The limited evidence available suggests that the interaction between chemical pollutants and biological particles may intensify respiratory diseases caused by air pollution in urban areas. Unlike air pollutants, which are routinely measured, records of biotic component are scarce. While pollen concentrations are daily surveyed in most cities, data related to airborne bacteria or fungi are not usually available. This work presents the first effort to understand atmospheric pollution integrating both biotic and abiotic agents, trying to identify relationships among the Proteobacteria, Actinobacteria and Ascomycota phyla with palynological, meteorological and air quality variables using all biological historical records available in the Madrid Greater Region. The tools employed involve statistical hypothesis contrast tests such as Kruskal-Wallis and machine learning algorithms. A cluster analysis was performed to analyse which abiotic variables were able to separate the biotic variables into groups. Significant relationships were found for temperature and relative humidity. In addition, the relative abundance of the biological phyla studied was affected by PM₁₀ and O₃ ambient concentration. Preliminary Generalised Additive Models (GAMs) to predict the biotic relative abundances based on these atmospheric variables were developed. The results (r = 0.70) were acceptable taking into account the scarcity of the available data. These models can be used as an indication of the biotic composition when no measurements are available. They are also a good starting point to continue working in the development of more accurate models and to investigate causal relationships.

现有的有限证据表明，化学污染物和生物颗粒之间的相互作用可能加剧城市空气污染引起的呼吸道疾病。与常规测量的空气污染物不同，缺乏生物成分的记录。尽管大多数城市每天都会对花粉浓度进行调查，但通常无法获得与空气传播的细菌或真菌有关的数据。这项工作是理解生物污染和非生物污染因子结合在一起的大气污染的第一项工作，试图利用马德里大区所有可用的生物学历史记录来鉴定变形杆菌，放线菌和子囊菌与古生物学，气象学和空气质量变量之间的关系。所使用的工具涉及统计假设对比测试，例如Kruskal-Wallis和机器学习算法。进行聚类分析以分析哪些非生物变量能够将生物变量分为几类。发现温度和相对湿度之间存在显着关系。此外，所研究的生物门的相对丰度受到PM的影响₁₀和O ₃环境浓度。建立了基于这些大气变量来预测生物相对丰度的初步通用加性模型（GAM）。考虑到可用数据的稀缺性，结果（r = 0.70）是可以接受的。当没有可用的测量值时，这些模型可以用作生物成分的指示。它们也是继续开发更准确的模型并调查因果关系的一个很好的起点。