Application of the Bayesian-based big data model in the analysis of the source of air pollution

WANG Lijun; ZHOU Yu; WAN Lijuan; CHENG Liangliang

doi:10.3969/j.issn.1673-6141.2023.03.004

Journal of Atmospheric and Environmental Optics, Volume. 18, Issue 3, 227(2023)

Application of the Bayesian-based big data model in the analysis of the source of air pollution

WANG Lijun^*, ZHOU Yu, WAN Lijuan, and CHENG Liangliang

Author Affiliations

School of Electronic Information and Electrical Engineering, Hefei Normal University, Hefei 230601, China

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Figures & Tables(6)

Fig. 1. 8 main influence factors to PM_2.5 and their interrelation scatter matrix

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Fig. 2. Distribution of PM_2.5 air pollution in Hefei from 2013 to 2018

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Table 1. Data collection table for weather and air quality of Hefei City from 2013 to 2018

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Table 1. Data collection table for weather and air quality of Hefei City from 2013 to 2018

PM₁₀ concentration/(µg·m^-3)	SO₂ concentration/(µg·m^-3)	NO₂ concentration/(µg·m^-3)	CO concentration/(µg·m^-3)	O₃ concentration/(µg·m^-3)	Max temp./℃	Min temp./℃	Date number	PM_2.5 level
200	37	44	1.35	42	24	11	298	3
247	38	55	1.58	46	24	15	299	3
88	16	27	0.87	46	21	15	300	2
55	15	35	1.08	25	18	15	301	2
…	…	…	…	…	…	…	…	…

Table 2. Analysis results of PM_2.5 pollution model in Hefei City
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Table 2. Analysis results of PM_2.5 pollution model in Hefei City
PM_2.5
pollution level
Number of days Correctly recognized days
Recognition
rate/%
0 376 363 96.54
1 141 125 88.65
2 38 29 76.32
3 5 3 60.00
4 2 1 50.00

Table 3. Confusion matrix for model analysis results
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Table 3. Confusion matrix for model analysis results
PM_2.5
pollution level
0 1 2 3 4
0 0.97 0 0.16 0 0
1 0.03 0.89 0.03 0.40 0
2 0 0.09 0.76 0.00 0.50
3 0 0.02 0.03 0.60 0
4 0 0 0.02 0 0.50

Table 4. Main influence factors to PM_2.5 pollution and their partial derivatives of Mahalanobis distance

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Table 4. Main influence factors to PM_2.5 pollution and their partial derivatives of Mahalanobis distance

PM_2.5pollution level	PM₁₀	SO₂	NO₂	CO	O₃	Max temp.	Min temp.	Date number
5 (Severely polluted)	0.516	0.067	0.171	0.481	-0.991	0.096	0.270	-0.115
4 (Heavily polluted)	0.234	0.173	-0.302	0.371	-1.417	-0.026	0.211	-0.050
3 (Moderated polluted)	0.262	0.137	-0.247	0.375	-1.332	0.014	0.257	-0.068
2 (Lightly polluted)	0.437	1.085	-0.124	0.097	0.157	-0.256	-0.580	0.234

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Lijun WANG, Yu ZHOU, Lijuan WAN, Liangliang CHENG. Application of the Bayesian-based big data model in the analysis of the source of air pollution[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(3): 227

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Paper Information

Category:

Received: Feb. 8, 2022

Accepted: --

Published Online: Jun. 29, 2023

The Author Email: WANG Lijun (lijwang@qq.com)

DOI:10.3969/j.issn.1673-6141.2023.03.004

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Data collection table for weather and air quality of Hefei City from 2013 to 2018

Table 1. Data collection table for weather and air quality of Hefei City from 2013 to 2018

Table 2. Analysis results of PM2.5 pollution model in Hefei City

Table 2. Analysis results of PM2.5 pollution model in Hefei City

Table 3. Confusion matrix for model analysis results

Table 3. Confusion matrix for model analysis results

Table 4. Main influence factors to PM2.5 pollution and their partial derivatives of Mahalanobis distance

Table 4. Main influence factors to PM2.5 pollution and their partial derivatives of Mahalanobis distance

Table 2. Analysis results of PM_2.5 pollution model in Hefei City

Table 2. Analysis results of PM_2.5 pollution model in Hefei City

Table 4. Main influence factors to PM_2.5 pollution and their partial derivatives of Mahalanobis distance

Table 4. Main influence factors to PM_2.5 pollution and their partial derivatives of Mahalanobis distance