Air pollution poses significant risks to built heritage, yet traditional methods for diagnosing degradation patterns remain largely fragmented, often relying on isolated data streams and/or subjective comparative interpretations. This study proposes a novel modular workflow that integrates Raman spectroscopy and micro-XRF spectrometry data with user-defined contextual metadata to automate the characterisation of pollution-induced degradation layers on monuments. This method utilises algorithms for peak detection, dimensionality reduction, unsupervised machine learning clustering, variance analysis across centroids, and correlation analysis, as well as steps for data re-encoding and visualisation of the results, allowing for scalable and reproducible analyses on heterogeneous multidimensional datasets. Applied to case studies from Athens, Piraeus, and Eleusis, Greece, the workflow successfully identified pollution sources and degradation patterns, while also quantifying the contribution of features, including contextual variables such as surface orientation and sampling height. The results validate the method’s capacity to combine molecular and elemental data streams, to enhance interpretive clarity, and to minimise manual effort and subjectivity. This work showcases the potential of algorithmic approaches in cultural heritage diagnostics to adapt dynamically and incorporate additional datasets and informs future applications of automated methods in the broader field of heritage science.

Analytical studies of archeological materials often face challenges, such as the merging of heterogeneous, multidimensional datasets from complementary analytical techniques, and incorporating site- and user-defined parameters. In this study, a data fusion methodology is applied that combines micro-X-ray fluorescence (micro-XRF) spectrometry and handheld Raman spectroscopy to investigate degradation layers and identify pollution sources on two monuments in an urban background: the Temple of Hephaestus and the Byzantine Church of Ag. Theodoroi, in Athens, Greece. A total of 12 samples were collected for laboratory measurements and 32 in situ measurements were conducted. Statistical and unsupervised machine learning tools, namely correlation analysis, Principal Component Analysis and k-means clustering, were applied to the merged datasets. Additionally, selected elements’ ratios were calculated to infer their sources. The black crusts were identified as heterogeneous mixtures of calcium sulfate dihydrate, calcite, and particulate pollutants, with their composition reflecting their preservation state. Vehicular emission indicators were dominant in both sites, while secondary domestic heating pollutant indicators were more prevalent at Ag. Theodoroi. Orientation had a minor role compared to pollutant sources in differentiating degradation patterns. The integrated comparison of the different outputs highlighted the interpretive potential of the approach, particularly in improving the readability of the multivariate structure and supporting the development of targeted conservation strategies for monuments in polluted urban contexts.