| 摘要: |
| 以汾渭平原典型城市——咸阳为研究区域，利用地面空气质量监测数据和欧洲中期天气预报中心（ECMWF）发布的第五代全球气候再分析资料数据集（ERA5），分析了咸阳市2018—2020年3 a采暖期污染物浓度变化特征和不同污染程度下的气象条件，采用统计学方法分析各项污染物浓度与气象因素间的相关性，使用多元线性回归模型评价各气象因素对PM2.5浓度的影响程度，使用二元Logistic回归分析气象因素对PM2.5超标风险的影响。咸阳市采暖期首要污染物以细颗粒物（PM2.5）和可吸入颗粒物（PM10）为主，采暖期超标最多的污染物为PM2.5，超标天数逐年递减；PM10的日变化呈“双峰双谷”型，PM2.5的谷值出现在17∶00且夜晚浓度较高。颗粒物浓度与相对湿度呈正相关，与风速、边界层高度、温度、气压呈负相关。多元线性回归预测模型显示PM2.5浓度预测值与实测值变化趋势保持一致，预测值的波动频率比实测值大，预测准确率为51.54%；二元Logistic回归模型显示：除相对湿度外，其他气象因素对PM2.5超标情况都是保护因素，边界层高度每增高1 m，日均浓度超标风险降低0.7%；相对湿度每升高1%，日均浓度超标风险升高5.3%；温度每升高1℃，日均浓度超标风险降低19.8%；气压每升高1 hPa，日均浓度超标风险降低9.7%。以上研究结果揭示了咸阳市采暖期主要气象因素对空气污染的影响程度，为我国北方城市今后的空气污染治理提供科学依据，为相关政策制定提供理论参考。 |
| 关键词: PM2.5 气象因素 相关性 多元线性回归模型 |
| DOI：10.7515/JEE222068 |
| CSTR：32259.14.JEE222068 |
| 分类号: |
| 基金项目:咸阳市大气污染防治“一市一策”驻点跟踪研究项目（DQGG-05-37）；国家重点研发计（2017YFC0212200） |
| 英文基金项目:Stagnation Point Tracking Research Project of “One City, One Policy” of Air Pollution Prevention and Control in Xianyang City (DQGG-05-37); National Key Research and Development Program of China (2017YFC0212200) |
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| The effect of meteorological conditions on PM2.5 and other air quality factors in winter heating period: a case study of Xianyang City |
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LI Rui, LIU Suixin, SU Xiaoli, ZHANG Ting, ZHANG Peiyun, SHI Julian
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1. Xi’an Institute for Innovative Earth Environment Research, Xi’an 710061, China
2. Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
3. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
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| Abstract: |
| Background, aim, and scope Xianyang City, situated in the western basin of the Guanzhong Plain, faces significant air pollution challenges due to geographical constraints. During the winter heating season, particulate pollutants increase, posing health and environmental risks. This study aims to investigate the influence of meteorological factors on air pollution and its formation mechanism. We analyzed ground air quality and meteorological data from 2018 to 2020, utilizing the fifth major global reanalysis (ERA5) dataset, to explore pollutant concentration spatiotemporal patterns and their correlation with meteorological conditions. Our findings provide valuable insights for air pollution prevention in Xianyang. Materials and methods Focusing on Xianyang as a typical city in the Fenwei Plain, we analyze pollutant concentration variations and meteorological conditions levels from 2018 to 2020 using ground air quality data and ERA5 reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF). We employ statistical methods to investigate correlations between pollutant concentrations and meteorological factors, establishing multivariate linear regression and binary logistic regression models. Results During the heating period, PM2.5 and PM10 emerged as primary pollutants, with PM2.5 exceeding standards the most frequently, albeit decreasing year by year. Diurnal patterns reveal double peaks for PM10 at 02∶00 and 11∶00, and double valleys at 07∶00 and 17∶00, while PM2.5 valleys appeared at 17∶00. Particle concentrations positively correlated with relative humidity and inversely with wind speed, boundary layer height, temperature, and air pressure. Severe PM2.5 pollution corresponds to a boundary layer height of (225.7±63.8) m, relative humidity of 65.6%±8.8%, wind speed of (1.3±0.4) m·s−1, and temperature of (3.5±3.0)℃. Severe PM10 pollution aligned with a boundary layer height of (192.8±17.0) m, relative humidity of 63.4%±1.4%, wind speed of (1.2±0.1) m·s−1, and temperature of (3.5±6.7)℃. Discussion The assessment of various meteorological factors on PM2.5 concentration indicates a consistent trend between predicted and observed PM2.5 levels. Notably, the predicted values exhibited a higher frequency of fluctuations compared to observed ones. This implies that the measured values remained stable for a certain duration, and alterations in specific meteorological factors did not cause an immediate increase or decrease in PM2.5 concentration. In other words, the sensitivity of PM2.5 concentration to meteorological factors was relatively low. The prediction accuracy was 51.54%, affirming the reliability of predictions. Binary logistic regression indicates that, except for relative humidity, meteorological factors acted as protective factors against PM2.5 exceedance. Briefly, a 1% rise in relative humidity increased the excessive risk of daily average PM2.5 concentration by 5.3%, while a 1℃ increase in temperature and a 1 hPa increase in air pressure reduced this risk by 19.8% and 9.7%, respectively. Conclusions Meteorological factors significantly affecting PM2.5 concentration in Xianyang City were boundary layer height, relative humidity, and wind speed. Recommendations and perspectives This study reveals the extent to which key meteorological factors influence air pollution in Xianyang during the heating period. These insights provide a scientific basis for future air pollution control strategies and serve as a theoretical reference for policy-making in northern Chinese cities. |
| Key words: PM2.5 meteorological factors correlation multiple linear regression model |
