## 4 Discussion  
In this study, we compared PM\({}_{2.5}\) concentrations measured with IRMs and RGMs to those measured with U.S. EPA FEMs. A relationship between the real-time PM\({}_{2.5}\) concentration and FEM were acceptable (R = 0.97 to 0.98: Indoor pan frying; 0.86 to 0.96: Indoor-SHS; 0.84 to 0.91: Outdoor-urban traffic, respectively). This study conducted indoor testing with common PM\({}_{2.5}\) sources of Korea, that is, frying pork in a pan or smoking. The indoor test was conducted in an indoor laboratory in which the room temperature was maintained at 20-22 \({}^{\circ}\)C and the relative humidity at 37% because we assumed that most low-cost PM sensing devices would be established inside susceptible populations' homes where the indoor temperature and humidity level would be relatively stable, compared to the outdoor environment. In addition, in this study, we extended our comparison test by performing them outside with a federal equivalent method. We discovered that, on days with a high level of outdoor RH (80% or higher), our IRMs overestimated the PM\({}_{2.5}\) level. Thus, finally, we got the correction model providing a correction factor for ESCOTAIR to adjust for the effect of temperature and relative humidity as such a process has been conducted in a regulatory monitoring site (South Coast Air Quality Monitoring District, SCAQMD) for the field use of these kinds of inexpensive sensing devices including PA [22; 23]. Several previous studies provided a correction factor for SIDEPAK monitors: 0.77 in Northern California, U.S.A. (ambient air), 0.43 or 0.52 in Italy (ambient air in urban or rural areas) and 0.42 in Italy (indoor-outdoor mixed environment) [24; 25; 26], which were comparable to our results (0.36). Our correction factor (0.36) for PDR-1500 was smaller than results reported by [PERSON] et al. (2016) [27] (0.71 compared to PDR-1500 using its own filtering method) but very similar to the values obtained by [PERSON] et al. (2000) (0.33) and [PERSON] et al. (2011) (0.38), who conducted their studies on atmospheric environments [28; 29]. A lack of quantitative information on the speciation of particles, traffic volume, type of vehicle or the particular sampling time or season limits further exploration of the basis for the differences in the correction factors between these studies and ours. Nevertheless, our correction model for ESCOTAIR, with consideration of the RH level, was derived in a similar way to that with which we obtained the factor for SIDEPAK or PDR-1500. Thus, we consider no significant systematic errors to have been involved in the calculation process. A good linear relationship has been obtained between the PM\({}_{2.5}\) mass concentration of FEM and the responses of low-cost PM sensors as reported previously in other country [8; 9; 25; 30]. We demonstrated the urban hotspot specific correction factor for a light-scattering sensor in Korean urban environment of interest to enable our findings and methodology to be extended and replicated by researchers who are interested in the utility of low-cost sensing device, such as ESCOTAIR, in South Korea. OPCs are reportedly good at estimating mass if they have numerous bins, such as GRIMM [31]. However, estimating the mass concentration from a limited number of bins may be subject to a measurement error during the conversion process with the factory-provided internal conversion algorithm. Therefore, we used an additional correction factor for ESCOTAIR after comparison to GRIMM for proper usage under Korean circumstances. It is well established that the response of monitors based on light scattering varies with aerosol size distribution, composition and optical properties and need a proper calibration process [13; 32; 33]. No single calibration model (or correction model) can enable accurate performance for all particle sources in microenvironments. This challenge applies to both research and consumer monitors.