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 (nushadir, 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 nushadir 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 NOx emissions leaves atmospheric NH3 in excess in many areas, contributing to particulate matter (PM2.5) 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'.

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'.

Accurately quantifying low-abundant targets amid similar sequences by revealing hidden correlations in oligonucleotide microarray data.

Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms interact to produce complex properties are hampered by theoretical and technological limitations. Here we describe a general method to accurately identify low-abundant targets in systems containing complex mixtures of homologous targets. We combined an analytical predictor of nonspecific probe-target interactions (cross-hybridization) with an optimization algorithm that iteratively deconvolutes true probe-target signal from raw signal affected by spurious contributions (cross-hybridization, noise, background, and unequal specific hybridization response). The method was capable of quantifying, with unprecedented specificity and accuracy, ribosomal RNA (rRNA) sequences in artificial and natural communities. Controlled experiments with spiked rRNA into artificial and natural communities demonstrated the accuracy of identification and quantitative behavior over different concentration ranges. Finally, we illustrated the power of this methodology for accurate detection of low-abundant targets in natural communities. We accurately identified Vibrio taxa in coastal marine samples at their natural concentrations (<0.05% of total bacteria), despite the high potential for cross-hybridization by hundreds of different coexisting rRNAs, suggesting this methodology should be expandable to any microarray platform and system requiring accurate identification of low-abundant targets amid pools of similar sequences.