## 1 Introduction  
High levels of particulate matter (PM) in ambient air are linked to morbidity as well as mortality [1, 2]. More precisely, exposure to polluted air is associated with coughing, wheezing, and shortness of breath among children [2], and a recently published study calculated that up to 166,000 premature deaths could be prevented if PM\({}_{2.5}\) levels were to decrease to the lowest measured concentrations in Europe, i.e., 3.7 \(\mathrm{\mu g/m^{3}}\)[1]. Experiments with PM analyzers mounted on cars have shown that spatial concentration differences in cities can be substantial; thus, personal exposure can vary significantly, and the spatial resolution provided by government-operated air quality stations can be considered insufficient [3]. It is no surprise that cities seek to increase spatial resolution in an affordable manner, which has led to an increasing demand for low-cost sensors for air quality monitoring in recent years [4, 5, 6]. Sensitivity to composition and particle size of the aerosol, influence of relative humidity (RH), low accuracy, poor repeatability, drifts due to aging, and high unit-to-unit variability are, however, limitations of (some) low-cost optical sensors [7, 8, 9, 10, 11]. In theory, each sensor must be regularly calibrated so that users can trust the sensor data. Furthermore, such calibrations aim to compensate for some of the interfering effects--e.g., artefacts due to RH--but can lead to spatial or temporal relocation issues (i.e., a sudden increase in measurement uncertainty) if performed in the field [12, 13, 14, 15]. The question arises to what extent governments can rely on such data to make informed policy decisions. Thus, main taining so-called \"digital trust\" with low-cost products during the operation of wireless sensor networks in smart cities remains a challenge [16; 17; 18; 19]. As aerosols vary across locations and seasons, examining low-cost PM sensors in laboratory facilities is essential for benchmarking them under different standardized environments, yielding reliable performance assessments independent of the location and time of the year. Recently, a setup known as PALMA (Production of Ambient-like Model Aerosols) was developed for the production of ambient-like aerosols (i.e., mixtures of inorganic salt, mineral dust, soot particles, and secondary organic matter) under controlled environmental conditions, and was successfully applied to the calibration of commercial PM monitors against the gravimetric reference method [20]. However, the setup in its original form was limited to the calibration of total PM mass concentration. In this work, we report on a further development of the PALMA setup. The sampling system of the gravimetric reference method was equipped with impactors as described in the EN 12341:2014 standard in order to determine PM\({}_{2,5}\) and PM\({}_{10}\) mass concentrations, which are more relevant for legislation and air quality assessment. Moreover, the method was scaled up so that a batch of low-cost PM sensors could be calibrated simultaneously, thereby also accounting for unit-to-unit variability. Compared to other setups reported in the literature (see [21] and references therein), this is the first laboratory-based facility that enables the calibration of PM sensors with multicomponent ambient-like aerosols instead of simple model aerosols, such as sodium chloride, sucrose, polystyrene spheres, or dry dust particles. Indeed, ambient PM does not refer to a single pollutant, but to a highly variable mixture of combustion particles, salts, mineral dust, organic substances, and other materials [22; 23]. PM sensors based on light scattering are sensitive to particle size, density, and refractive index distribution as well as hygroscopic growth, which can lead to considerable measurement artefacts [24; 25]. Thus, complex model aerosols with realistic size distribution and chemical composition are essential for reliable calibration.