Outdoor air pollution with particulate matter has become more severe in Korea. Ambient particle concentration affects the indoor environment through various routes through building envelopes. In this study, we investigated particle exposure in residential buildings. Indoor and outdoor particle sources determined the indoor concentrations and particle exposure. This paper measured indoor particles and CO2 concentrations in two different apartment buildings and conducted the survey for 24 hours. The I/O ratio of the occupant awake period was higher than the asleep period. The I/O ratio in the awake period is 0.93-3.65, while the I/O ratio in the asleep period is 0.31- 0.76.Indoor peak events such as cooking or cleaning temporarily increase the I/O ratio and emit the indoor particle sources. Decay rate constant is 0.49-6.84 (1/h) in the indoor peak events during the operation of the exhaust hood and natural ventilation. The size range of 0.3-0.5 μm size is over half for the proportions of emitted particles (55.6%). Daily exposure is divided into indoor sources (45.2%) and outdoor sources (54.8%). We found the differences for the proportion of particle exposure. The ratio of daily exposure in particles for 0.3-0.5 μm size is 43.1 (indoor)/ 56.9 (outdoor) %. However, indoor sources are higher than outdoor sources for the ratio of daily exposure in particles for the 0.5-10.0 μm size.

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.

Recently, a number of researchers have produced research and reports in order to forecast more exactly air quality such as particulate matter and odor. However, such research mainly focuses on the atmospheric diffusion models that have been used for the air quality prediction in environmental engineering area. Even though it has various merits, it has some limitation in that it uses very limited spatial attributes such as geographical attributes. Thus, we propose the new approach to forecast an air quality using a deep learning based ensemble model combining temporal and spatial predictor. The temporal predictor employs the RNN LSTM and the spatial predictor is based on the geographically weighted regression model. The ensemble model also uses the RNN LSTM that combines two models with stacking structure. The ensemble model is capable of inferring the air quality of the areas without air quality monitoring station, and even forecasting future air quality. We installed the IoT sensors measuring PM2.5, PM10, H2S, NH3, VOC at the 8 stations in Jeonju in order to gather air quality data. The numerical results showed that our new model has very exact prediction capability with comparison to the real measured data. It implies that the spatial attributes should be considered to more exact air quality prediction.

PURPOSES: The purpose of this study is to compare the concentrations of fine particulate matter (PM2.5) at different types of roadside bus stops in an urban environment, and analyze the tendencies in PM2.5 concentrations according to the air quality index. METHODS : To compare and analyze the characteristics of fine particulate matter at roadside bus stops, we collected data such as PM2.5 concentration, temperature, humidity etc., and performed a comparative analysis of their concentration levels at different types of bus stops (a partially closed bus stop with a front and back partition, a partially closed bus stop with only a back partition, and a bus stop with an open space). In addition, the daily variation in fine particulate matter concentration was analyzed. RESULTS: The average daily concentration levels of fine PM2.5 in the target area for a partially closed bus stop with a front and back partition, a partially closed bus stop with a back partition, and a bus stop with an open space were 18.40㎍/㎥ to 108.27㎍/㎥, 22.81㎍/㎥ to 135.51㎍/㎥, and 16.62㎍/㎥ to 81.52㎍/㎥, respectively. According to air quality index levels during the target measurement period, the bus stop with an open space had the least concentration levels of PM2.5 compared to the other bus stops. Furthermore, this study revealed that the PM2.5 concentration levels usually increased during the peak hour period in the morning and gradually increased after 2 pm until the end of the peak hour period at night, regardless of the bus stop type. CONCLUSIONS: Based on the results of this study, we demonstrated the effect of PM2.5 concentration levels on the atmospheric, weather, environmental, and transportation conditions in a target area, and the variation in concentration levels depending on the type of bus stop.

The objective of this study was to investigate the effect of shoe dust on the indoor environment, and the effectiveness of shoe dust control on indoor air quality. Test dust was resuspended to reach a mass concentration of particles (2.5-10 μm size) more than 3 times compared to background level, and 1.5 times for particles less than 1 μm in size. The shoes, which were used for actual walking purposes in the outdoor environment, increased indoor PM10 concentration by 118±9%. The removal of shoe dust by water washing and mechanical suction brought about an improvement of indoor air quality. In particular, in circumstances where 27 people walked for one hour into the indoor environment, the mechanical suction of shoe dust decreased PM10 concentration by about 17% (based on the mass balance analysis).

오늘날, 세계적으로 미세먼지(Particulate Matters:PM)의 심각성에 대한 인식과 해결을 위한 다양한 방법이 소개되고 있다. 영국과 같은 선진국에서는 정책을 바탕으로 커뮤니티가 주도하는 활성화된 PM저감형 가든 조성 및 관리를 통해 PM저감에 기여하고 있다. 이에 대한 연구는 국내 PM문제 해결을 위한 대안이 될 수 있다. 그러므로, 본 연구의 목적은 커뮤니티 주도 PM저감형 가든에 대한 영국 런던의 사례 분석과 경영 구조에 대한 특징을 고찰하고자 하였다. 이를 바탕으로, 적극적인 커뮤니티 참여를 통한 PM저감형 가든 조성 및 운영 활성화를 위한 시사점을 도출하고자 하였다. 체계적인 분석, 고찰 및 시사점 도출을 위해, Place-keeping 분석 틀을 이용하였다. 그 결과, 적극적인 거버넌스를 강조하는 지방정부의 정책적 가이드라인이 PM저감을 위한 구심점으로 작용하였다. 또한, 이해관계자들의 참여를 통한 파트너십, 커뮤니티에 의한 추가적인 재원 마련, 정기적인 모니터링, 장기적 관점에서의 관리가 동시에 적용되고 있음을 발견하였다. 이에, 본 연구에서는 PM저감형 커뮤니티 가든은 적극적 거버넌스 개념 도입을 통한 지속 가능한 자립형 경영 구조를 강조하는 바이다. 이러한 주장을 뒷받침하기 위해 Place-keeping의 여섯 가지 개념을 적용하여 시사점을 도출하였다. 첫째, 정책은 커뮤니티 참여 활성화를 강조하는 실현 가능한 대안을 제시해야 한다. 둘째, 적극적 거버넌스 개념을 촉진하기 위해 커뮤니티의 의사결정 참여를 위한 기회 제공을 의무화해야 한다. 셋째, 추가적인 재원 마련을 위해 민간 및 비지니스 분야와 같은 외부 자금 지원이 확대돼야 한다. 넷째, PM저감 쟁점에 대해 일반 국민이 책임을 공유해야 한다. 다섯째, 커뮤니티 주도 PM모니터링 확대와 대학과 같은 잠재적인 이해관계자들의 PM모니터링 참여를 확대해야 한다. 여섯째, 저관리형 PM저감 식재를 통해 재원 및 인력 부족에 대한 한계점을 최소화하여 장기적인 유지관리 구조를 조성해야 한다. 이와 같은 시사점의 반영은 추후, 공동체 의식의 증가뿐만 아니라, 결과적으로 PM저감형 커뮤니티 가든의 활성화로 이어질 것이다. 이에 본 연구를 통해 밝혀진 PM저감형 커뮤니티 가든의 긍정성이 국내 PM문제 해결을 위한 매개체가 되기를 기대한다.