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Abstract 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. Highlights • High-res air pollutant concentrations combined with residence/workplace populations • Exposures to NO 2 , PM 2.5 & O 3 including workplace hours compared with residence only • Population-weighted mean NO 2 exposure 0.3 μg m−3 (2%) higher; less for PM 2.5 and O 3 • Individual exposures vary substantially accounting for home and workplace location. • Greatest spatio-temporal variation when considering exposure to NO 2 [ABSTRACT FROM AUTHOR]

The impacts of climate on health and wellbeing occur in time and space and through a range of indirect, complicated mechanisms. This diversity of pathways has major implications for national public health planning and influence on interventions that might help to mitigate and adapt to rapidly changing environmental conditions, nationally and internationally. This paper draws upon evidence from public health and adverse impact studies across climate science, hydrology, agriculture, public health, and the social sciences. It presents a conceptual model to support decision-making by recognizing both the proximal and distal pathways from climate-induced environmental change to national health and wellbeing. The proximal and distal pathways associated with food security, migration and mobility illustrate the diverse climate change influences in different geographic locations over different timescales. We argue that greater realization and articulation of proximal and distal pathways should radically alter how climate change is addressed as a national and international public health challenge. [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]

Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing ‘Big Data’ and, more importantly, make the vital step from ‘Big Data’ to ‘Big Information’. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example. [ABSTRACT FROM AUTHOR]

The aim of this work was to compare the variability in an urban area of fine particles (PM 2.5 ), ultrafine particles (UFP) and black carbon (BC) and to evaluate the relationship between each particle metric and potential factors (local traffic, street topography and synoptic meteorology) contributing to the variability. Concentrations of the three particle metrics were quantified using portable monitors through a combination of mobile and static measurements in the city of Edinburgh, UK. The spatial variability of UFP and BC was large, of similar magnitude and about 3 times higher than the spatial variability of PM 0.5-2.5 (the PM size fraction actually quantified in this work). Highest inter-daily variability was observed for PM 0.5-2.5 , which was approximately 2 times higher than inter-daily variability of BC and UFP. Elevated concentrations of UFP and BC were observed along streets with high traffic volumes whereas PM 0.5-2.5 showed less variation between streets and a footpath without road traffic. Both BC and UFP were significantly correlated with traffic counts, while no significant correlation between PM 0.5-2.5 and traffic counts was observed. BC was significantly correlated with UFP, with significantly different regression slopes between working days and non-working days implying that the increased number of diesel powered heavy goods vehicles during working days contributed more to BC than to UFP. It is concluded that variations in BC and UFP concentrations were mainly determined by the nearby traffic count and varying background concentrations between days, while variation in PM 0.5-2.5 concentration was mainly associated with regional sources. These findings imply the need for different policies for managing human exposure to these different particle components: control of much BC and UFP appears to be manageable at local scale by restricting traffic emissions; however, abatement of PM 2.5 requires a more strategic approach, in cooperation with other regions and countries on emissions control to curb long-range transport of PM 2.5 precursors. [ABSTRACT FROM AUTHOR]

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 PM 2.5 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. [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]

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 58C 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. [ABSTRACT FROM AUTHOR]

Abstract: 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 &y& Elsevier]

Abstract: In this paper, crucial aspects of the implications and the complexity of interconnected multi-pollutant multi-effect assessments of both air pollution control strategies and the closely related reduction of greenhouse gas emissions will be discussed. The main aims of the work described here are to identify the core problems which occur when trying to apply current state-of-the-art methodology to conduct integrated assessments – in this context, cost-benefit assessment (CBA) as well as cost-effectiveness assessment (CEA) – using sophisticated computer models and propose solutions to the problems identified. The approaches described will display the integrated use of databases, efficient algorithms and already existing software tools and models in a unified model framework. The first part of the paper discusses the need for new developments in one particular field of Integrated Assessment Models (IAMs), which is the use of (typically) country-specific single pollutant abatement cost curves, which have been applied in a large number of modelling approaches with the aim to find cost-effective solutions for given air quality targets. However, research conducted to find such cost-effective solutions for the non-linear problem of tropospheric ozone abatement (dealing with two primary pollutants and their rather complex relationship to form tropospheric ozone, [see] [Friedrich, R., Reis, S. (Eds.), 2000. Tropospheric Ozone Abatement – Developing Efficient Strategies for the Reduction of Ozone Precursor Emissions in Europe. Springer Publishers] identified basic problems of cost curve based approaches even in this two-pollutant case. The approach discussed here promises to solve the key problems identified, making extensive use of databases in order to provide fast and high quality model input for CEA and CBA. In addition to that, the application of Genetic Algorithms will be discussed as a means to address extremely complex, vast solution spaces which are typical for the tasks IAMs are set to solve nowadays. As the application of the model in extensive assessment studies is currently under way, it is yet too early for a full evaluation of lessons learned. However, initial tests of performance and behaviour have shown robust and promising results. [Copyright &y& Elsevier]

In 2015, all 193 member states of the United Nations (UN) agreed on the 2030 Agenda for Sustainable Development. The special section contains the following articles: The special section opens with a conceptual article (Eckert et al. [10]) discussing the surprising fact that the risk concept is almost absent in the SDGs, and similarly, most risks assessments are narrowed down to sectoral approaches without references to the SDGs. The agreement comprises 17 Sustainable Development Goals (SDGs), specified by 169 individual targets, and a common understanding that these global goals need to be treated as being interconnected and interdependent rather than handled separately or even confined in silos (Dodds et al. [7]; UN [24]). [Extracted from the article]

The article offers information on the 2016 Quadrennial Ozone Symposium (QOS-2016) organized by the International Ozone Commission (IO3C) in Edinburgh, Scotland on September 4-9, 2016

Synthetic nitrogen (N) fertilizers support global food production and feed over half of the global population. However, more than half of the N fertilizers applied to croplands are not absorbed by crops, but lost to the environment, leading to low N use efficiency (NUE) globally. Identifying and overcoming socioeconomic barriers to achieve an improvement of NUE and a reduction in N loss is thus key for N management and to attain agricultural and environmental sustainability. In this paper, we compiled the global cropland N budgets at a national scale (1961–2018) and developed robust econometric models to explore the relationship between cropland N use with socioeconomic factors. The results demonstrate that economic development and farm size are the key drivers to improve NUE for agricultural and environmental sustainability. In less developed countries, it is difficult for farmers to access chemical fertilizers, leading to croplands receiving too little N input and at a high NUE, but suffering from soil N depletion. As economic development progresses, more fertilizer is produced and applied, but smallholder farming typically leads to over-fertilization and consequently N losses to the environment. Large-scale farming improves NUE, reduces N loss and benefits agricultural production and environmental protection. Also, interactions between farm size and economic development, international trade, crop and livestock systems, and related policies are important causes for changes in the NUE of the whole agricultural system. Overcoming these socioeconomic barriers especially combining large-scale farming could effectively reduce excessive N inputs and sustain agricultural and environmental N management, while maintaining food production, resulting in a triple-win for food security, poverty alleviation, and environmental protection. • Overcoming socio-economic barriers is vital to achieve N sustainability. • Economy and farm size are key drivers of sustaining agriculture and environment. • Large-scale farming improves NUE and benefit environmental protection. • Farm size interacted with economy, crop-livestock systems, and policies affect NUE. [ABSTRACT FROM AUTHOR]

Abstract: Science in general and modelling in particular provide in-depth understanding of environmental processes and clearly demonstrate the present unsustainable use of resources on a global scale. The latest report by the Intergovernmental Panel on Climate Change (IPCC), for instance, shows that climate is changing and with a 95% certainty it is the humans have caused the change. The future climatic conditions are shown to be largely adversely affecting human wellbeing on this planet. Yet we see in numerous examples that societies are very slow in reacting to this rapid depletion of natural resources. What still seems lacking is the translation of scientific reports and the results of analysis and modelling into corrective actions. We argue that one of the reasons for this is the traditional workflow of environmental modelling, which starts with the purpose, the goal formulation, and ends with problem solutions or decision support tools. Instead, modelling, and applied science in general, has to enhance its scope beyond the problem solving stage, to do more on the problem definition and solution implementation phases. Modelling can be also used for identification of societal values and for setting purposes by appropriate communication of the modelling process and results. We believe this new approach for modelling can impact and bring the social values to the forefront of socio-environmental debate and hence turn scientific results into actions sooner rather than later. Instead of being separated from the modelling process, the translation of results should be an intrinsic part of it. We discuss several challenges for recent socio-environmental modelling and conclude with ten propositions that modellers and scientists in general can follow to improve their communication with the society and produce results that can be understood and used to improve awareness and education and spur action. [Copyright &y& Elsevier]

To feed an increasingly affluent population, the nitrogen input into global food production systems is currently two times the safe planetary boundary leading to adverse impacts on the local and global environments. The nitrogen footprint is an important index to understand the impact of human activities on the environment, however, it is rarely applied at a watershed scale. By using the C oupled H uman A nd N atural S ystem (CHANS) model, it was found that the total nitrogen input to the Lake Tai watershed has increased from 141.1 Gg N yr−1 in 1995 to 244.3 Gg N yr−1 in 2010, and then decreased again to 201.2 Gg N yr−1 in 2015. The study findings are consistent with the change in the total nitrogen concentration observed in Lake Tai. While the per capita nitrogen footprint remained stable at around 30 kg N yr−1 before 2010, a substantial decrease to approximately 25 and 20 kg N yr−1 in 2010 and 2015, respectively, has occurred. Dominant sources of nitrogen emissions contributing to the nitrogen footprint gradually have changed from agricultural production to wastewater and nitrogen oxides emissions from fossil fuel combustion. A reduction of the nitrogen footprint is beneficial for effective nitrogen pollution control strategies and better wastewater treatment techniques should be prioritized for future policymaking. • The nitrogen footprint has been reduced in the Lake Tai watershed. • The nitrogen footprint can link water pollution and human activities. • Urban areas have become new hotspots of the nitrogen footprint for Lake Tai. • Optimization of the nitrogen footprint of urban areas can achieve sustainable development in the watershed. [ABSTRACT FROM AUTHOR]

Cropland ammonia (NH3) emission is a critical driver triggering haze pollution. Many agricultural policies were enforced in past four decades to improve nitrogen (N) use efficiency while maintaining crop yield. Inadvertent reductions of NH3 emissions, which may be induced by such policies, are not well evaluated. Here, we quantify the China's cropland‐NH3 emission change from 1980 to 2050 and its response to policy interventions, using a data‐driven model and a survey‐based dataset of the fertilization scheme. Cropland‐NH3 emission in China doubled from 1.93 to 4.02 Tg NH3‐N in period 1980–1996, and then decreased to 3.50 Tg NH3‐N in 2017. The prevalence of four agricultural policies may avoid ~3.0 Tg NH3‐N in 2017, mainly located in highly fertilized areas. Optimization of fertilizer management and food consumption could mitigate three‐quarters of NH3 emission in 2050 and lower NH3 emission intensity (emission divided by crop production) close to the European Union and the United States. Our findings provide an evidence on the decoupling of cropland‐NH3 from crop production in China and suggest the need to achieve cropland‐NH3 mitigation while sustaining crop yields in other developing economies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 PM2.5, 30% (22%) for SO2, 24% (19%) for NH3 and 15% (13%) for O3, compared with a desert scenario. By contrast, urban vegetation reduces average urban PM2.5 by only approximately 1%. Even large-scale conversion of half of existing open urban greenspace to forest would lower urban PM2.5 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 NO2 are small, and urban tree planting is even forecast to increase urban NO2 and NOx concentrations, due to the chemical interaction with changes in BVOC emissions and O3, 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'. [ABSTRACT FROM AUTHOR]

The Quadrennial Ozone Symposium 2016 Sophie GODIN-BEEKMANN, Irina PETROPAVLOVSKIKH, Stefan REIS, Paul NEWMAN, Wolfgang STEINBRECHT, Markus REX, Michelle L. SANTEE, Richard S. ECKMAN, Xiangdong ZHENG, Matthew B. TULLY, David S. STEVENSON, Paul YOUNG, John PYLE, Mark WEBER, Johanna TAMMINEN, Gina MILLS, Alkiviadis F. BAIS, Clare HEAVISIDE, and Christos ZEREFOS Observatoire de Versailles Saint-Quentin en Yvelines, Université de Versailles Saint-Quentin-en-Yvelines, CNRS, 78280 Guyancourt, France CIRES, University of Colorado, Boulder, CO 80309, USA NERC Centre for Ecology & Hydrology, Edinburgh EH26 0QB, UK Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA Hohenpeissenberg Meteorological Observatory, Deutscher Wetterdienst, 82383 Hohenpeissenberg, Germany Alfred Wegener Institute, 14401 Potsdam, Germany Jet Propulsion Laboratory, California Institute of Technology, CA 91109, USA NASA Headquarters, Earth Science Division, Washington, DC, USA Chinese Academy of Meteorological Sciences, Beijing, 100081, China Bureau of Meteorology, Melbourne, Victoria 3001, Australia University of Edinburgh, School of GeoSciences, Edinburgh EH9 3FE, UK Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK University of Cambridge, Department of Chemistry, Cambridge CB2 1EW, UK University of Bremen, Institute of Environmental Physics, 28359 Bremen, Germany Finnish Meteorological Institute, Earth Observation, FI-00101 Helsinki, Finland NERC Centre for Ecology and Hydrology, Bangor, Gwynedd LL57 2UW, Wales, UK Aristotle University of Thessaloniki, Thessaloniki, Greece Public Health England, Centre for Radiation, Chemical and Environmental Hazards, London, UK Research Center for Atmospheric Physics & Climatology, Academy of Athens, Athens 10680, Greece University of Exeter Medical School, Truro TR1 3HD, UK [ABSTRACT FROM AUTHOR]

Abstract Farm size plays a critical role in agricultural sustainability. This may have far-reaching consequences for the economic and environmental performance of agricultural production, resulting, for instance, in an excessive use of mineral fertilizers. However, the magnitude of such effects and their main causes are not well understood, while being essential for effective policy development, especially for countries like China where the agricultural sector is still largely dominated by smallholder farms. In this paper, we review the current understanding of how farm size affects agricultural sustainability using China's crop farming as an example from economic, environment and social aspects. We analyze impacts from both a Chinese and a global perspective to identify intervention points to improve agricultural performance. We found that increasing farm size has a positive impact on farmer's net profit, as well as economic, technical and labor efficiency with mean coefficients of 0.005, 0.02 and 2.25, respectively. Nevertheless, the relationships between farm size and overall productivity, total factor productivity and allocative efficiency are still not well understood and therefore require more research. Meanwhile, increase in farm size is associated with statistically significant decrease in fertilizer and pesticide use per hectare, showing clear benefits for environmental protection. In line with the experiences documented for the evolution of agricultural practices in developed countries, the expansion of large-scale farming is a critical path for modernizing agricultural production and ensuring sustainable food production from the social perspective. Measures concerning farm size should be implemented in an interaction between farmers and the government to promote the green development of agriculture. Highlights • Farm size plays an important role in the performance of agriculture. • Increasing farm size shows clear benefits for environmental protection. • Large-scale farming is a critical path for modernizing and sustaining agriculture. • Smallholders prefer to use more non-fixed inputs to increase yields. [ABSTRACT FROM AUTHOR]

Abstract Urbanization is a global trend, and consequently the quality of urban environments is increasingly important for human health and wellbeing. Urban life-style is typically associated with low physical activity and sometimes with high mental stress, both contributing to an increasing burden of diseases. Nature-based solutions that make effective use of ecosystem services, particularly of cultural ecosystem services (CES), can provide vital building blocks to address these challenges. This paper argues that, the salutogenic, i.e. health-promoting effects of CES have so far not been adequately recognised and deserve more explicit attention in order to enhance decision making around health and wellbeing in urban areas. However, a number of research challenges will need to be addressed to reveal the mechanisms, which underpin delivery of urban CES. These include: causal chains of supply and demand, equity, and equality of public health benefits promoted. Methodological challenges in quantifying these are discussed. The paper is highly relevant for policy makers within and beyond Europe, and also serves as a review for current researchers and as a roadmap to future short- and long-term research opportunities. Graphical abstract Unlabelled Image Highlights • Concerns positive public health impacts of urban nature's cultural ecosystem services (CES). • Discusses global development trends' implications for the provision and demand of CES. • Discusses current research and key research questions for a new research agenda. [ABSTRACT FROM AUTHOR]

Rapid urbanization in China is leading to substantial adverse air quality issues, particularly for NO2 and particulate matter (PM). Land-use regression (LUR) models are now being applied to simulate pollutant concentrations with high spatial resolution in Chinese urban areas. However, Chinese urban areas differ from those in Europe and North America, for example in respect of population density, urban morphology and pollutant emissions densities, so it is timely to assess current LUR studies in China to highlight current challenges and identify future needs. Details of twenty-four recent LUR models for NO2 and PM2.5/PM10 (particles with aerodynamic diameters <2.5 µm and <10 µm) are tabulated and reviewed as the basis for discussion in this paper. We highlight that LUR modelling in China is currently constrained by a scarcity of input data, especially air pollution monitoring data. There is an urgent need for accessible archives of quality-assured measurement data and for higher spatial resolution proxy data for urban emissions, particularly in respect of traffic-related variables. The rapidly evolving nature of the Chinese urban landscape makes maintaining up-to-date land-use and urban morphology datasets a challenge. We also highlight the importance for Chinese LUR models to be subject to appropriate validation statistics. Integration of LUR with portable monitor data, remote sensing, and dispersion modelling has the potential to enhance derivation of urban pollution maps. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input-output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40% variation range for the UK emissions of SO2, NOx, and NH3, from which regression coefficients for each input-output combination and each model grid (>10 000 across the UK) were calculated. Surface concentrations of SO2, NOx, and NH3 (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO3 and particulate SO42-, NO3-, and NH4+ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO2, NOx, and NH3 (±4, ±10, and ±20% respectively). The uncertainties in the surface concentrations of NH3 and NOx and the depositions of NHx and NOy were dominated by the uncertainties in emissions of NH3, and NOx respectively, whilst concentrations of SO2 and deposition of SOy were affected by the uncertainties in both SO2 and NH3 emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH4+, NO3-, SO42-, and HNO3) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input-output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40% variation range for the UK emissions of SO2, NOx and NH3, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO2, NOx and NH3 (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO3 and particulate SO42-, NO3- and NH4+ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO2, NOx and NH3 (±4%, ±10% and ±20% respectively). The uncertainties in the surface concentrations of NH3 and NOx and the depositions of NHx and NOy were dominated by the uncertainties in emissions of NH3, and NOx respectively, whilst concentrations of SO2 and deposition of SOy were affected by the uncertainties in both SO2 and NH3 emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH4+, NO3-, SO42- and HNO3) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs. [ABSTRACT FROM AUTHOR]

To reduce inaccuracies in the measurement of air pollutants by portable monitors it is necessary to establish quantitative calibration relationships against their respective reference analyser. This is usually done under controlled laboratory conditions or one-off static co-location alongside a reference analyser in the field, neither of which may adequately represent the extended use of portable monitors in exposure assessment research. To address this, we investigated ways of establishing and evaluating portable monitor calibration relationships from repeated intermittent deployment cycles over an extended period involving stationary deployment at a reference site, mobile monitoring, and completely switched off. We evaluated four types of portable monitors: Aeroqual Ltd. (Auckland, New Zealand) S500 O3 metal oxide and S500 NO2 electrochemical; RTI (Berkeley, CA, USA) MicroPEM PM2.5; and, AethLabs (San Francisco, CA, USA) AE51 black carbon (BC). Innovations in our study included: (i) comparison of calibrations derived from the individual co-locations of a portable monitor against its reference analyser or from all the co-location periods combined into a single dataset; and, (ii) evaluation of calibrated monitor estimates during transient measurements with the portable monitor close to its reference analyser at separate times from the stationary co-location calibration periods. Within the ~7 month duration of the study, 'combined' calibration relationships for O3, PM2.5, and BC monitors from all co-locations agreed more closely on average with reference measurements than 'individual' calibration relationships from co-location deployment nearest in time to transient deployment periods. 'Individual' calibrations relationships were sometimes substantially unrepresentative of the 'combined' relationships. Reduced quantitative consistency in field calibration relationships for the PM2.5 monitors may have resulted from generally low PM2.5 concentrations that were encountered in this study. Aeroqual NO2 monitors were sensitive to both NO2 and O3 and unresolved biases. Overall, however, we observed that with the 'combined' approach, 'indicative' measurement accuracy (±30% for O3, and 50% for BC and PM2.5) for 1 h time averaging could be maintained over the 7-month period for the monitors evaluated here. [ABSTRACT FROM AUTHOR]

China is a global hotspot of atmospheric ammonia (NH3) emissions and, as a consequence, very high nitrogen (N) deposition levels are documented. However, previous estimates of total NH3 emissions in China were much lower than inference from observed deposition values would suggest, highlighting the need for further investigation. Here, we reevaluated NH3 emissions based on a mass balance approach, validated by N deposition monitoring and satellite observations, for China for the period of 2000 to 2015. Total NH3 emissions in China increased from 12.1 ± 0.8 Tg N yr ' in 2000 to 15.6 ± 0.9 Tg N yr' in 2015 at an annual rate of 1.9%, which is a approximately 40% higher than existing studies suggested. This difference is mainly due to more emission sources now having been included and NH3 emission rates from mineral fertilizer application and livestock having been underestimated previously. Our estimated NH3 emission levels are consistent with the measured deposition of NH, (including NH,,' and NH3) on land (11-14 Tg N yr') and the substantial increases in NH3 concentrations observed by satellite measurements over China. These findings substantially improve our understanding on NH3 emissions, implying that future air pollution control strategies have to consider the potentials of reducing NH3 emission in China. [ABSTRACT FROM AUTHOR]

• We show how reduced form and complex models can be applied in combination to inform air pollution policy development. • We compare PM 2.5 concentrations calculated by both a reduced-form and a complex ACTM, showing good agreement. • Both models agree well with observations of PM 2.5 and SIA concentrations from national monitoring networks. • We present this comparison for the current (2018) and selected future (2040) emissions scenarios in the United Kingdom. • We confirm the robustness of a reduced-form model to evaluate alternative futures, through comparison with an Eulerian ACTM. 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. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

• Life course air pollution exposure and biological ageing markers were investigated in 525 older adults. • Young-to-middle adulthood exposure was associated with higher Horvath DNAmAge. • In males, air pollution in middle adulthood was linked to shorter DNAm telomere length. • Air pollution around birth was associated with shorter DNAm telomere length among females. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Optimal nitrogen (N) management is critical for efficient crop production and agricultural pollution control. Approximate 210–220 kg ha−1 N fertilizer was applied in millions of small plots through broadcasting way in China, resulting in over and loss of N fertilizers. However, it is difficult to implement advanced management practices on smallholder farms due to a lack of knowledge. Here, using 35,502 on-farm fertilization experiments, we demonstrated that smallholders in China could actually produce more grain with less N fertilizer use only through optimizing N application rate. The yields of wheat, maize and rice were shown to increase between 10% and 19% while N application rates were reduced by 15–19%. These changes resulted in an increase in N use efficiency (NUE) by 32–46% and a reduction in N surplus by 40% without actually changing farmers' operational practices. By reducing N application rates in line with official recommendations would not only save fertilizer cost while increasing crop yield, but also at the same time reduce environmental N pollution in China. Beyond of optimizing N application rate, improved management practices were required to produce more grain with less pollution, which would need about 11.8 billion US dollars for the implementation and reducing N loss reduction by 1.75 million tons to the environment. [Display omitted] • Optimizing N fertilizer rate was estimated based on 35,502 field experiments nationally. • Optimizing N rate increased NUE by 32–46% with 40% lower N surplus. • Environmental N loss was predicted to be reduced by 5.06 million t by optimizing N rate. • Best management strategies could result in 1.75 million tons N loss reduction. [ABSTRACT FROM AUTHOR]

Abstract: 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 &y& Elsevier]

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 Nyr-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 (NO-3) 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 Nyr-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 Nyr-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 TgNyr-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 10²-10³ 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. [ABSTRACT FROM AUTHOR]

Reference 1 Schneider R., Vicedo-Cabrera A.M., Sera F., Masselot P., Stafoggia M., de Hoogh K., Kloog I., Reis S., Vieno M., Gasparrini A. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM 2.5 Concentrations across Great Britain. Figure Graph: Figure 3 Stage-4 predicted PM2.5 concentrations across Great Britain (Top) and London (Bottom) for 2008, 2013, and 2018 aggregated by annual means. [Extracted from the article]

In a recent study, Magnani et al. report how atmospheric nitrogen deposition drives stand-lifetime net ecosystem productivity (NEPav) for midlatitude forests, with an extremely high C to N response (725 kg C kg−1 wet-deposited N for their European sites). We present here a re-analysis of these data, which suggests a much smaller C : N response for total N inputs. Accounting for dry, as well as wet N deposition reduces the C : N response to 177 : 1. However, if covariance with intersite climatological differences is accounted for, the actual C : N response in this dataset may be <70 : 1. We then use a model analysis of 22 European forest stands to simulate the findings of Magnani et al. Multisite regression of simulated NEPav vs. total N deposition reproduces a high C : N response (149 : 1). However, once the effects of intersite climatological differences are accounted for, the value is again found to be much smaller, pointing to a real C : N response of about 50–75 : 1. [ABSTRACT FROM AUTHOR]

Humans utilise about 40% of the earth's net primary production (NPP) but the products of this NPP are often managed by different sectors, with timber and forest products managed by the forestry sector and food and fibre products from croplands and grasslands managed by the agricultural sector. Other significant anthropogenic impacts on the global carbon cycle include human utilization of fossil fuels and impacts on less intensively managed systems such as peatlands, wetlands and permafrost. A great deal of knowledge, expertise and data is available within each sector. We describe the contribution of sectoral carbon budgets to our understanding of the global carbon cycle. Whilst many sectors exhibit similarities for carbon budgeting, some key differences arise due to differences in goods and services provided, ecology, management practices used, land management personnel responsible, policies affecting land management, data types and availability, and the drivers of change. We review the methods and data sources available for assessing sectoral carbon budgets, and describe some of key data limitations and uncertainties for each sector in different regions of the world. We identify the main gaps in our knowledge/data, show that coverage is better for the developed world for most sectors, and suggest how sectoral carbon budgets could be improved in the future. Research priorities include the development of shared protocols through site networks, a move to full carbon accounting within sectors, and the assessment of full greenhouse gas budgets. [ABSTRACT FROM AUTHOR]

The atmospheric chemistry and deposition model has been applied for calculation of nitrogen and sulphur depositions to the entire North Sea area for the year 1999. The total atmospheric nitrogen and sulphur depositions to the North Sea area were determined to 709 kton (kt) N and 551 kt S, respectively. Since the North Sea area was calculated to be 747,988 km2, this is equivalent to an average deposition of 0.9 ton N km−2 and 0.7 ton S km−2, respectively. The depositions decrease strongly from the south end (about 2–3 kt N km−2) to the north end (about 0.2 kt N km−2) of the North Sea, due to increasing distance to the large source areas in the northern part of the European continent. The territorial waters of Belgium, the Netherlands and Germany receive about 50% higher deposition densities than the average value for the entire North Sea area. For the remaining territorial waters of the North Sea the depositions follow more or less the fraction of the area. The results furthermore show that about 60% of the total nitrogen deposition is related to emissions from combustion sources (nitrogen oxides) and about 40% from emissions related to agricultural activities (ammonia). [Copyright &y& Elsevier]

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]