Knowledge Management System Of Guangzhou Institute of Geochemistry，CAS
Zhao, Qilong1; Jiang, Kui2; Talifu, Dilinuer1; Gao, Bo3; Wang, Xinming4,5 ; Abulizi, Abulikemu1; Zhang, Xiaohui1; Liu, Bowen1
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| Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model | |
| Source Publication | ATMOSPHERE
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| 2023 | |
| Volume | 14Issue:1Pages:16 |
| DOI | 10.3390/atmos14010160 |
| Language | 英语 |
| WOS Research Area | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| Abstract | Accurate ozone concentration simulation can provide a health reference for people's daily lives. Simulating ozone concentrations is a complex task because near-surface ozone production is determined by a combination of volatile organic compounds (VOCs) and NOx emissions, atmospheric photochemical reactions, and meteorological factors. In this study, we applied a genetic algorithm-optimized back propagation (GA-BP) neural network, multiple linear regression (MLR), BP neural network, random forest (RF) algorithm, and long short-term memory network (LSTM) to model ozone concentrations in three regions of Xinjiang, China (Urumqi, Hotan, and Dushanzi districts) for the first time by inputting wind speed, humidity, visibility, temperature, and wind direction. The results showed that the average relative errors of the model simulations in the Urumqi, Hotan, and Dushanzi districts were BP (61%, 14%, and 16%), MLR (97%, 14%, and 23%), RF (39%, 11%, and 14%), LSTM (50%, 12%, and 16%), and GA-BP (16%, 4%, and 6%) and that the significance coefficients R-2 were BP (0.73, 0.65, and 0.83), MLR (0.68, 0.62, and 0.74), RF (0.85, 0.80, and 0.88), LSTM (0.78, 0.74, and 0.85), and GA-BP (0.92, 0.93, and 0.94), respectively, with the simulated values of GA-BP being the closest to the true values. The GA-BP model results showed that among the 100 samples with the same wind speed, humidity, visibility, temperature, and wind direction data, the highest simulated ozone concentrations in the Urumqi, Hotan, and Dushanzi districts were 173.5 mu g/m(3), 114.3 mu g/m(3), and 228.4 mu g/m(3), respectively. The results provide a theoretical basis for the effective control of regional ozone pollution in urban areas (Urumqi), dusty areas (Hotan), and industrial areas (Dushanzi) in Xinjiang. |
| Keyword | meteorological factors atmospheric conditions ozone concentration genetic algorithm BP neural network Xinjiang region |
| WOS ID | WOS:000914347900001 |
| Indexed By | SCI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.gig.ac.cn/handle/344008/72576 |
| Collection | 有机地球化学国家重点实验室 |
| Corresponding Author | Talifu, Dilinuer |
| Affiliation | 1.Xinjiang Univ, Coll Chem Engn, Xinjiang Key Lab Coal Clean Convers & Chem Engn, Urumqi 830017, Peoples R China 2.Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan 430072, Peoples R China 3.Minist Ecol & Environm, South China Inst Environm Sci, Guangdong Prov Key Lab Water & Air Pollut Control, Guangzhou 510655, Peoples R China 4.Chinese Acad Sci, Guangzhou Inst Geochem, State Key Lab Organ Geochem, Guangzhou 510640, Peoples R China 5.Chinese Acad Sci, Guangzhou Inst Geochem, Guangdong Hong Kong Macao Joint Lab Environm Pollu, Guangzhou 510640, Peoples R China |
| Recommended Citation GB/T 7714 | Zhao, Qilong,Jiang, Kui,Talifu, Dilinuer,et al. Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model[J]. ATMOSPHERE,2023,14(1):16. |
| APA | Zhao, Qilong.,Jiang, Kui.,Talifu, Dilinuer.,Gao, Bo.,Wang, Xinming.,...&Liu, Bowen.(2023).Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model.ATMOSPHERE,14(1),16. |
| MLA | Zhao, Qilong,et al."Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model".ATMOSPHERE 14.1(2023):16. |
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