Abstract：
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans\' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
摘要：
土地利用回归（LUR）模型广泛用于流行病学和环境研究，以估计人类在城市地区的空气污染暴露。然而,早期的模型，使用线性回归和来自固定监测站和被动采样的数据开发，主要设计用于对传统和标准空气污染物进行建模，并且在捕获高分辨率的空气污染时空变化方面存在局限性。在过去的十年里，低成本监视器的多源观测有了显著的发展，移动监控,和卫星，结合先进的统计方法和时空动态预测因子的整合，这促进了LUR方法的显著扩展和进步。本文从空气质量数据采集变化的角度回顾和综合了LUR方法的最新进展，新颖的预测变量，模型开发方法的进展，验证方法的改进，模型可转移性，以及2011年至2023年发表的155项LUR研究报告的建模软件。我们证明，这些发展使LUR模型能够为更大的研究领域开发，并涵盖更广泛的标准和不受管制的空气污染物。传统空间结构中的LUR模型得到了更复杂的时空结构的补充。与线性模型相比，当处理具有复杂关系和相互作用的数据时，先进的统计方法会产生更好的预测。最后,这项研究探索了新的发展，确定了LUR方法进一步突破的潜在途径，并提出了未来的研究方向。在这种情况下，LUR方法有可能对未来为城市人口长期和短期暴露于空气污染的模式建模做出重大贡献。
