基于深度学习方法,准确预测近表面酸 (HONO) 的时空变化
Xuan Li1, Can Ye1,2, Keding Lu1
1State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
Environmental science & technology
|July 10, 2024
概括
一个深度神经网络使用空气质量数据准确预测酸 (HONO) 水平. 减少氧化 (NOx) 会减少HONO,有助于控制污染.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 气态酸 (HONO) 是基基 (OH) 的关键前体,影响大气氧化和二次污染物形成.
- 当前的化学模型在模拟HONO时面临不确定性,因为对其形成和消除机制的理解不完全.
研究的目的:
- 开发一个深度神经网络 (DNN) 模型,使用常规空气质量和气象数据来预测HONO度.
- 评估二氧化 (NO2) 对HONO形成的影响,并预测不同NOx减排场景下的未来HONO趋势.
主要方法:
- 通过中国大城市的空气质量数据 (O3,NO2,CO,PM2.5) 和气象参数 (温度,湿度,太阳顶角,季节) 来训练DNN模型.
- 使用SHapely添加式扩展 (SHAP) 方法来确定关键预测因素,NO2被发现是最有影响力的.
- DNN模型的性能与独立的测试集进行了验证,并与受观察约束的盒子模型进行了比较.
主要成果:
- DNN模型表现出强大的性能,准确地复制了HONO的时空变化 (r2 = 0.94在训练中,r2 = 0.79在测试组).
- 二氧化 (NO2) 被确定为影响HONO预测的最重要因素.
- 模拟表明,减少30-50%的NOx排放可能导致夏季HONO水平下降27-44%.
结论:
- 深度学习为预测HONO度提供了可行和有效的方法,补充了传统的化学模型.
- 减少氧化物排放具有双重好处:减少臭氧和二次颗粒物,同时降低HONO和原始基的生产率.
- 建议采取严格的NOx减排战略,共同控制空气污染,包括臭氧和二次PM2.5.5.
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