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Halving nitrogen pollution is crucial for achieving Sustainable Development Goals (SDGs). However, how to reduce nitrogen pollution from multiple sources remains challenging. Here we show that reactive nitrogen (Nr) pollution could be roughly halved by managed urban development in China by 2050, with NH3, NOx and N2O atmospheric emissions declining by 44%, 30% and 33%, respectively, and Nr to water bodies by 53%. While rural-urban migration increases point-source nitrogen emissions in metropolitan areas, it promotes large-scale farming, reducing rural sewage and agricultural non-point-source pollution, potentially improving national air and water quality. An investment of approximately US$ 61 billion in waste treatment, land consolidation, and livestock relocation yields an overall benefit of US$ 245 billion. This underscores the feasibility and cost-effectiveness of halving Nr pollution through urbanization, contributing significantly to SDG1 (No poverty), SDG2 (Zero hunger), SDG6 (Clean water), SDG12 (Responsible consumption and production), SDG14 (Climate Action), and so on. Here the authors demonstrate how managed urbanization in China could halve reactive nitrogen pollution to both the atmosphere and water resources. Investing 61 billion USD could provide 245 billion USD in benefits, while contributing to multiple SDG goals. [ABSTRACT FROM AUTHOR]

Rapid demographic ageing substantially affects socioeconomic development1–4 and presents considerable challenges for food security and agricultural sustainability5–8, which have so far not been well understood. Here, by using data from more than 15,000 rural households with crops but no livestock across China, we show that rural population ageing reduced farm size by 4% through transferring cropland ownership and land abandonment (approximately 4 million hectares) in 2019, taking the population age structure in 1990 as a benchmark. These changes led to a reduction of agricultural inputs, including chemical fertilizers, manure and machinery, which decreased agricultural output and labour productivity by 5% and 4%, respectively, further lowering farmers’ income by 15%. Meanwhile, fertilizer loss increased by 3%, resulting in higher pollutant emissions to the environment. In new farming models, such as cooperative farming, farms tend to be larger and operated by younger farmers, who have a higher average education level, hence improving agricultural management. By encouraging the transition to new farming models, the negative consequences of ageing can be reversed. Agricultural input, farm size and farmer’s income would grow by approximately 14%, 20% and 26%, respectively, and fertilizer loss would reduce by 4% in 2100 compared with that in 2020. This suggests that management of rural ageing will contribute to a comprehensive transformation of smallholder farming to sustainable agriculture in China.Rural population ageing reduces the sustainability of smallholder farming in China, but the transition to a new farming model could reverse the negative effects of rural population ageing. [ABSTRACT FROM AUTHOR]

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

Producing more food with less pollution and greenhouse gas emissions is a grand challenge for the 21st century. Strategies to successfully promote win‐win outcomes for both food security and environmental health are not easy to identify. Here we comprehensively assess an ecological rice‐animal co‐culture system (RAC) (e.g., rice‐fish, rice‐duck, and rice‐crayfish) through a global meta‐analysis and identify the potential benefits of global promotion. Compared to traditional monoculture of rice or animal production, the RAC can not only reduce the demand for agricultural land areas, but also increase rice yields (+4%) as well as nitrogen use efficiency of rice (+6%). At the same time, RAC reduces nitrogen losses (−16% runoff and −13% leaching) and methane emissions (−11%), except for rice‐fish coculture systems, which are likely to increase methane emissions (+29%). Furthermore, RAC increases the net income of farmers through reducing cost of fertilizer and pesticide input and achieving higher outputs with more marketable products. According to the development stage of different countries, promotion of RAC will thus realize multiple benefits and aid sustainable intensification. Plain Language Summary: How to feed the growing world population with less pollution is a grand challenge for the 21st century. Rice‐animal co‐culture systems present an ecological integrated farming approach to tackle the challenge. Through a global consensus by synthesizing evidence from existing literature, we found that rice‐animal co‐culture systems could produce more diverse food types and nutrient sources, enhance resource use efficiency and reduce methane emissions, while increasing farmers' income. Global promotion of rice‐animal coculture system has the potential to benefit sustainable development goals. Key Points: Rice‐animal co‐culture systems could produce more diverse food with higher rice yield and extra animal proteinGlobal promotion of rice‐animal co‐culture will reduce nitrogen loss and methane emissions from rice paddiesGlobal promotion of rice‐animal co‐culture will increase income of producers by 152–171 billion US dollars annually [ABSTRACT FROM AUTHOR]

The Chinese government recently proposed ammonia (NH 3) emission reductions (but without a specific national target) as a strategic option to mitigate fine particulate matter (PM 2.5) pollution. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH 3 and acid gas (SO 2 and NO x) emissions. We found that PM 2.5 concentrations decreased from 2000 to 2019, but annual mean PM 2.5 concentrations still exceeded 35 µ g m -3 at 74 % of 1498 monitoring sites during 2015–2019. The concentration of PM 2.5 and its components were significantly higher (16 %–195 %) on hazy days than on non-hazy days. Compared with mean values of other components, this difference was more significant for the secondary inorganic ions SO 42- , NO 3- , and NH 4+ (average increase 98 %). While sulfate concentrations significantly decreased over this period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50 % NH 3 emission reduction for controlling secondary inorganic aerosol (SIA) concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the COVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8 % lower than those in 2017. In addition, the reduction in SIA concentrations in 2017 was greater for 50 % acid gas reductions than for the 50 % NH 3 emission reductions. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by emissions of acid gases, while an additional control of NH 3 emissions would become more important as reductions of SO 2 and NO x emissions progress. [ABSTRACT FROM AUTHOR]

China's agriculture is dominated by smallholder farms, which have become major sources of negative environmental impacts including eutrophication, formation of haze, soil acidification and greenhouse gas emissions. To mitigate these environmental impacts, new farming models including family farming, cooperation farming and industrial farming have emerged in recent years. However, whether these new farming practices would improve the economic and environmental performance as compared to the current smallholder farming has yet to be verified on ground level. In this paper, by using pilot farming cases within the watershed of Tai Lake, we found that alternative farming models produced 7% more crop yield, while using 8% less fertilizer, leading to a 28% decrease in pollutant emission per hectare. These alternative farming models have a 17% higher fertilizer use efficiency and 50% higher profit per hectare. Compared to smallholder farming, these alternative farming practices invest 27% more resources into agricultural facilities, including advanced machinery, and have a younger, better educated labor force as a consequence of a larger farm size and more specialization. These input changes substantially increase fertilizer use efficiency and reduce agricultural pollution. Policy arrangements to support and facilitate the uptake of these farming models will further promote the green development and sustainable intensification of agricultural production. [ABSTRACT FROM AUTHOR]

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

The Chinese government recently proposed ammonia (NH3) emissions reductions (but without a specific national target) as a strategic option to mitigate PM2.5 pollution. We combined a meta-analysis of nationwide measurements and air quality modelling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m-3 at 74% of 1498 monitoring sites in 2015-2019. Secondary inorganic aerosols (SIA) were the dominant contributor to ambient PM2.5 concentrations. While sulfate concentrations significantly decreased over the time period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50% NH3 emission reduction for controlling SIA concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the CoVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8% lower than that in 2017. In addition, the reduction of SIA concentrations in 2017 was greater for 50% acid gas reductions than for the 50% NH3 emissions reduction. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by acid gases emissions, while an additional control on NH3 emissions would become more important as reductions of SO2 and NOx emissions progress. [ABSTRACT FROM AUTHOR]

Background: Air pollution has been consistently linked with dementia and cognitive decline. However, it is unclear whether risk is accumulated through long-term exposure or whether there are sensitive/critical periods. A key barrier to clarifying this relationship is the dearth of historical air pollution data.Objective: To demonstrate the feasibility of modelling historical air pollution data and using them in epidemiologicalmodels.Methods: Using the EMEP4UK atmospheric chemistry transport model, we modelled historical fine particulate matter (PM2.5) concentrations for the years 1935, 1950, 1970, 1980, and 1990 and combined these with contemporary modelled data from 2001 to estimate life course exposure in 572 participants in the Lothian Birth Cohort 1936 with lifetime residential history recorded. Linear regression and latent growth models were constructed using cognitive ability (IQ) measured by the Moray House Test at the ages of 11, 70, 76, and 79 years to explore the effects of historical air pollution exposure. Covariates included sex, IQ at age 11 years, social class, and smoking.Results: Higher air pollution modelled for 1935 (when participants would have been in utero) was associated with worse change in IQ from age 11-70 years (β = -0.006, SE = 0.002, p = 0.03) but not cognitive trajectories from age 70-79 years (p > 0.05). There was no support for other critical/sensitive periods of exposure or an accumulation of risk (all p > 0.05).Conclusion: The life course paradigm is essential in understanding cognitive decline and this is the first study to examine life course air pollution exposure in relation to cognitive health. [ABSTRACT FROM AUTHOR]

The potential to capture additional air pollutants by introducing more vegetation or changing existing short vegetation to woodland on first sight provides an attractive route for lowering urban pollution. Here, an atmospheric chemistry and transport model was run with a range of landcover scenarios to quantify pollutant removal by the existing total UK vegetation as well as the UK urban vegetation and to quantify the effect of large-scale urban tree planting on urban air pollution. UK vegetation as a whole reduces area (population)-weighted concentrations significantly, by 10% (9%) for PM2.5, 30% (22%) for SO2, 24% (19%) for NH3 and 15% (13%) for O3, compared with a desert scenario. By contrast, urban vegetation reduces average urban PM2.5 by only approximately 1%. Even large-scale conversion of half of existing open urban greenspace to forest would lower urban PM2.5 by only another 1%, suggesting that the effect on air quality needs to be considered in the context of the wider benefits of urban tree planting, e.g. on physical and mental health. The net benefits of UK vegetation for NO2 are small, and urban tree planting is even forecast to increase urban NO2 and NOx concentrations, due to the chemical interaction with changes in BVOC emissions and O3, but the details depend on tree species selection. By extrapolation, green infrastructure projects focusing on non-greenspace (roadside trees, green walls, roof-top gardens) would have to be implemented at very large scales to match this effect. Downscaling of the results to micro-interventions solely aimed at pollutant removal suggests that their impact is too limited for their cost–benefit analysis to compare favourably with emission abatement measures. Urban vegetation planting is less effective for lowering pollution than measures to reduce emissions at source. The results highlight interactions that cannot be captured if benefits are quantified via deposition models using prescribed concentrations, and emission damage costs. This article is part of a discussion meeting issue 'Air quality, past present and future'. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry transport models (ACTMs) are extensively used to provide scientific support for the development of policies to mitigate the detrimental effects of air pollution on human health and ecosystems. Therefore, it is essential to quantitatively assess the level of model uncertainty and to identify the model input parameters that contribute the most to the uncertainty. For complex process-based models, such as ACTMs, uncertainty and global sensitivity analyses are still challenging and are often limited by computational constraints due to the requirement of a large number of model runs. In this work, we demonstrate an emulator-based approach to uncertainty quantification and variance-based sensitivity analysis for the EMEP4UK model (regional application of the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre-West). A separate Gaussian process emulator was used to estimate model predictions at unsampled points in the space of the uncertain model inputs for every modelled grid cell. The training points for the emulator were chosen using an optimised Latin hypercube sampling design. The uncertainties in surface concentrations of O3 , NO2 , and PM 2.5 were propagated from the uncertainties in the anthropogenic emissions of NOx , SO2 , NH3 , VOC, and primary PM 2.5 reported by the UK National Atmospheric Emissions Inventory. The results of the EMEP4UK uncertainty analysis for the annually averaged model predictions indicate that modelled surface concentrations of O3 , NO2 , and PM 2.5 have the highest level of uncertainty in the grid cells comprising urban areas (up to ±7 %, ±9 %, and ±9 %, respectively). The uncertainty in the surface concentrations of O3 and NO2 were dominated by uncertainties in NOx emissions combined from non-dominant sectors (i.e. all sectors excluding energy production and road transport) and shipping emissions. Additionally, uncertainty in O3 was driven by uncertainty in VOC emissions combined from sectors excluding solvent use. Uncertainties in the modelled PM 2.5 concentrations were mainly driven by uncertainties in primary PM 2.5 emissions and NH3 emissions from the agricultural sector. Uncertainty and sensitivity analyses were also performed for five selected grid cells for monthly averaged model predictions to illustrate the seasonal change in the magnitude of uncertainty and change in the contribution of different model inputs to the overall uncertainty. Our study demonstrates the viability of a Gaussian process emulator-based approach for uncertainty and global sensitivity analyses, which can be applied to other ACTMs. Conducting these analyses helps to increase the confidence in model predictions. Additionally, the emulators created for these analyses can be used to predict the ACTM response for any other combination of perturbed input emissions within the ranges set for the original Latin hypercube sampling design without the need to rerun the ACTM, thus allowing for fast exploratory assessments at significantly reduced computational costs. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry transport models (ACTMs) are extensively used to provide scientific support for the development of policies to mitigate against the detrimental effects of air pollution on human health and ecosystems. Therefore, it is essential to quantitatively assess the level of model uncertainty and to identify the model input parameters that contribute the most to the uncertainty. For complex process-based models, such as ACTMs, uncertainty and global sensitivity analyses are still challenging and are often limited by computational constraints due to the requirement of a large number of model runs. In this work, we demonstrate an emulator-based approach to uncertainty quantification and variance-based sensitivity analysis for the EMEP4UK model (regional application of the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre-West). A separate Gaussian process emulator was used to estimate model predictions at unsampled points in the space of the uncertain model inputs for every modelled grid cell. The training points for the emulator were chosen using an optimised Latin hypercube sampling design. The uncertainties in surface concentrations of O3, NO2, and PM2.5 were propagated from the uncertainties in the anthropogenic emissions of NOx, SO2, NH3, VOC, and primary PM2.5 reported by the UK National Atmospheric Emissions Inventory. The results of the EMEP4UK uncertainty analysis for the annually averaged model predictions indicate that modelled surface concentrations of O3, NO2, and PM2.5 have the highest level of uncertainty in the grid cells comprising urban areas (up to ±7%, ±9%, and ±9% respectively). The uncertainty in the surface concentrations of O3 and NO2 were dominated by uncertainties in NOx emissions combined from non-dominant sectors (i.e. all sectors excluding energy production and road transport) and shipping emissions. Additionally, uncertainty in OO3 was driven by uncertainty VOC emissions combined from sectors excluding solvent use. Uncertainties in the modelled PM2.5 concentrations were mainly driven by uncertainties in primary PM2.5 emissions and NH3 emissions from the agricultural sector. Uncertainty and sensitivity analyses were also performed for five selected grid sells for monthly averaged model predictions to illustrate the seasonal change in the magnitude of uncertainty and change in the contribution of different model inputs to the overall uncertainty. Our study demonstrates the viability of a Gaussian process emulator-based approach for uncertainty and global sensitivity analyses, which can be applied to other ACTMs. Conducting these analyses helps to increase the confidence in model predictions. Additionally, the emulators created for these analyses can be used to predict the ACTM response for any other combination of perturbed input emissions within the ranges set for the original Latin hypercube sampling design without the need to re-run the ACTM, thus allowing fast exploratory assessments at significantly reduced computational costs. [ABSTRACT FROM AUTHOR]

Evidence is accumulating that emissions of primary particulate matter (PM) from residential wood and coal combustion in the UK may be underestimated and/or spatially misclassified. In this study, different assumptions for the spatial distribution and total emission of PM from solid fuel (wood and coal) burning in the UK were tested using an atmospheric chemical transport model. Modelled concentrations of the PM components were compared with measurements from aerosol mass spectrometers at four sites in central and Greater London (ClearfLo campaign, 2012), as well as with measurements from the UK black carbon network. The two main alternative emission scenarios modelled were Base4x and combRedist. For Base4x, officially reported PM2.5 from the residential and other non-industrial combustion source sector were increased by a factor of four. For the combRedist experiment, half of the baseline emissions from this same source were redistributed by residential population density to simulate the effect of allocating some emissions to the smoke control areas (that are assumed in the national inventory to have no emissions from this source). The Base4x scenario yielded better daily and hourly correlations with measurements than the combRedist scenario for yearlong comparisons of the solid fuel organic aerosol (SFOA) component at the two London sites. However, the latter scenario better captured mean measured concentrations across all four sites. A third experiment, Redist - all emissions redistributed linearly to population density, is also presented as an indicator of the maximum concentrations an assumption like this could yield. The modelled elemental carbon (EC) concentrations derived from the combRedist experiments also compared well with seasonal average concentrations of black carbon observed across the network of UK sites. Together, the two model scenario simulations of SFOA and EC suggest both that residential solid fuel emissions may be higher than inventory estimates and that the spatial distribution of residential solid fuel burning emissions, particularly in smoke control areas, needs re-evaluation. The model results also suggest the assumed temporal profiles for residential emissions may require review to place greater emphasis on evening (including "discretionary") solid fuel burning. [ABSTRACT FROM AUTHOR]

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

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

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

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

Evidence is accumulating that emissions of primary particulate matter (PM) from residential solid fuel combustion in the UK may be underestimated and/or spatially misclassified. In this study, different assumptions for the spatial distribution and total emission of PM from solid fuel (wood and coal) burning in the UK were tested using an atmospheric chemical transport model. Modelled concentrations of the PM components were compared with measurements from aerosol mass spectrometers at four sites in central and Greater London (ClearfLo campaign, 2012), as well as with measurements from the UK black carbon network. The two main alternative emission scenarios modelled were Base4x and combRedist. For Base4x, officially reported PM2.5 from the residential and other non-industrial combustion source sector were increased by a factor of 4. For the combRedist experiment, half of the baseline emissions from this same source were redistributed by residential population density to simulate the effect of allocating some emissions to the smoke control areas (that are assumed in the national inventory to have no emissions from this source). The Base4x scenario yielded better daily and hourly correlations with measurements than the combRedist scenario for year-long comparisons of the solid fuel organic aerosol (SFOA) component at the two London sites, whereas the latter scenario better captured mean measured concentrations across all four sites. The modelled elemental carbon (EC) concentrations derived from the combRedist experiments also compared well with seasonal-average concentrations of black carbon observed across the network of UK sites. Together, the two model scenario simulations of SFOA and EC suggest both that residential solid-fuel emissions may be higher than inventory estimates and that the spatial distribution of residential solid-fuel burning emissions, particularly in smoke control areas, needs re-evaluation. The model results also suggest the assumed temporal profiles for residential emissions may require review to place greater emphasis on evening (including 'discretionary') solid-fuel burning. [ABSTRACT FROM AUTHOR]

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

Cooking organic aerosol (COA) is currently not included in European emission inventories. However, recent positive matrix factorization (PMF) analyses of aerosol mass spectrometer (AMS) measurements have suggested important contributions of COA in several European cities. In this study, emissions of COA were estimated for the UK, based on hourly AMS measurements of COA made at two sites in London (a kerbside site in central London and an urban background site in a residential area close to central London) for the full calendar year of 2012 during the Clean Air for London (ClearfLo) campaign. Iteration of COA emissions estimates and subsequent evaluation and sensitivity experiments were conducted with the EMEP4UK atmospheric chemistry transport modelling system with a horizontal resolution of 5 km × 5 km. The spatial distribution of these emissions was based on workday population density derived from the 2011 census data. The estimated UK annual COA emission was 7.4 Gg per year, which is an almost 10% addition to the officially reported UK national total anthropogenic emissions of PM2:5 (82 Gg in 2012), corresponding to 320 mgperson-1 day-1 on average. Weekday and weekend diurnal variation in COA emissions were also based on the AMS measurements. Modelled concentrations of COA were then independently evaluated against AMS-derived COA measurements from another city and time period (Manchester, January-February 2007), as well as with COA estimated by a chemical mass balance model of measurements for a 2-week period at the Harwell rural site (~80 km west of central London). The modelled annual average contribution of COA to ambient particulate matter (PM) in central London was between 1 and 2 μgm-3 (~20% of total measured OA1) and between 0.5 and 0.7 μgm-3 in other major cities in England (Manchester, Birmingham, Leeds). It was also shown that cities smaller than London can have a central hotspot of population density of smaller area than the computational grid cell, in which case higher localized COA concentrations than modelled here may be expected. Modelled COA concentrations dropped rapidly outside of major urban areas (annual average of 0.12 μgm-3 for the Harwell location), indicating that although COA can be a notable component in urban air, it does not have a significant effect on PM concentrations on rural areas. The possibility that the AMS-PMF apportionment measurements overestimate COA concentrations by up to a factor of 2 is discussed. Since COA is a primary emission, any downward adjustments in COA emissions would lead to a proportional linear downward scaling in the absolute magnitudes of COA concentrations simulated in the model. [ABSTRACT FROM AUTHOR]

Cooking organic aerosol (COA) is currently not included in European emission inventories. However, recent positive matrix factorization (PMF) analyses of aerosol mass spectrometer (AMS) measurements have suggested important contributions of COA in several European cities. In this study, emissions of COA were estimated for the UK, based on hourly AMS measurements of COA made at two sites in London (a kerbside site in central London and an urban background site in a residential area close to central London) for the full calendar year of 2012 during the Clean Air for London (ClearfLo) campaign. Iteration of COA emissions estimates and subsequent evaluation and sensitivity experiments were conducted with the EMEP4UK atmospheric chemistry transport modelling system with a horizontal resolution of 5 km × 5 km. The spatial distribution of these emissions was based on workday population density derived from the 2011 census data. The estimated UK annual COA emission was 7.4 Gg per year, which is an almost 10% addition to the officially reported UK national total anthropogenic emissions of PM2.5 (82 Gg in 2012), corresponding to 320 mg person-1 day-1 on average. Weekday and weekend diurnal variation in COA emissions were also based on the AMS measurements. Modelled concentrations of COA were then independently evaluated against AMS-derived COA measurements from another city and time period (Manchester, Jan-Feb 2007), as well as with COA estimated by a chemical mass balance model of measurements for a two-week period at the Harwell rural site (~ 80 km west of central London). The modelled annual average contribution of COA to ambient particulate matter (PM) in central London was between 1-2 μg m-3, and between 0.5-0.7 μg m-3 in other major cities in England (Manchester, Birmingham, Leeds). It was also shown that cities smaller than London can have a central hot-spot of population density of smaller area than the computational grid cell, in which case higher localised COA concentrations than modelled here may be expected. Modelled COA concentrations dropped rapidly outside of major urban areas (annual average of 0.12 μg m-3 for the Harwell location), indicating that although COA can be a notable component in urban air, it does not have a significant effect on PM concentrations on rural areas. The possibility that the AMS PMF-apportionment measurements overestimate COA concentrations by up to a factor of 2 is discussed. Since COA is a primary emission, any downward adjustments in COA emissions would lead to a proportional linear downward scaling in the absolute magnitudes of COA concentrations simulated in the model. [ABSTRACT FROM AUTHOR]

We present high-resolution (5 km × 5 km) atmospheric chemical transport model (ACTM) simulations of the impact of newly estimated traffic-related emissions on secondary organic aerosol (SOA) formation over the UK for 2012. Our simulations include additional diesel-related intermediate-volatility organic compound (IVOC) emissions derived directly from comprehensive field measurements at an urban background site in London during the 2012 Clean Air for London (ClearfLo) campaign. Our IVOC emissions are added proportionally to VOC emissions, as opposed to proportionally to primary organic aerosol (POA) as has been done by previous ACTM studies seeking to simulate the effects of these missing emissions. Modelled concentrations are evaluated against hourly and daily measurements of organic aerosol (OA) components derived from aerosol mass spectrometer (AMS) measurements also made during the ClearfLo campaign at three sites in the London area. According to the model simulations, diesel-related IVOCs can explain on average ~30% of the annual SOA in and around London. Furthermore, the 90th percentile of modelled daily SOA concentrations for the whole year is 3.8 µgm-3, constituting a notable addition to total particulate matter. More measurements of these precursors (currently not included in official emissions inventories) is recommended. During the period of concurrent measurements, SOA concentrations at the Detling rural background location east of London were greater than at the central London location. The model shows that this was caused by an intense pollution plume with a strong gradient of imported SOA passing over the rural location. This demonstrates the value of modelling for supporting the interpretation of measurements taken at different sites or for short durations. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to review, from a sustainable management perspective, a range of conceptual frameworks; determine the efficacy and utility of three different data sources in generating indicators collectively; and consider the utility of a single index of total ecosystem services (TESI). The ecosystem service (ES) concept has been discussed as an important model to aid sustainable land-use management. Design/methodology/approach – The historical development and the relative merits of sustainable management frameworks which can be implemented in a decision-making context were examined. The efficacy of a single index TESI was examined considering three data sources for 11 contrasting sites within the UK. Findings – The choice of conceptual framework and data source depends on the specific question and scale being addressed. Publicly available data through the Eurostat route is primarily limited to the assessment of the provisioning services. Research limitations/implications – Limitation of the study is that both bottom-up and top-down sourcing of data to conduct an ES assessment were considered. Practical implications – The scale of enquiry when conducting ES assessment to aid sustainable management dictate the most useful data source. If conducting local assessments that give local data is more appropriate while conducting European Union (EU)-wide assessment gives less local precision, it does provide some insight when conducting larger-scale regional assessment which cannot otherwise be achieved. Originality/value – The various data set analysed in this study all provided insight for sustainable management. [ABSTRACT FROM AUTHOR]

Existing descriptions of bi-directional ammonia (NH3) land-atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission-deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 58C warming would increase emissions by 42 per cent (28-67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45-85) Tg N in 2008 to reach 132 (89-179) Tg by 2100. [ABSTRACT FROM AUTHOR]

Global nitrogen fixation contributes 413 Tg of reactive nitrogen (Nr) to terrestrial and marine ecosystems annually of which anthropogenic activities are responsible for half, 210 Tg N. The majority of the transformations of anthropogenic Nr are on land (240 Tg Nyr-1) within soils and vegetation where reduced Nr contributes most of the input through the use of fertilizer nitrogen in agriculture. Leakages from the use of fertilizer Nr contribute to nitrate (NO-3) in drainage waters from agricultural land and emissions of trace Nr compounds to the atmosphere. Emissions, mainly of ammonia (NH3) from land together with combustion related emissions of nitrogen oxides (NOx), contribute 100 Tg Nyr-1 to the atmosphere, which are transported between countries and processed within the atmosphere, generating secondary pollutants, including ozone and other photochemical oxidants and aerosols, especially ammonium nitrate (NH4NO3) and ammonium sulfate (NH4)2SO4. Leaching and riverine transport of NO3 contribute 40-70 Tg Nyr-1 to coastal waters and the open ocean, which together with the 30 Tg input to oceans from atmospheric deposition combine with marine biological nitrogen fixation (140 TgNyr-1) to double the ocean processing of Nr. Some of the marine Nr is buried in sediments, the remainder being denitrified back to the atmosphere as N2 or N2O. The marine processing is of a similar magnitude to that in terrestrial soils and vegetation, but has a larger fraction of natural origin. The lifetime of Nr in the atmosphere, with the exception of N2O, is only a few weeks, while in terrestrial ecosystems, with the exception of peatlands (where it can be 10²-10³ years), the lifetime is a few decades. In the ocean, the lifetime of Nr is less well known but seems to be longer than in terrestrial ecosystems and may represent an important long-term source of N2O that will respond very slowly to control measures on the sources of Nr from which it is produced. [ABSTRACT FROM AUTHOR]

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

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

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

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5. [ABSTRACT FROM AUTHOR]

To feed an increasingly affluent population, reactive nitrogen (Nr) inputs to China's lands and waters have substantially increased over the past century. Today, China's Nr emissions account for over one third of global total emissions, leading to serious environmental pollution and health damages. Quantifying the spatial variability of Nr inputs is crucial for the identification of intervention points to mitigate Nr pollution, which, however, is not well known. Here, we present a database describing Nr inputs to China for the year 2017 with a 1 km × 1 km resolution, considering land use and Nr sources, compiled by using the CHANS model. Results show that the North China Plain, the Sichuan Basin and the Middle-Lower Yangtze River Plain are hotspots of Nr inputs, where per hectare Nr input is an order of magnitude higher than that in other regions. Cropland and surface water bodies receive much higher Nr inputs than other land use types. This unique database will provide basic data for research on environmental health and global change modelling. Measurement(s) reactive nitrogen species Technology Type(s) computational modeling technique Factor Type(s) land use • geographic location • reactive nitrogen source Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12971939 [ABSTRACT FROM AUTHOR]

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

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