Open Access

The adoption of Directive (EU) 2024/2881 on ambient air quality and cleaner air for Europe marks a paradigm shift in regulatory expectations: tighter pollutant limits—including new obligations for ultrafine particles (UFPs), elemental and organic carbon (EC/OC), and ammonia (NH3)—expanded monitoring requirements for hotspots and supersites, and the formal recognition of indicative and supplementary measurement methods alongside fixed reference stations. Against this backdrop, drones (often also termed uncrewed/unmanned aerial vehicles (UAVs), uncrewed/unmanned aerial systems (UASs), remotely piloted aircraft systems (RPASs) or multicopters) equipped with low-cost sensor (LCS) payloads have emerged as a technically promising but methodologically contested instrument for bridging spatial gaps on small scales in conventional monitoring networks. For readability, the term “drone” is used throughout this review interchangeably with uncrewed/unmanned aerial vehicle (UAV), uncrewed/unmanned aerial system (UAS), remotely piloted aircraft system (RPAS), and multicopter; no distinction in platform type, regulatory classification, or degree of autonomy is implied by the choice of term. This review critically synthesizes the peer-reviewed literature on drone-based air quality monitoring (AQM), with emphasis on its potential to support—rather than replace—stationary monitoring within the regulatory architecture established by the European Air Quality Directive. In line with the screening protocol adopted here, the evidence synthesized is centred on particulate matter and aerosol measurements; gas-phase pollutants are addressed only where co-reported by the included studies and are discussed as a secondary, contextual finding. A structured literature search of Web of Science (23 July 2026) using a three-component Boolean keyword string identified 1657 records (2016–2026). A four-criterion sequential screening protocol—requiring, in turn, (i) a thematic focus on particulate/aerosol air quality, (ii) evidence of an airborne sensor or in-flight concentration data, (iii) thematic relevance (excluding acronym and keyword coincidences), and (iv) an unambiguous, platform-attributable particulate/aerosol measurement—narrowed this to a corpus of 312 records. Full-text retrieval and assessment excluded a further 124 records found on full reading to be off-scope (e.g., records without any onboard particulate/aerosol measurement, gas-phase-only measurements, meteorology-only platforms, non-drone studies that had passed the keyword screen, or reviews without original data), leaving 188 records for methodological quality appraisal against a six-domain rubric (calibration transparency, co-location validation, uncertainty reporting, meteorological controls, repeatability/reproducibility, regulatory relevance; each scored 0–2, maximum 12) grounded in the philosophy of data quality objectives (DQOs) of CEN/TS 17660 (parts 1 and 2). A quality-gate threshold of ≥4 points, applied here as an inclusion criterion, yielded a final corpus of 164 studies for qualitative synthesis. These were classified into three analytical groups (platform and sensor performance, Group I, n = 44, 26.8%; spatial and vertical measurement applications, Group II, n = 129, 78.7%; regulatory and methodological integration, Group III, n = 35, 21.3%); 44 studies (26.8%) received multiple-group assignment. The synthesis reveals that while drone–LCS systems offer robust gradient detection and hotspot characterization capabilities, persistent challenges in absolute calibration accuracy, rotor-induced measurement artefacts, temporal representativeness, and the absence of standardized protocols limit their current applicability as indicative measurements under the DQO framework of CEN/TS 17660 (parts 1 and 2). Four development priorities are derived—in-flight DQO-validated calibration, source-type-specific flight and sampling protocols, temporal representativeness frameworks, and integration into EU assessment workflows—and translated into five standardization domains and operational recommendations for the regulatory use of drone-based aerosol measurements.

Das kreative Problemlösen ist ein wesentlicher Bestandteil des Ingenieurwesens. Ingenieurinnen und Ingenieure sind mit ihrer Kreativität wirksame Innovationstreiber in Unternehmen. Zwar lösen sie auch wohlstrukturierte Probleme mit der Hilfe von Algorithmen. Aber einen Großteil ihrer Zeit verwenden sie auf schlechtstrukturierte Probleme, die meist nur durch die Anwendung heuristischer Kreativitätstechniken lösbar sind. Der vorliegende Beitrag versteht Ingenieurkreativität als domänen-spezifische Form der Kreativität und stellt einen Test zur Messung der Ingenieurkreativität (TZMI) vor, der auf Basis dieser Annahme entwickelt wurde. Der TZMI besteht aus drei Arten von schlechtstrukturierten Problemen, die neben rein divergenten Aufgaben auch erfinderische Widersprüche und funktionale Fixierung umfassen und die darüber hinaus technisches Wissen beim Teilnehmenden voraussetzen. Es werden erste empirische Ergebnisse des Tests von wissenschaftlichen Mitarbeitenden und Master-Studierenden vorgestellt. Die Ergebnisse zeigen, dass der Test in sich plausibel erscheint und den Studierenden die Gelegenheit gibt, ihr eigenes Kreativitätsniveau zu reflektieren und ihre kreativen Fähigkeiten zu verbessern.