Regional Predictions of Air Pollution in Guangzhou: Preliminary Results and Multi-Model Cross-Validations

doi:10.3390/atmos13101527

HFCAS OpenIR

	Regional Predictions of Air Pollution in Guangzhou: Preliminary Results and Multi-Model Cross-Validations
	Qiao, Zhi 1,2,3; Cui, Shengcheng 1,2; Pei, Chenglei 4,5,6,7,8; Ye, Zhou 1,2,3; Wu, Xiaoqing1,2 ; Lei, Lei 8; Luo, Tao 1,2; Zhang, Zihan 1,2; Li, Xuebin1,2 ; Zhu, Wenyue1,2
	2022-10-01
发表期刊	ATMOSPHERE
通讯作者	Cui, Shengcheng(csc@aiofm.ac.cn)
摘要	A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13 degrees N, 113.26 degrees E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, -11.96% and -0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale.
关键词	WRF-Chem back propagation neural network long short-term memory air pollution Guangzhou
DOI	10.3390/atmos13101527
关键词[WOS]	PARTICULATE MATTER CONCENTRATION ; ARTIFICIAL NEURAL-NETWORK ; SHORT-TERM-MEMORY ; WRF-CHEM ; PM2.5 CONCENTRATIONS ; MODELING SYSTEM ; EASTERN CHINA ; QUALITY ; SENSITIVITY ; SATELLITE
收录类别	SCI
语种	英语
资助项目	Foundation of Key Laboratory of Science and the Technology Innovation of the Chinese Academy of Sciences[CXJJ-21S028] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA17010104]
项目资助者	Foundation of Key Laboratory of Science and the Technology Innovation of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000872188200001
出版者	MDPI
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/129920
专题	中国科学院合肥物质科学研究院
通讯作者	Cui, Shengcheng
作者单位	1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Atmospher Opt, Hefei 230031, Peoples R China 2.Adv Laser Technol Lab Anhui Prov, Hefei 230037, Peoples R China 3.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, 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 Key Lab Environm Protect & Resources Ut, Guangzhou 510640, Peoples R China 6.CAS Ctr Excellence Deep Earth Sci, Guangzhou 510640, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 8.Guangzhou Subbranch Guangdong Ecol & Environm Mon, Guangzhou 510060, Peoples R China
第一作者单位	中科院安徽光学精密机械研究所
通讯作者单位	中科院安徽光学精密机械研究所
推荐引用方式 GB/T 7714	Qiao, Zhi,Cui, Shengcheng,Pei, Chenglei,et al. Regional Predictions of Air Pollution in Guangzhou: Preliminary Results and Multi-Model Cross-Validations[J]. ATMOSPHERE,2022,13.
APA	Qiao, Zhi.,Cui, Shengcheng.,Pei, Chenglei.,Ye, Zhou.,Wu, Xiaoqing.,...&Zhu, Wenyue.(2022).Regional Predictions of Air Pollution in Guangzhou: Preliminary Results and Multi-Model Cross-Validations.ATMOSPHERE,13.
MLA	Qiao, Zhi,et al."Regional Predictions of Air Pollution in Guangzhou: Preliminary Results and Multi-Model Cross-Validations".ATMOSPHERE 13(2022).