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

空气污染是人为和自然来源的各种物质的复杂混合物。这些污染源与气象和化学/生物转化等因素相结合，决定了空气污染浓度和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通过有针对性的行动控制和减少污染的有效和高效战略的关键要素。此外，空气污染是一个重大的公共卫生问题，与人类人口发病和死亡的风险越来越相关。最近的证据表明，来自不同来源的颗粒物混合可能对健康产生不同的有害贡献；这使得了解污染物来源变得更加重要，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。虽然主要污染源的知识可以在时间和/或地理局部环境下有效地获得，但对动态和物理化学过程的某些方面的建模和理解仍然是一个巨大的挑战。本项目主要侧重于：(I)开发颗粒物(PM)源分配(SA)的方法，该方法使用依赖于动态因素(例如气象学)的非参数过程来模拟基本的时空结构和污染物的分布，以确定源；(Ii)量化分配的空气污染物对脆弱人群的影响；以及(Iii)将这一方法论方法转化为现实生活中的决策，通过预测由于潜在的政策实施而导致的污染组合变化情景下的健康结果。所提出的方法将与最先进的空气污染SA工具进行对比，并使用模拟实例进行测试。对与空气颗粒源相关的不良反应的评估是通过比较两阶段程序与SA和健康影响评估的联合模型来实现的。我们将考虑两个真实的案例研究：(I)确定大伦敦地区颗粒物(PM2.5)的时间变化源，并在时间序列框架内评估其对脆弱人群(0-14岁，65岁以上)呼吸系统住院的急性影响；(Ii)将英格兰东南部颗粒物(PM2.5)的空间变化来源分离开来，并在小区域框架内评估其对同一地区的呼吸道慢性影响。通过使用严格和创新的方法，我们相信拟议的研究(I)将提供PM化学成分的不同有害影响的科学证据，(Ii)将有助于了解哪些来源是可控制的，以及(Iii)将有可能为空气污染政策的实施和监管提供信息，以改善英国人口健康。

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.