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.

空气污染是人类和自然来源的各种物质的复杂混合物。这些来源与气象和化学/生物转化等因素结合使用，确定空气污染的浓度以及跨时空的生理化学成分的变化。这些来源的识别是制定有效且有效的策略以通过有针对性的行动来控制和减少污染的关键要素。此外，空气污染是一个主要的公共卫生问题，与人口发病率和死亡率的风险越来越大。最近的证据指出，来自不同来源的颗粒的混合物对健康有不同的有害贡献。这使得对污染物来源的理解更加重要，以便使空气质量经理充分了解污染物混合物的潜在健康结果。虽然可以有效地获得有关污染主要来源的知识，但在时间和/或地理位置的本地化环境中，对动态和生理化学过程的某些方面的建模和理解仍然是一个重大的挑战。该项目主要侧重于：（i）对粒子问题（pm）源分配方法的开发（sa）的发展（sa），这些方法是依赖于evallience（sa）的（sa）的依赖性（sa），这些方法（sa）是依赖依赖性的（sa）。潜在的空间或时间结构以及识别来源的污染物的分布； （ii）量化分配空气污染物对脆弱人群的影响； （iii）通过对潜在的政策实施的污染组合情况变化，这种方法学方法通过对健康结果的预测进行了对现实生活决策的翻译。使用模拟示例，将对所提出的方法针对空气污染的最新工具进行测试。通过比较两阶段的程序与SA和健康效应评估的联合模型，可以评估与空气颗粒来源相关的不良反应。我们将考虑两个实际案例研究：（i）在大伦敦确定颗粒的时变源（PM2.5），并评估其对弱势群体（0-14岁以上65岁以上）在时间序列框架中对呼吸系统住院的急性影响； （ii）在小区域框架内，在英格兰东南部解开颗粒颗粒来源（PM2.5），并评估其在同一地区的呼吸道慢性效应。使用严格而创新的方法，我们认为拟议的研究（i）将为pm and the PM的影响（II）提供（II）（II）（II）（II）（II）（II）的科学证据（I）告知空气污染政策实施和法规，以改善英国人口健康。

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