机构地区:[1]安徽建筑大学环境与能源工程学院,安徽合肥230601 [2]安徽省环境污染控制与资源再利用重点实验室,安徽合肥230601
出 处:《环境工程》2025年第1期155-166,共12页Environmental Engineering
基 金:安徽建筑大学博士启动基金(2020QDZ31);环境污染控制与废弃物资源化利用安徽省重点实验室开放课题(2022EPC07);国家重点研发计划项目(2018YFC0213806);国家自然科学基金(41005016,41105031)。
摘 要:为了解中国东部地区细颗粒物中SNA离子的沉降特征差异,对2015—2022年中国东部地区98个城市的PM2.5浓度数据,采用K-means算法进行分析,将其按照浓度由低到高归集为LPC、MPC和HPC三类。结果表明:几乎所有城市的PM2.5日平均浓度均呈下降趋势,但不同分类属性的城市间差异显著,类型分布与区域位置密切相关。采用WRF/CAMx模式模拟了济南、郑州、合肥、南京、杭州、上海6个典型城市的颗粒物组分和干湿沉降速率,发现PM2.5中重要的水溶性离子——SNA离子(NO_(3)^(-)、SO_(4)^(2-)、NH_(4)^(+))存在着显著的季节变化,1月MPC类型城市NO_(3)^(-)质量浓度为HPC、LPC城市的1.46~1.88倍,4,7,10月NO_(3)^(-)质量浓度下降且不同类别间的差异缩小;NH_(4)^(+)的变化规律与NO_(3)^(-)相近;SO_(4)^(2-)浓度相对较低且差异最不显著。模拟结果表明,不同离子组分的湿沉降通量不仅与PM2.5浓度及离子质量浓度相关,而且受降水量影响显著。干湿沉降作用可以持续和稳定地降低空气中的颗粒物浓度,特别是在污染过程后期其贡献更加显著。To comprehend the disparities in the deposition characteristics of SNA ions in fine particulate matter in eastern China, this study initially utilized PM_(2.5) concentration data from 98 cities in the region spanning from 2015 to 2022. The data underwent analysis via the K-means algorithm and were categorized into three groups: LPC, MPC, and HPC, based on ascending concentration levels. The findings revealed a decreasing trend in the daily average PM_(2.5) concentration across most cities. However, noteworthy distinctions emerged among cities categorized under different attributes, with the distribution of types closely linked to regional location. The study employed the WRF/CAMx model to simulate particulate matter fractions and wet and dry deposition rates in six representative cities: Jinan, Zhengzhou, Hefei, Nanjing, Hangzhou, and Shanghai. It revealed significant seasonal variations in important water-soluble ions, namely SNA ions(NO_(3)^(-), SO_(4)^(2-), and NH_(4)^(+)), within PM_(2.5). Specifically, in January, the mass concentration of NO_(3)^(-) in MPC-type cities was approximately 1.46 to 1.88 times higher than that in HPC and LPC-type cities. However, this discrepancy decreased in April, July, and October, resulting in a narrowed difference between different categories. Similar patterns were observed for NH_(4)^(+), while SO_(4)^(2-) exhibited relatively lower concentration levels with the least significant differences. Furthermore, the simulation results indicated that wet deposition fluxes of various ionic components were not only correlated with PM_(2.5) concentration and ion mass concentration but also significantly influenced by precipitation. Dry and wet deposition continuously and steadily reduced the concentration of particulate matter in the air, especially in the later stages of the pollution process. Its contribution was more significant. By comparing the difference in wet sedimentation flux and precipitation between different cities, it was concluded that SNA ion wet sedimentation flux was
关 键 词:PM2.5 SNA离子 WRF/CAMx模型 大气沉降 沉降通量
分 类 号:X513[环境科学与工程—环境工程]
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