课题基金 / 基金详情

Process analysis, observations and modelling - Integrated solutions for cleaner air for Delhi (PROMOTE)

Process analysis, observations and modelling - Integrated solutions for cleaner air for Delhi (PROMOTE)
过程分析、观察和建模 - 德里清洁空气综合解决方案(PROMOTE)
批准号:
NE/P016391/1
负责人:
Ranjeet Sokhi
金额:
$93.09万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2016
资助国家:
英国
项目状态:
已结题
起止时间:
2016 至 --

项目摘要

项目成果

Ranjeet Sokhi的其他基金

相似基金

相关文献

中文摘要
翻译
标题:过程分析、观察和建模--德里清洁空气综合解决方案(促进)空气污染已被广泛认为是一个主要的全球健康风险。鉴于每10人死亡中就有1人可归因于空气污染(世界银行2016年),这对世界城市有重大影响。作为印度-恒河平原(IGP)的一部分,德里受到来自复杂来源的空气污染的影响。由于该地区复杂的排放和气象,颗粒物(PM10和PM2.5)、氮氧化物(NOx、NO2)、二氧化硫(SO2)、一氧化碳(CO)和黑碳(BC)都在季风期后达到峰值,并在冬季保持较高水平,使国家首都地区(NCR)成为污染最严重的地区之一。模型无法准确预测冬季雾事件期间的空气污染,无法量化从远距离进入德里的空气污染。在4年多的时间里,PROCESS的目标是通过开展面向过程的观测和模拟分析来减少德里空气质量预测和预报中的不确定性,并得出最有效的缓解解决方案,以减少城市和周围地区的空气污染。Promote汇集了一个由来自印度和英国的顶尖研究人员组成的跨学科团队,以实现该项目的目标。它的调查将解决三个关键问题:Q1气溶胶对德里空气污染负担有何贡献?Q2低大气边界层如何影响进入德里的空气污染的远程传输?Q3缓解干预措施的最有效排放控制措施将导致德里和更广泛的国家首都地区空气污染和暴露水平的显著降低?为了解决三个关键问题,我们将:1通过为亚热带城市环境开发一个新的代表性模式方案,研究次生气溶胶对德里空气污染负担的贡献;2调查边界层相互作用如何导致影响德里的季风前和稳定的冬季雾期期间的高空气污染事件;3通过改进对气溶胶、远程输送和边界层过程的理解,量化当地、城市和区域对德里空气质量的贡献;4测试德里的空气质量预报系统,包括对气溶胶污染和大气边界层过程的更好的理解;5开发用于预测PM2.5、PM10、NO2和其他污染物的高分辨率浓度的第一个多尺度模拟系统,然后为为德里制定有效的缓解战略提供分析;6与其他APHH项目合成和翻译PRESSION的结果,为暴露和健康研究提供数据集,并为实施有效的地方和区域缓解战略的路线图作出贡献,以满足德里和NCR当前和未来的遵约和健康要求。通过我们的分析,我们将提供有关地方、城市和区域(LRT)空气污染源如何影响德里空气质量的新知识。随着对气溶胶和低层大气动力学的更好了解,空气污染物浓度与当地(如交通、工业)和区域贡献变化之间的敏感性将通过一个新的多尺度模拟系统进行量化,以建议德里的干预措施和缓解方案。
英文摘要
Title: Process analysis, observations and modelling - Integrated solutions for cleaner air for Delhi (PROMOTE)Air pollution has been widely recognized as a major global health risk. Given that 1 in every 10 total deaths can be attributed to air pollution (World Bank 2016), there are major implications for the cities of the world. As part of the Indo-Gangetic Plain (IGP), Delhi is subject to air pollution from a complex mixture of sources. As a consequence of the complex emissions and meteorology of the region, particulate matter (PM as PM10 and PM2.5), nitrogen oxides (NOx, NO2), sulphur dioxide (SO2), carbon monoxide (CO) and black carbon (BC) all peak during post-monsoon periods and remain elevated during winter making the National Capital Region (NCR) one of the most polluted areas. Open questions remain regarding the inability of models to accurately predict air pollution during winter time fog events and quantifying incoming air pollution from large distances into Delhi.Over 4 years, PROMOTE aims to reduce uncertainties in air quality prediction and forecasting for Delhi by undertaking process orientated observational and modelling analyses and to derive the most effective mitigation solutions for reducing air pollution over the urban and surrounding region. PROMOTE brings together a cross-disciplinary team of leading researchers from India and the UK to deliver the project aims. Its investigations will address three key questions:Q1 What contribution is made by aerosols to the air pollution burden in Delhi?Q2 How does the lower atmospheric boundary layer affect the long range transport of air pollution incoming into Delhi?Q3 What are the most effective emission controls for mitigation interventions that will lead to significant reductions in air pollution and exposure levels over Delhi and the wider National Capital Region? To address the three key questions we will:1 Examine the contribution of secondary aerosols to the air pollution burden in Delhi during distinct meteorological seasons by developing a new representative model scheme for subtropical urban environments;2 Investigate how boundary layer interactions lead to high air pollution events during pre-monsoon and stable winter fog periods affecting Delhi; 3 Quantify local, urban and regional contributions to Delhi's air quality through an improved understanding of aerosols, long-range transport and boundary layer processes;4 Test the Delhi's air quality forecasting system incorporating improved understanding of aerosol pollution and atmospheric boundary layer processs;5 Develop the first multiscale modelling system for predicting high resolution concentrations of PM2.5, PM10, NO2 and other pollutants and then provide the analysis for developing effective mitigation strategies for Delhi;6 Synthesise and translate the outcomes of PROMOTE with other APHH projects to provide datasets for exposure and health studies and contribute to a roadmap for implementing effective local and regional mitigation strategies to meet current and future compliance and health requirements in Delhi and NCR. Through our analysis, we will deliver new knowledge on how local, urban and regional (LRT) sources of air pollution affect Delhi's air quality. With an improved understanding of aerosols and lower atmosphere dynamics, sensitivities between air pollutant concentrations and changes in local (e.g. traffic, industrial) and regional contributions will be quantified with a new multiscale modelling system for recommending interventions and mitigation options for Delhi.
期刊论文(6)
专著(0)
科研奖励(0)
会议论文
Quantifying effects of long-range transport of air pollutants over Delhi using back-trajectories and satellite NO 2 data
使用反向轨迹和卫星 NO 2 数据量化德里上空空气污染物远距离输送的影响
DOI: 10.5194/egusphere-2023-382
发表时间: 2023
期刊:
影响因子: --
作者: [Graham A]
通讯作者: Graham A
DOI: 10.5194/acp-2020-1023
发表时间: 2020
期刊:
影响因子: --
作者: [Biswal A]
通讯作者: Biswal A
High Resolution Forecasting of Air Quality and Exposure for Healthier Cities (HiRAE)
  • 批准号:
    NE/M021971/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $10.67万
  • 财政年份:
    2015
  • 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:
    $11.42万
  • 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:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Intelligent Patent Analysis for Optimized Technology Stack Selection:Blockchain BusinessRegistry Case Demonstration
  • 批准号:
    --
  • 项目类别:
    外国学者研究基金项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    USHARANI HAREESH GOVINDARA JAN
  • 依托单位:
利用全基因组关联分析和QTL-seq发掘花生白绢病抗性分子标记
基于SERS纳米标签和光子晶体的单细胞Western Blot定量分析技术研究
  • 批准号:
    31900571
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2019
  • 负责人:
    刘兵
  • 依托单位: