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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (6): 1296-1304    DOI: 10.3785/j.issn.1008-973X.2024.06.019
    
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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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     
Received: 29 May 2023      Published: 25 May 2024
CLC:  X 51  
Fund:  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）.
Corresponding Authors: Yini SHE     E-mail: qiche@zjut.edu.cn
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Hongzhao DONG
Can JIN
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Cite this article:

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.

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


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

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 
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
Tab.1 Atmospheric stability classification of Gaussian plume model
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}} $
Tab.2 Diffusion coefficient of Gaussian plume model
Fig.1 Algorithm process of genetic-pattern search algorithm
Fig.2 Distribution of air pollution sensors
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
Tab.3 Sensors locations and data volume of atmospheric perceptron network
Fig.3 PM2.5 mass concentration monitoring data of atmospheric perceptron network
Fig.4 Pre-processed data of PM2.5 mass concentration
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
Tab.4 Sensors locations and PM2.5 mass concentration monitoring values
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
Tab.5 Inverse results of genetic-pattern search algorithm
Fig.5 Search hotspot of October pollution source location
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
Tab.6 Comparison of response time and search accuracy of various algorithms
Fig.6 Search-hotspot comparison of various algorithms
Fig.7 Comparison of stability in coordinate inversion of different algorithms
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