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作者简介:

张凌,女,讲师,研究方向为风景园林规划设计.125481553@qq.com;

孔德政(通信作者),男,博士,教授,研究方向为风景园林规划设计.kzxy303@163.com

中图分类号:X513

文献标识码:A

DOI:10.13878/j.cnki.jnuist.2023.01.002

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目录contents

    摘要

    为探讨不同植物群落对大气颗粒物浓度的影响,以郑州市金水区为例,在2020年冬季(2020年12月—2021年2月)对园林绿化区(数码公园)、居住区(正弘·蓝堡湾)、文教区(河南农业大学)内植物群落的PM2.5和PM10质量浓度及气象因子(温度、相对湿度和风速)进行监测.结果表明:每个功能区中不同样地之间的 PM2.5、PM10质量浓度日变化趋势基本一致,一般为早高晚低;不同植物群落之间PM2.5和PM10质量浓度存在显著差异性,其中广场样地与其他样地的差异性最显著;3个功能区中各样地对PM2.5和PM10质量浓度的阻滞率均表现为乔灌草结构最高,乔灌结构和乔草结构次之,且多表现为乔灌样地大于乔草样地,灌草结构和草坪最低;研究区域PM2.5、PM10质量浓度与温度呈负相关,与相对湿度呈正相关,与风速呈负相关.

    Abstract

    To explore the blocking impact of plant communities on atmospheric particulate matters,we monitored the PM2.5 and PM10 concentrations and related meteorological factors near plant communities in three functional areas of Zhengzhou's Jinshui district,which were garden area (Digital Park),residential area (Zhengzhou Blue Bay),and cultural & educational area (Henan Agricultural University).The monitoring period covered a whole winter from December 2020 to February 2021.The results indicated that the diurnal variation trends of PM2.5 and PM10 concentrations were basically the same for all sampling plots,which were generally high in early morning and low in evening;obvious differences in PM2.5 and PM10 concentrations were observed among plant communities,which were most significant between the square plot and other plots;for the three functional areas,the blocking of PM2.5 and PM10 were all the strongest by the combined structure of arbor,shrub and grass,followed by arbor & shrub and arbor & grass structures,and the lowest by structure of shrub & grass and single structure of grass;the PM2.5 and PM10 concentrations were observed to be negatively correlated with temperature and wind speed,and positively correlated with relative humidity.

  • 0 引言

  • 近年来,随着我国城市化进程的加快,雾霾天气不断发生.颗粒物(PM)作为雾霾产生的罪魁祸首之一,是许多城市大气环境的主要污染物[1-2].河南省是中国最严重的空气污染地区之一[3].根据郑州市人民政府统计结果发现:2013—2018年区域空气污染主要以颗粒物污染为主,其中PM2.5、PM10、NO2、O3年均浓度均超过国家二级标准限值,超标现象严重; 此外研究区域季节特征显著,秋冬季是郑州市雾霾天气的高发季节,污染物主要以PM2.5、PM10为主[4].

  • 研究发现,植物对于大气颗粒物具有阻滞和吸收作用,是改善空气环境质量的有效措施之一[5].不同植物群落对颗粒物的消减能力不同[6].杨貌等[7]在春季城区道路不同绿地配置模式对大气颗粒物的消减作用中提出,复合配置模式比单一配置模式下空气颗粒物浓度稳定程度高.刘宇等[8]在夏季不同结构绿地对PM2.5浓度的影响研究中提出,由乔木组成的郁闭度高和地被覆盖高的垂直复层结构能有效降低 PM2.5浓度水平.目前国内学者大多从同一场地对不同植物群落的PM2.5、PM10浓度进行研究[9-11],研究对象较少易造成结果具片面性,此外PM2.5、PM10季节特征显著,冬季质量浓度最高[12],因此研究不同场地冬季颗粒物浓度变化具有现实意义.本研究从郑州市金水区选取3个功能区,从每个功能区选取典型植物群落监测其空气颗粒物浓度及气象因子变化,分析不同功能区、不同植物群落、环境因子等多个方面与颗粒物浓度变化的关系,比较冬季不同植物群落内的大气颗粒物变化情况及其阻滞颗粒物的能力,为改善城市环境空气质量提供理论和实践依据.

  • 1 试验场地与研究方法

  • 1.1 试验场地

  • 研究区域位于郑州市金水区(113°40′~113°47′E,30°50′~34°57′N).该区域属温带大陆性季风气候,四季分明,日照时间长,自然降水偏少.主要气候特征是:春季干旱多风,冷暖无常; 夏季炎热多雨,水热同期; 秋季凉爽,日照充足; 冬季干燥,风多雪少.年平均气温14.8℃,降雨量586.1 mm/a,日照2 052.6 h/a.截至2021年,金水区常住人口161.7万,是河南全省人口最多、经济最发达的城区.但随着人车流量的增加及化石燃料的燃烧,大气环境污染问题也日益严重.

  • 研究区域包括园林绿化区的数码公园、居住区的正弘·蓝堡湾和文教区的河南农业大学校园.每个功能区选择5~6个监测样地.各样地监测点选择郑州市具有典型性和代表性的植被类型,主要以女贞、国槐、悬铃木等乔木结合本土灌木和沿阶草等地被草坪形成,其中乔木层高度约为8~12 m,灌木层高度约为0.5~1.5 m,盖度约为55%.

  • 数码公园地处金水区东风路与渠西路交叉口,北临东风渠.总占地面积17.2亩,绿地率约68.8%.园区内植物群落整体空间分布均匀,以半闭合阔叶多层结构、半闭合阔叶单层结构和针阔叶混交林多层结构为主.

  • 正弘·蓝堡湾位于东风路花园路,总占地面积431.199亩,绿地率约51.8%.场地周边用地为写字楼、商业街区等.居住区内高楼林立,场地内绿化条件好,植物种类丰富,相间分布于道路两旁,多为观赏植物群落.

  • 河南农业大学位于金水区文化路街道,总占地面积330.6亩,绿地率约40%.校园内植物种类多样性较高,有56科89属121种.其中乔木占比最多,达60种且以悬铃木为主,灌木中大叶黄杨数量最多,草坪地被种类较少.

  • 本实验选取的每个样方为20 m×10 m,除数码公园外,其他功能区没有合适的草坪区域,因此未选择单一草坪下垫面,校外道路监测点位于河南农业大学东门口,与校内广场距离较近.不同样地情况具体如表1所示.

  • 表1 不同功能绿地基本情况

  • Table1 Basic situation of plant communities in three functional areas

  • 1.2 研究方法

  • 在天气晴朗微风条件下,从2020年12月至2021年2月,每月3天,对每个功能区不同监测点的PM2.5、PM10质量浓度及气象因子进行监测(08:00—18:00),每隔2 h监测1次.

  • 1)PM2.5、PM10质量浓度监测

  • 采用英国Turnkey仪器公司生产的Dustmate粉尘监测仪,分辨率为0.001 mg/m3,测量范围为0~6 000 μg/m3,每2 h的整点同步测定各监测点人体呼吸高度1.5 m处PM2.5、PM10的质量浓度,每个时间点在东西南北4个方向测取4次.阻滞能力用阻滞率Pn来表示,它能够直接反映出各监测点的颗粒净化效益,计算公式为

  • Pn=Co-Cn/Co,

  • 式中,Co为对照点处空气颗粒物质量浓度,Cn为公园内不同监测点空气颗粒物质量浓度.

  • 2)气象因子测定

  • 采用美国产Kestrel 4500手持自动气象仪,测定各监测点的气温、相对湿度、风速等气象因子.温度测定范围-30~70℃,分辨率0.1℃; 相对湿度测定范围5%~95%,分辨率为0.1%.每次在整点前后10 min内完成并在东西南北4个方向进行4次重复,测定高度为1.5 m.

  • 2 研究结果与分析

  • 2.1 不同植物群落内大气颗粒物日变化

  • 由图1可知,各样地中PM2.5、PM10质量浓度变化趋势基本保持一致,上午08:00—10:00达到最大值,中午慢慢降低,在下午14:00或16:00达到最低值.从下午14:00开始颗粒物浓度会缓慢上升,但幅度较小,且低于早上08:00的浓度.

  • 数码公园中6个样地内的PM2.5质量浓度呈“L”型变化,早上空气环境污染较为严重,PM2.5质量浓度将近140 μg/m3,PM10质量浓度为180 μg/m3.从08:00—14:00,PM2.5质量浓度一直处于持续下降趋势,14:00—18:00,PM2.5质量浓度有缓慢回升的趋势; 而PM10质量浓度同样是在14:00达到最低值,随后有缓慢回升趋势.正弘·蓝堡湾中5个样地内的PM2.5质量浓度变化呈现“L”型变化,PM10呈“V”型变化,样地Z5的PM2.5和PM10质量浓度最高,Z1和Z3最低,各样地在12:00—18:00之间空气质量较好.河南农业大学中6个样地PM2.5质量浓度从08:00—12:00持续下降,12:00之后各监测点PM2.5质量浓度较为平稳,14:00是一天的低谷值,在16:00之后又有回升趋势; 而PM10质量浓度变化不明显,整体呈缓慢下降趋势.整体来看各样地中S5、S6、Z5、H5、H6颗粒物质量浓度最高且明显高于其他样地,而S1、S3、Z1、Z3、H1和H2质量浓度最低.对照组(样地S5、Z5、H5)的颗粒物质量浓度相对较高.

  • 2.2 不同植物群落内大气颗粒物差异性分析

  • 为了探究各样地颗粒物浓度在冬季是否存在显著差异,对各样地PM2.5、PM10质量浓度进行方差分析,结果如图2所示.数码公园PM2.5质量浓度经单因素方差分析得出F值为9.383、P<0.05.各样地之间PM2.5质量浓度存在显著差异,各监测点PM2.5质量浓度的大小顺序为S5>S6>S4>S2>S3>S1; PM10质量浓度得出F值为32.350、P<0.05,各监测点PM10质量浓度的大小顺序与PM2.5排序一致,均为S1样地最低,S5样地最高.

  • 正弘·蓝堡湾PM2.5质量浓度经单因素方差分析得出F值为6.825、P值为0.001<0.05,PM10质量浓度得出F值为14.533、P<0.05.各监测点PM2.5、PM10质量浓度的大小顺序为Z5>Z2>Z4>Z3>Z1.综合上述结果可得出Z1的PM2.5、PM10质量浓度最低,Z5的PM2.5、PM10质量浓度最高.

  • 河南农业大学和文化路PM2.5质量浓度经单因素方差分析得出F值为4.308,P值为0.005<0.05.从总的PM2.5质量浓度变化来看,冬季河南农业大学不同植物群落PM2.5质量浓度存在显著差异,校园各监测点PM2.5质量浓度的大小顺序为H6>H5>H4>H3>H2>H1; PM10质量浓度得出F值为31.952、P<0.05,各样地PM10质量浓度的大小顺序与PM2.5排序一致.可以看出以常绿树为主的阔叶乔灌草(H1)样地内PM2.5和PM10质量浓度最低,而H6样地PM2.5和PM10质量浓度均为最高.

  • 2.3 不同植物群落阻滞大气颗粒物的能力分析

  • 不同植物群落对PM2.5的阻滞率如图3所示.乔灌草结构对PM2.5的阻滞率最高,数码公园、正弘·蓝堡湾和河南农业大学中乔灌草结构对PM2.5的阻滞率分别为33.13%、46.51%、20.80%; 灌草和草坪结构最小,灌草结构阻滞率分别为16.47%、20.24%、7.84%,其中河南农业大学的植物种类较少,对PM2.5的阻滞率最低,为7.84%,而草坪结构阻滞率仅为3.57%.在乔草结构中,数码公园对PM2.5的阻滞率最高,达到了22.65%.此外,同一种绿地类型对颗粒物的阻滞能力也不同,在乔灌草结构和乔灌结构中正弘·蓝堡湾植物种类与层次较数码公园、河南农业大学丰富,其对PM2.5的阻滞率也明显高于另外两个样地,阻滞率分别达到46.51%和39.77%.

  • 图1 不同功能绿地PM2.5、PM10质量浓度日变化

  • Fig.1 Diurnal variations of PM2.5 and PM10 near plant communities in three functional areas

  • 不同植物群落对PM10的阻滞率均为乔灌草结构最高,灌草和草坪结构最小,与各样地对PM2.5阻滞率情况相同.乔灌草结构对PM10的阻滞率分别为33.98%、26.04%、14.44%; 乔草结构对PM10的阻滞率分别为24.59%、11.60%、10.10%; 乔灌结构阻滞率分别为32.10%、19.13%、10.32%; 灌草结构阻滞率分别为15.47%、11.65%、5.86%; 草坪结构为5.775%.数码公园和正弘·蓝堡湾的乔灌草、乔灌结构阻滞率较高,河南农业大学内乔灌草和乔灌结构阻滞率最低.

  • 2.4 城市不同功能区阻滞大气颗粒物的能力分析

  • 将各功能区中各样地的PM2.5、PM10阻滞率均值作为该功能区的PM2.5、PM10阻滞率,可得各功能区对PM2.5、PM10阻滞率情况(图4).整体来看数码公园和正弘·蓝堡湾的阻滞PM2.5和PM10的能力要高于河南农业大学.各功能区对PM2.5阻滞率由大到小为正弘·蓝堡湾、数码公园、河南农业大学,阻滞率分别为18.88%、15.4%、13.14%; 对PM10阻滞率由高到低为数码公园、河南农业大学、正弘·蓝堡湾,阻滞率分别为14.14%、11.38%、8.11%.

  • 图2 不同植物群落内PM2.5、PM10质量浓度方差分析与多重比较

  • Fig.2 Variance analysis and multiple comparison of PM2.5 and PM10 concentrations near plant communities, (a) for plots in Digital Park, (b) for plots in Zhengzhou Blue Bay, and (c) for plots in Henan Agricultural University

  • 图3 不同植物群落对颗粒物浓度阻滞率

  • Fig.3 Blocking rates of particulate matters by plant communities

  • 2.5 大气颗粒物浓度与气象因子的相关性分析

  • 为探究不同植物群落内PM2.5、PM10浓度与气象因子的相关关系,对各样地的PM2.5、PM10质量浓度和气象因子数据(温度、相对湿度、风速)作相关性分析.

  • 由表2可知:冬季河南农业大学、数码公园、正弘·蓝堡湾中观测样地PM2.5质量浓度与温度呈显著负相关,而校外道路PM2.5质量浓度与温度不呈相关性; PM10质量浓度与温度均不呈相关性.空气相对湿度是影响颗粒物浓度的一个重要指标,研究发现冬季大部分样地PM2.5质量浓度与相对湿度呈显著正相关,少量样地与PM2.5质量浓度不呈相关性; PM10质量浓度同样与相对湿度呈正相关,但与PM2.5相比相关性较低,仅有少部分样地PM10质量浓度与相对湿度呈显著正相关,因此PM2.5与PM10质量浓度与相对湿度均呈正相关.风速对于颗粒物在空气中的扩散具有重要影响,冬季部分样地中PM2.5、PM10质量浓度均与风速呈显著负相关,风速越大,研究区域PM2.5、PM10质量浓度则会越低[13].

  • 图4 不同功能区对颗粒物浓度阻滞率

  • Fig.4 Blocking rates of particulate matters by plant communities in different functional areas

  • 表2 不同植物群落内PM2.5、PM10质量浓度与气象因子的相关性

  • Table2 Correlation between PM2.5, PM10 concentrations and meteorological factors

  • 3 讨论与结论

  • 本研究得出PM2.5、PM10质量浓度日变化一般呈现出早高晚低的特征,这与张建设等[14]、任晓旭等[15]的研究相似,造成这种日变化的主要原因是:夜晚低气温下容易产生逆温层,空气颗粒物PM2.5和PM10在底层沉淀聚集,导致观测初始的PM2.5和PM10质量浓度较高; 太阳上升后,地温逐渐升高,形成暖气团,携带细颗粒物逐渐上升到 1.5 m 以上,因此各样地 PM2.5、PM10质量浓度总体变化呈下降趋势[16].在监测快结束时下班高峰期到来,逐渐增多的车流量以及机动车尾气尘,导致了 PM2.5、PM10质量浓度缓慢回升的趋势[17].

  • 刘畅等[18]在东北林业大学不同植物群落对大气颗粒物浓度的消减作用中发现,层次合理、植被丰富的绿地配置模式对颗粒物的消减效果明显.本研究发现不同植物群落内PM2.5和PM10质量浓度存在显著差异,其中植被较为单一的广场和草坪样地的PM2.5、PM10质量浓度与其他样地的差异性最显著.综合几个功能区的颗粒物浓度变化情况,发现郁闭度较高、具有多层植物且林下盖度较高的群落,PM2.5和PM10质量浓度较低,表明复合群落结构的绿地对颗粒物滞尘能力大于单一群落结构的绿地.

  • 本文比较了各样地和各功能区不同植物群落对颗粒物的阻滞率,发现3个功能区中各样地对PM2.5和PM10的阻滞率均表现为乔灌草最高,乔草结构和乔灌结构次之,且多表现为乔灌样地大于乔草样地,灌草结构最低,草坪样地滞尘能力最弱.这与多位学者[19-20]的研究结果相似.研究还发现,各功能区阻滞PM2.5的能力要略大于PM10.

  • 在PM2.5、PM10质量浓度与气象因子相关性分析中发现,气象因素对颗粒物浓度有着显著影响.受太阳辐射的影响,随着气温逐渐升高,颗粒物随着空气对流向上扩散,进而使得颗粒物浓度降低.此外,研究发现PM2.5、PM10质量浓度与相对湿度呈显著正相关关系,且各样地间相对湿度与PM2.5、PM10质量浓度的相关性最显著,表明相对湿度是影响颗粒物浓度的重要因素,这与赵松婷等[21]、赵晨曦等[22]的研究基本一致.风速通过影响对流运动对颗粒物浓度变化产生影响.而王会霞等[23]在研究中发现风速过高或过低时,植物滞尘能力都会减弱,只有在合适风速时,才能够更好地阻滞颗粒物.本研究中风速均为小风,各样地中PM2.5、PM10质量浓度与风速呈显著负相关,随着风速的增大,颗粒物浓度降低.此外有学者研究发现PM2.5、PM10、PM1、TSP这4种颗粒物与风速、最大风速、平均风速、气温、光照强度、相对湿度的关系趋势并不是一直相同的[22].因此,对于单一气象因子影响大气颗粒物浓度变化的研究,也是今后研究的具体方向.

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