Fine particulate matter (PM2.5) poses a significant risk to human health, with long-term exposure being linked to conditions such as asthma, chronic bronchitis, lung cancer, atherosclerosis, etc. In order to improve current pollution control strategies and to better shape public policy, the development of a more comprehensive understanding of this air pollutant is necessary. To this end, this work attempts to quantify the relationship between certain meteorological drivers and the levels of PM2.5. It is expected that the set of important meteorological drivers will vary both spatially and within the conditional distribution of PM2.5 levels. To account for these characteristics, a new local linear penalized quantile regression methodology is developed. The proposed estimator uniquely selects the set of important drivers at every spatial location and for each quantile of the conditional distribution of PM2.5 levels. The performance of the proposed methodology is illustrated through simulation, and it is then used to determine the association between several meteorological drivers and PM2.5 over the Eastern United States (US). This analysis suggests that the primary drivers throughout much of the Eastern US tend to differ based on season and geographic location, with similarities existing between "typical" and "high" PM2.5 levels.

中文翻译：

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通过局部线性惩罚分位数回归估计器对美国东部 PM2.5 气象驱动因素的影响进行空间建模

细颗粒物 (PM2.5) 对人类健康构成重大风险，长期接触与哮喘、慢性支气管炎、肺癌、动脉粥样硬化等疾病有关。 为了改进当前的污染控制策略并更好地制定公共政策，制定对这种空气污染物的更全面的了解是必要的。为此，这项工作试图量化某些气象驱动因素与 PM2.5 水平之间的关系。预计这组重要的气象驱动因素将在空间上和 PM2.5 水平的条件分布内发生变化。为了说明这些特征，开发了一种新的局部线性惩罚分位数回归方法。建议的估计器在每个空间位置和 PM2.5 水平条件分布的每个分位数上唯一地选择一组重要的驱动因素。通过模拟说明了所提出方法的性能，然后将其用于确定美国东部 (US) 的几个气象驱动因素与 PM2.5 之间的关联。该分析表明，美国东部大部分地区的主要驱动因素往往因季节和地理位置而异，“典型”和“高”PM2.5 水平之间存在相似性。5 在美国东部 (US)。该分析表明，美国东部大部分地区的主要驱动因素往往因季节和地理位置而异，“典型”和“高”PM2.5 水平之间存在相似性。5 在美国东部 (US)。该分析表明，美国东部大部分地区的主要驱动因素往往因季节和地理位置而异，“典型”和“高”PM2.5 水平之间存在相似性。