4 results match your criteria: "Zhejiang Hangzhou Ecological and Environmental Monitoring Center[Affiliation]"

Currently, the absence of a nonlinear response between air quality and industrial emissions, which accurately captures the complex relationship between pollution levels and emission sources, poses a significant challenge in the formulation of effective control policies. For the purpose of effectively managing industrial emissions and mitigating severe pollution peaks, a new method for evaluating the synergistic effects of atmospheric emission reduction was proposed. The objective of this method is to simulate the impact of emissions on air quality.

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Aiming at the problem that the single machine learning model has low prediction accuracy of daily average ozone concentration, an ozone concentration prediction method based on the fusion class Stacking algorithm (FSOP) was proposed, which combined the statistical method ordinary least squares (OLS) with machine learning algorithms and improved the prediction accuracy of the ozone concentration prediction model by integrating the advantages of different learners. Based on the principle of the Stacking algorithm, the observation data of the daily maximum 8h ozone average concentration and meteorological reanalysis data in Hangzhou from January 2017 to December 2022 were used. Firstly, the specific ozone concentration prediction models based on the light gradient boosting machine (LightGBM) algorithm, long short-term memory model (LSTM), and Informer model were established, respectively.

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Evaluation of Low-Cost CO Sensors Using Reference Instruments and Standard Gases for Indoor Use.

Sensors (Basel)

April 2024

Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA.

CO monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO sensors from ±30 ppm to 0.

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Improved estimation of CO emissions from thermal power plants based on OCO-2 XCO retrieval using inline plume simulation.

Sci Total Environ

February 2024

Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China.

CO emissions from power plants are the dominant source of global CO emissions, thus in the context of global warming, accurate estimation of CO emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions.

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