• Proceedings of the Korean Environmental Sciences Society Conference
• /
• 1996.04a
• /
• pp.22-22
• /
• 1996

;The Kwinana Shoreline Fumigation Experiment(KSFE) took place in Fremantle, WA, Australia between 23 January and 8 February, 1995. All measurement systems performed to expectation. The CSIRO DAR(Division of Atmospheric Research) LIDAR measured plume sections from near the Kwinana Power Station(KPS) stacks to up to about 5 km downstream. It also measured boundary layer aerosols and the structure of the boundary layer on some occasions. Both stages A and C of KPS were used as tracers at different times. Radiosonde and double theodolite sounding systems measured temperature, humidity, air pressure and wind structure at the coast(Woodman Point) and at the inland(ALCOA residue dump) site at intervals of roughly two hours. These were supplemented by mid afternoon soundings(radiosonde and single theodolite) by Department of Environmental Protection(DEP) at Swanbourne. The Flinders aircraft measured wind, turbulence and temperature structure of the atmospheric boundary layer, concentrations of $C0_2,\;0_3,\;S0_2\;and\;NO_x$ in the smoke plumes and surface radiation over both land and sea. CSIRO DCET(Division of Coal and Energy Technology) vehicle successfully interceptde many smoke plumes and using a range of tracers will be able to identify the various sources much of the time. Routine data from the DEP and Kwinana Industrial Council(KIC) air quality monitoring networks were also automatically logged. Murdoch University measured surface heat flux at Hope Valldy monitoring station and also at Wattleup monitoring station for the last five days. The heart of the LIDAR system is a Neodymium-doped Yttrium-aluminumgarnet(Nd:Y AG) laser operating at a fundamental wavelength of 1064 nm, with harmonics fo 532 nm and 355 nm. A small fraction of the laser beam is scattered back to the LIDAR, collected by a telescope and detedted by a photomultiplier tube. The intensity of the signal as a function of time is a measure of the particle concentration as a function of distance along the line of the laser shot. The results of nine days special field observations are summarized in detail.etail.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.24 no.2
• /
• pp.113-121
• /
• 2014

Objectives: This study is intended to seek credible and efficient measurements on airborne asbestos concentrations that allow immediate action by establishing complementary data through comparative analysis with existing PCM and KF-100 method real-time monitoring equipment in working areas in Seoul where asbestos-containing buildings are being demolished, including living environment surroundings. Materials: We measured airborne asbestos concentrations using PCM and KF-100 at research institutes, monitoring networks, subway stations and demolition sites of asbestos-containing buildings. Through this measurement data and KF-100 performance testing, we drew a conversion factor and applied it via KF-100. Finally we verified the relationship between PCM and KF-100 with statistical methods. Results: The airborne asbestos concentrations by PCM for the objects of study were less than the detection limit(7 fiber/$mm^2$) in three (20%) out of 15 samples. The highest concentration was 0.009 f/cc. The airborne asbestos concentrations by PCM in laboratories, monitoring networks, subway stations and demolition sites of asbestos-containing buildings were respectively $0.002{\pm}0.000$ f/cc, $0.004{\pm}0.001$ f/cc, $0.009{\pm}0.001$ f/cc, and $0.002{\pm}0.000$ f/cc. As a result of KF-100 performance testson rooftops, the conversion factor was 0.1958. Applying the conversion factor to KF-100 for laboratories, the airborne asbestos concentrations ratio of the two ways was nearly 1:1.5($R^2$=0.8852). Also,the airborne asbestos concentration ratio of the two ways was nearly 1:1($R^2$=0.9071) for monitoring networks, subway stations, and demolition sites of asbestos-containing buildings. As a result of independent sample t-tests, there was no distinction between airborne asbestos concentrations monitored in the two ways. Conclusions: In working areas where asbestos-containing buildings are being demolished, including living environment surroundings, quickly and accurately monitoring airborne asbestos scattered in the air around the working area is highly important. For this, we believea mutual interface of existing PCM and a real-time monitoring equipment method is possible.

• Journal of Biosystems Engineering
• /
• v.43 no.2
• /
• pp.148-159
• /
• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.