Ozone pollution contributes to the yield gap for beans in Uganda, East Africa, and is co-located with other agricultural stresses

Air quality negatively impacts agriculture, reducing the yield of staple food crops. While measured data on African ground-level ozone levels are scarce, experimental studies demonstrate the damaging impact of ozone on crops. Common beans (Phaseolus vulgaris), an ozone-sensitive crop, are widely grown in Uganda. Using modelled ozone flux, agricultural surveys, and a flux-effect relationship, this study estimates yield and production losses due to ozone for Ugandan beans in 2015. Analysis at this scale allows the use of localised data, and results can be presented at a sub-regional level. Soil nutrient stress, drought, flood risk, temperature and deprivation were also mapped to investigate where stresses may coincide. Average bean yield losses due to ozone were 17% and 14% (first and second growing season respectively), equating to 184 thousand tonnes production loss. However, for some sub-regions, losses were up to 27.5% and other crop stresses also coincided in these areas. This methodology could be applied widely, allowing estimates of ozone impact for countries lacking air quality and/or experimental data. As crop productivity is below its potential in many areas of the world, changing agricultural practices to mitigate against losses due to ozone could help to reduce the crop yield gap.

Higher air pollution exposure in early life is associated with worse health among older adults: A 72-year follow-up study from Scotland

Air pollution increases the risk of mortality and morbidity. However, limited evidence exists on the very long-term associations between early life air pollution exposure and health, as well as on potential pathways. This study explored the relationship between fine particle (PM2.5) exposure at age 3 and limiting long-term illness (LLTI) at ages 55, 65 and 75 using data from the Scottish Longitudinal Study Birth Cohort 1936, a representative administrative cohort study. We found that early life PM2.5 exposure was associated with higher odds of LLTI in mid-to-late adulthood (OR = 1.10, 95% CI: 1.06, 1.14 per 10 μg m-3 increment) among the 2085 participants, with stronger associations among those growing up in disadvantaged families. Path analyses suggested that 15-21% of the association between early life PM2.5 concentrations and LLTI at age 65 (n = 1406) was mediated through childhood cognitive ability, educational qualifications, and adult social position. Future research should capitalise on linked administrative and health data, and explore causal mechanisms between environment and specific health conditions across the life course.

Early life PM2.5 exposure, childhood cognitive ability and mortality between age 11 and 86: A record-linkage life-course study from Scotland

BACKGROUND

Living in areas with high air pollution concentrations is associated with all-cause and cause-specific mortality. Exposure in sensitive developmental periods might be long-lasting but studies with very long follow-up are rare, and mediating pathways between early life exposure and life-course mortality are not fully understood.

METHODS

Data were drawn from the Scottish Longitudinal Study Birth Cohort of 1936, a representative record-linkage study comprising 5% of the Scottish population born in 1936. Participants had valid age 11 cognitive ability test scores along with linked mortality data until age 86. Fine particle (PM2.5) concentrations estimated with the EMEP4UK atmospheric chemistry transport model were linked to participants' residential address derived from the National Identity Register in 1939 (age 3). Confounder-adjusted Cox regression estimated associations between PM2.5 and mortality; regression-based causal mediation analysis explored mediation through childhood cognitive ability.

RESULTS

The final sample consisted of 2734 individuals with 1608 deaths registered during the 1,833,517 person-months at risk follow-up time. Higher early life PM2.5 exposure increased the risk of all-cause mortality (HR = 1.03, 95% CI: 1.01-1.04 per 10 μg m-3 increment), associations were stronger for mortality between age 65 and 86. PM2.5 increased the risk of cancer-related mortality (HR = 1.05, 95% CI: 1.02-1.08), especially for lung cancer among females (HR = 1.11, 95% CI: 1.02-1.21), but not for cardiovascular and respiratory diseases. Higher PM2.5 in early life (≥50 μg m-3) was associated with lower childhood cognitive ability, which, in turn, increased the risk of all-cause mortality and mediated 25% of the total associations.

CONCLUSIONS

In our life-course study with 75-year of continuous mortality records, we found that exposure to air pollution in early life was associated with higher mortality in late adulthood, and that childhood cognitive ability partly mediated this relationship. Findings suggest that past air pollution concentrations will likely impact health and longevity for decades to come.

Future impacts of O3 on respiratory hospital admission in the UK from current emissions policies

Exposure to ambient ozone (O3) O3 is associated with impacts on human health. O3 is a secondary pollutant whose concentrations are determined inter alia by emissions of precursors such as oxides of nitrogen (NOx) and volatile organic compounds (VOCs), and thus future health burdens depend on policies relating to climate and air quality. While emission controls are expected to reduce levels of PM2.5 and NO2 and their associated mortality burdens, for secondary pollutants like O3 the picture is less clear. Detailed assessments are necessary to provide quantitative estimates of future impacts to support decision-makers. We simulate future O3 across the UK using a high spatial resolution atmospheric chemistry model with current UK and European policy projections for 2030, 2040 and 2050, and use UK regional population-weighting and latest recommendations on health impact assessment to quantify respiratory emergency hospital admissions associated with short-term effects of O3. We estimate 60,488 admissions in 2018, increasing by 4.2%, 4.5% and 4.6% by 2030, 2040 and 2050 respectively (assuming a fixed population). Including future population growth, estimated emergency respiratory hospital admissions are 8.3%, 10.3% and 11.7% higher by 2030, 2040 and 2050 respectively. Increasing O3 concentrations in future are driven by reduced nitric oxide (NO) in urban areas due to reduced emissions, with increases in O3 mainly occurring in areas with lowest O3 concentrations currently. Meteorology influences episodes of O3 on a day-to-day basis, although a sensitivity study indicates that annual totals of hospital admissions are only slightly impacted by meteorological year. While reducing emissions results in overall benefits to population health (through reduced mortality due to long-term exposure to PM2.5 and NO2), due to the complex chemistry, as NO emissions reduce there are associated local increases in O3 close to population centres that may increase harms to health.

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Air pollution is the greatest environmental risk to public health. Future air pollution concentrations are primarily determined by precursor emissions, which are driven by environmental policies relating to climate and air pollution. Detailed health impact assessments (HIA) are necessary to provide quantitative estimates of the impacts of future air pollution to support decision-makers developing environmental policy and targets. In this study we use high spatial resolution atmospheric chemistry modelling to simulate future air pollution concentrations across the UK for 2030, 2040 and 2050 based on current UK and European policy projections. We combine UK regional population-weighted concentrations with the latest epidemiological relationships to quantify mortality associated with changes in PM_2.5 and NO₂ air pollution. Our HIA suggests that by 2050, population-weighted exposure to PM_2.5 will reduce by 28% to 36%, and for NO₂ by 35% to 49%, depending on region. The HIA shows that for present day (2018), annual mortality attributable to the effects of long-term exposure to PM_2.5 and NO₂ is in the range 26,287 - 42,442, and that mortality burdens in future will be substantially reduced, being lower by 31%, 35%, and 37% in 2030, 2040 and 2050 respectively (relative to 2018) assuming no population changes. Including population projections (increases in all regions for 30+ years age group) slightly offsets these health benefits, resulting in reductions of 25%, 27%, and 26% in mortality burdens for 2030, 2040, 2050 respectively. Significant reductions in future mortality burdens are estimated and, importantly for public health, the majority of benefits are achieved early on in the future timeline simulated, though further efforts are likely needed to reduce impacts of air pollution to health.

Modelling benzo(a)pyrene concentrations for different meteorological conditions - Analysis of lung cancer cases and associated economic costs

Air pollution originating from the household presents a significant burden to public health, especially during the wintertime in countries, such as Poland, where coal substantially contributes to the energy market. One of the most hazardous components of particulate matter is benzo(a)pyrene (BaP). This study focusses on the impact of different meteorological conditions on BaP concentrations in Poland and associated impacts on human health and economic burdens. For this study, we used the EMEP MSC-W atmospheric chemistry transport model with meteorological data from the Weather Research and Forecasting model to analyze the spatial and temporal distribution of BaP over Central Europe. The model setup has two nested domains, with the inner domain at 4 km × 4 km over Poland, which is a hotspot for BaP concentrations. The outer domain covers countries surrounding Poland in coarser resolution (12 × 812 km), to ensure that transboundary pollution is properly characterized in the modelling. We investigated the sensitivity to variability in winter meteorological conditions on BaP levels and impacts using data from 3 years: 1) 2018, which represents average meteorological conditions during the winter season (BASE run), 2) 2010 with a cold winter (COLD), and 3) 2020 with a warm winter (WARM). The ALPHA-RiskPoll model was used to analyze the lung cancer cases and associated economic costs. The results show that the majority of Poland exceeds the target level of benzo(a)pyrene (1 ng m^-3) mainly due to high concentrations during the cold months. High concentrations of BaP have serious health implications and the number of lung cancers in Poland due to BaP exposure varies from 57 to 77 cases for the WARM and COLD years, respectively. It is reflected in the economic costs, which ranged from 136, through 174 to 185 million euros/year for the WARM, BASE and COLD model runs, respectively.

Reduced-form and complex ACTM modelling for air quality policy development: A model inter-comparison

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.

Mitigating the impact of air pollution on dementia and brain health: Setting the policy agenda

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.

Life-course exposure to air pollution and biological ageing in the Lothian Birth Cohort 1936

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 (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) 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, SO₂ and NO₂.

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.

Abating ammonia is more cost-effective than nitrogen oxides for mitigating PM2.5 air pollution

[Figure: see text].

Quantifying the impact of ozone on crops in Sub-Saharan Africa demonstrates regional and local hotspots of production loss

Tropospheric ozone can have a detrimental effect on vegetation, including reducing the quantity of crop yield. This study uses modelled ozone flux values (POD₃IAM; phytotoxic ozone dose above 3 nmol m^-2 s^-1, parameterised for integrated assessment modelling) for 2015, together with species-specific flux-effect relationships, spatial data on production and growing season dates to quantify the impact of ozone on the production of common wheat (Triticum aestivum) and common beans (Phaseolus vulgaris) across Sub-Saharan Africa (SSA). A case study for South Africa was also done using detailed data per province. Results suggest that ozone pollution could decrease wheat yield by between 2 and 13%, with a total annual loss of 453,000 t across SSA. The impact on bean production depended on the season; however, estimated yield losses were up to 21% in some areas of SSA, with an annual loss of ~300,000 t for each of the two main growing seasons. Production losses tended to be greater in countries with the highest production, for example, Ethiopia (wheat) and Tanzania (beans). This study provides an indication of the location of areas at high risk of crop losses due to ozone. Results emphasise that efforts to reduce ozone precursors could contribute to reducing the yield gap in SSA. More stringent air pollution abatement policies are required to reduce crop losses to ozone in the future.

Nitrogen Challenges and Opportunities for Agricultural and Environmental Science in India

In the last six decades, the consumption of reactive nitrogen (Nr) in the form of fertilizer in India has been growing rapidly, whilst the nitrogen use efficiency (NUE) of cropping systems has been decreasing. These trends have led to increasing environmental losses of Nr, threatening the quality of air, soils, and fresh waters, and thereby endangering climate-stability, ecosystems, and human-health. Since it has been suggested that the fertilizer consumption of India may double by 2050, there is an urgent need for scientific research to support better nitrogen management in Indian agriculture. In order to share knowledge and to develop a joint vision, experts from the UK and India came together for a conference and workshop on “Challenges and Opportunities for Agricultural Nitrogen Science in India.” The meeting concluded with three core messages: (1) Soil stewardship is essential and legumes need to be planted in rotation with cereals to increase nitrogen fixation in areas of limited Nr availability. Synthetic symbioses and plastidic nitrogen fixation are possibly disruptive technologies, but their potential and implications must be considered. (2) Genetic diversity of crops and new technologies need to be shared and exploited to reduce N losses and support productive, sustainable agriculture livelihoods. (3) The use of leaf color sensing shows great potential to reduce nitrogen fertilizer use (by 10–15%). This, together with the usage of urease inhibitors in neem-coated urea, and better management of manure, urine, and crop residues, could result in a 20–25% improvement in NUE of India by 2030.

A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain

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 (PM_2.5) 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-PM_2.5 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 PM_2.5. Stage-4 applies Stage-3 models to estimate daily PM_2.5 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 PM_2.5.

Life Course Air Pollution Exposure and Cognitive Decline: Modelled Historical Air Pollution Data and the Lothian Birth Cohort 1936

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.

Alkaline air: changing perspectives on nitrogen and air pollution in an ammonia-rich world

Ammonia and ammonium have received less attention than other forms of air pollution, with limited progress in controlling emissions at UK, European and global scales. By contrast, these compounds have been of significant past interest to science and society, the recollection of which can inform future strategies. Sal ammoniac (nūshādir, nao sha) is found to have been extremely valuable in long-distance trade (ca AD 600-1150) from Egypt and China, where 6-8 kg N could purchase a human life, while air pollution associated with nūshādir collection was attributed to this nitrogen form. Ammonia was one of the keys to alchemy-seen as an early experimental mesocosm to understand the world-and later became of interest as 'alkaline air' within the eighteenth century development of pneumatic chemistry. The same economic, chemical and environmental properties are found to make ammonia and ammonium of huge relevance today. Successful control of acidifying SO₂ and NO_x emissions leaves atmospheric NH₃ in excess in many areas, contributing to particulate matter (PM_2.5) formation, while leading to a new significance of alkaline air, with adverse impacts on natural ecosystems. Investigations of epiphytic lichens and bog ecosystems show how the alkalinity effect of NH₃ may explain its having three to five times the adverse effect of ammonium and nitrate, respectively. It is concluded that future air pollution policy should no longer neglect ammonia. Progress is likely to be mobilized by emphasizing the lost economic value of global N emissions ($200 billion yr^-1), as part of developing the circular economy for sustainable nitrogen management. This article is part of a discussion meeting issue 'Air quality, past present and future'.

A chronology of global air quality

Air pollution has been recognized as a threat to human health since the time of Hippocrates, ca 400 BC. Successive written accounts of air pollution occur in different countries through the following two millennia until measurements, from the eighteenth century onwards, show the growing scale of poor air quality in urban centres and close to industry, and the chemical characteristics of the gases and particulate matter. The industrial revolution accelerated both the magnitude of emissions of the primary pollutants and the geographical spread of contributing countries as highly polluted cities became the defining issue, culminating with the great smog of London in 1952. Europe and North America dominated emissions and suffered the majority of adverse effects until the latter decades of the twentieth century, by which time the transboundary issues of acid rain, forest decline and ground-level ozone became the main environmental and political air quality issues. As controls on emissions of sulfur and nitrogen oxides (SO₂ and NO_x) began to take effect in Europe and North America, emissions in East and South Asia grew strongly and dominated global emissions by the early years of the twenty-first century. The effects of air quality on human health had also returned to the top of the priorities by 2000 as new epidemiological evidence emerged. By this time, extensive networks of surface measurements and satellite remote sensing provided global measurements of both primary and secondary pollutants. Global emissions of SO₂ and NO_x peaked, respectively, in ca 1990 and 2018 and have since declined to 2020 as a result of widespread emission controls. By contrast, with a lack of actions to abate ammonia, global emissions have continued to grow. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Potential and limitation of air pollution mitigation by vegetation and uncertainties of deposition-based evaluations

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 SO₂, 24% (19%) for NH₃ and 15% (13%) for O₃, 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 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'.

Impact of long-term nitrogen deposition on the response of dune grassland ecosystems to elevated summer ozone

Nitrogen deposition and tropospheric ozone are important drivers of vegetation damage, but their interactive effects are poorly understood. This study assessed whether long-term nitrogen deposition altered sensitivity to ozone in a semi-natural vegetation community. Mesocosms were collected from sand dune grassland in the UK along a nitrogen gradient (5-25 kg N/ha/y, including two plots from a long-term experiment), and fumigated for 2.5 months to simulate medium and high ozone exposure. Ozone damage to leaves was quantified for 20 ozone-sensitive species. Soil solution dissolved organic carbon (DOC) and soil extracellular enzymes were measured to investigate secondary effects on soil processes. Mesocosms from sites receiving the highest N deposition showed the least ozone-related leaf damage, while those from the least N-polluted sites were the most damaged by ozone. This was due to differences in community-level sensitivity, rather than species-level impacts. The N-polluted sites contained fewer ozone-sensitive forbs and sedges, and a higher proportion of comparatively ozone-resistant grasses. This difference in the vegetation composition of mesocosms in relation to N deposition conveyed differential resilience to ozone. Mesocosms in the highest ozone treatment showed elevated soil solution DOC with increasing site N deposition. This suggests that, despite showing relatively little leaf damage, the 'ozone resilient' vegetation community may still sustain physiological damage through reduced capacity to assimilate photosynthate, with its subsequent loss as DOC through the roots into the soil. We conclude that for dune grassland habitats, the regions of highest risk to ozone exposure are those that have received the lowest level of long-term nitrogen deposition. This highlights the importance of considering community- and ecosystem-scale impacts of pollutants in addition to impacts on individual species. It also underscores the need for protection of 'clean' habitats from air pollution and other environmental stressors.

The influence of residential and workday population mobility on exposure to air pollution in the UK

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 NO₂, PM_2.5 and O₃. 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 NO₂, with an estimated 0.3 μg m^-3 (equivalent to 2%) increase in population-weighted annual exposure to NO₂ across the whole UK population. Population-weighted exposure to PM_2.5 and O₃ 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 NO₂ 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.

Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol

INTRODUCTION

Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks.

METHODS AND ANALYSIS

We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km×5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices' populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case-control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland.Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype.

ETHICS AND DISSEMINATION

The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.

VOC emission rates over London and South East England obtained by airborne eddy covariance

Volatile organic compounds (VOCs) originate from a variety of sources, and play an intrinsic role in influencing air quality. Some VOCs, including benzene, are carcinogens and so directly affect human health, while others, such as isoprene, are very reactive in the atmosphere and play an important role in the formation of secondary pollutants such as ozone and particles. Here we report spatially-resolved measurements of the surface-to-atmosphere fluxes of VOCs across London and SE England made in 2013 and 2014. High-frequency 3-D wind velocities and VOC volume mixing ratios (made by proton transfer reaction – mass spectrometry) were obtained from a low-flying aircraft and used to calculate fluxes using the technique of eddy covariance. A footprint model was then used to quantify the flux contribution from the ground surface at spatial resolution of 100 m, averaged to 1 km. Measured fluxes of benzene over Greater London showed positive agreement with the UK’s National Atmospheric Emissions Inventory, with the highest fluxes originating from central London. Comparison of MTBE and toluene fluxes suggest that petroleum evaporation is an important emission source of toluene in central London. Outside London, increased isoprene emissions were observed over wooded areas, at rates greater than those predicted by a UK regional application of the European Monitoring and Evaluation Programme model (EMEP4UK). This work demonstrates the applicability of the airborne eddy covariance method to the determination of anthropogenic and biogenic VOC fluxes and the possibility of validating emission inventories through measurements.

Myocardial infarction, ST-elevation and non-ST-elevation myocardial infarction and modelled daily pollution concentrations: a case-crossover analysis of MINAP data

Objectives

To investigate associations between daily concentrations of air pollution and myocardial infarction (MI), ST-elevation MI (STEMI) and non-ST-elevation MI (NSTEMI).

Methods

Modelled daily ground-level gaseous, total and speciated particulate pollutant concentrations and ground-level daily mean temperature, all at 5 km×5 km horizontal resolution, were linked to 202 550 STEMI and 322 198 NSTEMI events recorded on the England and Wales Myocardial Ischaemia National Audit Project (MINAP) database. The study period was 2003–2010. A case-crossover design was used, stratified by year, month and day of the week. Data were analysed using conditional logistic regression, with pollutants modelled as unconstrained distributed lags 0–2 days. Results are presented as percentage change in risk per 10 µg/m³ increase in the pollutant relevant metric, having adjusted for daily mean temperature, public holidays, weekly influenza consultation rates and a sine-cosine annual cycle.

Results

There was no evidence of an association between MI or STEMI and any of O₃, NO₂, PM_2.5, PM₁₀ or selected PM_2.5 components (sulfate and elemental carbon). For NSTEMI, there was a positive association with daily maximum 1-hour NO₂ (0.27% (95% CI 0.01% to 0.54%)), which persisted following adjustment for O₃ and adjustment for PM_2.5. The association appeared to be confined to the midland and southern regions of England and Wales.

Conclusions

The study found no evidence of an association between the modelled pollutants (including components) investigated and STEMI but did find some evidence of a positive association between NO₂ and NSTEMI. Confirmation of this association in other studies is required.

Assessing chemistry schemes and constraints in air quality models used to predict ozone in London against the detailed Master Chemical Mechanism

Air pollution is the environmental factor with the greatest impact on human health in Europe. Understanding the key processes driving air quality across the relevant spatial scales, especially during pollution exceedances and episodes, is essential to provide effective predictions for both policymakers and the public. It is particularly important for policy regulators to understand the drivers of local air quality that can be regulated by national policies versus the contribution from regional pollution transported from mainland Europe or elsewhere. One of the main objectives of the Coupled Urban and Regional processes: Effects on AIR quality (CUREAIR) project is to determine local and regional contributions to ozone events. A detailed zero-dimensional (0-D) box model run with the Master Chemical Mechanism (MCMv3.2) is used as the benchmark model against which the less explicit chemistry mechanisms of the Generic Reaction Set (GRS) and the Common Representative Intermediates (CRIv2-R5) schemes are evaluated. GRS and CRI are used by the Atmospheric Dispersion Modelling System (ADMS-Urban) and the regional chemistry transport model EMEP4UK, respectively. The MCM model uses a near-explicit chemical scheme for the oxidation of volatile organic compounds (VOCs) and is constrained to observations of VOCs, NOx, CO, HONO (nitrous acid), photolysis frequencies and meteorological parameters measured during the ClearfLo (Clean Air for London) campaign. The sensitivity of the less explicit chemistry schemes to different model inputs has been investigated: Constraining GRS to the total VOC observed during ClearfLo as opposed to VOC derived from ADMS-Urban dispersion calculations, including emissions and background concentrations, led to a significant increase (674% during winter) in modelled ozone. The inclusion of HONO chemistry in this mechanism, particularly during wintertime when other radical sources are limited, led to substantial increases in the ozone levels predicted (223%). When the GRS and CRIv2-R5 schemes are run with the equivalent model constraints to the MCM, they are able to reproduce the level of ozone predicted by the near-explicit MCM to within 40% and 20% respectively for the majority of the time. An exception to this trend was observed during pollution episodes experienced in the summer, when anticyclonic conditions favoured increased temperatures and elevated O3. The in situ O3 predicted by the MCM was heavily influenced by biogenic VOCs during these conditions and the low GRS [O3] : MCM [O3] ratio (and low CRIv2-R5 [O3] : MCM [O3] ratio) demonstrates that these less explicit schemes under-represent the full O3 creation potential of these VOCs. To fully assess the influence of the in situ O3 generated from local emissions versus O3 generated upwind of London and advected in, the time since emission (and, hence, how far the real atmosphere is from steady state) must be determined. From estimates of the mean transport time determined from the NOx : NOy ratio observed at North Kensington during the summer and comparison of the O3 predicted by the MCM model after this time, ∼60% of the median observed [O3] could be generated from local emissions. During the warmer conditions experienced during the easterly flows, however, the observed [O3] may be even more heavily influenced by London's emissions.

Integrating modelling and smart sensors for environmental and human health

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.

Quantifying missing annual emission sources of heavy metals in the United Kingdom with an atmospheric transport model

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.

Measurement error in time-series analysis: a simulation study comparing modelled and monitored data

Background

Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data.

Methods

Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003–2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban log_e(daily 1-hour maximum NO₂).

Results

When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background log_e(NO₂) and 38% for rural log_e(NO₂). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural log_e(NO₂) but more marked for urban log_e(NO₂).

Conclusion

Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.

Health burdens of surface ozone in the UK for a range of future scenarios

Exposure to surface ozone (O3), which is influenced by emissions of precursor chemical species, meteorology and population distribution, is associated with excess mortality and respiratory morbidity. In this study, the EMEP-WRF atmospheric chemistry transport model was used to simulate surface O3 concentrations at 5km horizontal resolution over the British Isles for a baseline year of 2003, for three anthropogenic emissions scenarios for 2030, and for a +5°C increase in air temperature on the 2003 baseline. Deaths brought forward and hospitalisation burdens for 12 UK regions were calculated from population-weighted daily maximum 8-hour O3. The magnitude of changes in annual mean surface O3 over the UK for +5°C temperature (+1.0 to +1.5ppbv, depending on region) was comparable to those due to inter-annual meteorological variability (-1.5 to +1.5ppbv) but considerably less than changes due to precursor emissions changes by 2030 (-3.0 to +3.5ppbv, depending on scenario and region). Including population changes in 2030, both the 'current legislation' and 'maximum feasible reduction' scenarios yield greater O3-attributable health burdens than the 'high' emission scenario: +28%, +22%, and +16%, respectively, above 2003 baseline deaths brought forward (11,500) and respiratory hospital admissions (30,700), using O3 exposure over the full year and no threshold for health effects. The health burdens are greatest under the 'current legislation' scenario because O3 concentrations increase as a result of both increases in background O3 concentration and decreases in UK NOx emissions. For the +5°C scenario, and no threshold (and not including population increases), total UK health burden increases by 500 premature deaths (4%) relative to the 2003 baseline. If a 35ppbv threshold for O3 effects is assumed, health burdens are more sensitive to the current legislation and +5°C scenarios, although total health burdens are roughly an order of magnitude lower. In all scenarios, the assumption of a threshold increases the proportion of health burden in the south and east of the UK compared with the no threshold assumption. The study highlights that the total, and geographically-apportioned, O3-attributable health burdens in the UK are highly sensitive to the future trends of hemispheric, regional and local emissions of O3 precursors, and to the assumption of a threshold for O3 effect.

Towards a climate-dependent paradigm of ammonia emission and deposition

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.

The importance of source configuration in quantifying footprints of regional atmospheric sulphur deposition

An atmospheric transport-chemistry model is applied to investigate the effects of source configuration in simulating regional sulphur deposition footprints from elevated point sources. Dry and wet depositions of sulphur are calculated for each of the 69 largest point sources in the UK. Deposition contributions for each point source are calculated for 2003, as well as for a 2010 emissions scenario. The 2010 emissions scenario has been chosen to simulate the Gothenburg protocol emission scenario. Point source location is found to be a major driver of the dry/wet deposition ratio for each deposition footprint, with increased precipitation scavenging of SO(x) in hill areas resulting in a larger fraction of the emitted sulphur being deposited within the UK for sources located near these areas. This reduces exported transboundary pollution, but, associated with the occurrence of sensitive soils in hill areas, increases the domestic threat of soil acidification. The simulation of plume rise using individual stack parameters for each point source demonstrates a high sensitivity of SO(2) surface concentration to effective source height. This emphasises the importance of using site-specific information for each major stack, which is rarely included in regional atmospheric pollution models, due to the difficulty in obtaining the required input data. The simulations quantify how the fraction of emitted SO(x) exported from the UK increases with source magnitude, effective source height and easterly location. The modelled reduction in SO(x) emissions, between 2003 and 2010 resulted in a smaller fraction being exported, with the result that the reductions in SO(x) deposition to the UK are less than proportionate to the emission reduction. This non-linearity is associated with a relatively larger fraction of the SO(2) being converted to sulphate aerosol for the 2010 scenario, in the presence of ammonia. The effect results in less-than-proportional UK benefits of reducing in SO(2) emissions, together with greater-than-proportional benefits in reducing export of UK SO(2) emissions.

Current and future climate- and air pollution-mediated impacts on human health

BACKGROUND

We describe a project to quantify the burden of heat and ozone on mortality in the UK, both for the present-day and under future emission scenarios.

METHODS

Mortality burdens attributable to heat and ozone exposure are estimated by combination of climate-chemistry modelling and epidemiological risk assessment. Weather forecasting models (WRF) are used to simulate the driving meteorology for the EMEP4UK chemistry transport model at 5 km by 5 km horizontal resolution across the UK; the coupled WRF-EMEP4UK model is used to simulate daily surface temperature and ozone concentrations for the years 2003, 2005 and 2006, and for future emission scenarios. The outputs of these models are combined with evidence on the ozone-mortality and heat-mortality relationships derived from epidemiological analyses (time series regressions) of daily mortality in 15 UK conurbations, 1993-2003, to quantify present-day health burdens.

RESULTS

During the August 2003 heatwave period, elevated ozone concentrations > 200 microg m-3 were measured at sites in London and elsewhere. This and other ozone photochemical episodes cause breaches of the UK air quality objective for ozone. Simulations performed with WRF-EMEP4UK reproduce the August 2003 heatwave temperatures and ozone concentrations. There remains day-to-day variability in the high ozone concentrations during the heatwave period, which on some days may be explained by ozone import from the European continent.Preliminary calculations using extended time series of spatially-resolved WRF-EMEP4UK model output suggest that in the summers (May to September) of 2003, 2005 & 2006 over 6000 deaths were attributable to ozone and around 5000 to heat in England and Wales. The regional variation in these deaths appears greater for heat-related than for ozone-related burdens.Changes in UK health burdens due to a range of future emission scenarios will be quantified. These future emissions scenarios span a range of possible futures from assuming current air quality legislation is fully implemented, to a more optimistic case with maximum feasible reductions, through to a more pessimistic case with continued strong economic growth and minimal implementation of air quality legislation.

CONCLUSION

Elevated surface ozone concentrations during the 2003 heatwave period led to exceedences of the current UK air quality objective standards. A coupled climate-chemistry model is able to reproduce these temperature and ozone extremes. By combining model simulations of surface temperature and ozone with ozone-heat-mortality relationships derived from an epidemiological regression model, we estimate present-day and future health burdens across the UK. Future air quality legislation may need to consider the risk of increases in future heatwaves.

Modelling the spatial distribution of ammonia emissions in the UK

Ammonia emissions (NH3) are characterised by a high spatial variability at a local scale. When modelling the spatial distribution of NH3 emissions, it is important to provide robust emission estimates, since the model output is used to assess potential environmental impacts, e.g. exceedance of critical loads. The aim of this study was to provide a new, updated spatial NH3 emission inventory for the UK for the year 2000, based on an improved modelling approach and the use of updated input datasets. The AENEID model distributes NH3 emissions from a range of agricultural activities, such as grazing and housing of livestock, storage and spreading of manures, and fertilizer application, at a 1-km grid resolution over the most suitable landcover types. The results of the emission calculation for the year 2000 are analysed and the methodology is compared with a previous spatial emission inventory for 1996.

Ammonia emission and deposition in Scotland and its potential environmental impacts

The main source of atmospheric ammonia (NH₃) in Scotland is livestock agriculture, which accounts for 85% of emissions. The local magnitude of emissions therefore depends on livestock density, type, and management, with major differences occurring in various parts of Scotland. Local differences in agricultural activities therefore result in a wide range of NH₃ emissions, ranging from less than 0.2 kg N ha⁻¹ year⁻¹ in remote areas of the Scottish Highlands to over 100 kg N ha⁻¹ year⁻¹ in areas with intensive poultry farming. Scotland can be divided loosely into upland and lowland areas, with NH₃ emission being less than and more than 5 kg N ha⁻¹ year⁻¹, respectively.Many semi-natural ecosystems in Scotland are vulnerable to nitrogen deposition, including bogs, moorlands, and the woodland ground flora. Because NH₃ emissions occur in the rural environment, the local deposition to sensitive ecosystems may be large, making it essential to assess the spatial distribution of NH₃ emissions and deposition. A spatial model is applied here to map NH₃ emissions and these estimates are applied in atmospheric dispersion and deposition models to estimate atmospheric concentrations of NH₃ and NH₄⁺, dry deposition of NH₃, and wet deposition of NHx. Although there is a high level of local variability, modelled NH₃ concentrations show good agreement with the National Ammonia Monitoring Network, while wet deposition is largest at high altitude sites in the south and west of Scotland. Comparison of the modelled NH_x deposition fields with estimated thresholds for environmental effects (“critical loads”) shows that thresholds are exceeded across most of lowland Scotland and the Southern Uplands. Only in the cleanest parts of the north and west is nitrogen deposition not a cause for concern. Given that the most intense effects occur within a few kilometres of sources, it is suggested that local spatial abatement policies would be a useful complement to traditional policies that mitigate environmental effects based on emission reduction technologies.

Seedling establishment from seeds and seed banks in forests under long-term pollution stress: a potential for vegetation recovery

Seed germinability of some common dwarf shrubs and seed-bank composition were studied in young pine forests along a pollution gradient from Severonickel smelter in Monchegorsk, northern Russia. Samples for seed germination and seed-bank trials were taken from sites representing different zones of pollution. Generally, germinability of dwarf shrub seeds was not affected by distance from pollution source, except for Empetrum nigrum ssp. hermaphroditium. The average density per site of seedlings that emerged from seed-bank samples varied between 278 and 416 seedlings/m2. Empetrum nigrum ssp. hermaphroditum and Betula sp. dominated in seed banks. Calluna vulgaris was also numerous at one site. As a whole, seed-bank taxa were well represented in the above ground vegetation. There were no significant differences in seedling density between sites for dominant taxa. Our results indicate that seeds can retain viability even under a heavy pollution load and thus form a potential for vegetation recovery in polluted sites. Key words: seed germination, seed bank, recovery, pollution, coniferous forest.

Seed bank composition in a subarctic pine–birch forest in Finnish Lapland: natural variation and the effect of simulated acid rain

The seed bank composition and seedling densities were determined for a subarctic pine–birch forest. Samples (12 × 12 cm) were taken from plots that had received sulphuric acid and (or) nitric acid irrigation of pH 3 for 5 years and from irrigated control plots. The average seedling density per plot calculated from seedbank samples was 912/m2, varying from 191 to 5240. All the main aboveground taxa were present in the emerging seedlings with Betula sp. and Empetrum nigrum ssp. hermaphroditum being dominant. Other common seed-bank species included Vaccinium uliginosum, Vaccinium vitis-idaea, and Festuca ovina. Acid treatment did not have any significant impact on the composition of the seed bank or on germinative capacity in general. These findings indicate the existence of a viable soil seed bank in northern subarctic forests and the persistence of viability in spite of moderate acid deposition. Key words: seed bank, simulated acid rain, subarctic forest, seedling.