Bayesian Spatial Multivariate Receptor Modeling for Multi-Site Multi-Pollutant Data

For the development of effective air pollution control strategies, it is crucial to identify the sources that are the principal contributors to air pollution and estimate how much each source contributes. Multivariate receptor modeling aims to address these problems by decomposing ambient concentrations of multiple air pollutants into components associated with different source types. With the expanded monitoring efforts that have been established over the past several decades, extensive multivariate air pollution data obtained from multiple monitoring sites (multi-site multi-pollutant data) are now available. Although considerable research has been conducted on modeling multivariate space-time data in other contexts, there has been little research on spatial multivariate receptor models for multi-site, multi-pollutant data. We present a Bayesian spatial multivairate receptor modeling (BSMRM) approach that can incorporate spatial correlations in multi-site, multi-pollutant data into the estimation of source composition profiles and contributions, based on discrete process convolution models for multivariate spatial processes. The new BSMRM approach enables predictions of source contributions at unmonitored sites as well as simultaneously dealing with model uncertainty caused by the unknown number of sources and identifiability conditions. The new approach can also provide uncertainty estimates for the predicted source contributions at any location, which was not possible in previous multivariate receptor modeling approaches. The proposed approach is applied to 24-hour ambient air concentrations of 17 Volatile Organic Compounds (VOCs) measured at nine monitoring sites in Harris County, Texas between 2003 and 2005. Supplementary materials for this article, including real data and MATLAB codes for implementing BSMRM, are available online on the journal web site.

为制定高效的空气污染管控策略，精准识别大气污染的主要贡献源并量化各源的排放占比，是至关重要的核心环节。多元受体模型（Multivariate receptor modeling）正是为解决此类问题而提出的，其核心思路是将环境中多种空气污染物的浓度分解为与不同污染源类别相关的组分。历经数十年的监测网络扩容与完善，目前已获取了来自多个监测站点的海量多元空气污染数据，即多站点多污染物数据（multi-site multi-pollutant data）。尽管已有大量研究针对其他场景下的多元时空数据建模展开，但针对多站点多污染物数据的空间多元受体模型相关研究仍较为匮乏。本文提出一种贝叶斯空间多元受体模型（Bayesian Spatial Multivariate Receptor Modeling, BSMRM），该方法基于多元空间过程的离散过程卷积模型，可将多站点多污染物数据中的空间相关性纳入源成分谱与源贡献的估算流程中。该新型BSMRM方法不仅可预测未布设监测站点区域的源贡献，还能同时处理由污染源数量未知以及可识别性条件所引发的模型不确定性问题。此外，该方法还可对任意位置的预测源贡献进行不确定性估算，而这一功能在以往的多元受体建模方法中无法实现。本文将所提方法应用于2003至2005年间美国德克萨斯州哈里斯县9个监测站点采集的17种挥发性有机物（Volatile Organic Compounds, VOCs）的24小时环境浓度数据。本文的补充材料（包含真实数据集与实现BSMRM的MATLAB代码）可在期刊官网在线获取。