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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]

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]

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]

Abstract 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 NO 2 , PM 2.5 and O 3. 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 NO 2 , with an estimated 0.3 μg m−3 (equivalent to 2%) increase in population-weighted annual exposure to NO 2 across the whole UK population. Population-weighted exposure to PM 2.5 and O 3 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 NO 2 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. Highlights • High-res air pollutant concentrations combined with residence/workplace populations • Exposures to NO 2 , PM 2.5 & O 3 including workplace hours compared with residence only • Population-weighted mean NO 2 exposure 0.3 μg m−3 (2%) higher; less for PM 2.5 and O 3 • Individual exposures vary substantially accounting for home and workplace location. • Greatest spatio-temporal variation when considering exposure to NO 2 [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]