利用人工智能和先进建模来精确控制臭氧:以数据为导向的空气质量管理方法
Guan-Yu Lin1, Jih-Wen Zheng1, Yen-Ping Peng2
1Department of Environmental Science and Engineering, Tunghai University, Taichung 407224, Taiwan.
Journal of hazardous materials
|May 30, 2025
概括
减少乙烯排放大大降低了臭氧污染. 这项研究强调乙烯 (C2H4) 是一种关键的臭氧前体,50%的削减可能会使臭氧减少38.5%.
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) 是由挥发性有机化合物 (VOC) 和氧化 (NOx) 形成的二次污染物.
- 臭氧及其前体有助于二次气溶,影响环境质量和人类健康.
- 有效的空气质量管理需要了解和量化臭氧前体的影响.
研究的目的:
- 开发一个数学建模框架来评估VOC和NOx变化的对臭氧形成的影响.
- 量化臭氧前体的光化学损失,并逆向计算初始排放度.
- 确定主要的臭氧前体污染物及其对台湾南部臭氧形成的贡献.
主要方法:
- 整合Kriging,臭氧异构层绘图研究 (OZIPR) 模型,光化学损失分析和臭氧形成潜力 (OFP) 评估.
- 机器学习技术的应用,特别是人工神经网络 (ANN),用于前体影响估计.
- 对光化学臭氧前体损失的量化和初始排放度的逆向计算.
主要成果:
- 该ANN模型表现出强的性能,R2 = 0.804和RMSE = 0.08ppbv.
- 乙烯 (C2H4) 被确定为台湾南部最有影响力的臭氧前体.
- 预计乙烯排放量减少50%可能会使臭氧度降低38.5%.
结论:
- 政策制定者应该优先考虑对乙烯排放的监管,特别是来自炼油和石化行业的排放.
- 开发的建模框架为有效的臭氧控制策略提供了宝贵的见解.
- 调查结果支持环境机构在空气质量管理和对臭氧减少的监管执法方面.
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