Lightning strikes, a prominent meteorological event, pose a significant risk of triggering technological disruptions within the process industry. To better understand this phenomenon, an analysis focused on past lightning-triggered events was carried out, examining open-source industrial-accident databases to compile a new NaTech-driven dataset of 689 records. First, an overall quantitative analysis revealed that over 80 % of these events involved incidents or loss of containment. Notably, 83.3 % of them occurred during the spring and summer, indicating a seasonal pattern. Based on the frequency of functional attributes, the chemical and petrochemical macro-sector was the most vulnerable, followed by storage and warehousing. About 40 % of all classifiable events happened on storage equipment, while 21 % happened on electric and electronic devices. Given the lack of valuable information for the principal source of data (NRC), the technological scenarios triggered were characterized using a refined subset of 127 observations, obtained considering the "other sources" of data. Fire scenarios predominated at 56 %; coincidentally, roughly 70 % of all scenarios involved hazardous substances classified as physical hazards. Estimated losses for the available information underscored the adverse consequences of lightning-triggered NaTech events, highlighting their major impact on both safety and the environment. An analysis of the event tree showed the logical path from the lightning strike to the final ignition scenarios (considering a subset of 107 records). This path accounted for 36 % of the classifiable records that directly affected the structure, while more than 50 % of them did not. Bayesian network structures made it possible to get conditional probabilities from the event tree and improved the model by adding attributes for vulnerable equipment and macro-sectors. In order to deal with the uncertain data, algorithms were used to generalize the models that were obtained from smaller subsets of data with more accurate information to the whole dataset. It provides an important additional view of unclassifiable data that otherwise remained in the dark. This novel insight contributes to increase the vulnerability awareness of industrial assets against lightning strikes.

Material degradation is a critical factor in assessing the vulnerability of industrial infrastructure, particularly in the presence of extreme natural events. This study shows that the relation between material degradation and NaTech risk is both bidirectional and systemic, with important implications for industrial safety. Through the analysis of an emblematic case study, it was demonstrated that latent defects, originating during the construction phase, can remain silent for decades and manifest critically under the action of extreme natural events. The objective is to provide a useful methodological tool for the early diagnosis of systemic risk conditions and for planning preventive and resilient strategies. The proposed approach overcomes the traditional separation between degradation analysis and environmental risk assessment, promoting a holistic and adaptive view of vulnerability. Specifically, integrating the concept of structural obsolescence into NaTech risk models allows for a more realistic representation of systemic exposure and supports the planning of more effective prevention strategies. The case study analysis highlights the interaction between latent structural defects and environmental stresses, offering insights for interpreting vulnerability in complex and multifactorial scenarios. The outcome provides perspectives for the integration of quantitative indicators into NaTech risk models.