| 引用本文: | 陈欣,王博,顾晨铭,李怡莹,张秀秀,李舒畅,刘国瑞.2026.基于植物磁学与ENVI-met模型的树木配置与降水对道路磁铁矿颗粒分布的影响[J].地球环境学报,17(4):1009-1022 |
| CHEN Xin,WANG Bo,GU Chenming,LI Yiying,ZHANG Xiuxiu,LI Shuchang,LIU Guorui.2026.The impacts of tree configuration and precipitation on the distribution of road traffic-related magnetite particles: a study based on biomagnetic monitoring and the ENVI-met model[J].Journal of Earth Environment,17(4):1009-1022 |
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| 基于植物磁学与ENVI-met模型的树木配置与降水对道路磁铁矿颗粒分布的影响 |
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陈欣1，2，王博1，2，顾晨铭1，2，李怡莹1，2，张秀秀1，2，李舒畅1，2，刘国瑞1，2
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1.浙江师范大学 地理与环境科学学院，金华 321004 ；2.浙江省流域环境数智监测与修复重点实验室，金华 321004
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| 摘要: |
| 交通源磁铁矿颗粒（MPs）对环境质量与人体健康存在潜在威胁，明确其道路微尺度分布及影响因素对城市污染防控与绿化优化至关重要。以杭州典型开放道路北塘东路为研究区，结合植物磁学监测与ENVI-met模型，探究树木配置（成排树（T1）/单株树（T2））与降水对MPs分布的影响。结果表明：（1）成排树滞尘能力显著优于单株树，降水前T1平均MPs浓度（380.3 mg/kg）为T2（289.6 mg/kg）的1.31倍，颗粒物富集于近地面层（＜160 cm）和面向道路侧；（2）降水使T1、T2 的MPs浓度分别下降65.79%、61.05%，但T1近地面层浓度仍高于T2，面向道路侧富集优势保持，说明树木配置是MPs垂直与方位分布差异的主导因素；（3）ENVI-met模拟PM2.5浓度随高度降低，与实测MPs趋势一致，面向道路侧模拟值与实测值高度相关（T1：r=0.77，T2：r=0.98，P＜0.01），但成排树因复杂冠层结构，背向道路侧模拟值与实测值呈负相关（r=-0.28，P＜0.01），降水后仅T1面向道路侧显著相关（r=0.93，P＜0.01），凸显模型在复杂冠层及湿沉降非稳态过程中的应用局限。研究证实植物磁学可补充监测网络，其与模型结合为行道树滞尘功能评估及道路绿化精准配置提供科学支撑。 |
| 关键词: 植物磁学监测 MPs ENVI-met模型 交通污染 杭州 |
| DOI：10.7515/JEE2025150 |
| CSTR：32259.14.JEE2025150 |
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| 基金项目:国家自然科学基金国际（地区）合作交流项目（W2512053） |
| 英文基金项目: |
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| The impacts of tree configuration and precipitation on the distribution of road traffic-related magnetite particles: a study based on biomagnetic monitoring and the ENVI-met model |
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CHEN Xin1，2，WANG Bo1，2，GU Chenming1，2，LI Yiying1，2，ZHANG Xiuxiu1，2，LI Shuchang1，2，LIU Guorui1，2
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1.College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004 , China ；2.Zhejiang Provincial Key Laboratory of Digital Intelligent Monitoring and Restoration of Watershed Environments, College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004 , China
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| Abstract: |
| Background, aim, and scope Traffic-derived magnetite particles (MPs) pose potential threats to environmental quality and human health. Understanding their road-scale distribution patterns and influencing factors is crucial for urban pollution control and green space optimization. This study integrates plant magnetic monitoring with the ENVI-met model to investigate how tree configurations and precipitation affect MPs distribution in typical open-road environments. Materials and methods This study was conducted on Beitang East Road, a densely populated, high-traffic area in Xiaoshan District, Hangzhou. The research focused on Juniperus formosana, with the tested trees having a circumference of approximately 4 m, height of about 5 m, dense canopies, and low pore density. Two tree configurations were selected for sampling: row-planted trees (T1) and individual trees (T2). Leaf samples were collected at four azimuths: road-facing side (F), back-facing side (B), lane-facing side (L), and reverse lane side (R), at heights ranging from 0.8 m to 5.0 m with a 20 cm interval, with 22 mature, intact leaves collected per azimuth. Sampling was conducted in two phases (before and after precipitation), with 88 samples per tree configuration and a total of 352 J. formosana leaf samples. All leaves were stored at 4℃ and promptly transported to the laboratory for analysis. Saturation isothermal remanent magnetization was used to quantitatively analyze the magnetic particle concentration in leaf-deposited dust. Simultaneously, an ENVI-met microenvironment model based on the actual road scale and vegetation layout was constructed to verify the consistency between model outputs and magnetic monitoring data. The applicability of the model was further defined through correlation analysis between simulated and measured concentrations. Results The results indicate that in a typical open-road environment in Hangzhou, the concentration of magnetite particles in leaf-deposited dust decreases with increasing height, and their typical coarse-grained spherical morphology confirms a traffic-related origin. These characteristics make them a reliable tracer for studying the dispersion behavior of traffic pollutants. Quantitatively, row-planted trees (T1) exhibit significantly higher MPs concentrations than solitary trees (T2): before precipitation, the average MPs concentration of T1 was 380.3 mg/kg, 1.31 times that of T2 (289.6 mg/kg), demonstrating stronger dust retention capacity. Although precipitation significantly reduces MPs concentrations: decreasing T1 and T2 concentrations by 65.79% (to 130.1 mg/kg) and 61.05% (to 112.8 mg/kg), respectively, and weakens vertical concentration gradients, it does not alter the superior retention capacity of T1 at heights below 160 cm (near-surface layer). Moreover, the barrier effect formed by T1 significantly enriches particles on the road-facing side (F): before precipitation, the MPs concentration on T1’s back-facing side (B) was 83.89% lower than that on the road-facing side, much higher than the 63.17% decrease observed for T2. Precipitation has a notable cleansing effect on leaf-deposited MPs, but tree configuration remains the dominant factor influencing MPs spatial distribution, as the enhanced enrichment effect on the road-facing side of T1 persists even after precipitation. Additionally, correlation analysis shows that the ENVI-met model’s simulation results are consistent with MPs monitoring data in terms of vertical distribution trends. Discussion The study verifies that row-planted J. formosana (T1) has better particle retention capacity than individual trees (T2), which is consistent with measured MPs concentrations. Model accuracy is significantly affected by tree configuration. Before precipitation, both T1 and T2 showed a significant correlation between simulated values and measured MPs on the road-facing side (F) (r=0.77 and 0.98, P＜0.01, respectively). However, T2 exhibited a moderate correlation on the back-facing side (r=0.57, P＜0.01), while T1 showed a significant negative correlation (r=-0.28, P＜0.01). This discrepancy for T1 is due to its dense conical canopy (narrow at the top and wide at the bottom), which reduces traffic particle influence on the lower back-facing side and conflicts with the model’s assumption of bottom-up PM2.5 diffusion. For T2, enhanced ventilation without adjacent tree interference explains its moderate correlation. Changes in correlation patterns after precipitation underscore the need for caution when applying the model to post-wet deposition environments, with only T1’s F side maintaining a significant correlation (r=0.93, P＜0.01). Plant magnetic monitoring effectively complements traditional methods, providing reliable data for model validation.Conclusions Plant-based magnetic monitoring at the road scale effectively complements existing monitoring networks, as magnetite particles (MPs) in leaf-deposited dust serve as a reliable tracer for traffic pollutant dispersion and a useful tool for validating urban micro-scale particulate dispersion models such as ENVI-met. This study confirms that tree configuration is the dominant factor regulating MPs spatial distribution, with row-planted J. formosana showing superior dust retention capacity compared to individual trees, an advantage that persists after precipitation. Additionally, the findings clarify the ENVI-met model’s limitations in simulating complex canopy-flow interactions and post-precipitation unsteady processes, providing a basis for model application and optimization. Recommendations and perspectives The integration of biomagnetic monitoring and ENVI-met modeling provides a novel approach for the precise quantification and dynamic evaluation of the ecological service functions of urban green spaces in traffic pollution retention. Future research should expand to multiple tree species and precipitation scenarios to improve the generalization of results, and optimize ENVI-met model parameters to address its limitations in simulating complex canopies, thereby providing more targeted support for urban pollution control and green space optimization. |
| Key words: biomagnetic monitoring MPs ENVI-met model traffic pollution Hangzhou |
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