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.

This study investigates the influence of particulate matter concentrations on the incidence of asthma, focusing on the delayed onset of symptoms and subsequent medical consultations. Analysis incorporates a four-day lag from the initiation of fine dust exposure and compares asthma patterns before and after the World Health Organization's (WHO) classification of fine dust as a Group 1 carcinogen in November 2013. Utilizing daily PM10 data and asthma-related medical visit counts in Seoul from 2008 to 2016, the study additionally incorporates Google search frequencies and newspaper article counts on fine dust to assess public awareness. Results reveal a surge in search frequencies and article publications after WHO announcement, indicating heightened public interest. To standardize the long-term asthma occurrence trend, the daily asthma patient numbers are ratio-adjusted based on annual averages. The analysis uncovers an increase in asthma medical visits 2 to 3 days after fine dust events. Additionally, greater public awareness of fine dust hazards correlates with a significant reduction in asthma occurrence after such events, even within 'normal' fine dust concentrations. Notably, behavioral changes, like limiting outdoor activities, contribute to this decrease. This study highlights the importance of analyzing accumulated medical data over an extended period to identify general public behavioral patterns, deviating from conventional survey methods in social sciences. Future research aims to extend data collection beyond 2016, exploring recent trends and considering the potential impact of decreased fine dust awareness amid the COVID-19 pandemic.

PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

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.

The Saemangeum has a dry surface characteristic with a low moisture content ratio due to the saline and silt soil, so the vegetation cover is low compared to other areas. In areas with low vegetation cover, wind erosion has a high probability of scattering dust. If the vegetation cover is increased by cultivating crops that can withstand the Saemangeum reclaimed environment, scattering dust can be reduced by reducing the flow rate at the bottom. Thus, the purpose of this study is to analyze the effect of suppressing the generation of fine dust and scattering dust by cultivating winter forage crops on the Saemangeum reclaimed land. While growing 0.5 ha of barley and 0.5 ha of triticale in Saemangeum reclaimed land, the concentration of fine dust was monitored according to agricultural work and growth stage. Changes in the concentrations of PM-10, PM-2.5, and PM-1.0 were monitored on the leeward, the windward and centering on the crop field. As a result of monitoring, PM-1.0 had little effect on crop cultivation. the concentration of PM-10 and PM-2.5 increased according to tillage and harvesting, and tillage had a higher increasing the concentration of PM-10 and PM-2.5 than that of harvesting. According to the growth stage of crops, the effect of suppressing scattering dust was shown, and the effect of suppressing scattering dust was higher in the heading stage than in the seedling stage. So, it was found that there was an effect of suppressing scattering dust other than the effect of land covering. Through this study, it was possible to know about the generation and suppression effect of scattering dust according to crop cultivation.

In this study, we investigated the Indoor and Outdoor concentrations of PM10 in Y area, Jeollanam-do. We conducted personal exposure concentration estimates, and Exposure and Risk Assessments using the Time-weighted Average Model. The concentration of Indoor PM10 was 49.38 μg/m3 and that of Outdoor PM10 was 48.02 μg/m3, with the Indoor/ Outdoor Ratio value being 1 or more, and it was found that there was an indoor source of pollution. The Indoor/Outdoor Cr ratio value was 1 or more, and the source of Cr was confirmed to be indoor. Based on our analysis, there was a positive correlation between heavy metals Ni, Cr, and Mn (p<0.05). Using the Time-Weighted Average model, we determined the PM10 personal exposure concentration to be 49.36 μg/m3 and confirmed the feasibility of this model in utilizing the PM10 personal exposure concentrations. In this study, the findings are likely to provide useful data that can be used to determine the concentration of indoor pollutants that are not easy to survey. However, to accurately evaluate indoor air quality, more factors need to be considered and evaluated.

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.

In order for records to be preserved for a long time without physical and chemical transformation, the preservation environment of the library is very important, and environmental problems must be improved through periodic investigation on the preservation environment. Against this background, this study derived fine dust (PM10) and ultrafine dust (PM2.5) concentration data for the libraries, hallways, and workrooms of the National Archives of Korea over two years. There was a difference in the concentration of fine dust and ultrafine dust among facilities, and there was a change in the concentration depending on the month. Both fine dust and ultrafine dust concentrations were present at less than 10 μg/m³ in the libraries. In the hallways, both fine and ultrafine dust concentrations were highest in July. In the workplaces, the monthly fluctuations in the concentration of fine dust and ultrafine dust were large. And the concentration of fine dust and ultrafine dust in the workplaces were higher than those in the library and hallways. Overall, the concentration of fine dust and ultra-fine dust was measured below the maintenance standards stipulated by the Indoor Air Quality Management Act of the Ministry of Environment of Korea in all the investigated facilities. The results of this study are expected to be used as fundamental information to manage the indoor air quality of the facilities of the National Archives.

This study obtained the following conclusions using the measurement results of indoor and outdoor PM10 with regard to cardiovascular disease patients in Cheongju-area in November 2020. Most of the PM10 has an I/O ratio of less than 1, which is an outdoor source. Since we measured once and twice time, Without the air purifier device’s working status, there were no concentration changes of PM10 in the first and second indoor areas. As for the concentration of PM10 according to the living environment, the distribution of PM10 is higher indoors than outdoors when the residential area is 30 m2 or more, and the outdoor PM10 concentration tends to be high when the distance to the road is within 50 m. The more time spent indoors, the higher the indoor PM10 concentration. The smaller the ventilation time and frequency, the longer the cooking time was, and the higher the number of cooking times, the higher the concentration of PM10 could be. The indoor PM10 contribution ratio through multiple regression analysis showed the possibility of increasing indoor PM10 as β = 28.590 when the time spent indoors was longer than 16 hours (p<0.05). The result regarding PM10 exposure reveals that PM10 can be inhaled not only indoors but also outdoors, and the subjects of this study appear to have lived indoors for about 16 hours or longer on a daily basis, which may affect their health regardless of gender.

본 연구는 도로에서 발생하는 미세먼지 농도가 도시 개발 형태에 따라 인접 생활권별로 어떻게 확산되는지 시뮬레이션을 통해 파악하고자 하였다. 연구는 경상남도 밀양시청 앞 6차선 도로를 중심으로 한 도로영향권 시가지를 대상으로 진행하였다. 시뮬레이션 프로그램인 ENVI-met 모델을 가로녹지 유무, 도로변 건축물의 높이에 따라 변수를 조정하여 미세먼지 농도의 확산정도를 파악하였다. 모델링 결과 도로변 건물이 고층으로 형성되어 있고 가로녹지가 조성되어 있는 경우 인접 생활권으로 확산된 미세먼지 농도가 가장 낮았으며, 다음으로는 고층건물군에 가로녹지가 없는 상태의 농도가 낮았다. 반면 저층건물군이 형성된 경우에는 가로녹지 유무에 관계없이 인접생활권으로 확산된 미세먼지 농도는 높게 나타났다. 고층건물의 경우 빌딩풍에 의해 건축물 주변으로 강한 바람이 형성되는 만큼 바람에 의해 미세먼지가 빠르게 외부로 확산되어 농도가 낮아지는 것으로 확인할 수 있었다. 반면 가로녹지 조성이 도로변 생활권에 미치는 미세먼지 저감효과는 뚜렷하지 않았다. 특히 도로변 건축물이 저층일 경우 가로녹지를 조성과 생활권미세먼지농도변화와 관련성은 없는 것으로 확인되었다. 본 연구는 미세먼지가 도로에서만 발생하는 것을 가정하여 모델링을 진행한 것으로 향후 다양한 변수에 따른 미세먼지 확산모형 연구 및 현장연구의 보완을 필요로 하였다.

본 연구에서는 K-means 군집 분석을 통하여 최근 5년간(2014-2018) 한반도 남동 지역의 고농도 미세먼지 발생에 영향을 미치는 주요 종관 기상 패턴을 분류하였다. 또한 고농도 미세먼지 사례일의 발생과 관련된 지역적 차이를 살펴보기 위하여 NCEP (National Centers for Environmental Prediction)/FNL (Final Operational Global Analysis) 재해석 기상자료를 이용하여 부산, 울산, 경남 지역의 미세먼지 발생 특성과 관련된 종관 규모 기상의 특성에 대한 비교 연구도 수행하였다. 한반도 남동 지역의 고농도 미세먼지 사례일과 관련된 종관 기상 패턴은 총 5개(C1-C5)로 분류된다. 각 군집의 발생빈도는 24.8% (C1), 21.3% (C2), 20.4% (C3), 17.3% (C4), 16.2% (C5)이다. 기상 패턴 분석을 통하여 제시된 남동 지역의 고농도 미세먼지를 유발하는 요인에는 지역 외부에서 장·단거리 수송(C1, C3, C5)에 의한 영향과 지역내 배출(C2, C4)에 의한 것임을 알 수 있었다. 또한 고농도 미세먼지 발생일에 대해 부산, 울산, 경남 세 지역의 기상장을 분석하였을 때, 500 hPa 지위 고도 및 풍속 등의 기상학적 특성이 지역별로 다르게 나타났다. 그리고 고기압 의 작은 위치 변화가 각 지역의 미세먼지 발원과 장거리 이동 경향성에 영향을 미치고 있었다.

PURPOSES : In this study, the characteristics of fine particulate matter (PM2.5) concentrations under different weather conditions of different types of bus stops, such as enclosed-type and open-type bus stops, were analyzed using statistical methods. METHODS : Data was collected inside and outside an enclosed bus stop on sunny and rainy days to compare and analyze the characteristics of fine particulate matter concentration in the target bus stop. The probability distributions were estimated for each data point using the Anderson–Darling test. Based on the estimated probability distributions, probability density functions were computed, and the values were used to estimate and compare probability for each air quality index inside and outside the bus stop under different weather conditions RESULTS : For the results of descriptive statistics, the average concentrations of fine particulate matter inside and outside the bus stop were 42.296 and 35.482 μg/m3 on a sunny day and 40.831 and 39.321 μg/m3 on a rainy day, respectively. Results of the statistical method, obtained using the Anderson-Darling test, indicate that the probability of the air quality index inside the bus stop reaching high concentrations on a sunny day was "high" or "very high," compared to that outside the bus stop. However, on rainy days, the differences in fine particulate matter concentrations inside and outside the bus stops were difficult to identify based on statistical evidence. CONCLUSIONS : It was found that the open-type bus stop had an advantage of preventing fine particulate matter effects on sunny days, compared to the enclosed-type bus stops. Furthermore, there were slight differences in fine particulate matter concentrations inside and outside the bus stop on a rainy day because of atmospheric flow and stormwater.

Since 1974, the urban subway has been used as a major form of public transportation in Seoul, Korea. The air quality in the subway environment depends on the introduction of air pollutants from roadway air and its generation is caused by subway operation in the tunnel. In the subway tunnel, PM10 concentration was monitored from March 8 to 15, 2018 and from March 26 to 28, 2018, and compared with concentrations that are routinely monitored at the subway concourse and the nearest roadside air quality monitoring station (RAQMS). Overall PM10 concentration at the concourse was similar to that of the RAQMS. However, PM10 concentration in the tunnel was significantly higher than those of the subway concourse and RAQMS, and showed distinct diurnal variation caused by train operation. The dominant peak concentrations were highly correlated with the number of train operations per hour. The minimum PM10 concentration was observed between 2 am to 5 am when the train was not operated. This was similar to that of the RAQMS. Although the diurnal variation of the PM10 concentration at the concourse is not significant, the overall trend is similar to that in the tunnel.

PURPOSES : This study analyzes the characteristics of generated fine particulate matter (PM2.5) and nitrogen oxide (NOX) at roadsides using a statistical method, namely, a generalized linear model (GLM). The study also investigates the applicability and capability of a machine learning methods such as a generalized regression neural network (GRNN) for predicting PM2.5 and NOX generations. METHODS : To analyze the characteristics of PM2.5 and NOX generations at roadsides, data acquisition was conducted in a specific segment of roads, and PM2.5 and NOX prediction models were estimated using GLM. In addition, to investigate the applicability and capability of a machine learning methods, PM2.5 and NOX prediction models were estimated using a GRNN and were compared with models employing previously estimated GLMs using r-square, mean absolute deviation (MAD), mean absolute percentage error (MAPE), and root mean square error (RMSE) as parameters. RESULTS : Results revealed that relative humidity, wind speed, and traffic volume were significant for both PM2.5 and NOX prediction models based on estimated models from a GLM. In addition, to compare the applicability and capability of the GLM and GRNN models (i.e., PM2.5 and NOX prediction models), the GRNN model of PM2.5 and NOX prediction was found to yield better statistical significance for r-square, MAD, MAPE, and RMSE as compared with the same parameters used in the GLM. CONCLUSIONS : Analytical results indicated that a higher relative humidity and traffic volume could lead to higher PM2.5 and NOX concentrations. By contrast, lower wind speed could affect higher PM2.5 and NOX concentrations at roadsides. In addition, based on a comparison of two statistical methods (i.e., GLM and GRNN models used to estimate PM2.5 and NOX), GRNN model yielded better statistical significance as compared with GLM.

This study was conducted as a part of the research for the “Development of Big Data Analysis Techniques and AI-based Integrated Support System for Energy-Environment Management.” We collected research results on characterization of distribution of fine dust and re-analyzed using meta-analysis techniques to build “big data” with high potential for school environments. The results of prior studies on the characteristics of fine dust concentration distribution in a school environment conducted in Korea were collected and re-analyzed the results using the metaanalysis technique. In this manner, the variables that could be used to derive the independent variables needed to produce the e-coding book prior to the big data collection, were first derived. The possibility of using the data as independent variables was then evaluated. In this study, three variables: “elementary school vs. middle school vs. high school,” “general classroom vs. special classroom,” and “new classroom vs. old classroom” were evaluated for their application as major classification variables with priority. The necessity of being derived as a major classification variable was examined by testing the difference in fine dust concentration distribution in the school environment by each variable case. Results showed that “elementary school vs. middle school vs high school” and “general classroom vs. special classroom” could be used as independent variables, while “new classroom vs. old classroom” was less likely to be used as an independent variable.

The goal of this study was to measure the indoor and outdoor fine and ultrafine particulate matter concentrations (PM10, PM1.0) of some houses in Yeosu and in S university in Asan from March to September 2018. PM10 concentration in indoor air in Yeosu area was 18.25 μg/m3, while for outdoor air it was 14.53 μg/m3. PM1.0 concentration in indoor air in the Asan area was 1.70 μg/m3, while for outdoor air it was 1.76 μg/m3, showing a similar trend. Heavy metal concentrations in the Yeosu region were the highest, at Mn 2.81 μg/m3, Cr 1.30 μg/ m3, and Ni 1.11 μg/m3 indoors. Outside, similar concentrations were found, at Cr 3.44 μg/m3, Mn, 2.60 μg/m3, and Ni 1.71 μg/m3. Our analysis of indoor and outdoor PM concentrations in the Asan region, which was carried out using the MOUDI (Micro-orifice Uniform Deposit Impactor) technique, found that PM concentration is related to each particle size concentration, as the concentration of 18 μm and 18-10 μm inside tends to increase by 3.2- 1.8 μm and 0.56-0.32 μm.

PURPOSES: The purpose of this study is to analyze characteristics of concentrations of fine particulate matter (PM2.5) among 3 different types of bus stops, specifically partially closed bus stop with front & back partition, partially closed bus stop with back partition, and bus stop with open space (referred to as bus stop types Ⅰ, Ⅱ, and Ⅲ, respectively) at urban roadside, using the Anderson-Darling test as statistical method. METHODS: For the purpose of this study, first of all, data on concentrations of PM2.5 on the 3 types of bus stops at urban roadside were acquired for certain days, with different levels of air quality index (AQI). Secondly, this study accomplished the data processing of removing outliers from acquired data, and the Anderson-Darling test was conducted to estimate probabilities of occurrence for concentrations of PM2.5 in the 3 types of bus stops. RESULTS : The average concentrations of PM2.5 for AQI‘ Very High’for bus stop types Ⅰ, Ⅱand Ⅲare 46-179㎍/m3, 66-194㎍/m3, 42- 134㎍/m3, respectively, and for AQI ‘High’for bus stop typesⅠ, Ⅱ and Ⅲ are 16-71㎍/m3, 26-84㎍/m3, and 14-69㎍/m3, respectively. Furthermore, probabilities of occurrence for concentration levels of PM2.5 in AQI were estimated for given measurement dates using the Anderson-Darling test as statistical method. As a result, for AQI ‘Very High,’the probabilities of occurrence for concentration levels ‘Very High’and‘ High’were determined more likely to occur regardless of bus stop type. With respect to each type of bus stop, the probabilities of ‘Very High’for bus stop type Ⅱ were 93.37% and 98.92%, higher than for the other bus stop types. For AQI ‘High’the probabilities of occurrence for concentration levels‘ Good’were found to be very low, at 0.00% to 3.07%, and occurred mainly for‘ Moderate’and‘ High’in this study. In particular, the probabilities of occurrence for concentration level‘ High’for bus stop type Ⅱwere analyzed to be greater than 90%, compared to those for the other bus stop types. CONCLUSIONS: Based on the result of this study, when PM2.5 is analyzed on certain days, probabilities of occurrence for concentration levels in AQI should be considered for each type of bus stop.