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浙江大学学报(工学版)
浙江大学学报(工学版)  2024, Vol. 58 Issue (6): 1296-1304    DOI: 10.3785/j.issn.1008-973X.2024.06.019
环境工程     
基于遗传-模式搜索算法的微尺度管控区域大气污染物PM2.5溯源
董红召1,金灿1,唐伟2,佘翊妮1,*(),林盈盈1
1. 浙江工业大学 智能交通系统研究所，浙江 杭州 310014
2. 杭州市生态环境科学研究院，浙江 杭州 310014
Back-calculation of outdoor PM2.5 pollutant source around microscale controlled area by genetic-pattern search algorithm
Hongzhao DONG1,Can JIN1,Wei TANG2,Yini SHE1,*(),Yingying LIN1
1. ITS Joint Research Institute, Zhejiang University of Technology, Hangzhou 310014, China
2. Hangzhou Institute of Environment Sciences, Hangzhou 310014, China
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摘要：

针对微尺度管控区域可能发生的大气污染提出有效的靶向诊断方法?结合高斯烟羽模型和遗传-模式搜索算法的大气污染物分布式溯源方法. 将污染源反算模型得到的污染物理论质量浓度与传感器网络观测值的数据对应关系作为目标函数，使用模式搜索算法嵌入遗传算法加快反算模型的搜索过程，反算得到污染源强度和位置. 依托杭州市亚运板球场馆大气感知器网络进行实验验证，监测2021年10月PM2.5质量浓度、气象数据，对所提出的混合式大气污染溯源方法进行实验验证. 实验结果表明：改进遗传-模式搜索算法对于多维变量的搜索效果较好，能快速精准地反算污染源的位置和强度，可以为微尺度管控区域突发性气体污染防治提供应急决策参考.
关键词： 源强反算;  遗传-模式搜索算法;  高斯烟羽模型;  微尺度管控;  颗粒物污染溯源    
Abstract:

An effective targeted diagnosis method, distributed traceability method for atmospheric pollutants combining Gaussian plume model and genetic-pattern search algorithm, was proposed, aiming at air pollutants that may occur in the micro-scale control area. The corresponding relationship between the calculated pollutant concentration obtained from pollution backcalculation model and the observation value of the monitoring sensor was used as the objective function. Pattern search algorithm was embedded in the genetic algorithm to speed up the search process of the inverse calculation model, then to inversely calculate the intensity and location of the pollution source. A validation experiment was conducted by monitoring the PM2.5 mass concentration, meteorology and other data based on the atmospheric sensor data of Hangzhou Asian Games cricket stadium in October 2021. Results showed, compared with other methods, the effect of the improved genetic-pattern search algorithm for multi-dimensional variables was better, and the location and intensity of pollution sources could be calculated more quickly and accurately. This research can provide suggested solution for environmental emergencies of air pollution in micro-scale control regions.
Key words: source inversion    genetic-pattern search algorithm    Gaussian plume model    micro-scale control    tracing of particulate matter pollution
收稿日期: 2023-05-29 出版日期: 2024-05-25
CLC:  X 51  
基金资助: 浙江省公益技术研究资助项目（LGF20F030001）；杭州市农业与社会发展科研资助项目（20201203B158）.
通讯作者: 佘翊妮     E-mail: qiche@zjut.edu.cn
作者简介: 董红召（1969—），男，教授，从事智能交通系统、智能环保研究. orcid.org/0000-0001-5905-597X. E-mail：its@zjut.edu.cn
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董红召
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佘翊妮
林盈盈

引用本文:

董红召,金灿,唐伟,佘翊妮,林盈盈. 基于遗传-模式搜索算法的微尺度管控区域大气污染物PM2.5溯源[J]. 浙江大学学报(工学版), 2024, 58(6): 1296-1304.

Hongzhao DONG,Can JIN,Wei TANG,Yini SHE,Yingying LIN. Back-calculation of outdoor PM2.5 pollutant source around microscale controlled area by genetic-pattern search algorithm. Journal of ZheJiang University (Engineering Science), 2024, 58(6): 1296-1304.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.06.019    或    https://www.zjujournals.com/eng/CN/Y2024/V58/I6/1296

v/（m·s?1）白天（太阳辐射）夜晚（云量）
强中弱多云无云
<2.0AA、BBEF
2.0~3.0A、BBCEF
3.0~5.0B、CB、CCDE
5.0~6.0C、DC、DDDD
>6.0DDDDD
表 1  高斯烟羽模型中的大气稳定度分类
大气稳定度$ {\mathrm{\sigma }}_{y} $$ {\mathrm{\sigma }}_{z} $
A、B$ {0.32(1+0.000\;4x）}^{-{1}/{2}} $$ {0.24(1+0.000\;1x）}^{-{1}/{2}} $
C$ {0.22(1+0.000\;4x）}^{-{1}/{2}} $$ 0.20x $
D$ {0.16(1+0.000\;4x）}^{-{1}/{2}} $$ {0.14(1+0.000\;3x）}^{-{1}/{2}} $
E、F$ {0.11(1+0.000\;4x）}^{-{1}/{2}} $$ {0.08(1+0.015\;0x）}^{-{1}/{2}} $
表 2  高斯烟羽模型中的扩散系数
图 1  遗传-模式搜索算法流程
图 2  大气污染感知器分布
编号数据类型经度纬度N
1PM2.5、PM10质量浓度120.0332° E30.2305° N996 906
2风速风向、PM2.5、PM10质量浓度120.0360° E30.2322° N62 166
3风速风向、PM2.5、PM10质量浓度120.0359° E30.2289° N588 295
4PM2.5、PM10质量浓度120.0336° E30.2283° N540 618
5风速风向、PM2.5、PM10质量浓度120.0311° E30.2274° N1 027 629
6PM2.5、PM10质量浓度120.0541° E30.2355° N488 188
表 3  大气感知网络中感知器位置及数据量
图 3  大气感知器网络中各感知器的PM2.5质量浓度监测数据
图 4  预处理后的PM2.5质量浓度数据
传感器序号坐标/m$ {\rho }_{\mathrm{m}\mathrm{e}\mathrm{a}}^{{i}} $/（μg·m?3）
1（287.5，345.7）19
2（70.6，135.1）75
3（252.3，37.1）20
4（83.9，?30.3）87
5（?56.2，?98.8）49
6（?230.0，?209.4）19
表 4  传感器位置及PM2.5质量浓度观测值
反算参数q0/（g·s?1）x0/my0/m
期望值1.3900
运行结果1.67?7.511.3
表 5  遗传-模式搜索算法反算结果
图 5  10月污染源位置搜索热点
算法t/sq0/（g·s?1）x0/my0/mΔq0/%Δx0/mΔy0/m
GA-PS2.441.29?7.14.87.2?7.14.8
GA-NM2.571.73?21.14.324.5?21.14.3
PSO-NM2.651.6541.1?35.018.741.1?35.0
表 6  各算法响应时间和搜索准确度比较
图 6  各算法搜索热点对比
图 7  不同算法坐标反演误差稳定性比较
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