• Journal of Environmental Health Sciences
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• v.44 no.5
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• pp.421-432
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• 2018

Objectives: In this study, we started to develop an indoor garden integrated IoT solution based on IAQ (indoor air quality) and interconnection with an environmental database for smart management of indoor gardens. The purpose of this study was to develop and apply an integrated solution for customized air purification from an indoor garden through big data analysis using IoT technology. Methods: An IoT-based IAQ monitoring system was established in three households within a new apartment building. Based on real-time and long-term data collected, $PM_{2.5}$, $CO_2$, temperature, and humidity changes were compared to those of indoor garden applications and the analyzed results were indexed. Results As a result of the installation, all three households had no results exceeding the standard for indoor air pollution on average $PM_{2.5}$ and $CO_2$ indices. In the case of indoor garden installation, the IAQ index increased to the "Good" section after the installation, and readings in the "Bad" section shown before the installation disappeared. The comfort index also did not dip into the "Uncomfortable" section, where it had been preinstallation, and significantly lowered the average score from "Uncomfortable for sensitive groups" to "Good". Overall, the IAQ composite index for the generation of installations decreased the "Good" interval, but "Bad" did not appear. Conclusions In this study on developing an integrated solution for IAQ based on IoT indoor gardens, big data was analyzed to determine IAQ and comfort indexes and an IAQ composite index. Through this process, it became understood that it is necessary to monitor IAQ based on IoT.

• Korean Journal of Environmental Agriculture
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• v.2 no.1
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• pp.35-44
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• 1983

In order to elucidate the change of plant community of natural forests caused by air pollution in the area of Mt. Dosjil of Ulsan city, 25 study subplots, 5 subplots from each 5 plots were set up. These plots lay along gradients of chronic exposure to airborne sulfur dioxide ($SO_2$), fluorides(F) and possibly other pollutants. Total nitrogen, organic matter, available $P_2O_5$, and CEC were generally depressed near industrial sourecs of air pollutants. The author found a total 7 species near sources of air pollutants and 13 species at the utmost plot from air pollutants. Number of individuals, species diversity, evenness, similarity index and biomass were generally depressed near the industrial source of air pollutants. Tolerant plants to air pollution is Cyperus amuricus, Calamagrostis arundinacea, Bidens bipinnata, Cocculus trilobus, Digtaria sanguinalis, Persicaria hydropiper, Pinus thunbergii, Ambrosia artemisiifolia var. elatior, Rhododendron schlippenbachii and Robinia pseudoacacia. It contains 4.41ton/ha of aboveground biomass of Robinia pseudoacacia in damaged forest by air pollution and 39.47ton/ha of it in the non-attacked forest. The annual net production is 0.67ton/ha. yr in damaged forest and $7.73ton/ha{\cdot}yr$ in the non-attacked forest.

• Atmosphere
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• v.30 no.4
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• pp.439-454
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• 2020

This study investigates the change in the fine particulate matter (PM2.5) concentration and World Health Organization (WHO) air quality index (AQI) in East Asia (EA) under Shared Socioeconomic Pathways (SSPs). AQI is an indicator of increasing levels about health concern, divided into six categories based on PM2.5 annual concentrations. Here, we utilized the ensemble results of UKESM1, the climate model operated in Met Office, UK, for the analysis of long-term variation during the historical (1950~2014) and future (2015~2100) period. The results show that the spatial distributions of simulated PM2.5 concentrations in present-day (1995~2014) are comparable to observations. It is found that most regions in EA exceeded the WHO air quality guideline except for Japan, Mongolia regions, and the far seas during the historical period. In future scenarios containing strong air quality (SSP1-2.6, SSP5-8.5) and medium air quality (SSP2-4.5) controls, PM2.5 concentrations are substantially reduced, resulting in significant improvement in AQI until the mid-21st century. On the other hand, the mild air pollution controls in SSP3-7.0 tend to lead poor AQI in China and Korea. This study also examines impact of increased in PM2.5 concentrations on downward shortwave energy at the surface. As a result, strong air pollution controls can improve air quality through reduced PM2.5 concentrations, but lead to an additional warming in both the near and mid-term future climate over EA.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.04a
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• pp.239-240
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• 2006

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.11a
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• pp.43-44
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• 2002

중국 서북건조 사막지역에서 봄철에 기온이 올라가면서 강한 한랭전선을 동반한 저기압이 발달할 때 그 전선 후면에 미세한 모래먼지가 바람에 휘감겨 올라가 황사가 발생한다. 발원지에서 생성된 황사는 약 30%가 발원지에서 재 침적되고, 20%는 주변지역으로 수송되며, 50%는 편서풍을 타고 1,500-2,000km이상의 거리를 이동하여 우리나라, 일본 및 하와이에까지 침적되는 것으로 알려져 있다. (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.11a
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• pp.314-315
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• 2002

대기중에는 다양한 크기와 모양, 그리고 화학성분을 가진 입자들이 존재한다. 이러한 입자들은 발생원이 다양하고 대기중에 존재하면서 광화학적인 변화를 거쳐 형성되기도 한다. 입자의 화학조성과 크기는 발생원을 구분하기 위한 일반적인 척도로 쓰이고 있다. 대기 에어러솔의 크기분포특성은 에어러솔의 변화 요소인 발생원과 제거 그리고 동역학적인 과정을 이해하는데 중요한 역할을 한다. (중략)

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.556-559
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• 2007

The Urban Environmental Quality (UEQ) indicates a complex and various parameters resulting from both human and natural factors in an urban area. Vegetation, climate, air quality, and the urban infrastructure may interact to produce effects in an urban area. There are relationships among air pollution, vegetation, and degrading environmental the urban heat island (UHI) effect. This study investigates the application of multi-spectral remote sensing data from the Landsat ETM and TM sensors for the mapping of air quality and UHI intensity in Seoul from 2000 to 2006 in fine resolution (30m) using the emissivity-fusion method. The Haze Optimized Transform (HOT) correction approach has been adopted for atmospheric correction on all bands except thermal band. The general UHI values (${\Delta}(T_{urban}-T_{rural})$) are 8.45 (2000), 9.14 (2001), 8.61 (2002), and $8.41^{\circ}C$ (2006), respectively. Although the UHI values are similar during these years, the spatial coverage of "hot" surface temperature (>$24^{\circ}C$) significantly increased from 2000 to 2006 due to the rapid urban development. Furthermore, high correlations between vegetation index and land surface temperature were achieved with a correlation coefficients of 0.85 (2000), 0.81 (2001), 0.84(2002), and 0.89 (2006), respectively. Air quality is shown to be an important factor in the spatial variation of UEQ. Based on the quantifiable fine resolution satellite image parameters, UEQ can promote the understanding of the complex and dynamic factors controlling urban environment.

• Advances in environmental research
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• v.4 no.4
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.15-26
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• 2017

The decrease of green space according to the urbanization has caused many environmental problems as the destruction of habitat, air pollution, heat island effect. With interest growing in natural view recently, proper management of evergreen tree which is lived even the winter season has been on the rise importantly. This study analyzed the distribution area of evergreen tree using vegetation index based on unmanned aerial vehicle (UAV). Firstly, RGB and NIR+RG camera were loaded in fixed-wing UAV and image mosaic was achieved using GCPs based on Pix4d SW. And normalized differences vegetation index (NDVI) and soil adjusted vegetation index (SAVI) was calculated by band math function from acquired ortho mosaic image. validation points were applied to evaluate accuracy of the distribution of evergreen tree for each range value and analysis showed that kappa coefficient marked the highest as 0.822 and 0.816 respectively in "NDVI > 0.5" and "SAVI > 0.7". The area of evergreen tree in "NDVI > 0.5" and "SAVI > 0.7" was $11,824m^2$ and $15,648m^2$ respectively, that was ratio of 4.8% and 6.3% compared to total area. It was judged that UAV could supply the latest and high resolution information to vegetation works as urban environment, air pollution, climate change, and heat island effect.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.339-351
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• 2011

This study investigates the potentials of satellite, ground measurement data, and geo-spatial information within an urban area for the mapping of the Urban Environmental Quality (UEQ) parameters. The UEQ indicates a complex and various parameters resulting from both human and natural factors, which are greenness, climate, air pollution, the urban infrastructure, and etc. Multi-spectral remote sensing data from the Landsat ETM and TM sensors for the mapping of air pollution by the Haze Optimized Transform (HOT) technique, Urban Heat Island (UHO using the emissivity-fusion method in Seoul from 2000 to 2006 in fine resolution (30m) were analyzed for the estimation of UEQ index. Although the UHI values are similar ($8.4^{\circ}C{\sim}9.1^{\circ}C$) during these years, the spatial coverage of "hot" surface temperature (> $24^{\circ}C$) significantly increased from 2000 to 2006 due to the rapid urban development. Furthermore, high correlations between vegetation index and land surface temperature were achieved with a correlation coefficients of 0.85 (2000), 0.81 (2001), 0.84 (2002), and 0.89 (2006), respectively. It was found that the proposed method was successfully analyzed spatial structure of the UEQ and the scenarios of the best and worst areas within the city were also identified. Based on the quantifiable fine resolution satellite image parameters, UEQ can promote the understanding of the complex and dynamic factors controlling urban environment.