Applying conditional analysis methods to manage fugitive air pollution from regulated industry sites.

应用条件分析方法来管理受监管工业场所的逸散空气污染。

• 批准号: NE/N012704/1
• 负责人: Arwa Sayegh
• 金额: $ 1.09万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FN012704%2F1
• 关键词: Applying; conditional; analysis; methods; manage

EA regulates industrial sites located within England and Wales which have the potential to emit significant amounts of harmful air pollutants. EA realises that measurement and data analysis techniques are now available to quantify emissions from certain types of industrial processes such as those emitted from point sources (stacks, chimneys, etc.) which help in better regulating and controlling them. However, it is also recognized that other types of emissions, referred to as fugitive emissions, are yet to be as well understood. Speranza (1) identified example fugitive emissions as those originating from a large number of very small, diverse locations around a building, a piece of equipment, a dusty road, or a storage pile. Recent DEFRA (2) report highlights the high uncertainty in the estimates of fugitive emissions of particulate matter, for instance, citing both measurement difficulties and their high dependence on topographical and meteorological conditions which are themselves highly variable in space and time. This work is intended to contribute towards reducing the uncertainties associated with the quantification, attribution, and management of fugitive releases from industrial sites.This project is divided into three main stages. The first stage is to review existing literature on industrial emissions, fugitive emissions, and existing measurement and evaluation methods previously applied to fugitive emissions. The second stage is dedicated to analysing existing data and developing an appropriate framework to applying the conditional statistical analysis method to air quality data surrounding industrial sites. In applying the conditional analysis method, it is also essential to study the level of uncertainty of the assumptions and outputs associated with each step. Therefore, the third stage of this project involves investigating how conditional analysis and uncertainty analysis can be integrated to reduce the uncertainty in the final quantification of fugitive emission source. The combination of the second and third stages could also provide a basis for routinely optimising the outputs of conditional analysis and investigating the impact of more subjective model input parameters.My MSc thesis project involved analysing and modelling environmental time series data using a classification and regression trees technique. This has allowed me to review environmental modelling techniques and understand the complex underlying processes linking emissions at the source to air quality level measured at the receptor and to understand how challenging it can be to account for the different meteorological and background variables involved in the atmospheric dispersion process. In the last 3 years, I have developed working experience of 'R statistical and computing' software and have used it to analyse and model environmental data. I am also currently using it to develop software to analyse and model time-series traffic and emissions data. Throughout my PhD as well, I have been interested in studying how uncertainty in the model inputs and model parameters has an influence on models outputs using for instance randomisation and sensitivity analysis methods. Combining EA/LEC researchers' experience of industrial processing/regulations and environmental analysis techniques with my existing knowledge in environmental data analysis, uncertainty analysis, and 'R' programming can help develop my knowledge in the field and accomplish the main stages of this project. In addition, I would greatly appreciate the opportunity to engage more fully in the 'data analysis to decision making' process, something I think this project, with its strong focus on data analysis to support EA fugitive emissions management activities, could provide.(1) Speranza, P.A. (1993). US Patent No. 5,206,818. Washington, DC: US Patent &Trademark Office.(2) DEFRA. (2014). Air Quality Pollutant Inventories, for England, Scotland, Wales & Northern Ireland.

环境署监管位于英格兰和威尔士境内的工业场所，这些场所有可能排放大量有害空气污染物。EA意识到，测量和数据分析技术现在可以量化某些类型的工业过程的排放，例如从点源（烟囱，烟囱等）排放的排放。这有助于更好地调节和控制它们。然而，人们也认识到，对其他类型的排放，即所谓的散逸性排放，还没有得到很好的了解。Speranza（1）确定了散逸性排放的例子，即那些源自建筑物、设备、尘土飞扬的道路或储存堆周围大量非常小的不同位置的排放。最近的DEFRA（2）报告强调了颗粒物散逸性排放估计的高度不确定性，例如，引用了测量困难及其对地形和气象条件的高度依赖，而地形和气象条件本身在空间和时间上变化很大。这项工作旨在减少与工业场所散逸性释放的量化、归属和管理相关的不确定性。第一阶段是审查关于工业排放、散逸性排放的现有文献，以及先前适用于散逸性排放的现有测量和评价方法。第二阶段致力于分析现有数据，并制定一个适当的框架，将条件统计分析方法应用于工业场所周围的空气质量数据。在应用条件分析方法时，还必须研究与每个步骤相关的假设和输出的不确定性水平。因此，本项目的第三阶段涉及研究如何将条件分析和不确定性分析相结合，以减少最终量化无组织排放源的不确定性。第二阶段和第三阶段的结合也可以提供一个基础，用于常规优化条件分析的输出和调查更主观的模型输入参数的影响。我的硕士论文项目涉及使用分类和回归树技术分析和建模环境时间序列数据。这使我能够审查环境建模技术，了解将源排放与受体测量的空气质量水平联系起来的复杂基本过程，并了解解释大气扩散过程中涉及的不同气象和背景变量的挑战性。在过去的3年里，我开发了“R统计和计算”软件的工作经验，并使用它来分析和建模环境数据。我目前还在使用它开发软件，以分析和建模时间序列的交通和排放数据。在我的博士学位期间，我一直对研究模型输入和模型参数的不确定性如何影响模型输出感兴趣，例如使用随机化和敏感性分析方法。将EA/LEC研究人员在工业处理/法规和环境分析技术方面的经验与我在环境数据分析，不确定性分析和“R”编程方面的现有知识相结合，可以帮助我发展该领域的知识并完成该项目的主要阶段。此外，我将非常感谢有机会更充分地参与“数据分析决策”过程，我认为这个项目，其重点是数据分析，以支持EA散逸性排放管理活动，可以提供。(1)Speranza，P.A.（1993年）。美国专利号5，206，818。华盛顿，DC：美国专利商标局。(2)德芙拉（2014年）。英格兰、苏格兰、威尔士和北方爱尔兰的空气质量污染物清单。