Multivariate Disaggregation Modeling of Air Pollutants: A Case-Study of PM2.5, PM10 and Ozone Prediction in Portugal and Italy

Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated levels, particularly when the goal is to model the underlying continuous processes and perform spatial predictions at varying resolutions. To address these issues, we propose a continuous multivariate spatial model based on Gaussian processes (GPs), naturally accommodating the support of aggregated sampling units. Computationally efficient inference is achieved using R-INLA, leveraging the connection between GPs and Gaussian Markov random fields (GMRFs). A custom projection matrix maps the GMRFs defined on the triangulation of the study region and the aggregated GPs at sampling units, ensuring accurate handling of changes in spatial support. This approach integrates shared information among pollutants and incorporates covariates, enhancing interpretability and explanatory power. This approach is used to downscale PM 2.5, PM₁₀ and ozone levels in Portugal and Italy, improving spatial resolution from 0.1° (10 km) to 0.02° (2 km), and revealing dependencies among pollutants. Our framework provides a robust foundation for analyzing complex pollutant interactions, offering valuable insights for decision-makers seeking to address air pollution and its impacts.

大气污染仍是一项严峻的环境与公共卫生挑战，亟需高分辨率空间数据以更深入地解析其空间分布与影响。本研究旨在应对聚合观测尺度下大气污染物开展多变量空间分析所面临的挑战，尤其是当研究目标为建模潜在连续过程并实现多分辨率空间预测时。为此，我们提出一种基于高斯过程（Gaussian Processes, GPs）的连续型多变量空间模型，可自然适配聚合采样单元的空间支撑域。借助R-INLA工具包，利用高斯过程与高斯马尔可夫随机场（Gaussian Markov Random Fields, GMRFs）之间的关联，可实现计算高效的统计推断。我们构建了自定义投影矩阵，可将研究区域三角剖分上定义的高斯马尔可夫随机场，与采样单元处的聚合型高斯过程进行映射，从而精准处理空间支撑域的变化。该方法整合了污染物间的共享信息并纳入协变量，提升了模型的可解释性与解释效力。我们将该方法应用于葡萄牙与意大利的PM2.5、PM10及臭氧浓度的空间降尺度任务，将空间分辨率从0.1°（约10千米）提升至0.02°（约2千米），并揭示了污染物间的相互依赖关系。本框架为分析复杂的污染物交互作用提供了稳健的研究基础，可为致力于应对大气污染及其健康影响的决策者提供极具价值的参考见解。