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Monitoring landfill volatile organic compounds emissions by an uncrewed aerial vehicle platform with infrared and visible-light cameras, remote monitoring, and sampling systems

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Journal of Environmental Management 2024年 365卷 121575页

作者： Liang, Chen-Wei Zheng, Zhong-Chun Chen, Ting-Nong Master Program in UAV Application and Precision Agriculture National Ilan University Yilan Taiwan Department of Biomechatronic Engineering National Ilan University Yilan Taiwan

An uncrewed aerial vehicle (uav) platform equipped with dual imaging cameras, a gas sampling system, and a remote synchronous monitoring system was developed to sample and analyze volatile organic compounds (VOCs) emitted from landfills. The remote synchronous monitoring system provided real-time video to administrators with specific permissions to assist in identifying sampling sites within extensive landfill areas. The sampling system included four kits capable of collecting samples from different locations during a single flight mission. Each kit comprised a 1 L Tedlar bag for measuring landfill VOC concentrations according to the TO-15 method prescribed by the US Environmental Protection Agency. The air sample was introduced into a Tedlar bag via pumping. A known volume of the sample was subsequently concentrated using a solid multisorbent concentrator. Following this, the sample underwent cold trap concentration and thermal desorption. The concentrated sample was then transferred to a chromatography-mass spectrometry system for separation and analysis. Since the anaerobic catabolism of organic waste is exothermic and emits VOCs, this study employed uav thermal imaging to locate principal emission sources for sampling. Visible-light imaging helped identify newer or older landfill sections, aiding in the selection of appropriate sampling sites, particularly when surfaces were thermally disturbed by solar radiation. Field measurements were conducted under three meteorological conditions: sunny morning, cirrus morning, and thin cloud evening (2 h after sunset), identifying 119, 122, and 111 chemical species respectively. The sequence of total VOC concentrations measured correlated with the meteorological conditions as follows: cirrus morning > thin cloud evening > sunny morning. The results indicated that ambient temperature and global solar radiation significantly influenced daytime gas emissions from landfills. Evening thermal images, unaffected by solar heating,

关键词： Volatile organic compounds

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Estimating the Roles of Three Components of Regional Haze on Traffic Accidents

SSRN

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SSRN 2022年

作者： Liang, Chen-Wei Chang, Chia-Chun Liang, Jeng-Jong Master Program in UAV Application and Smart Agriculture National Ilan University Yilan Taiwan Department of Biomechatronic Engineering National Ilan University Yilan Taiwan Department of Environmental Engineering and Science Feng Chia University Taichung Taiwan

This study estimated changes in the levels of three haze components at the time of two severe traffic accidents on an expressway and a freeway to determine environmental factors contributing to the accidents. Monitoring data and surveillance images were collected to determine the precise causes of the poor visibility–related accidents. The haze extraction method was used to assess the relationship between the components and visibility during the accidents. The results revealed that the moisture was significantly not the main haze–fog component. The haze components were ordered as in terms of their correlation with local visibility: PM2.5 > SOAs > RH. The spatial distributions and evolutions of the three components indicated that the PM2.5 remained high from midnight until early morning but decreased slightly at the time of both accidents. PM2.5 and SOAs were the main causes of the heavy haze, and SOAs had the greatest impact on visibility. © 2022, The Authors. All rights reserved.

关键词： Extraction

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Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

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Heliyon 2023年第8期9卷 e19281页

作者： Liang, Chen-Wei Chang, Chia-Chun Hsiao, Chun-Yun Liang, Chen-Jui Department of Biomechatronic Engineering National Ilan University Yilan Taiwan Master Program in UAV Application and Smart Agriculture National Ilan University Yilan Taiwan Department of Environmental Engineering and Science Feng Chia University Taichung Taiwan International School of Technology and Management Feng Chia University Taichung Taiwan Artificial Intelligence Technology and Application Bachelor Program Feng Chia University Taichung Taiwan

Scattering visiometers are widely used to measure atmospheric visibility;however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predictin

关键词： Air pollutants Extinction coefficient Fog-haze Koschmieder's law Machine learning Terrain effects

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