This review clarifies particulate matter (PM) pollution, including its levels, the factors affecting its distribution, and its health effects on passengers waiting at bus stations. The usual factors affecting the characteristics and composition of PM include industrial emissions and meteorological factors (temperature, humidity, wind speed, rain volume) as well as bus-station-related factors such as fuel combustion in vehicles, wear of vehicle components, cigarette smoking, and vehicle flow. Several studies have proven that bus stops can accumulate high PM levels, thereby elevating passengers’ exposure to PM while waiting at bus stations, and leading to dire health outcomes such as cardiovascular disease (CVD), respiratory effects, and diabetes. In order to accurately predict PM pollution, an artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) have been developed. ANN is a data modeling method of proven effectiveness in solving complex problems in the fields of alignment, prediction, and classification, while the ANFIS model has several advantages including non-requirement of a mathematical model, simulation of human thinking, and simple interpretation of results compared with other predictive methods.

Resuspension of particulate matter (PM) in classrooms, which increases the risk of negative impact on student health from exposure to PM, is influenced by humidity level in the indoor environment. The goal of this study is to investigate the properties of PM resuspension in accordance with relative humidity through classroom test chamber experiments. In actual classrooms, it is challenging to control factors influencing resuspension. Therefore, the classroom chamber that reflects the environment of elementary school classroom (e.g., structure, floor material) is used in this study. The humidity of the classroom chamber is adjusted to 35%, 55%, 75%, and 85% by placing it inside a real-size environmental chamber, which allows artificial control of climatic conditions. At the respective humidity conditions, PM resuspension concentration and resuspension factor caused by occupant walking across the classroom chamber are analyzed. The results show that both of the resuspension concentration and resuspension factor reveal a linear negative correlation to humidity increase. Furthermore, coefficient of determination (R²) indicating goodness-of-fit of the linear regression model between the resuspension concentration and humidity is 0.88 for PM₁₀ and 0.93 for PM_2.5. It implies that accuracy of the regression model for estimating PM₁₀ and PM_2.5 resuspension concentrations is 88% and 93%, respectively.

This study characterized emissions of particulate matter (PM), volatile organic compounds (VOCs), heavy metals, and anions from Mongolian bituminous coals in a controlled heating experiment. Three coal samples from Alag Tolgoi (coal 1), Baganuur (coal 2), and Nalaikh (coal 3) were combusted at a constant heat flux of 50 kW/m² using a dual-cone calorimeter. The coal samples were commonly used in ger district of Ulaanbaatar, Mongolia. PM₁₀ emission factors were 1122.9 ± 526.2, 958.1 ± 584.0, and 472.0 ± 57.1 mg/kg for coal samples 1, 2, and 3, respectively. PM with a diameter of 0.35–0.45 µm was dominant and accounted for 41, 34, and 48% of the total PM for coal samples 1, 2, and 3, respectively. The emissions of PM and VOC from coals commonly used in Ulaanbaatar, Mongolia were significant enough to cause extremely high levels of indoor and outdoor air pollution.

Use of diesel locomotives in transport is gradually decreasing due to electrification and the introduction of high-speed electric rail. However, in Korea, up to 30% of the transportation of passengers and cargo still uses diesel locomotives and diesel vehicles. Many studies have shown that exhaust gas from diesel locomotives poses a threat to human health. This study examined the characteristics of particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons in diesel locomotive engine exhaust. Emission concentrations were evaluated and compared with the existing regulations. In the case of PM and NOx, emission concentrations increased as engine output increased. High concentrations of CO were detected at engine start and acceleration, while hydrocarbons showed weakly increased concentrations regardless of engine power. Based on fuel consumption and engine power, the emission patterns of PM and gaseous substances observed in this study were slightly higher than the U.S. Environmental Protection Agency Tier standard and the Korean emission standard. Continuous monitoring and management of emissions from diesel locomotives are required to comply with emission standards. The findings of this study revealed that emission factors varied based on fuel consumption, engine power, and actual driving patterns. For the first time, a portable emission measurement system (PEMS), normally used to measure exhaust gas from diesel vehicles, was used to measure exhaust gas from diesel locomotives, and the data acquired were compared with previous results. This study is meaningful as the first example of measuring the exhaust gas concentration by connecting a PEMS to a diesel locomotive, and in the future, a study to measure driving characteristics and exhaust gas using a PEMS should be conducted.

According to the stringent regulations on particulate matter (PM) concentrations in Seoul, Korea, the PM₁₀ and PM_2.5 concentrations in subway stations must be maintained below 50 and 30 μg/m³, respectively, by 2024. Therefore, the PM concentrations in a subway station were analyzed considering air-conditioning diffuser arrangement and filtration efficiency, with the total ventilation flow rate of the station maintained constant. Dynamic analysis was performed under a worst-case scenario, wherein outdoor air was introduced through ground entrances and high-concentration dust (PM₁₀, PM_2.5) was introduced from stationary train cabins into the platforms through open platform screen doors (PSDs). Although the average PM concentrations were predicted to satisfy the reinforced criteria of Seoul under the existing operating conditions, the recommended limits were exceeded in certain local areas. To address this, the PM concentrations were predicted by changing the diffuser arrangement in the waiting room and maintaining the total ventilation flow rate constant. When the diffusers were placed near the waiting room walls, the PM₁₀ and PM_2.5 concentrations were reduced by approximately 10.5 and 5%, respectively, compared to the previous diffuser arrangement. Thus, the required PM concentration criteria were satisfied in nearly all areas of the target station, except for certain areas close to PSDs. The study findings can form the basis for improving the air quality of other subway stations.