Particulate matter is known to have adverse effects on health, making it crucial to accurately gauge its concentration levels. While the recent advent of low-cost air sensors has enabled real-time measurement of particulate matter, discrepancies in concentrations can arise depending on the sensor used, the measuring environment, and the manufacturer. In light of this, we aimed to propose a method to calibrate measurements between low-cost air sensor devices. In our study, we introduced decision tree techniques, commonly used in machine learning for classification and regression problems, to categorize particulate matter concentration intervals. For each interval, both univariate and multivariate multiple linear regression analyses were conducted to derive calibration equations. The concentrations of PM10 and PM2.5 measured indoors and outdoors with two types of LCS equipment and the GRIMM 11-A device were compared and analyzed, confirming the necessity for distinguishing between indoor and outdoor spaces and categorizing concentration intervals. Furthermore, the decision tree calibration method showed greater accuracy than traditional methods. On the other hand, during univariate regression analysis, the proportion exceeding a PM2.5/PM10 ratio of 1 was significantly high. However, using multivariate regression analysis, the exceedance rate decreased to 79.1% for IAQ-C7 and 89.3% for PMM-130, demonstrating that calibration through multivariate regression analysis considering both PM10 and PM2.5 is more effective. The results of this study are expected to contribute to the accurate calibration of particulate matter measurements and have showcased the potential for scientifically and rationally calibrating data using machine learning.

The mechanism of resonant light scattering in single-layer graphene is discussed. A new concept of electron–hole selfphotorecombination is proposed, which makes it possible to clearly separate the phenomena of resonant light scattering and resonant photoluminescence. It is established that Rayleigh resonant radiation has been found to consist of virtual and non-virtual components. It has been shown that Rayleigh radiation is mainly caused by resonant non-virtual optical transitions. The band of Rayleigh radiation due to resonant virtual transitions is quite wide, and the intensity is extremely low. On the basis of the presented theory, the results of numerical estimates of the linewidth and intensity of the Rayleigh band of single-layer graphene are in fairly good agreement with the experimental data.

Low-cost particulate matter (PM) sensors based on the light scattering principle measure the concentration of particles by the change of scattering intensity after light is irradiated onto the particles. It has been reported that when the relative humidity is high, water vapor may cause the expansion of airborne particles and affect the accuracy of the light scattering method for PM measurement, but it has also been shown that the effect of humidity is not significant or even negligible. Therefore, to determine the effect of humidity on the Plantower PMS7003 light scattering sensor, in this study, a BAM1020 (Beta Attenuation Monitoring) was installed alongside to continuously monitor the ambient atmospheric PM concentration for approximately four weeks. The sensors collected data at 10-minute intervals, resulting in a 1-hour average for comparative analysis. To accurately measure humidity, the performance of the Arduino + DHT22 humidity sensor was also pre-evaluated using a series of saturated salt solutions. The humidity was grouped into five intervals and analyzed by visual analysis. The results confirmed that there was no significant correlation between PM2.5 differences and humidity, which were randomly and uniformly distributed around the mean. However, since in the very low and high concentration ranges based on the beta-ray monitor measurements, the difference between the light scattering sensor PM10 measurement and the reference value is much larger than the difference between the PM2.5 and the reference value., there is an additional need to investigate the appropriate correction method for dust season or PM10. The results show that the outcomes of the light scattering sensor are more influenced by particle size and concentration than by humidity.

Most of the white fumes from the tenter process of a textile plant in an industrial complex are generated by water vapor and oil mist. While general water vapor disappears when the humidity is lowered, the white fume generated in the tenter process does not disappear and is continuously maintained, resulting in environmental problems and complaints. Efforts to reduce white fume are being conducted, but it is vitally important to develop a performance index that quantitatively calculates and deduces the degree by which white fume has been reduced, so that a tangible and visible result can be obtained in the performance evaluation of prevention facilities. In this study, the removal efficiency or performance of a general wet scrubber and a wet electrostatic precipitator (electrical fume collector, EFC) installed in the actual textile tenter process was analyzed by the light scattering method that can measure the concentration of particles up to a high level. The white fume removal efficiency of the EFC was 92%, much higher than the 17% removal efficiency of the general scrubber. In addition, the EFC was more effective in removing toluene, 1,1'- [oxybis(methylene)]bis- Benzene, and benzothiazole, which are the major substances generated from the textile tenter process, as well as complex odors. From these results, it was found that the light scattering method is one of the useful tools to evaluate the performance of white fume prevention facilities in the industrial field in terms of satisfying the urgent need for measurement and the ability to obtain a clear and precise result on site. This approach is meaningful in that real-time quantification is applicable more intuitively than the gravimetric method in assessing the fume removal performance as it can be observed with the naked eye.

In this study, Target strength for multi-frequency (38 kHz, 70 kHz, 120 kHz and 200 kHz) of chub mackerel (Scomber japonicus) was estimated using by the KRM model. The body shape of the Chub mackerel was described by an X-ray system and the body length of 20 individuals ranged from 16 cm to 28 cm. The swimbladder tilt angle ranged between – 8 and – 14°, the maximum TS value according to the swimming angle of chub mackerel was – 33.0 dB at – 11°. The averaged TScm according to fork length was – 66.02 dB at 38 kHz, – 66.50 dB at 70 kHz, – 66.00 dB at 120 kHz and – 67.35 dB at 200kHz, respectively.

The purpose of this study is to analyze the effect of temperature and humidity on the measured Particulate Matter (PM) concentrations recorded by PMS5003T, a low-cost light scattering type measuring tool. A regression analysis was performed on the ratio of PM concentrations measured by the light scattering method and the beta-ray absorption method according to temperature and humidity in an outdoor environment. As the temperature decreased, the PM concentration ratio increased, and this tendency intensified below 0oC. As the humidity increased, the PM concentration ratio increased, but the effect was less than the temperature effect. The coefficients of determination for temperature and humidity were R2 = 0.325 and 0.003, respectively, and the effects of temperature and humidity on the measured values w ere formulated and compensated for. As a result of the compensation, R2, relative precision, accuracy and RMSE improved from 0.927 to 0.958, from 91.183% to 96.651%, from 31.383% to 74.058%, and from 13.517 μg/m³ to 6.690 μg/m³, respectively. Finally, results from this study indicate that the reliability of the low-cost light scattering type PM sensor can be improved by applying the temperature and humidity compensation method.