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High Resolution Forecasting of Air Quality and Exposure for Healthier Cities (HiRAE)

High Resolution Forecasting of Air Quality and Exposure for Healthier Cities (HiRAE)
高分辨率空气质量和暴露预测,打造更健康的城市 (HiRAE)
批准号:
NE/M021971/1
负责人:
Ranjeet Sokhi
金额:
$10.67万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2015
资助国家:
英国
项目状态:
已结题
起止时间:
2015 至 --

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中文摘要
翻译
赫特福德大学和国家大气科学中心目前正在运行一个空气质量预报系统。就像天气预报一样,该系统使用计算机模型来预测空气污染物的浓度,如臭氧,颗粒物和氮氧化物。该预测运行整个英国,并预测未来三天,它产生的每种污染物的地图在10公里的空间分辨率。除了空气质量预测能力,赫特福德郡大学还开发了模型和专业知识,在城市地区的空气质量建模。空气质量可能会受到空气质量预测所预测的区域和国家规模效应的高度影响,但也会受到非常局部的影响,例如特定道路上的交通排放,当地天气甚至当地建筑物和景观。因此,我们有一个单独的系统,用于城市地区,其分辨率非常高。例如,城市系统使用描述单个道路污染的数据。这个城市模型也有数据来描述人们生活和工作的地方,所以我们可以计算一个城市不同地区和一天中不同时间的污染物浓度,然后使用这些信息来估计他们生活和工作的当地人口所面临的污染“暴露”。由于“暴露”结合了污染物水平和人们在污染地区度过的时间,它使我们能够了解空气污染对人群可能产生的健康影响。在这个项目中,我们将预测大曼彻斯特和布里斯托两个城市地区的空气质量和暴露。该项目的独特创新是将空气质量和暴露预测降低到街道和城市规模,同时首次向地方当局提供所有数据。为此,我们将继续运行英国空气质量预报,并将其预测输入城市规模模型。这将使我们能够创建污染地图,三天的预测和暴露估计在当地规模的布里斯托和大曼彻斯特。这些数据将使地方当局能够研究空气质量趋势和统计数据,并为骑自行车者和行人找到低污染路线。他们将能够利用这些数据做出更好的规划决策,改善教育计划,优化减少污染的措施,以产生最大的影响。提供暴露数据以及污染浓度对于最大限度地提高改善健康的策略的有效性尤为重要。我们将通过创建在线数据仪表板向地方当局提供所有模型数据。这将允许地方当局通过易于使用的图形用户界面访问所有模型数据。通过创建数据仪表板,我们将消除目前阻碍更广泛利用空气质量模型数据的障碍,并释放建模空气质量数据对地方当局和公众的潜在好处。该项目将直接满足地方当局的需求,以履行其监测和管理空气质量的法定责任。满足欧盟空气质量限值的责任移交给他们。我们的地方当局合作伙伴,布里斯托和大曼彻斯特,分别将每年约200人和1300人的过早死亡归因于空气质量。该项目将帮助布里斯托和大曼彻斯特当局,通过新的独特数据集支持和告知他们改善健康和减少空气污染的战略。这些数据将使地方当局能够最佳地实施新的和现有的举措,如促进骑自行车,步行和公共交通,管理货车,交通管理,低排放区,规划指导和教育,并告知公众空气质量健康风险。
英文摘要
The University of Hertfordshire and the National Centre for Atmospheric Science currently operate an Air Quality forecasting system. Like a weather forecast, the system uses a computer model to make predictions of the concentrations of air pollutants such as Ozone, Particulate Matter and Nitrogen Oxides. The forecast runs for the whole of the UK and predicts three days into the future and it produces maps of each pollutant at a spatial resolution of 10km.Alongside the Air Quality Forecasting capability the University of Hertfordshire has also developed models and expertise in modelling air quality in urban areas. Air Quality can be highly influenced by the regional and national scale effects predicted by the Air Quality forecast but it is also influenced by very local effects such as emissions from traffic on a particular road, the local weather and even the local buildings and landscape. For this reason we have a separate system for urban areas which operates at a very high resolution. The urban system uses data that describes the pollution from individual roads for example. This urban model also has data to describe where people live and work so we can calculate pollutant concentrations in different parts of a city and at different times of day, then use that information to estimate the 'exposure' to pollution faced by the local population where they live and work. Because 'exposure' combines both pollutant levels and the time people spend in polluted areas, it is allows us to understand the likely health impact air pollution is having on the population. In this project we will forecast air quality and exposure for two urban areas, Greater Manchester and Bristol. The unique innovations in the project are to bring air quality and exposure forecasts down to the street and city scales, whilst making all the data available to the local authorities for the first time. To do this we will continue to operate the UK Air Quality Forecast and feed its predictions into the urban scale model. This will allow us to create pollution maps, a three day forecast and exposure estimates at a local scale for Bristol and Greater Manchester. The data will allow the local authorities to study air quality trends and statistics and find low pollution routes for cyclists and pedestrians. They will be able to use the data to make better planning decisions, improve education schemes and optimise pollution reduction measures to have the greatest impact. The delivery of exposure data alongside pollution concentrations is especially important for maximising the effectiveness of strategies to improve health.We will make all of the model data available to the local authorities by creating an online data dashboard. This will allow the local authorities access to all the model data via an easy to use graphical user interface. By creating the data dashboard we will remove a barrier currently preventing wider exploitation of air quality model data and unlock the potential benefits of modeled air quality data to local authorities and the general public.This project will directly address the needs of Local Authorities to meet their statutory responsibility to monitor and manage Air Quality. The responsibility for meeting EU air quality limit values is devolved to them. Our Local Authority partners, Bristol and Greater Manchester, attribute the premature death of approximately 200 and 1300 people annually to Air Quality respectively. This project will help the Bristol and Greater Manchester authorities by underpinning and informing their strategies for improving health and reducing air pollution with new, unique datasets. This data will allow the Local Authorities to optimally implement new and exiting initiatives such as promoting cycling, walking and public transport, managing goods vehicle, traffic management, low emission zones, planning guidance and education and informing the public of Air Quality health risks.
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Process analysis, observations and modelling - Integrated solutions for cleaner air for Delhi (PROMOTE)
  • 批准号:
    NE/P016391/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $93.09万
  • 财政年份:
    2016
  • 负责人:
    Ranjeet Sokhi
  • 依托单位:
ClearfLo: Clean Air for London
  • 批准号:
    NE/H003185/1
  • 项目类别:
    Research Grant
  • 资助金额:
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  • 财政年份:
    2010
  • 负责人:
    Ranjeet Sokhi
  • 依托单位:
Mesoscale Modelling for Air Pollution Applications (Meso-NET)
  • 批准号:
    NE/E002617/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $1.38万
  • 财政年份:
    2007
  • 负责人:
    Ranjeet Sokhi
  • 依托单位:
Mesoscale Modelling for Air Pollution Applications (Meso-NET)
  • 批准号:
    NE/E002692/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $17.99万
  • 财政年份:
    2007
  • 负责人:
    Ranjeet Sokhi
  • 依托单位:
海外基金