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The oxidation of an organic compound in the presence of air or oxygen is called autoxidation. For example, cumene reacts with oxygen to form hydroperoxide. Autoxidation involves initiation, propagation, and termination steps. Many organic compounds are susceptible to autoxidation—especially ethers in the presence of oxygen, which form hydroperoxides. Even though this reaction is slow, old ether bottles contain small amounts of peroxide, which leads to laboratory explosions during ether...
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In ozonolysis, ozone is used to cleave a carbon–carbon double bond to form aldehydes and ketones, or carboxylic acids, depending on the work-up.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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[基于可解释的机器学习的城市臭氧驱动因素]

Chao-Long Wang1, Lian Xue2, Yi-Sheng Zhang1

  • 1School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266525, China.

Huan jing ke xue= Huanjing kexue
|May 20, 2025
PubMed
概括

气象因素在青岛显著推动臭氧 (O3) 污染,为其形成贡献了67.7%. 污染物排放占32.3%,太阳辐射,温度和湿度发挥着关键作用.

关键词:
青岛 青岛 青岛在SHAP模块中,我们可以使用SHAP模块.驱动因素 驱动因素 驱动因素机器学习是机器学习.臭氧 (O3) 是一种有毒的物质.

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科学领域:

  • 大气化学 大气化学
  • 环境科学 环境科学
  • 数据科学数据科学数据科学

背景情况:

  • 臭氧 (O3) 污染是城市地区的重大环境问题.
  • 了解O3形成的驱动因素对于有效的污染控制策略至关重要.
  • 青岛是一个沿海城市,面临着与空气质量和O3污染有关的挑战.

研究的目的:

  • 调查气象因素和大气污染物排放对青岛O3污染的影响.
  • 量化气象因素和排放对O3形成的贡献.
  • 确定影响O3度的主导因素的空间和时间变化.

主要方法:

  • 使用极端梯度增强 (XGBoost) 模型与夏普利添加式扩展 (SHAP) 结合进行分析.
  • 从2019年到2023年在青岛的16个地点收集的数据.
  • 分析了表面太阳辐射,温度,相对湿度,边界层高度,风,NO2,PM2.5和PM10对O3度的影响.

主要成果:

  • 气象因素对O3形成有67.7%的贡献,而排放量占32.3%.
  • 表面的太阳辐射,温度和相对湿度对O3形成产生了重大影响,具有特定的时间模式.
  • NO2和PM2.5与O3呈现复杂的关系,因时间,季节和地点而异. 表面太阳辐射和NO2是超过O3标准的日子里的主要驱动因素.

结论:

  • 天气条件是青岛O3污染的主要驱动因素.
  • 污染物排放,特别是NO2和PM2.5,起着重要的作用,特别是在冬季和高污染日.
  • 有效的O3控制策略必须考虑气象影响和减排,根据特定地区和季节量身定制.