A statistical framework for the apportionment of particulate contaminants and their health effect determination

颗粒污染物分配及其健康影响确定的统计框架

• 批准号: MR/T044713/1
• 负责人: Marta Blangiardo
• 金额: $ 65.28万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT044713%2F1
• 关键词: statistical; framework; apportionment; particulate; contaminants

Air pollution is a complex mixture of diverse substances from anthropogenic and natural sources. These sources in combination with factors such as meteorology and chemical/biological transformations, determine the air pollution concentration and the variation in the physiochemical components across space and time. The identification of these sources is a key element for developing effective and efficient strategies to control and reduce pollution through targeted actions. In addition, air pollution is a major public health concern, being increasingly associated with risk of morbidity and mortality of human populations. Recent evidence points out that mixture of particles from different sources can have a different detrimental contribution on health; this makes the understanding of pollutant sources even more important in order for air quality managers to fully understand the potential health outcomes of pollutant mixtures. While knowledge of the main sources of pollution can be effectively obtained on temporal and/or geographical localised setting, the modelling and the understanding of some aspects of the dynamic and physiochemical processes remain a substantial challenge.This project focuses primarily upon: (i) the development of a methodological approach for particle matter (PM) source apportionment (SA) which uses nonparametric processes with dependence on dynamic factors (e.g. meteorology) to model the underlying spatial or temporal structure and the distribution of contaminants to identify sources; (ii) the quantification of the impact of apportioned air contaminants upon vulnerable populations; and (iii) the translation of this methodological approach to real-life decision making through the predictions of the health outcomes under changing scenarios of pollution mix as a result of potential policy implementations. The proposed approach will be tested against the state-of-the-art tools for SA of air pollution, using simulated examples. Evaluation of the adverse responses associated with air particulate sources is reached by comparing two-stage procedures vs joint models for SA and health-effect assessment. We will consider two real case studies: (i) to identify time-varying sources of particles (PM2.5) in Greater London and evaluate their acute effects on respiratory hospital admissions in vulnerable populations (0-14 years, 65+) in a time-series framework; (ii) to disentangle spatially-varying sources of particles (PM2.5) in South East England and evaluate their respiratory chronic effects in the same region in a small-area framework.By using rigorous and innovative methodologies, we believe that the proposed research (i) will provide scientific evidence of the differential harmful effect of PM chemical components, (ii) will help understand the sources that can be controlled, and (iii) will have the potential to inform air pollution policy implementation and regulation to improve UK population health.

空气污染是人为和自然来源的各种物质的复杂混合物。这些来源与气象学和化学/生物转化等因素相结合，决定了空气污染的浓度以及物理化学成分在空间和时间上的变化。查明这些污染源是制定有效和高效的战略，通过有针对性的行动控制和减少污染的一个关键因素。此外，空气污染是一个重大的公共卫生问题，与人口发病率和死亡率的风险日益相关。最近的证据表明，来自不同来源的颗粒物的混合物可能对健康产生不同的有害影响;这使得对污染物来源的了解变得更加重要，以便空气质量管理人员充分了解污染物混合物的潜在健康后果。虽然可以根据时间和/或地理局部环境有效地了解主要污染源，但模拟和了解动态和物理化学过程的某些方面仍然是一项重大挑战。（i）发展一套以非参数方法分析颗粒物来源的方法，而该方法须依赖动力因素（ii）量化分配的空气污染物对脆弱人群的影响;（iii）通过预测由于可能的政策实施而导致的污染组合不断变化的情景下的健康结果，将这种方法学方法转化为现实生活中的决策。所提出的方法将进行测试，对国家的最先进的工具SA的空气污染，使用模拟的例子。通过比较SA和健康效应评估的两阶段程序与联合模型，达到与空气颗粒物源相关的不良反应的评估。我们将考虑两个真实的案例研究：（i）识别随时间变化的粒子源（PM2.5）在大伦敦，并评估其急性影响呼吸系统医院入院的弱势群体（0-14岁，65岁以上）;（ii）解开空间变化的粒子源（PM2.5），并在小区域框架内评估其对同一地区呼吸系统的慢性影响。通过使用严格和创新的方法，我们相信，拟议的研究（i）将为PM化学成分的不同有害影响提供科学证据，（ii）将有助于了解可以控制的来源，以及（iii）将有可能为空气污染政策的实施和监管提供信息，以改善英国人口的健康。

项目成果

A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.

• DOI: 10.1002/env.2763
• 发表时间: 2023-02
• 期刊: ENVIRONMETRICS
• 影响因子: 1.7
• 作者: Baerenbold, Oliver;Meis, Melanie;Martinez-Hernandez, Israel;Euan, Carolina;Burr, Wesley S.;Tremper, Anja;Fuller, Gary;Pirani, Monica;Blangiardo, Marta
• 通讯作者: Blangiardo, Marta