## 4 Conclusions  
This paper presents an experimental comparison of aerosol sampling methods by means of real-time samplers based on low-cost optical sensors, a cascade impactor, and a PMI for collection of PM\({}_{2.5}\) on a Teflon filter membrane. Measurements of PM\({}_{2.5}\) were performed in four environmental conditions, namely, indoor busy, indoor quiet, outdoors, and confined space (garage), where different registered activities were ongoing. The results that emerged from the research evidenced:  
1. The underestimation of PM\({}_{2.5}\) concentration indirectly calculated by low-cost optical sensors with respect to concentrations provided by the PMI, and the good alignment between the latter and the cascade impactor;
2. Different LODs for the optical sensor of the AirVisual Pro (250 nm) and KORA (380 nm) systems, estimated through the comparison with PM obtained by summing up size-segregated fractions of ELPI+ and supported by statistical analysis (PARAFAC model); and
3. An interesting correlation among size-segregated PM fractions measured by the cascade impactor (ELPI+) to discriminate specific emission sources indoors, in a confined environment, and outdoors. In conclusion, the performance assessment of optical low-cost sensors showed the representativeness in providing a good evolution of concentrations over time and the cause-effect relationship between aerosol emission sources and their impact on air quality. Therefore, they can successfully be used to detect PM\({}_{2.5}\) critical concentrations and eventual hot spots to drive local policies. Data analysis revealed the impossibility of discriminating aerosol emission sources and detecting particles in the ultrafine, and part of the fine, region. In order to use low-cost sensors for health impact assessments, the technology should be oriented toward the detection of particles comprising the nanoscale range. The following are available online at [[https://www.mdpi.com/article/10.3390/app11104360/s1](https://www.mdpi.com/article/10.3390/app11104360/s1)]([https://www.mdpi.com/article/10.3390/app11104360/s1](https://www.mdpi.com/article/10.3390/app11104360/s1)), Table S1: Classification of the type of events occurred during the monitoring campaign with specific brief descriptions and labels; Table S2: Results of the least square method applied to the comparison of KORA and AirVisual Pro with the sum of various ELPI + fractions: 8-11, 7-11, 6-11, 1-11. The sum of the squares of deviations for each comparison is reported from columns 3 to 5, minimum values are in bold; Table S3: Correlation matrices of PN concentrations associated to ELPI + Stages from 1 to 14. High correlation is highlighted for coefficients larger than 0.7, reported in bold; Figure S1: Calendar of sampling days during March 2018; Figure S2: Daily concentrations of PM\({}_{2.5}\) (averaged over 1 min sampling time) of KORA and AirVisual Pro collected outdoor (left) and in the garage (right). The starting time of different events is labelled with the corresponding letter as specified in Table S1; Figure S3: Daily concentrations of PM\({}_{2.5}\) (averaged over 1 min sampling time) of KORA and AirVisual Pro collected in the warehouse. It should be noted the different y-axis scale up to 20 ug/m\({}^{3}\) for \"indoor quiet\" and up to 45 ug/m\({}^{3}\) for \"indoor busy\". The starting time of different events is labelled with the corresponding letter as specified in Table S1; Figure S4: Daily PM\({}_{2.5}\) concentrations (averaged over 1 min sampling time) of ELPI+ data from Stage 2, Stage 3, Stage 8 and Stage 11; Figure S5: Daily concentrations pattern measured by ELPI + Stage 4 (grey) and the corresponding linear correlation plots with Stages from 1 to 6. Data highlighted in black refer to the event of truck entering the warehouse; Figure S6: Altman Plots representing the difference of PM\({}_{2.5}\) hourly concentrations between ELPI + and KORA(a), ELPI + and AirVisual Pro (b) and AirVisual Pro and KORA (c) measurements.