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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]

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]

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

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]

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]