Towards Digital Twins for PM Emission Management on Urban Construction Sites: A Systematic Review

Reducing particulate matter (PM) emissions from construction activities is essential for improving urban air quality and safeguarding the health of workers and nearby communities. As the sector embraces digital and data-driven approaches, the Digital Twin (DT) paradigm offers a promising framework for more intelligent and responsive environmental management. However, its application to PM emission monitoring remains fragmented and underexplored. This paper presents a systematic review of scientific articles published between 2015 and 2025, conducted in alignment with PRISMA guidelines, to consolidate current knowledge and assess the maturity of DT-related developments in this context. The analysis of 23 publications is structured around three key dimensions: technology, data management, and DT capabilities. Findings reveal a predominance of isolated solutions—such as sensing systems or predictive models—rather than integrated DT frameworks. Key research gaps include sensor limitations, inconsistent exposure thresholds, and the absence of semantic approaches to support heterogeneous data integration. The review highlights the need for more holistic, interoperable DT solutions and provides guidance for researchers, developers, and policymakers advancing digital innovation in construction and environmental management.