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.

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 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'.

Nitrous oxide emission factors of mineral fertilisers in the UK and Ireland: A Bayesian analysis of 20 years of experimental data.

In this study, we analysed datasets of N2O emission factors (EFs) from 21 separate studies carried out on arable and managed grasslands across the UK and Ireland over the past 20 years. A total of 641 separate events were collated from 40 experimental field sites. Individual EFs ranged over an order of magnitude (0-12% of applied N) for each fertiliser type, following a log-normal distribution in all cases. Our study shows that a Bayesian approach can provide a robust statistical method that is capable of performing uncertainty analysis on log-normal distributed data in a more defensible manner than conventional statistical methods allow. This method allowed for a national scale comparison of EFs between the most commonly applied mineral fertilisers based solely on previously published data (UK and Ireland in this case). The study shows that ammonium nitrate (AN) and Calcium ammonium nitrate (CAN) are the largest emitting fertiliser types by mass across the British Isles (temperate climate zone), with EFs of 1.1 (1.0-1.2) % and 1.0 (0.7-1.3) % for all recorded events, respectively; however, emissions from AN applications were significantly lower for applications to arable fields (0.6%) than to grasslands (1.3%). EFs associated with urea (CO(NH2)2) were significantly lower than AN for grasslands with an EF of 0.6 (0.5-0.7) %, but slightly higher for arable fields with an EF of 0.7 (0.4-1.4) %. The study highlights the potential effectiveness of microbial inhibitors at reducing emissions of N2O from mineral fertilisers, with Dicyandiamide (DCD) treated AN reducing emissions by approximately 28% and urea treated with either DCD or N-(n)-butyl) thiophosphorictriamide (NBTP) reducing emissions by approximately 40%. Although limited by a relatively small sample size (n = 11), urea treated with both DCD and NBPT appeared to have the lowest EF of all treatments at 0.13 (0.08-0.21) %, highlighting the potential to significantly reduce N2O emissions at regional scales if applied instead of conventional nitrogen fertilisers.

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.

What is the most ecologically-meaningful metric of nitrogen deposition?

Nitrogen (N) deposition poses a severe risk to global terrestrial ecosystems, and managing this threat is an important focus for air pollution science and policy. To understand and manage the impacts of N deposition, we need metrics which accurately reflect N deposition pressure on the environment, and are responsive to changes in both N deposition and its impacts over time. In the UK, the metric typically used is a measure of total N deposition over 1-3 years, despite evidence that N accumulates in many ecosystems and impacts from low-level exposure can take considerable time to develop. Improvements in N deposition modelling now allow the development of metrics which incorporate the long-term history of pollution, as well as current exposure. Here we test the potential of alternative N deposition metrics to explain vegetation compositional variability in British semi-natural habitats. We assembled 36 individual datasets representing 48,332 occurrence records in 5479 quadrats from 1683 sites, and used redundancy analyses to test the explanatory power of 33 alternative N metrics based on national pollutant deposition models. We find convincing evidence for N deposition impacts across datasets and habitats, even when accounting for other large-scale drivers of vegetation change. Metrics that incorporate long-term N deposition trajectories consistently explain greater compositional variance than 1-3 year N deposition. There is considerable variability in results across habitats and between similar metrics, but overall we propose that a thirty-year moving window of cumulative deposition is optimal to represent impacts on plant communities for application in science, policy and management.

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, PM2.5 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 PM2.5 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.

Impact of two centuries of intensive agriculture on soil carbon, nitrogen and phosphorus cycling in the UK.

This paper describes an agricultural model (Roth-CNP) that estimates carbon (C), nitrogen (N) and phosphorus (P) pools, pool changes, their balance and the nutrient fluxes exported from arable and grassland systems in the UK during 1800-2010. The Roth-CNP model was developed as part of an Integrated Model (IM) to simulate C, N and P cycling for the whole of UK, by loosely coupling terrestrial, hydrological and hydro-chemical models. The model was calibrated and tested using long term experiment (LTE) data from Broadbalk (1843) and Park Grass (1856) at Rothamsted. We estimated C, N and P balance and their fluxes exported from arable and grassland systems on a 5km×5km grid across the whole of UK by using the area of arable of crops and livestock numbers in each grid and their management. The model estimated crop and grass yields, soil organic carbon (SOC) stocks and nutrient fluxes in the form of NH4-N, NO3-N and PO4-P. The simulated crop yields were compared to that reported by national agricultural statistics for the historical to the current period. Overall, arable land in the UK have lost SOC by -0.18, -0.25 and -0.08MgCha-1y-1 whereas land under improved grassland SOC stock has increased by 0.20, 0.47 and 0.24MgCha-1y-1 during 1800-1950, 1950-1970 and 1970-2010 simulated in this study. Simulated N loss (by leaching, runoff, soil erosion and denitrification) increased both under arable (-15, -18 and -53kgNha-1y-1) and grass (-18, -22 and -36kgNha-1y-1) during different time periods. Simulated P surplus increased from 2.6, 10.8 and 18.1kgPha-1y-1 under arable and 2.8, 11.3 and 3.6kgPha-1y-1 under grass lands 1800-1950, 1950-1970 and 1970-2010.

Nutrient fluxes from domestic wastewater: A national-scale historical perspective for the UK 1800-2010.

Nutrient emissions in human waste and wastewater effluent fluxes from domestic sources are quantified for the UK over the period 1800-2010 based on population data from UK Census returns. The most important drivers of change have been the introduction of the water closet (flush toilet) along with population growth, urbanization, connection to sewer, improvements in wastewater treatment and use of phosphorus in detergents. In 1800, the population of the UK was about 12 million and estimated emissions in human waste were 37kt N, 6.2kt P and 205ktorganicC/year. This would have been recycled to land with little or no sewage going directly to rivers or coastal waters. By 1900, population had increased to 35.6 million and some 145kt N were emitted in human waste but, with only the major urban areas connected to sewers, only about 19kt N were discharged in sewage effluent. With the use of phosphorus in detergents, estimated phosphorus emissions peaked at around 63.5ktP/year in the 1980s, with about 28ktP/year being discharged in sewage effluent. By 2010, population had increased to 63 million with estimated emissions of 263kt N, 43.6kt P and 1460ktorganicC/year, and an estimated effluent flux of 104kt N, 14.8kt P and 63kt organic C. Despite improvements in wastewater treatment, current levels of nutrient fluxes in sewage effluent are substantially higher than those in the early 20th century.