The potential to capture additional air pollutants by introducing more vegetation or changing existing short vegetation to woodland on first sight provides an attractive route for lowering urban pollution. Here, an atmospheric chemistry and transport model was run with a range of landcover scenarios to quantify pollutant removal by the existing total UK vegetation as well as the UK urban vegetation and to quantify the effect of large-scale urban tree planting on urban air pollution. UK vegetation as a whole reduces area (population)-weighted concentrations significantly, by 10% (9%) for PM _2.5 , 30% (22%) for SO ₂ , 24% (19%) for NH ₃ and 15% (13%) for O ₃ , compared with a desert scenario. By contrast, urban vegetation reduces average urban PM _2.5 by only approximately 1%. Even large-scale conversion of half of existing open urban greenspace to forest would lower urban PM _2.5 by only another 1%, suggesting that the effect on air quality needs to be considered in the context of the wider benefits of urban tree planting, e.g. on physical and mental health. The net benefits of UK vegetation for NO ₂ are small, and urban tree planting is even forecast to increase urban NO ₂ and NO x concentrations, due to the chemical interaction with changes in BVOC emissions and O ₃ , but the details depend on tree species selection. By extrapolation, green infrastructure projects focusing on non-greenspace (roadside trees, green walls, roof-top gardens) would have to be implemented at very large scales to match this effect. Downscaling of the results to micro-interventions solely aimed at pollutant removal suggests that their impact is too limited for their cost–benefit analysis to compare favourably with emission abatement measures. Urban vegetation planting is less effective for lowering pollution than measures to reduce emissions at source. The results highlight interactions that cannot be captured if benefits are quantified via deposition models using prescribed concentrations, and emission damage costs. This article is part of a discussion meeting issue ‘Air quality, past present and future’.

Air pollution presents a major risk to human health, resulting in premature deaths and reduced quality of life. Quantifying the role of vegetation in reducing air pollution concentrations is an important contribution to urban natural capital accounting. However, most current methods to calculate pollution removal are static, and do not represent atmospheric transport of pollutants, or interactions among pollutants and meteorology. An additional challenge is defining urban extent in a way that captures the green and blue infrastructure providing the service in a consistent way. We developed a refined urban morphology layer which incorporates urban green and blue space. We then applied an atmospheric chemistry transport model (EMEP4UK) to calculate pollutant removal by urban natural capital for pollutants including PM_2.5, NO₂, SO₂, O₃. We calculated health benefits directly from the change in pollutant concentrations (i.e. exposure) rather than from tonnes of pollutant removed. Urban natural capital across Britain removes 28,700 tonnes of PM_2.5, NO₂, SO₂, O₃. The economic value of the health benefits are substantial: £136 million in 2015, resulting from 900 fewer respiratory hospital admissions, 220 fewer cardiovascular hospital admissions, 240 fewer deaths and 3600 fewer Life Years Lost.

The dissolved organic carbon (DOC) export from land to ocean via rivers is a significant term in the global C cycle, and has been modified in many areas by human activity. DOC exports from large global rivers are fairly well quantified, but those from smaller river systems, including those draining oceanic regions, are generally under-represented in global syntheses. Given that these regions typically have high runoff and high peat cover, they may exert a disproportionate influence on the global land–ocean DOC export. Here we describe a comprehensive new assessment of the annual riverine DOC export to estuaries across the island of Great Britain (GB), which spans the latitude range 50–60° N with strong spatial gradients of topography, soils, rainfall, land use and population density. DOC yields (export per unit area) were positively related to and best predicted by rainfall, peat extent and forest cover, but relatively insensitive to population density or agricultural development. Based on an empirical relationship with land use and rainfall we estimate that the DOC export from the GB land area to the freshwater-seawater interface was 1.15 Tg C year⁻¹in 2017. The average yield for GB rivers is 5.04 g C m⁻² year⁻¹, higher than most of the world’s major rivers, including those of the humid tropics and Arctic, supporting the conclusion that under-representation of smaller river systems draining peat-rich areas could lead to under-estimation of the global land–ocean DOC export. The main anthropogenic factor influencing the spatial distribution of GB DOC exports appears to be upland conifer plantation forestry, which is estimated to have raised the overall DOC export by 0.168 Tg C year⁻¹. This is equivalent to 15% of the estimated current rate of net CO₂uptake by British forests. With the UK and many other countries seeking to expand plantation forest cover for climate change mitigation, this ‘leak in the ecosystem’ should be incorporated in future assessments of the CO₂sequestration potential of forest planting strategies.

Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R² = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R² = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km² of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m² resolution to underpin evidence-based management and conservation of seagrass.

Noise pollution from road traffic is ubiquitous in modern cities and is the second greatest environmental risk to health in Western Europe. Urban woodland can provide substantial noise mitigation if located properly, yet such considerations are often absent from the urban planning process. Current approaches for quantifying this important ecosystem service (ES) do not account adequately for important spatial factors and are unable to identify effectively the best locations to place new woodland for noise mitigation. We present new methods, in which we exploit the concept of least-cost-distance, to map and value the mitigating effect of urban woodland, and to identify optimal locations to place new woodland. Applying these methods, we show that urban woodland currently provides Birmingham City (UK) with over GBP 3.8 million in noise mitigation benefits, annually. We also show that our new ‘opportunity’ mapping methods effectively identify the best locations for new woodland, achieving close to a maximum service with less than a quarter of the additional woodland needed to achieve it. This has important implications for the design and implementation of urban tree planting for noise mitigation, and these methods can be adapted for other ES, allowing consideration of multiple service outcomes.