| 引用本文: | 彭婷，赵美玲，吴莹，张钰莹，沙桐，严殊祺，杨文武.2024.2022年江苏省PM2.5与臭氧污染特征及其与气象要素关系[J].地球环境学报,15(3):459-473 |
| PENG Ting, ZHAO Meiling, WU Ying, ZHANG Yuying, SHA Tong, YAN Shuqi, YANG Wenwu.2024.Spatiotemporal characteristic of PM2.5 and ozone and their relationships with meteorology over Jiangsu Province in 2022[J].Journal of Earth Environment,15(3):459-473 |
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| 摘要: |
| 基于2022年江苏省PM2.5、臭氧环境监测站数据和地面气象站数据，分析污染时空分布特征及其与污染源排放、气象要素的关系，从而为排放管控、污染治理提供科学依据。PM2.5整体呈北高南低、西高东低的空间特征，冬季浓度最高（30—75 μg∙m−3），西北部城市的PM2.5全年污染天数为全省最多（25—40 d）。臭氧呈南高北低的空间特征，夏季浓度最高（120—160 μg∙m−3），苏南城市的臭氧全年污染天数最多可达60—70 d。二者浓度在夏季具有较强的正相关性，在冬季相关性很弱。各季节PM2.5与温度、相对湿度、风向呈弱相关性，与风速关系稍强。PM2.5污染事件多发于温度为2—6 ℃、相对湿度为60%—85%、风速＜3 m∙s−1的条件。各季节臭氧均与温度呈强正相关性，与相对湿度为负相关。风向为偏西风时容易出现高浓度，为第四象限风（东风到南风）时浓度通常较低。臭氧污染集中于日最高气温＞28 ℃、相对湿度为60%—75%、风向为偏西风的条件，以此作为预报指标，正确率约为50%。 |
| 关键词: PM2.5 臭氧 时空特征 气象要素 |
| DOI：10.7515/JEE242006 |
| CSTR：32259.14.JEE242006 |
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| 基金项目:泰州市大气PM2.5和O3协同控制研究（TS202230） |
| 英文基金项目:Study of Associated Control of PM2.5 and Ozone Pollution in Taizhou (TS202230) |
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| Spatiotemporal characteristic of PM2.5 and ozone and their relationships with meteorology over Jiangsu Province in 2022 |
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PENG Ting, ZHAO Meiling, WU Ying, ZHANG Yuying, SHA Tong, YAN Shuqi, YANG Wenwu
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1. Taizhou Environmental Monitoring Center of Jiangsu Province, Taizhou 225300, China
2. School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
3. Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
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| Abstract: |
| Background, aim, and scope In recent years, the associated pollution between PM2.5 and ozone occurs more and more frequently in Jiangsu Province which is one of the most developed regions in China. As meteorological conditions and emissions are responsible for the formation of PM2.5 and ozone pollution, while the reasons of the pollution formation still need to be explored. This study reveals the spatiotemporal characteristics of PM2.5 and ozone pollution and their relations with relevant meteorological factors and emissions; as well as provides the potential meteorological indicators in PM2.5 and ozone concentration prediction, furthermore, to provide a basic emission management in air pollution control in Jiangsu Province. Materials and methods Based on the PM2.5 and ozone data from air quality monitoring stations and the ground meteorological observation data over Jiangsu Province in 2022, this study analyzes the spatiotemporal characteristic of PM2.5 and ozone pollution and their relationships with meteorological elements. Results The concentration of PM2.5 is generally higher in the north and west, and lower in the south and east of Jiangsu Province. In winter, the PM2.5 concentration is the largest, around 30—75 μg·m−3. It is more likely to be polluted by PM2.5 in the north-western cities in Jiangsu with 25 to 40 polluted days in 2022. The concentration of ozone is higher in the south and lower in the north, and the highest concentration (about 120—160 μg·m−3) occurs in summer. There are 60 to 70 days with ozone pollution in the southern Jiangsu which takes place most frequently in 2022. The concentrations of PM2.5 and ozone are strongly positively correlated in summer but weakly correlated in winter. It also shows weak correlations of PM2.5 concentration with temperature, relative humidity (RH) and wind speed (WS) in all seasons, respectively, while it is a bit closely correlated with wind speed. PM2.5 pollution events are mostly concentrated in the interval from 2—6 ℃, RH of 60%—85% and WS＜3 m·s−1. The ozone concentration shows strong positive correlations with temperature and negative correlations with RH in all seasons. Higher (lower) concentration of ozone tends to occur with westerly (easterly to southerly) winds. Ozone pollution events are usually concentrated with daily maximum temperature greater than 28 ℃, RH of 60%—75% and westerly winds. Using these indicators in the prediction of ozone pollution, the accuracy ratio is approximately 50%. Discussion Under the effect of meteorological impacts and emissions, the PM2.5 and ozone concentrations show spatiotemporal differences over Jiangsu Province. PM2.5 pollution is stronger in north-western cities, hence, these cities need to optimize their industrial structure and develop clean energy. Ozone pollution is stronger in southern Jiangsu, those industries with high VOC emissions should effectively mitigate emission. Additionally, the duration days of successive PM2.5 or ozone pollution are higher in those cities where the pollution itself is more serious. The occurrences of successive pollution events would increase in the future due to the weakening of East Asian winter monsoon and the increasing of summer hot temperature. By analyzing the relations of PM2.5 and ozone concentration with meteorological factors, this study raises potential meteorological indicators in air pollution prediction. In the future, the effect of emissions and precursors should be considered to improve the prediction accuracy. Besides, atmospheric chemistry numerical models and artificial intelligence algorithms could further enhance PM2.5 and ozone pollution prediction ability. Conclusions PM2.5 and ozone concentrations show spatiotemporal variabilities over Jiangsu, and their correlations with meteorological elements are different. The concentrations of the PM2.5 and ozone have a strong positive correlation in summer and a weak correlation in winter. Recommendations and perspectives In the future, we should further explore the driving factors of PM2.5 and ozone pollution through observations and numerical models. More effective methods are needed to improve the forecast ability of PM2.5 and ozone concentration. |
| Key words: PM2.5 ozone spatiotemporal characteristic meteorological elements |
