城市绿地类型对大气PM2.5浓度的影响

doi:10.3969/j.issn.1000-2006.201811039

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南京林业大学学报（自然科学版） ›› 2020, Vol. 44 ›› Issue (3): 179-184.doi: 10.3969/j.issn.1000-2006.201811039

城市绿地类型对大气PM_2.5浓度的影响

俞琳琳(), 胡海波(), 余伟

南京林业大学, 南方现代林业协同创新中心，江苏省水土保持与生态修复重点实验室，江苏南京 210037

收稿日期:2018-11-26 修回日期:2019-07-11 出版日期:2020-05-30 发布日期:2020-06-11
通讯作者: 胡海波
作者简介:俞琳琳(913987849@qq.com)，工程师。
基金资助:
高等学校博士学科点专项科研基金项目(20133204110005);江苏高校优势学科建设工程资助项目

Effects of urban green spaces on PM_2.5concentrations in atmosphere

YU Linlin(), HU Haibo(), YU Wei

Co -Innovation Center for the Sustainable Forestry in Southern China, Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China

Received:2018-11-26 Revised:2019-07-11 Online:2020-05-30 Published:2020-06-11
Contact: HU Haibo

摘要/Abstract

摘要： 目的

揭示不同类型城市绿地大气PM_2.5浓度的变化规律，以期为科学规划城市绿地、改善居民游憩环境提供理论依据。

方法

选取南京市玄武区具有代表性的3种类型城市绿地——综合性公园绿地（玄武湖公园）、公共设施绿地（南京林业大学校园）、道路绿地（玄武大道）作为研究对象，观测大气PM_2.5浓度的季节、月和日变化特征，并对不同天气下的变化规律进行研究。

结果

3种城市绿地大气PM_2.5质量浓度季节性差异显著，冬季最高，夏季次之，然后是秋季，春季最低，分别为175.27、146.76、104.77和96.83 μg/m³。不同绿地类型大气PM_2.5浓度在各季节变化趋势相同，由大到小依次为道路绿地、公共设施绿地、综合性公园绿地。就全年各月份来看，大气PM_2.5质量浓度1月最高为195.12 μg/m³，4月最低为69.06 μg/m³。春季和秋季城市绿地大气PM_2.5日均浓度变化（监测时段7:00—19:00）曲线呈近“U”形；夏季呈单谷单峰型，先下降后上升再下降；冬季呈单峰型或单谷型，先上升再下降或先下降再上升。阴天大气PM_2.5浓度比晴天高44.89%，雨后比雨前低30.94%，降雨能有效降低大气PM_2.5浓度。

结论

3种城市绿地大气PM_2.5浓度变化特征明显，综合性公园绿地内大气PM_2.5浓度低于公共设施绿地和道路绿地，说明合理的复层结构绿地在一定程度上对PM_2.5有滞留作用，对提高城市空气质量有益。

关键词: PM_2.5, 城市绿地, 季节变化, 月变化, 日变化

Abstract: Objective

In recent years, atmospheric pollution due to fine particles in urban areas has attracted concern since the phenomena of haze weather have become increasingly prominent. At present, little research exists from the macroscopic view on the effect of urban green spaces on PM_2.??5 concentrations. Most research concentrated on two sides of this issue, the dust catching effect and the effect of PM_2.5 concentrations on different urban vegetation types. The aim of this study was to reveal the variation rule of PM_2.5 concentrations for different urban green spaces, to provide a theoretical basis for planning of urban green spaces, and to improve the recreational environment.

Method

PM_2.5 concentrations in three types of urban green spaces (comprehensive park, public utilities green space and road green space) were measured on typical days over the four seasons in Nanjing. In this study, seasonal and diurnal variation in PM_2.5 concentrations in three types of urban green spaces were analyzed and the variation rule for PM_2.5 concentration under different weather conditions was discerned.

Result

The PM_2.5 concentrations in three types of urban green spaces (comprehensive park, public utilities green space and road green space) changed seasonally. The PM_2.5 mean concentration was 96.83 and 104.77 μg/m³ in spring and autumn, whereas it was 175.27 and 146.76 μg/m³ in winter and summer, respectively. At the seasonal scale, there was no significant difference among different plots during the same season, with concentrations mainly in the form of road green space>public utilities green space>comprehensive park. Seasonally, the PM_2.5 mean concentration of road green space was the highest in winter and lowest in autumn, whereas the PM_2.5 mean concentration of the other two types of urban green spaces (comprehensive park and public utilities green space) was the highest in winter and lowest in spring. It was not surprising to see that there were high levels of PM_2.5 caused by ground dust with low soil moisture, while these curves showed “one vale and one peak” in summer, “one peak” or “one vale”in winter. These diurnal variations suggested that anthropogenic activities and weather conditions were major contributors to higher particulate concentration. However, there were no significant differences among the PM_2.5 diurnal variation concentration of the three types of urban green spaces, which may be due to air motion. The mean concentration of PM_2.5 was 44.89% higher on cloudy days than on sunny days, but decreased by 30.94% after rainfall, which might be due to the change in solar radiation and precipitation. The reduction was remarkable after rainfall.

Conclusion

It was showed that the variety characteristic of PM_2.5 concentration was obvious. The PM_2.5 concentrations in comprehensive park was lower than that in other two urban green spaces, which means green spaces with multi-layer structure are very important to improve the quality of air by their PM_2.5 retention capacity.

Key words: PM_2.5, urban green space, seasonal variation, monthly variation, diurnal variation

中图分类号:

S719

俞琳琳,胡海波,余伟. 城市绿地类型对大气PM_2.5浓度的影响[J]. 南京林业大学学报（自然科学版）, 2020, 44(3): 179-184.

YU Linlin, HU Haibo, YU Wei. Effects of urban green spaces on PM_2.5concentrations in atmosphere[J].Journal of Nanjing Forestry University (Natural Science Edition), 2020, 44(3): 179-184.DOI: 10.3969/j.issn.1000-2006.201811039.

图/表 4

表1

图1

图2

表2

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绿地类型 urban green spaces	观测点 observation site	PM_2.5浓度/（μg·m^-3） PM_2.5 concentration
绿地类型 urban green spaces	观测点 observation site	春季 spring	夏季 summer	秋季 autumn	冬季 winter
年均值annual mean value		96.83	146.76^*	104.77	175.27^*
G11	XUG	81.82	129.16	98.89	170.31
G42	NL	86.22	151.11	99.57	171.92
G46	XUD	122.44	160.00	115.84	183.58

天气条件 weather condition	均值/(μg·m^-3) average	标准差/(μg·m^-3) standard deviation	标准误/(μg·m^-3) standard error	t	相伴概率 probability
晴天sunshine	114.73	41.71	12.04	-2.642	0.015
阴天cloudy	166.23	53.09	15.33	-2.642	0.015
雨前before precipitation	174.67	48.47	15.62	3.128	0.009
雨后after precipitation	120.63	18.48	6.16	3.128	0.009

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