基于DSM的城市公园对PM_(2.5)和PM_(10)的消减特征研究——以南昌市人民公园为例

Study on reduction characteristics of PM_(2.5)and PM_(10)in urban parks based on DSM:a case study of Nanchang People’s Park

【目的】PM_(2.5)、PM_(10)等空气颗粒物是城市空气首要污染物,在城市空气污染中占主导地位。了解固定外源下PM_(2.5)、PM_(10)在城市绿地的消减特征,可为城市阻控空气颗粒物、缓解空气污染提供有利依据。然而目前空气颗粒物的研究大多以点测定方式量化空间结构及植被类型对空气颗粒物的影响,对固定外源污染下PM_(2.5)、PM_(10)在城市绿地空间尺度上的影响机制研究较少。【方法】研究结合DSM与地统计学,以南昌市人民公园为例,探索城市公园阻隔外源污染的空间梯度效应及空间结构类型差异。利用克里金插值法对其空间分布特征进行可视化模拟;利用Arcgis和R语言等软件分析不同空间结构PM_(2.5)、PM_(10)的浓度差异。【结果】人民公园PM_(2.5)、PM_(10)的浓度在空间分布上趋势一致,均表现为以固定外源点为核心,浓度随距离增加呈极显著梯度递减的趋势,且在中部(约距外源点150~220 m处)消减效率最高,约为全园PM_(2.5)平均消减值的7.5倍,PM_(10)平均消减值的3.8倍;PM_(2.5)、PM_(10)受多种因子影响:与空气温度、距离(主导因子)显著负相关、与相对湿度显著正相关,且PM_(2.5)、PM_(10)对不同因子响应特征存在差异;城市公园不同绿地空间结构对PM_(2.5)、PM_(10)的消减及扩散作用差异显著,受其双重影响,PM_(2.5)、PM_(10)的浓度表现为水体>广场>树林>草坪,其中PM_(2.5)受影响更显著;此外,受各因子和绿地空间结构耦合影响,部分区域PM_(2.5)、PM_(10)分布异常。【结论】以固定外源点为核心,PM_(2.5)、PM_(10)浓度随距离增加呈极显著梯度递减的趋势,且在中部消减效率最高;PM_(2.5)、PM_(10)浓度与相对湿度显著正相关,与空气温度与距离显著负相关,其中PM_(10)对距离和相对湿度响应较为明显,而PM_(2.5)受空气温度影响较大;在随距离变化基础上,不同城市绿地空间结构对PM_(2.5)、PM_(10)消减和扩散作用差异导致了局部分布差异。

[Objective]Air particulate matter such as PM_(2.5)and PM_(10)are the primary pollutants in urban air,occupying a dominant position in urban air pollution.Understanding the reduction characteristics of PM_(2.5)and PM_(10)in urban green spaces under fixed external sources can provide favorable basis for controlling air particulate matter and alleviating air pollution in cities.Most of the current research on air particulate matter quantify the impact of spatial structure and vegetation type on air particulate matter through point measurement,however,there is little research on the impact mechanism of PM_(2.5)and PM_(10)on the spatial scale of urban green spaces under fixed external pollution.[Method]Taking Nanchang People’s Park as an example,this study combines DSM and geostatistics to explore the spatial gradient effect and spatial structure type difference of urban parks in blocking exogenous pollution.The Kriging interpolation method was used to visually simulate its spatial distribution characteristics.Arcgis and R language were used to analyze the concentration differences of PM_(2.5)and PM_(10)in different spatial structures.[Result]The concentrations of PM_(2.5)and PM_(10)in the park had a consistent spatial distribution trend,with fixed external points as the core.The concentration decreased significantly with the increase of distance,and the reduction efficiency was the highest in the middle(about 150-220 m from the external point),which was about 7.5 times of the average reduction value of PM_(2.5)and 3.8 times of the average reduction value of PM_(10).PM_(2.5)and PM_(10)are influenced by multiple factors.They were significantly and negatively correlated with air temperature and distance(the dominant factor),as well as significantly and positively correlated with relative humidity.Moreover,there are differences in the response characteristics of PM_(2.5)and PM_(10)to different factors.The reduction and diffusion effects of different green space structures on PM_(2.5)and PM_(10)in urban parks were significantly different.The concentration of PM_(2.5)and PM_(10)in different areas ranked as follows:water>square>forest>lawn,with PM_(2.5)being more significantly affected;In addition,due to the coupling effect of various factors and green space structure,the distribution of PM_(2.5)and PM_(10)in some areas was abnormal.[Conclusion]With fixed external sources as the core,the concentrations of PM_(2.5)and PM_(10)show a highly significant gradient decreasing trend with distance,and the highest reduction efficiency is observed in the central region.The concentrations of PM_(2.5)and PM_(10)are significantly correlated with relative humidity,while they are significantly and negatively correlated with air temperature and distance.Among them,PM_(10)has a more positive response to distance and relative humidity,while PM_(2.5)is significantly affected by air temperature;On the basis of distance variation,the differences in the reduction and diffusion effects of different urban green space structures on PM_(2.5)and PM_(10)lead to local distribution differences.