Gabriel Oduori

Data fusion (DF) addresses the challenge of integrating heterogeneous data sources to improve decision-making and inference. Although DF has been widely explored, no prior systematic review has specifically focused on its application to low-cost sensor (LCS) data in environmental monitoring. To address this gap, we conduct a systematic literature review (SLR) following the PRISMA framework, synthesising findings from 82 peer-reviewed articles. The review addresses three key questions: (1) What fusion methodologies are employed in conjunction with LCS data? (2) In what environmental contexts are these methods applied? (3) What are the methodological challenges and research gaps? Our analysis reveals that geostatistical and machine learning approaches dominate current practice, with air quality monitoring emerging as the primary application domain. Additionally, artificial intelligence (AI)-based methods are increasingly used to integrate spatial, temporal, and multimodal data. However, limitations persist in uncertainty quantification, validation standards, and the generalisability of fusion frameworks. This review provides a comprehensive synthesis of current techniques and outlines key directions for future research, including the development of robust, uncertainty-aware fusion methods and broader application to less-studied environmental variables.

In a complete theory there is an element corresponding to each element of reality. A sufficient condition for the reality of a physical quantity is the possibility of predicting it with certainty, without disturbing the system. In quantum mechanics in the case of two physical quantities described by non-commuting operators, the knowledge of one precludes the knowledge of the other. Then either (1) the description of reality given by the wave function in quantum mechanics is not complete or (2) these two quantities cannot have simultaneous reality. Consideration of the problem of making predictions concerning a system on the basis of measurements made on another system that had previously interacted with it leads to the result that if (1) is false then (2) is also false. One is thus led to conclude that the description of reality as given by a wave function is not complete.