Pollution and Climate Smart Agriculture in China (PaCSAC)

中国污染与气候智能型农业 (PaCSAC)

• 批准号: ST/V002481/1
• 负责人: Lisa Emberson
• 金额: $ 29.27万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FV002481%2F1
• 关键词: Pollution; Climate; Smart; Agriculture; China

Agriculture is a substantial contributor to emissions of greenhouse gases (GHGs) such as carbon dioxide, methane and nitrous oxide but is also impacted by the climate change caused by increased atmospheric concentrations of these GHGs. This situation has led to the 'climate smart agriculture' (CSA) approach that identifies agricultural management practices and technologies to reduce emissions, whilst simultaneously enhancing productivity and improving farmers' livelihoods. However, agriculture also contributes substantially to air pollution through emissions of nitrogen oxides, ammonia and aerosols in addition to the more well-known GHGs. Together these emissions contribute to air pollutants of which secondary ozone and aerosols (PM) are arguably the most important, damaging both human health and arable crop productivity.These agricultural emissions contribute to substantial air quality problems in China. Poor air quality (due to elevated PM) is estimated to be responsible for between 0.35 to 1.2 million premature deaths per year in China. Ozone pollution has also been estimated to induce wheat yield losses of between 3 and 12% per year. However, the nature of these environmental challenges also offer opportunities for innovative solutions, specifically to extend the CSA approach to include air pollutant emissions and impacts and apply this in China. This will be explored in PaCSAC within four key research areas: The first will involve stakeholder engagement with farmer representatives and organisations to gain a better understanding of the feasibility of implementing alternative agricultural practices and technologies to reduce emissions. The second will translate this knowledge of feasible measures into quantified estimates of emissions associated with these different interventions; here we will partner with colleagues in Laos to explore the transferability of methods developed in this project to support agricultural burn assessment and subsequent air quality. Thirdly, we will combine existing models (TOMCAT and WRFChem), remote and in-situ observations to estimate the atmospheric ozone and aerosol concentrations, with a focus on Eastern China where agriculture is particularly important. Finally, impact assessments of the consequences of pollutant concentrations and associated climate variables, for a range of agricultural emission reduction scenarios, on crop productivity (namely rice and wheat) and PM effects on human health will be assessed using two existing tools - the DO3SE-crop model (for crops) and the LEAP-IBC tool (for human health and crops). These scenarios will be developed in partnership with IIASA who are supporting policy makers in China on low emission development. Importantly, these estimates of emissions, concentrations and impacts will incorporate new satellite, aerial and in situ observation monitoring technologies coupled with the expertise of our Chinese partners to improve modelling and ultimately, the identification of the sustainable solutions for agriculture.To enable transfer of these new methodologies for application in other countries, PaCSAC will further develop and apply the LEAP-IBC tool. This is a decision support tool and represents a simplified, consolidated tool that incorporates theemissions, concentration and impact estimates that are provided by the combination of the more complex models used in this project. LEAP-IBC is currently used by countries around the world to develop low emission development pathways, supported by the international 'Climate and Clean Air Coalition' organisation. This offers an exciting opportunity for the results of our project to be disseminated to a large number of countries supported by the CCAC. In the first instance we will work closely with Bangladesh to ensure that the improvement of the LEAP-IBC tool (and specifically the agricultural emissions associated with different interventions) is relevant outside of China.

农业是二氧化碳、甲烷和一氧化二氮等温室气体排放的主要来源，但也受到这些温室气体大气浓度增加造成的气候变化的影响。这种情况导致了“气候智能型农业”（CSA）方法，该方法确定了农业管理实践和技术，以减少排放，同时提高生产力并改善农民的生计。然而，农业也通过排放氮氧化物、氨和气溶胶以及更为人所知的温室气体，对空气污染起到了很大的作用。这些排放物共同造成了空气污染物，其中二次臭氧和气溶胶（PM）无疑是最重要的污染物，对人类健康和农作物生产力都造成了损害。这些农业排放物导致了中国严重的空气质量问题。空气质量差（由于PM升高）估计每年导致中国35万至120万人过早死亡。据估计，臭氧污染还导致小麦产量每年损失3%至12%。然而，这些环境挑战的性质也为创新解决方案提供了机会，特别是将CSA方法扩展到包括空气污染物排放和影响，并在中国应用。这将在PaCSAC的四个关键研究领域进行探索：第一个将涉及利益相关者与农民代表和组织的接触，以更好地了解实施替代农业实践和技术以减少排放的可行性。第二个项目将把这些可行措施的知识转化为与这些不同干预措施相关的排放量的量化估计;在这里，我们将与老挝的同事合作，探索该项目中开发的方法的可移植性，以支持农业燃烧评估和随后的空气质量。第三，我们将结合联合收割机现有的模式（TOMCAT和WRFChem），远程和现场观测，以估计大气臭氧和气溶胶浓度，重点是中国东部，农业是特别重要的。最后，将使用两种现有工具--DO 3SE-作物模型（用于作物）和LEAP-IBC工具（用于人类健康和作物），评估污染物浓度和相关气候变量对一系列农业减排情景的影响，以及对作物生产力（即水稻和小麦）和PM对人类健康的影响。这些情景将与IIASA合作制定，IIASA正在支持中国的政策制定者进行低排放发展。重要的是，这些排放、浓度和影响的估算将结合新的卫星、空中和现场观测监测技术，再加上我们中国合作伙伴的专业知识，以改进建模，最终确定农业的可持续解决方案。为了将这些新方法转移到其他国家应用，PaCSAC将进一步开发和应用LEAP-IBC工具。这是一个决策支持工具，是一个简化的综合工具，其中包括排放量、浓度和影响估计数，这些估计数是由本项目中使用的更复杂的模型组合提供的。LEAP-IBC目前被世界各国用于开发低排放发展途径，并得到国际“气候与清洁空气联盟”组织的支持。这是一个令人振奋的机会，让我们的项目成果得以推广到众多得到廉政公署支持的国家。首先，我们将与孟加拉国密切合作，确保LEAP-IBC工具（特别是与不同干预措施相关的农业排放）的改进在中国以外具有相关性。

项目成果

Lower air pollution during COVID-19 lock-down: improving models and methods estimating ozone impacts on crops.

• DOI: 10.1098/rsta.2020.0188
• 发表时间: 2020-10-30
• 期刊: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
• 作者: Dentener F;Emberson L;Galmarini S;Cappelli G;Irimescu A;Mihailescu D;Van Dingenen R;van den Berg M
• 通讯作者: van den Berg M

Integrated assessment of global climate, air pollution, and dietary, malnutrition and obesity health impacts of food production and consumption between 2014 and 2018

• DOI: 10.1088/2515-7620/ac0af9
• 发表时间: 2021
• 期刊: Environmental Research Communications
• 影响因子: 2.9
• 作者: Christopher S. Malley;W. K. Hicks;Johan C I Kulyenstierna;E. Michalopoulou;Amy Molotoks;J. Slater;C. Heaps;Silvia Ulloa;Jason Veysey;D. Shindell;D. Henze;O. Nawaz;S. Anenberg;B. Mantlana;T. Robinson