**1. Introduction**

It has been continuously necessary to control indoor air quality more precisely and with more detailed information. However, indoor air is quite di fferent from atmospheric air in the aspects of air

flow characteristics and type of contaminants [1]. Depending on the type of building and purpose of usage, the contaminant type and its level vary due to di fferent human activities and emission sources. For a residential house, cooking is reported as a primary factor for the emission of gaseous pollutants such as formaldehyde, CO and Total Volatile Organic Compounds (TVOC). Particulate pollutant PM2.5 is also one of the contaminants highly detected in indoor air during cooking [2]. In addition, human activities such as ironing, vacuum cleaning, lighting candles and smoking are also known to increase the level of pollutants, and even walking can increase the PM level by resuspension [3]. Relatively large particulate matters such as soil dust, flower pollen and PM10 are well-known to be transported into the indoors by air flow from the outside and their generation and behavior have been reported quite di fferently [4].

Even though various technologies to detect both gaseous and particulate contaminants have been developed and widely applied to practical fields, any sensing data to inform us with both the contaminant source and its concentration simultaneously does not exist, and even its accuracy remains low [5]. Most commercial sensors to detect particulate matters are generally used as dust sensors and are mostly based on the light scattering principle. As many particles exist in a specific volume of the sensor when used inside, more light is scattered and reflected to the detector and represented as particle levels. For this reason, it is necessary to introduce su fficient air containing contaminants that can represent a statistically mean concentration per volume into the sensor inside by fan or air compressor for reliable accuracy. The other factor to govern the dust level is the interaction between the light source and particulate matters. In previous research, light sources such as laser diode, infrared and LED photodiode were used to examine how light source can influence the sensing of particulate matters [6–8].

Depending on the light source, single point detection, uniformity issue and brightness di fference were reported to limit the sensitivity of dust sensors [9]. For more accurate concentration, particle counters utilizing a beta ray absorption method were tested and authorized to report daily data of particulate matters that have an aerodynamic diameter of less than 10 and 2.5 μm in Korea [10]. According to the purpose of measurement, both optical sensing and beta attenuation monitoring (BAM) were adopted to research the area or air pollution forecast, but simple light-scattering-based sensors were mostly utilized in daily life measurement for a single household's air quality monitoring, including a dust sensor, air conditioner and air purifier. As recognized in the above explanations, the concentration of particulate matter is primary information for sensors in monitoring particulate matter contaminants and is provided relatively su fficiently with various methods. However, other information such as the type of particle, and the chemical composition to inform us of its origin and where it is generated and transported from, is still under laboratory level observation [11].

Nowadays, characterization to determine the origin of contaminants, especially for particulate matters, is a major concern in Korea. This is because daily concentration of particulate matters PM2.5 and PM10 have caused a noticeable increase in the reported number of patients with respiratory disease, and personal protective equipment (PPE) including air pollution masks, filters and air purifiers are selling significantly above production amounts [12]. In several reports, particulate matters are characterized and chemical compositions have reported that PM10 and PM2.5 contain organic compound and heavy metal ions, which may cause health issues [13]. Furthermore, it is necessary to analyze particulate matters at the laboratory level to know the source of particulate matters and their chemical properties that can potentially be harmful to respiratory health. However, chemical analysis is expensive and it takes a long time to reach to the desired results. As a result, there is at least demand to identify the types of contaminants using a simple dust sensor at an economic cost as a prescreening level test.

In this study, two approaches were tested. A small-scale spectral sensor was utilized to find the feasibility of light wavelength in terms of position and intensity to discriminate the type of particulate matter. The other approach was to use a chromameter to reveal the color data of particulate matter in a chromaticity diagram. Five di fferent particles, household dust, soil, pine tree pollen, talc and

gypsum powder were chosen and tested to find the feasibility of optical approaches using color and reflected light to distinguish di fferent particulate matters. It is our expectation that the intrinsic color of particles can be a key parameter to identify particulate matters having unique colors. Particulate matters which havea tendency to react easily with water and refractive index liquid can be selectively detected by observing reflected light and characterizing its spectrum. Our study can assist current light-scattering-based sensors to identify the type of particulate matter contaminants and concentration with higher accuracy for reliable indoor environment management.

#### **2. Materials and Methods**

Five di fferent particulate matters were collected in Korea and prepared for the characterization as they were. Household dust was collected by a regular vacuum cleaner from a living room in a typical apartment complex in Goyang city. Korean pine tree pollen was collected during spring season by washing a glass plate located under a pine tree bush in Jeongbal mountain, located in Goyang city for one day. Illite powder, a commonly found yellow soil in Korea, was used for the representative soil sample. It was purchased from Yong Gung Illite ®Inc., and the average size of illite powder was characterized to be less than 200 μm. Talc powder, a raw material widely used as a construction material and usually suspended in indoor air during the construction process was purchased from a chemical company to have the chemical formula Mg3H2(SiO3)4; H2Mg3O12Si4. Gypsum powder was prepared by grinding gypsum insulation board manufactured by KCC Inc., Korea, which has a 9.5 mm thickness, 900 mm width and 1800 mm length in general grade. A total of 20 samples for five di fferent particulate matters were ground and filtered with Whatman ®qualitative paper filter having 20 μm particle retention by flushing with distilled water to exclude the size-induced di fference. After drying at room temperature, the collected powders were used for the experiment. All samples were prepared by cutting them into pieces small enough to grind and sieve to make a desired powder size of 20 μm. Those powders were denoted "as prepared" to distinguish between untreated powders and other powders treated by chemical additives.

Filters and liquid additives to modulate the reflected light of particle samples were tested. Cellophane filters ranged from red, orange, yellow, green, blue, pink and violet in a visible light range as shown in Figure 1. Three color filters, dark blue, green and yellow were utilized, having 400–450 nm, 500–550 nm, and 550–600 nm in wavelength, respectively. Two liquid additives, refractive index liquid (n = 1550, Cargille Inc. Cedar Grove, NJ, USA) and distilled water, were tested.

Reflected light was observed in the same 10 cm distance from the sample surface to the detectors, spectral sensor and chromameter. An 80 W–6500 K white LED light bulb was used for the light source to provide su fficient light in the visible light range and avoid a light color e ffect. In addition to this, a UV light with 365 nm in wavelength was used.

As shown in Figure 2, a schematic (a) and a picture (b) of the experimental apparatus, chromameter (c) and spectral sensor (d) were prepared. A spectral sensor, Apollo ™, developed by NanoLambda in Korea, was used to di fferentiate reflected light into the light spectrum in a small chamber and to examine the applicability to a small-scale sensor. The configuration of the chamber and detailed

experimental method was described in our previous study [14]. A chromameter CR-400 by Konica Minolta was used to acquire color data in terms of chromaticity values.

**Figure 2.** Schematics and pictures of experimental apparatus. (**a**) A schematic of chamber, (**b**) Black-coated closed chamber, (**c**) Chromameter, and (**d**) Spectral sensor (20 mm width and 60 mm length).
