Analysis of PM2.5 Components During Continuous Spring Pollution in Sichuan Basin Based on WRF-CMAQ Model

doi:10.16258/j.cnki.1674-5906.2024.08.006

Ecology and Environment ›› 2024, Vol. 33 ›› Issue (8): 1214-1226.DOI: 10.16258/j.cnki.1674-5906.2024.08.006

• Research Article [Environmental Science] • Previous Articles Next Articles

Analysis of PM_2.5 Components During Continuous Spring Pollution in Sichuan Basin Based on WRF-CMAQ Model

WANG Congcong¹^,²(), ZHANG Xiaoling¹^,³^,^*(), LEI Yu¹, HUANG Xiaojuan¹^,⁴, WANG Jingyi¹, YIN Lihao¹, WANG Chuanyang¹

1. Plateau Atmosphere and Environment Key Laboratory of Sichuan Province/College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, P. R. China
2. Tai’an Meteorological Bureau, Tai’an 271001, P. R. China
3. Chengdu Plain Urban Meteorology and Environment Field Scientific Observation and Research Station of Sichuan Province, Chengdu 610225, P. R. China
4. Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3)/Department of Environmental Science & Engineering/Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, P. R. China

Received:2023-10-18 Online:2024-08-18 Published:2024-09-25

基于WRF-CMAQ模型的四川盆地春季持续臭氧污染过程中PM_2.5组分解析

王聪聪¹^,²(), 张小玲¹^,³^,^*(), 雷雨¹, 黄小娟¹^,⁴, 王婧怡¹, 尹黎昊¹, 王传扬¹

1.成都信息工程大学大气科学学院/高原大气与环境四川省重点实验室，四川成都 610225
2.泰安市气象局，山东泰安 271001
3.成都平原城市气象与环境四川省野外科学观测研究站，四川成都 610225
4.复旦大学环境科学与工程系/上海市大气颗粒物污染防治重点实验室，上海 200438

通讯作者: *张小玲。E-mail: xlzhang@ium.cn
作者简介:王聪聪（1999年生），女，硕士研究生，主要从事大气物理学与大气环境研究。E-mail: 529890272@qq.com
基金资助:
国家重点研发计划课题(2023YFC3709301);国家自然科学基金项目(42205100);成都信息工程大学引进人才科研启动项目(KYTZ202127);成都信息工大学科技创新能力提升计划重大团队项目(KYTD202202)

Abstract

Abstract:

To investigate the pollution characteristics of PM_2.5 components and their relationship with atmospheric oxidation in typical cities in the Sichuan Basin, this study used the WRF-CMAQ model to simulate and analyze the PM_2.5 components during a sustained O₃ pollution process in the spring of 2020. O_{3, max} was used as an indicator to classify the photochemical activity as light, low, and medium photochemical levels to analyze the characteristics of PM_2.5 components and their contributions to PM_2.5at different photochemical levels. The results showed that high-level air was dominated by either stable weather conditions or downwelling airflow during the O₃ pollution period in the Sichuan Basin, and that the sea-level pressure was high, which corresponded to sunny weather. This is beneficial for photochemical reactions, and facilitates the generation of O₃ and secondary aerosols. Pollution hotspots were mainly concentrated in the Chengdu Plain and western Chongqing. The high concentration areas of PM_2.5, and NO₂ coincided with areas of O₃ pollution. In the early stage of pollution, OC and NO₃⁻ contributed the most to PM_2.5, whereas in the late stage, OC and SO₄²⁻ contributed the most to PM_2.5. In Dazhou, NO₃⁻ and NH₄⁺ concentrations reached their highest levels at low photochemical levels, and then decreased with increasing photochemical levels. OC and SO₄²⁻ showed strong positive correlations with photochemical levels. For the daily averaged variations, O₃ and PM_2.5 were positively correlated with photochemical levels, and the daily changes in EC, NO₂, and NO₃⁻ showed similar variations to those of PM_2.5. The concentration of NO₃⁻ at night was higher in Zigong than that in the other three cities. OC had a higher concentration at noon, and later, the concentration of SO₄²⁻ did not show significant variation at light and low photochemical levels, but was directly proportional to the photochemical level. In Chengdu and Dazhou, the proportion of Secondary Organic Carbon (SOC) increased with changes in photochemical levels from light to medium. In Deyang and Zigong, the proportion of SOC switched from increasing to decreasing when photochemical levels ranged from light to medium. The nitrogen oxidation rate (NOR) and sulfur oxidation rate (SOR) at different photochemical levels in the four cities exceeded 0.1, indicating that secondary conversion became remarkable. The SOR value was significantly greater than that of NOR, and the secondary conversion rate of SO₂ was higher. The methods and conclusions of this study are not only valuable for comprehensive air environment remediation in the Sichuan Basin but can also provide a reference for the prevention and control of air pollution in other regions.

Key words: persistent ozone pollution, PM_2.5 components, atmospheric oxidation, numerical simulation, meteorological condition, Sichuan Basin

摘要：

为探究四川盆地典型城市PM_2.5组分的污染特征及其与大气氧化性的关系，利用WRF-CMAQ模型对2020年春季一次持续O₃污染过程中PM_2.5组分进行模拟分析，并将O_{3, max}作为光化学活性指标，划分出轻、低、中3种光化学水平，对不同光化学水平下PM_2.5主要组分及贡献的变化特征。结果表明，四川盆地发生O₃污染期间，高空为稳定的天气形势或下沉气流，海平面气压场为高压，对应晴好天气，有利于光化学反应及O₃和二次气溶胶的生成。污染热点区域主要集中在成都平原和重庆西部，PM_2.5和NO₂的高浓度区与O₃污染发生的区域相吻合。污染前期，OC和NO₃⁻对PM_2.5的贡献较大；污染后期，OC和SO₄²⁻对PM_2.5的贡献较大。达州的NO₃⁻和NH₄⁺等离子在低光化学水平下浓度最高，光化学水平再升高反而出现下降的趋势。OC和SO₄²⁻与光化学水平呈较好的正相关关系。对于平均日变化情况，O₃和PM_2.5与光化学水平呈正相关，EC、NO₂和NO₃⁻的日变化情况均与PM_2.5类似，自贡夜间 $N O 3 −$ 浓度相对于其他3个城市较高，OC在中午和午后出现较高值，SO₄²⁻在轻和低光化学水平下变化不明显，浓度与光化学水平成正比。成都和达州，二次有机碳（SOC）随着光化学水平由轻到中的变化，占比不断增加。而对德阳和自贡，SOC随着光化学水平由轻到中占比先增加后减少。4个城市不同光化学水平下的氮氧化率（NOR）和硫氧化率（SOR）基本超过了0.1，说明二次转化较为明显，且SOR的值明显大于NOR，SO₂二次转化率更高。该文的研究方法和结论不仅对四川盆地的空气环境综合整治有重要价值，而且可为其它地区空气污染的预防与控制提供参考。

关键词: 持续臭氧污染, PM_2.5组分, 大气氧化性, 数值模拟, 气象条件, 四川盆地

CLC Number:

WANG Congcong, ZHANG Xiaoling, LEI Yu, HUANG Xiaojuan, WANG Jingyi, YIN Lihao, WANG Chuanyang. Analysis of PM_2.5 Components During Continuous Spring Pollution in Sichuan Basin Based on WRF-CMAQ Model[J]. Ecology and Environment, 2024, 33(8): 1214-1226.

王聪聪, 张小玲, 雷雨, 黄小娟, 王婧怡, 尹黎昊, 王传扬. 基于WRF-CMAQ模型的四川盆地春季持续臭氧污染过程中PM_2.5组分解析[J]. 生态环境学报, 2024, 33(8): 1214-1226.

Figures/Tables 10

Figure 1 Model simulation area and distribution and elevation of 18 cities in Sichuan Basin

Figure 2 Results of hourly O3 concentration observation (black line) and simulation (red line) in four cities from April 20 to May 10, 2020

Figure 3 Results of hourly PM2.5 concentration observation (black line) and simulation (red line) in four cities from April 20 to May 10, 2020

Figure 4 Simulated surface O3_8h and PM2.5 during the pollution process in Sichuan Basin

Figure 5 Time series of O3, PM2.5 and PM2.5 components in four cities from April 20 to May 10

Figure 6 Components and proportion characteristics of PM2.5 in four cities at different photochemical levels

Figure 7 Average daily changes of each pollutant at different photochemical levels

Table 1 Ratio of OC/EC at different photochemical levels in four cities

光化学水平	OC/EC的比值
光化学水平	成都	德阳	自贡	达州
轻		3.12	2.19	0.77
低	1.97	3.28	3.21	1.45
中	2.48	3.08	3.07	2.60

Figure 8 The concentration and proportion characteristics of PM2.5 carbon components (EC, POC, SOC) at different photochemical levels in four cities

Figure 9 Characteristics of nitrogen oxidation rate (NOR) and sulfur oxidation rate (SOR) at different photochemical levels in four cities

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