Air-pollution Innovation in Regional-forecasts utilising operational Satellite Applications and Technologies (AIRSAT)

利用卫星应用和技术（AIRSAT）进行区域预测的空气污染创新

• 批准号: NE/Y005147/1
• 负责人: Richard Pope
• 金额: $ 31.01万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FY005147%2F1
• 关键词: Air; pollution; Innovation; Regional; forecasts

Deteriorating air quality (AQ) is an ongoing challenge for all major global economies and is now recognised as the largest environmental stress on human health. Key air pollutants include gases like ozone (O3), nitrogen dioxide (NO2) and aerosols (particles of pollution suspended in the air), which can cause health ailments such as respiratory and cardiovascular illness. Globally, it has been estimated that air pollution is the cause of ~9 million premature deaths/year, while in the UK, it results in ~40,000 premature deaths/year. As a result, the UK Met Office (UKMO) uses its AQ forecast model, to provide the forewarning of hazardous AQ episodes for the general public (e.g. individuals with respiratory illnesses) and government departments/bodies (e.g. the NHS to prepare for increases hospital emissions). The UKMO uses observations from surface sites (known as the Automated Urban and Rural network, AURN) to evaluate the performance of their AQ forecasts and developed a statistical scheme to correct them (e.g. if the model surface ozone concentrations are too large when compared to the AURN observations, the forecast values will be lowered in that region).While this is a powerful method to improve the skill of the UKMO AQ forecasts, the surface network only consists of ~100 sites. Therefore, there are large data gaps across the UK where the forecasts cannot be verified. However, in the last decade, there has been rapid development and advancements in observing air pollution from space. We now have satellite instruments which can detect pollution hotspots (e.g. cities) at a spatial resolution of several kilometres. The satellite platforms can provide daily coverage across the globe (and thus the UK) and provide the exciting opportunity to exploit this data for model forecast evaluation and improvement. As such, these satellite AQ products can be integrated into the same statistical scheme as the surface AURN observations to provide daily forecast corrections and updates. This proposal aims to do this in three steps:1. Develop the necessary processing and data analysis tools (e.g. programing codes) to compare past model forecasts with readily available satellite data products. This will help identify which satellite AQ products are most useful for model forecast verification and correction.2. Use well established statistical methods to extract useful surface information from the satellite data. Most satellite products provide information on an air pollutant throughout the atmosphere, so known statistical relationships between atmospheric and surface pollution can be used to extract important surface information. 3. Integrate the satellite surface information into the UKMO's statistical bias correction scheme for historical case studies (e.g. forecasts of previous AQ episodes), which can then be independently assessed against the AURN observations. Once fully functional off-line, the UKMO can then assimilate the satellite component into their operational statistical bias correct scheme to improve the public AQ forecasts.Ultimately, this project aims to integrate satellite data sets into the UKMO operational AQ forecasts to improve the quality of this important public service. As very few national meteorological agencies (e.g. including the UKMO) include Earth observation (EO) products into their routine evaluation of AQ forecast models, this represents an innovative step to utilise satellite AQ products beyond their most common use with in academia (e.g. used in scientific studies).

对于所有主要的全球经济体来说，空气质量恶化（AQ）是一个持续的挑战，现在被认为是对人类健康的最大环境压力。主要的空气污染物包括臭氧（O3），氮二氧化氮（NO2）和气溶胶（空气中悬浮污染的颗粒）等气体，这会导致呼吸道和心血管疾病等健康疾病。在全球范围内，据估计，空气污染是每年约900万人过早死亡的原因，而在英国，每年造成约40,000个早期死亡。结果，英国大都会办公室（UKMO）使用其AQ预测模型，为公众（例如患有呼吸系统疾病的人）和政府部门/机构（例如，NHS准备增加医院发射）为公众（例如患有呼吸系统疾病的人）提供危险的AQ发作。 UKMO使用地面站点（称为自动化城市和农村网络，Aurn）的观察来评估其AQ预测的性能并制定了统计方案来纠正它们（例如，与Aurn Ountress相比，模型的Ozone浓度太大，与该区域相比，预测仅在该区域中降低了，这是一个强大的方法。由约100个站点组成。因此，整个英国都有较大的数据差距，无法验证预测。但是，在过去的十年中，在观察空气污染的迅速发展和进步。现在，我们有卫星仪器可以以几公里的空间分辨率检测污染热点（例如城市）。卫星平台可以在全球（以及英国）提供每日覆盖范围，并提供令人兴奋的机会，以利用此数据进行模型预测评估和改进。因此，这些卫星AQ产品可以集成到与表面Aurn观测值相同的统计方案中，以提供每日的预测校正和更新。该提案旨在通过三个步骤完成此操作：1。开发必要的处理和数据分析工具（例如编程代码），以将过去的模型预测与随时可用的卫星数据产品进行比较。这将有助于确定哪些卫星AQ产品对于模型预测验证和校正最有用2。使用良好的统计方法从卫星数据中提取有用的表面信息。大多数卫星产品在整个大气中提供有关空气污染物的信息，因此可以使用大气和表面污染之间的已知统计关系来提取重要的表面信息。 3。将卫星表面信息整合到UKMO的历史案例研究（例如对先前AQ发作的预测）中的统计偏置校正方案中，然后可以独立评估Aurn观察结果。一旦功能齐全的离线功能，UKMO就可以将卫星组件吸收到其运营统计偏差的正确方案中，以改善公共AQ预测。最初，该项目旨在将卫星数据集集成到UKMO操作AQ预测中，以提高这项重要公共服务的质量。由于很少有国家气象机构（例如，包括UKMO）将地球观测（EO）产品包括在其对AQ预测模型的常规评估中，因此这代表了利用卫星AQ产品的创新步骤，而不是学术界最常见的使用（例如，在科学研究中使用）。