莆田市道路交通环境颗粒物污染分布特征研究

发布时间：2018-07-05 03:17

本文选题：道路交通 + PM_(2.5)　；参考：《福建农林大学》2017年硕士论文

【摘要】：近年来,PM2.5(细颗粒物)已经成为我国空气质量的首要影响因素。而交通活动造成的颗粒物排放已成为城市大气污染的重要来源之一。本研究拟以莆田市8座环境监测站数据为基础,利用GIS对莆田市8座监测站数据进行空间插值,对莆田市主城区颗粒物浓度空间分布情况进行分析。以不同的道路绿化模式的典型路段为研究对象,对这些道路路侧环境中的颗粒物、气象参数以及路段车量等进行监测。在此基础上,通过对不同绿化模式、不同车流量条件下的道路环境颗粒物污染的对比,寻找不同车流量条件下道路环境颗粒物污染的规律,同时为探讨一种较好的道路绿化模式提供科学依据。主要研究结果如下:(1)莆田市城区全年颗粒物浓度均处于较低水平;春、冬两季的PM10(可吸入颗粒物)和PM2.5的平均浓度值明显高于夏、秋两季;荔城区监测点位颗粒物污染最为严重;东圳水库监测点位颗粒物浓度最低;远离城区、绿化和植被覆盖好,及邻近水域的地区颗粒物浓度较低;风速与PM10浓度呈正相关性,而与PM2.5浓度之间存在强烈的负相关性;温度与PM10和PM2.5浓度均呈现明显的负相关关系;相对湿度与PM10和PM2.5浓度呈负相关性。(2)本文分别使用传统车流量以及基于排放因子的车流量计算方式对车流量进行统计,发现基于排放因子的标准化车流量与颗粒物浓度的相关性更好;PM2.5的浓度与车流量的相关性较好,而PM1(可入肺颗粒物)、PM10和TSP(总悬浮颗粒物)的浓度与车流量的相关性较差;对车流量及颗粒物浓度的时间分布进行分析,发现由于颗粒物的扩散需要一定的时间,颗粒物浓度的变化相对交通量有一定的延后性;根据监测站周边交通量大小以及繁华度从高到低将8座空气自动监测子站分为三类,发现颗粒物污染较严重的区域的主要特征为交通发达、人类活动频繁;对于不同等级道路,PM10及TSP浓度呈现出主干道次干道支路;而PM1及PM2.5的浓度呈现出次干道主干道支路;莆田市城区节假日与工作日颗粒物浓度变化情况有很大的区别,而车流量在节假日明显降低,平均降幅达到了 13.44%;颗粒物浓度水平也有明显的降低,特别是PM10和TSP,降幅分别达到了 35.36%和39.09%,呈现出明显的"节假日效应"。(3)"乔-灌-草"配置模式的道路路侧绿化带对各个粒径颗粒物浓度的削减作用最高;PM1和PM2.5的污染源相似,而PM10和TSP则是来自于另外的相似污染源;道路路侧绿化带对不同粒径的颗粒物的削减率不同,大小依次为:TSPPM10PM2.5PM1。道路路侧绿化带对粒径较大的颗粒物有较好的阻挡及吸附作用,"乔-灌-草"模式对PM10和TSP的削减率达到了 44.9%和43.7%。而对小粒径颗粒物的净化效果较差。
[Abstract]:In recent years, PM2.5 (fine particulate matter) has become the primary influence factor of air quality in China. Particulate matter emissions caused by traffic activities have become one of the important sources of urban air pollution. Based on the data of 8 environmental monitoring stations in Putian City, the spatial distribution of particulate matter concentration in Putian City is analyzed by using GIS to interpolate the data of 8 stations in Putian City. Taking typical road sections with different road greening models as the research object, the particles, meteorological parameters and traffic volume of these road side environments were monitored. On this basis, through the comparison of road environmental particulate pollution under different greening modes and different vehicle flow conditions, the laws of road environmental particulate pollution under different traffic flow conditions are explored. At the same time, it provides a scientific basis for exploring a better road greening mode. The main results are as follows: (1) the average concentrations of PM10 (respirable particulate matter) and PM2.5 in spring and winter were significantly higher than those in summer and autumn. The concentration of particulate matter was the lowest in Dongzhen Reservoir, far away from the urban area, the greening and vegetation coverage was good, and the concentration of particulate matter in the adjacent waters was lower, and the wind speed was positively correlated with the concentration of PM10. However, there was a strong negative correlation between temperature and PM2.5 concentration, and there was a significant negative correlation between temperature and PM10 and PM2.5 concentration. There is a negative correlation between relative humidity and PM10 and PM2.5 concentration. (2) in this paper, the traditional traffic flow and the calculation method based on emission factor are used to calculate the traffic flow. The results showed that the correlation between the concentration of PM2.5 and the concentration of PM _ (2.5) was better than that of the concentration of PM _ (1) and tsp (total suspended particulate matter). By analyzing the time distribution of vehicle flow and particle concentration, it is found that the change of particle concentration has a certain delay because the diffusion of particulate matter takes a certain time. According to the traffic volume around the monitoring station and the degree of prosperity from high to low, eight air automatic monitoring sub-stations can be divided into three categories. It is found that the main characteristics of the area with serious particulate pollution are developed traffic and frequent human activities. The concentration of PM10 and tsp in different grades of roads showed sub-trunk roads, while the concentrations of PM1 and PM2.5 showed sub-trunk roads, and the concentration of particulate matter in Putian city was different from that in working days. On the other hand, the traffic flow significantly decreased during the holidays, with an average drop of 13.44%. The level of particulate matter concentration also decreased significantly. Especially for PM10 and TSPs, the decreases were 35.36% and 39.09%, respectively, showing obvious "holiday effect". (3) the pollution sources of PM1 and PM2.5 were similar to those of "Arbor, Irrigation and Grass" allocation mode. But PM10 and tsp come from other similar pollution sources, and the road side green belt has different reduction rate of different particle size, the order is: TSPPM10PM2.5PM1. The green belt on the side of the road has a good blocking and adsorption effect on the larger particle size. The reduction rates of PM10 and tsp in the "Joe Irrigation Grass" model have reached 44.9% and 43.7% respectively. However, the purifying effect of small particle size was poor.
【学位授予单位】：福建农林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X513;X73

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