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In collisional gas–solid flows, dense particle clusters are often observed that greatly affect the transport properties of the mixture. The characterisation and prediction of this phenomenon are challenging due to limited optical access, the wide range of scales involved and the interplay of different mechanisms. Here, we consider a laboratory setup in which particles fall against upward-moving air in a square vertical duct: a classic configuration in riser reactors. The use of non-cohesive, monodispersed, spherical particles and the ability to independently vary the solid volume fraction ( $$\varPhi _V = 0.1\,\% - 0.8\,\%$$ ) and the bulk airflow Reynolds number ( $$Re_{bulk} = 300 - 1200$$ ) allows us to isolate key elements of the multiphase dynamics, providing the first laboratory observation of cluster-induced turbulence. Above a threshold $$\varPhi _V$$ , the system exhibits intense fluctuations of concentration and velocity, as measured by high-speed imaging via a backlighting technique which returns optically depth-averaged fields. The space–time autocorrelations reveal dense and persistent mesoscale structures falling faster than the surrounding particles and trailing long wakes. These are shown to be the statistical footprints of visually observed clusters, mostly found in the vicinity of the walls. They are identified via a percolation analysis, tracked in time, and characterised in terms of size, shape, location and velocity. Larger clusters are denser, longer-lived and have greater descent velocity. At the present particle Stokes number, the threshold $$\varPhi _V \sim 0.5$$ % (largely independent from $$Re_{bulk}$$ ) is consistent with the view that clusters appear when the typical interval between successive collisions is shorter than the particle response time.