more robust association with the spatiotemporal variation of PM 2.5 levels than emissions of SO 2 and NO x . As a consequence, we argue that urban PM 2.5 pollution in China in many locations is substantially affected by NH 3 emissions. We highlight that more efforts should be directed to the reduction of NH 3 emissions that help mitigate PM 2.5 pollution more efficiently than other PM 2.5 precursors. Such efforts will yield substantial co-benefits by improving nitrogen use efficiency in farming systems. As a consequence, such integrated strategies would not only improve urban air quality, but also contribute to China's food-security goals, prevent further biodiversity loss, reduce greenhouse gas emissions and lead to economic savings., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Adverse health effects from exposure to air pollution are a global challenge and of widespread concern. Recent high ambient concentration episodes of air pollutants in European cities highlighted the dynamic nature of human exposure and the gaps in data and knowledge about exposure patterns. In order to support health impact assessment it is essential to develop a better understanding of individual exposure pathways in people's everyday lives by taking account of all environments in which people spend time. Here we describe the development, validation and results of an exposure method applied in a study conducted in Scotland. A low-cost particle counter based on light-scattering technology - the Dylos 1700 was used. Its performance was validated in comparison with equivalent instruments (TEOM-FDMS) at two national monitoring network sites (R(2)=0.9 at a rural background site, R(2)=0.7 at an urban background site). This validation also provided two functions to convert measured PNCs into calculated particle mass concentrations for direct comparison of concentrations with equivalent monitoring instruments and air quality limit values. This study also used contextual and time-based activity data to define six microenvironments (MEs) to assess everyday exposure of individuals to short-term PM2.5 concentrations. The Dylos was combined with a GPS receiver to track movement and exposure of individuals across the MEs. Seventeen volunteers collected 35 profiles. Profiles may have a different overall duration and structure with respect to times spent in different MEs and activities undertaken. Results indicate that due to the substantial variability across and between MEs, it is essential to measure near-complete exposure pathways to allow for a comprehensive assessment of the exposure risk a person encounters on a daily basis. Taking into account the information gained through personal exposure measurements, this work demonstrates the added value of data generated by the application of low-cost monitors., (Copyright © 2014. Published by Elsevier B.V.)

An atmospheric chemical transport model was adapted to simulate the concentration and deposition of heavy metals (arsenic, cadmium, chromium, copper, lead, nickel, selenium, vanadium, and zinc) in the United Kingdom. The model showed that wet deposition was the most important process for the transfer of metals from the atmosphere to the land surface. The model achieved a good correlation with annually averaged measurements of metal concentrations in air. The correlation with measurements of wet deposition was less strong due to the complexity of the atmospheric processes involved in the washout of particulate matter which were not fully captured by the model. The measured wet deposition and air concentration of heavy metals were significantly underestimated by the model for all metals (except vanadium) by factors between 2 and 10. These results suggest major missing sources of annual heavy metal emissions which are currently not included in the official inventory. Primary emissions were able to account for only 9%, 21%, 29%, 21%, 36%, 7% and 23% of the measured concentrations for As, Cd, Cr, Cu, Ni, Pb and Zn. A likely additional contribution to atmospheric heavy metal concentrations is the wind driven re-suspension of surface dust still present in the environment from the legacy of much higher historic emissions. Inclusion of two independent estimates of emissions from re-suspension in the model was found to give an improved agreement with measurements. However, an accurate estimate of the magnitude of re-suspended emissions is restricted by the lack of measurements of metal concentrations in the re-suspended surface dust layer., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Existing descriptions of bi-directional ammonia (NH3) land-atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission-deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28-67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45-85) Tg N in 2008 to reach 132 (89-179) Tg by 2100.

Global nitrogen fixation contributes 413 Tg of reactive nitrogen (Nr) to terrestrial and marine ecosystems annually of which anthropogenic activities are responsible for half, 210 Tg N. The majority of the transformations of anthropogenic Nr are on land (240 Tg N yr(-1)) within soils and vegetation where reduced Nr contributes most of the input through the use of fertilizer nitrogen in agriculture. Leakages from the use of fertilizer Nr contribute to nitrate (NO3(-)) in drainage waters from agricultural land and emissions of trace Nr compounds to the atmosphere. Emissions, mainly of ammonia (NH3) from land together with combustion related emissions of nitrogen oxides (NOx), contribute 100 Tg N yr(-1) to the atmosphere, which are transported between countries and processed within the atmosphere, generating secondary pollutants, including ozone and other photochemical oxidants and aerosols, especially ammonium nitrate (NH4NO3) and ammonium sulfate (NH4)2SO4. Leaching and riverine transport of NO3 contribute 40-70 Tg N yr(-1) to coastal waters and the open ocean, which together with the 30 Tg input to oceans from atmospheric deposition combine with marine biological nitrogen fixation (140 Tg N yr(-1)) to double the ocean processing of Nr. Some of the marine Nr is buried in sediments, the remainder being denitrified back to the atmosphere as N2 or N2O. The marine processing is of a similar magnitude to that in terrestrial soils and vegetation, but has a larger fraction of natural origin. The lifetime of Nr in the atmosphere, with the exception of N2O, is only a few weeks, while in terrestrial ecosystems, with the exception of peatlands (where it can be 10(2)-10(3) years), the lifetime is a few decades. In the ocean, the lifetime of Nr is less well known but seems to be longer than in terrestrial ecosystems and may represent an important long-term source of N2O that will respond very slowly to control measures on the sources of Nr from which it is produced.

Quantifying human exposure to air pollutants is a challenging task. Ambient concentrations of air pollutants at potentially harmful levels are ubiquitous in urban areas and subject to high spatial and temporal variability. At the same time, every individual has unique activity-patterns. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process. Traditionally, approaches to quantify human exposure have relied on pollutant concentrations from fixed air quality network sites and static population distributions. New developments in sensor technology now enable us to monitor personal exposure to air pollutants directly while people are moving through their activity spaces and varying concentration fields. The literature review on which this paper is based on reflects recent developments in the assessment of human exposure to air pollution. This includes the discussion of methodologies and concepts, and the elaboration of approaches and study designs applied in the field. We identify shortcomings of current approaches and discuss future research needs. We close by proposing a novel conceptual model for the integrated assessment of human exposure to air pollutants taking into account latest technological capabilities and contextual information., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Halving nitrogen pollution is crucial for achieving Sustainable Development Goals (SDGs). However, how to reduce nitrogen pollution from multiple sources remains challenging. Here we show that reactive nitrogen (Nr) pollution could be roughly halved by managed urban development in China by 2050, with NH3, NOx and N2O atmospheric emissions declining by 44%, 30% and 33%, respectively, and Nr to water bodies by 53%. While rural-urban migration increases point-source nitrogen emissions in metropolitan areas, it promotes large-scale farming, reducing rural sewage and agricultural non-point-source pollution, potentially improving national air and water quality. An investment of approximately US$ 61 billion in waste treatment, land consolidation, and livestock relocation yields an overall benefit of US$ 245 billion. This underscores the feasibility and cost-effectiveness of halving Nr pollution through urbanization, contributing significantly to SDG1 (No poverty), SDG2 (Zero hunger), SDG6 (Clean water), SDG12 (Responsible consumption and production), SDG14 (Climate Action), and so on. Here the authors demonstrate how managed urbanization in China could halve reactive nitrogen pollution to both the atmosphere and water resources. Investing 61 billion USD could provide 245 billion USD in benefits, while contributing to multiple SDG goals. [ABSTRACT FROM AUTHOR]

Land-use regression (LUR) models are increasingly used to estimate exposure to air pollution in urban areas. An appropriate monitoring network is an important component in the development of a robust LUR model. In this study concentrations of NO 2 were simulated by a dispersion model at ‘virtual’ monitoring sites in 54 network designs of varying numbers and types of site, using a 25 km 2 area in Edinburgh, UK, as an example location. Separate LUR models were developed for each network. The LUR models were then used to estimate NO 2 concentration at all residential addresses, which were evaluated against the dispersion-modelled concentration at these addresses. The improvement in predictive capability of the LUR models was insignificant above ∼30 monitoring sites, although more sites tended to yield more precise LUR models. Monitoring networks containing sites located within highly populated areas better estimated NO 2 concentrations across all residential locations. LUR models constructed from networks containing more roadside sites better characterised the high end of residential NO 2 concentrations but had increased errors when considering the whole range of concentrations. No particular composition of monitoring network resulted in good estimation simultaneously across all residential NO 2 concentration and of the highest NO 2 levels. This evaluation with dispersion modelling has shown that previous LUR model validation methods may have been optimistic in their assessment of the model's predictive performance at residential locations. [ABSTRACT FROM AUTHOR]

Anthropogenic activities are responsible for the emission of gaseous and particulate pollutants that modify atmospheric composition. Such changes are, in turn, responsible for the degradation of air quality at the regional/local scale as well as for changes of climate. Air pollution and climate change are two intimately connected environmental issues. However, these two environmental challenges are still viewed as separate issues, which are dealt with by different science communities and within different policy frameworks. Indeed, many mitigation options offer the possibility to both improve air quality and mitigate climate change but, at the same time, mitigation options that may provide benefits to one aspect, are worsening the situation in the other. Therefore, coordinated actions taking into account the air quality-climate linkages are required. These actions need to be based on strong scientific grounds, as recognised by the European Commission that in the past few years has promoted consultation processes among the science community, the policy makers and the relevant stakeholders. Here, the main fields in which such coordinated actions are needed are examined from a policy perspective. [ABSTRACT FROM AUTHOR]

Urban air quality in China has been declining substantially in recent years due to severe haze episodes. The reduction of sulfur dioxide (SO 2 ) and nitrogen oxide (NO x ) emissions since 2013 does not yet appear to yield substantial benefits for haze mitigation. As the reductions of those key precursors to secondary aerosol formation appears not to sufficient, other crucial factors need to be considered for the design of effective air pollution control strategies. Here we argue that ammonia (NH 3 ) plays a - so far - underestimated role in the formation of secondary inorganic aerosols, a main component of urban fine particulate matter (PM 2.5 ) concentrations in China. By analyzing in situ concentration data observed in major cities alongside gridded emission data obtained from remote sensing and inventories, we find that emissions of NH 3 have a more robust association with the spatiotemporal variation of PM 2.5 levels than emissions of SO 2 and NO x . As a consequence, we argue that urban PM 2.5 pollution in China in many locations is substantially affected by NH 3 emissions. We highlight that more efforts should be directed to the reduction of NH 3 emissions that help mitigate PM 2.5 pollution more efficiently than other PM 2.5 precursors. Such efforts will yield substantial co-benefits by improving nitrogen use efficiency in farming systems. As a consequence, such integrated strategies would not only improve urban air quality, but also contribute to China's food-security goals, prevent further biodiversity loss, reduce greenhouse gas emissions and lead to economic savings. [ABSTRACT FROM AUTHOR]

In this virtual thematic issue (VTI) the authors of 14 papers address key challenges of environmental modelling and software to support the identification of human and ecological health risks. The contributions address different pathways how environmental exposure can affect human health with a focus on models, data and software and can be divided into the following research areas: (1) modelling approaches to quantify air pollution concentrations and exposure, (2) models and methods to determine health effects of air pollution, (3) models and software to predict and quantify disease and health risks and (4) data interoperability and integration. These contributions highlight that further advances in this research field are required, especially from highly polluted regions, as so far papers from these regions are scarce. The majority of contributions covers exposure to ambient air pollution, which is a key public health risk in both industrialised and developing countries. While not a focus of this VTI, human exposure to water and soil contamination as well as exposure pathways through other environmental media are equally relevant. Further research into how modelling, software and data can support an integrated assessment of the whole Exposome is therefore essential. [ABSTRACT FROM AUTHOR]

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5. [ABSTRACT FROM AUTHOR]