减少大气排放的协同效应评估方法基于深度学习的融合模型
Dong Hong-Zhao1, Guo Hong-Mei1, Liao Shi-Kai1
1Joint Institute of Intelligent Transportation and Environment, Zhejiang University of Technology, Hangzhou, China.
Journal of hazardous materials
|December 5, 2024
概括
一个新的深度学习模型GR-BILSTM准确地预测工业排放对空气质量的影响. 这有助于制定针对工业园区的有针对性的污染控制政策,确定主要贡献者,如SO2,NOx和TSP.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 由于缺乏将工业排放与污染水平联系起来的非线性模型,有效的空气质量管理受到阻碍.
- 精确模拟排放影响对于减轻严重的污染峰值和为政策提供信息至关重要.
研究的目的:
- 提出一种新的方法来评估大气排放减少的协同效应.
- 模拟工业排放对空气质量的影响,并制定有针对性的控制政策.
主要方法:
- 开发了一个深度学习融合模型,GR-BILSTM,集成生成对抗网络 (GAN) 和ResNet-BILSTM.
- 采用扰动分析来量化工业园区排放对PM2.5度的影响.
- 根据LSTM,BILSTM和CNN-BILSTM验证了模型性能,评估了适配的准确性.
主要成果:
- 与现有模型相比,GR-BILSTM 模型显示出更高的适配精度.
- 干扰分析量化了排放减排比对后续PM2.5度的影响.
- 确定了SO2 (16%),NOx (15%) 和TSP (18%) 作为工业园区PM2.5污染的主要原因.
结论:
- 该GR-BILSTM模型提供了准确的排放-空气质量关系的模拟.
- 这些发现为制定工业园区目标减排政策提供了定量基础.
- 了解SO2,NOx和TSP等污染物的具体贡献对于有效的环境管理至关重要.
相关概念视频
01:13Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
01:28Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...