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atmospheric environment monitoring technology and equipment in China:A review and outlook

Journal of Environmental Sciences 2023年第1期123卷 41-53页

作者： Youwen Sun Ting Yang Huaqiao Gui Xin Li Weigang Wang Jun Duan Shushuai Mao Hao Yin Bin Zhou Jianlei Lang Haijin Zhou Cheng Liu Pinhua Xie Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine MechanicsHFIPSChinese Academy of SciencesHefei 230031China Department of Precision Machinery and Precision Instrumentation University of Science and Technology of ChinaHefei 230026China State Key Laboratory of atmospheric Boundary Layer Physics and atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of ChemistryChinese Academy of SciencesBeijing 100190China Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental&Energy EngineeringBeijing University of TechnologyBeijing 100124China Shanghai Key Laboratory of atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science and EngineeringFudan UniversityShanghai 200438China Center for Excellence in Regional atmospheric Environment Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes University of Science and Technology of ChinaHefei 230026China Anhui Province Key Laboratory of Polar Environment and Global Change University of Science and Technology of ChinaHefei 230026China

Accurate monitoring of the atmospheric environment and its evolution are important for understanding the sources,chemical mechanisms,and transport processes of air pollution and carbon emissions in China,and for regulatory and control purposes.This study gives an overview of atmospheric environment monitoring technology and equipment in China and summarizes the major achievements obtained in recent years.China has made great progress in the development of atmospheric environment monitoring technology and equipment with decades of effort.The manufacturing level of atmospheric environment monitoring equipment and the quality of products have steadily improved,and a technical&production system that can meet the requirements of routine monitoring activities has been initiated.It is expected that domestic atmospheric environment monitoring technology and equipment will be able to meet future demands for routine monitoring activities in China and provide scientific assistance for addressing air pollution problems.

关键词： atmospheric pollution atmospheric environment atmospheric monitoring Environmental instrument

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A review of Space-Air-Ground integrated remote sensing techniques for atmospheric monitoring

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Journal of Environmental Sciences 2023年第1期123卷 3-14页

作者： Bin Zhou Sanbao Zhang Ruibin Xue Jiayi Li Shanshan Wang Shanghai Key Laboratory of atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science and EngineeringFudan UniversityShanghai 200433China Institute of Eco-Chongming(IEC) Shanghai 202162China Zhuhai Fudan Innovation Institute Zhuhai 519000China Institute of atmospheric Sciences Fudan UniversityShanghai 200433China

Currently,the three-dimensional(3D)distribution and characteristics of air pollution cannot be understood based on the application of any single atmospheric monitoring technology.Long-term,high-precision and large-scale 3D atmospheric monitoring might become practical by combining heterogeneous modern technologies;for this purpose,the SpaceAir-Ground integrated system is a promising concept.In this system,optical remote sensing technologies employing fixed or mobile platforms are used as the main means for groundbased observations.Tethered balloons,unmanned aerial vehicles(UAV)and airborne platforms serve as the air-based observation segment.The final part,satellite remote sensing,corresponds to space-based observations.Aside from obtaining the 3D distribution of air pollution,research on emission estimation and pollution mechanisms has been extensively implemented based on the strengths of this system or some portion of it.Moreover,further research on the fusion of multi-source data,optimization of inversion algorithms,and coupling with atmospheric models is of great importance to the realization of this system.

关键词： Space-Air-Ground atmospheric monitoring 3D distribution

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Advances in coastal ocean boundary layer detection technology and equipment in China

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Journal of Environmental Sciences 2023年第1期123卷 156-168页

作者： Haitao Zheng Youwen Sun Tao Luo Xueling Cheng Shiyong Shao Shouyin Zheng Bangyi Tao Bin Chen Qianguang Tu Kan Huang Bingbing Wang Mian Wang Xiaoquan Song Tianshu Zhang Yin Cheng Jianguo Liu Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine MechanicsHFIPSChinese Academy of SciencesHefei 230031China Department of Precision Machinery and Precision Instrumentation University of Science and Technology of ChinaHefei 230026China Key Laboratory of atmospheric Optics Anhui Institute of Optics and Fine MechanicsChinese Academy of SciencesHefei 230031China Advanced Laser Technology Laboratory of Anhui Province Hefei 230031China Institute of atmospheric Physics Chinese Academy of SciencesBeijing 100029China College of Earth Sciences University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou 310012China School of Surveying and Municipal Engineering Zhejiang University of Water Resources and Electric PowerHangzhou 310018China Center for atmospheric Chemistry Study Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3)Department of Environmental Science and EngineeringFudan UniversityShanghai 200433China State Key Laboratory of Marine Environmental Science College of Ocean and Earth SciencesXiamen UniversityXiamen 361005China Meteorological Observation Centre China Meteorological AdministrationBeijing 100081China College of Marine Technology Faculty of Information Science and EngineeringOcean University of ChinaQingdao 266100China

Accurate and comprehensive knowledge of the atmospheric environment and its evolution within the coastal ocean boundary layer are necessary for understanding the sources,chemical mechanisms,and transport processes of air pollution in land,sea,and atmosphere.We present an overview of coastal ocean boundary layer detection technology and equipment in China and summarize the progress and main achievements in recent years.China has developed a series of coastal ocean boundary layer detection technologies,including Light Detection and Ranging(LIDAR),turbulent exchange analyzer,air-sea flux analyzer,stereoscopic remote sensing of air pollutants,and oceanic aerosol detection equipment to address the technical bottleneck caused by harsh environmental conditions in coastal ocean regions.Advances in these technologies and equipment have provided scientific assistance for addressing air pollution issues and understanding land-sea-atmosphere interactions over coastal ocean regions in China.In the future,routine atmospheric observations should cover the coastal ocean boundary layer of China.

关键词： atmospheric pollution Coastal ocean atmospheric monitoring Environmental instrument

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Highly-sensitive NO,NO2,and NH3 measurements with an open-multipass cell based on mid-infrared wavelength modulation spectroscopy

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Chinese Physics B 2018年第4期27卷 250-257页

作者：陈祥杨晨光胡迈沈建康牛二超许振宇范雪丽姚路何亚柏刘建国阚瑞峰魏敏 Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine MechanicsChinese Academy of Sciences University of Science and Technology of China Jiangsu Environmental monitoring Center

A compact prototype based on mid-infrared wavelength modulation spectroscopy（WMS）is developed for the simul-taneous monitoring of NO,NO2,and NH3 in the urban area.Three quantum cascade lasers（QCLs）with central frequencies around 1900.0 cm^-1,1600.0 cm^-1,and 1103.4 cm^-1are used for NO,NO2,and NH3detections,respectively,by timedivision multiplex.An open-path multi-pass cell of 60-m optical path length is applied to the instrument for high sensitivity and reducing the response time to less than 1 s.The prototype achieves a sub-ppb detection limit for all the three target gases with an average time of about 100 s.The instrument is installed in the Jiangsu environmental monitoring center to conduct performance tests on ambient air.Continuous 24-hour measurements show good agreement with the results of a reference instrument based on the chemiluminescence technique.

关键词： wavelength modulation spectroscopy optical sensing atmospheric monitoring time division multiplexing quantum cascade laser mid-infrared laser spectroscopy

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Application of Regularized Logistic Regression and Artificial Neural Network Model for Ozone Classification across El Paso County, Texas, United States

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Journal of Data Analysis and Information Processing 2023年第3期11卷 217-239页

作者： Callistus Obunadike Adekunle Adefabi Somtobe Olisah David Abimbola Kunle Oloyede Department of Computer Science Austin Peay State University Clarksville USA

This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. The LR and ANN algorithms are employed to train the datasets. The models demonstrate a remarkably high classification accuracy of 89.3% in predicting ozone levels on a given day. Evaluation metrics reveal that both the ANN and LR models exhibit accuracies of 89.3% and 88.4%, respectively. Additionally, the AUC values for both models are comparable, with the ANN achieving 95.4% and the LR obtaining 95.2%. The lower the cross-entropy loss (log loss), the higher the model’s accuracy or performance. Our ANN model yields a log loss of 3.74, while the LR model shows a log loss of 6.03. The prediction time for the ANN model is approximately 0.00 seconds, whereas the LR model takes 0.02 seconds. Our odds ratio analysis indicates that features such as “Solar radiation”, “Std. Dev. Wind Direction”, “outdoor temperature”, “dew point temperature”, and “PM10” contribute to high ozone levels in El Paso, Texas. Based on metrics such as accuracy, error rate, log loss, and prediction time, the ANN model proves to be faster and more suitable for ozone classification in the El Paso, Texas area.

关键词： Machine Learning Ozone Prediction Pollutants Forecasting atmospheric monitoring Air Quality Logistic Regression Artificial Neural Network

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