论文标题
使用道路网络的结构特性估算周围空气污染
Estimating Ambient Air Pollution Using Structural Properties of Road Networks
论文作者
论文摘要
近年来，世界越来越关注空气污染。特别是在全球北部，各国正在实施系统以大规模监测空气污染以帮助决策。这样的努力至关重要，但至少有三个缺点：（1）它们成本高昂，难以方便地实施； （2）他们专注于大多数人居住的城市地区，但是这种选择容易出现不平等； （3）估计空气污染的过程缺乏透明度。在本文中，我们证明了我们可以使用有关道路结构特性的开源信息来估算空气污染；尽管此处描述的方法并不依赖于特定数据集，但我们专注于英国（英国）的英格兰和威尔士（英国）。我们的方法使实施一种廉价的方法可以将空气污染浓度估算到准确的水平上，这可以支持决策者的决定，同时在所有地区（不仅是城市地区）提供估算，并在一个透明且可解释的过程中提供估算。影响声明。我们表明，使用单个结构性特性的线性回归模型 - 轨道的长度和未分类的道路网络在英格兰和威尔士（在英国）的0.36％之内 - 可以准确估计哪些地区是最受污染的地区。该模型提供了一种透明，低成本但有效的替代品，替代了更昂贵的模型，例如Defra当前在英国使用的模型。该模型对于想要寻求清洁空气倡议但缺乏投资全面监控网络的资本的决策者具有明显的实用用途。它的低实施成本，可访问的模型设计以及数据集的全球覆盖范围为实施系统估算低收入国家的空气污染浓度提供了基础。
In recent years, the world has become increasingly concerned with air pollution. Particularly in the global north, countries are implementing systems to monitor air pollution on a large scale to aid decision-making. Such efforts are essential but they have at least three shortcomings: (1) they are costly and are difficult to implement expediently; (2) they focus on urban areas, which is where most people live, but this choice is prone to inequalities; and (3) the process of estimating air pollution lacks transparency. In this paper, we demonstrate that we can estimate air pollution using open-source information about the structural properties of roads; we focus on England and Wales in the United Kingdom (UK) in this paper although the methods here described are not dependent on specific datasets. Our approach makes it possible to implement an inexpensive method of estimating air pollution concentrations to an accuracy level that can underpin policymakers' decisions while providing an estimate in all districts, not just urban areas, and in a process that is transparent and explainable. Impact Statement. We show that a linear regression model using a single structural property -- length of the track and unclassified road network within 0.36% of districts within England and Wales (in the UK) -- can accurately estimate which districts are the most polluted. The model presents a transparent and low-cost, yet effective, alternative to more expensive models such as the one currently used by DEFRA in the UK. The model has apparent practical uses for policymakers who want to pursue clean-air initiatives but lack the capital to invest in comprehensive monitoring networks. Its low implementation cost, accessible model design, and worldwide coverage of the dataset provide a basis for implementing systems to estimate air pollution concentrations in low-income countries.
