杭州城市森林区和居民区大气颗粒物浓度特征的差异性研究
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摘要
【目的】本文以杭州国家半山森林公园和采荷社区为研究地点,揭示城市森林对TSP、PM_(10)、PM_(2.5)和PM_14种不同类型大气颗粒物浓度变化的影响。【方法】对TSP、PM_(10)、PM_(2.5)和PM_14种不同类型大气颗粒物的浓度进行了周期为1年的定点监测,通过对比分析城市森林区和城区的不同类型大气颗粒物浓度的变化差异。【结果】半山监测点的TSP、PM_(10)、PM_(2.5)以及PM_14种大气颗粒物浓度的日变化曲线为早上9:00到达最大值,然后随时间变化而持续减少,在下午14:00到达最小值,分别为:182.2、124.2、68.2、20.6μg·m~(-3);在采荷社区,4种大气颗粒物浓度由早上9:00开始降低但到下午17:00时又基本上升到早上9:00的水平;在半山监测点相比采荷,PM_(2.5)和PM_1浓度年变化的峰值均延迟了1个月的时间,城市森林区和城区的不同类型大气颗粒物浓度的日变化和月变化规律存在明显的不同。【结论】4种不同类型大气颗粒物的平均浓度均为半山森林公园高于采荷社区;在半山和采荷2个监测点的PM_(2.5)和PM_1浓度的差异分别显示为有高度统计意义和有统计意义。
【Objective】The concentration of different types of atmospheric particulate matter(TSP,PM_(10),PM_(2.5)and PM_1)were monitored in the Hangzhou Banshan national forest park and Caihe community for one year.【Method】Through the contrast analysis of different types of atmospheric particulate matter concentration in typical urban forest area and urban area,the effect of urban forest on atmospheric particulate matter concentration was revealed.【Result】The results showed that the concentration of TSP,PM_(10),PM_(2.5)and PM_1in Bashan monitoring site both reached the maximum at 9:00 am and then decreased continuously with time and reached the minimum at 14:00 pm,182.2,124.2,68.2,20.6μg·m~(-3),respectively,but the four atmospheric particulate matter decreased from 9:00 to 17:00 when they basically rose to the level of 9:00 am at Caihe community.【Conclusion】The peak values of annual changes in concentrations of PM_(2.5)and PM_1were delayed by one month at Banshan site compared to those at Caihe sites.The diurnal and annual variation patterns of atmospheric particulate matter concentrations in typical urban forest areas and urban areas were significantly different in Hangzhou.The mean concentrations of four different types of airborne particles were both higher than those of the community.The concentrations of PM_(2.5)and PM_1at the Banshan and Caihe have highly statistically significant and statistically significant,respectively.
【Objective】The concentration of different types of atmospheric particulate matter(TSP,PM_(10),PM_(2.5)and PM_1)were monitored in the Hangzhou Banshan national forest park and Caihe community for one year.【Method】Through the contrast analysis of different types of atmospheric particulate matter concentration in typical urban forest area and urban area,the effect of urban forest on atmospheric particulate matter concentration was revealed.【Result】The results showed that the concentration of TSP,PM_(10),PM_(2.5)and PM_1in Bashan monitoring site both reached the maximum at 9:00 am and then decreased continuously with time and reached the minimum at 14:00 pm,182.2,124.2,68.2,20.6μg·m~(-3),respectively,but the four atmospheric particulate matter decreased from 9:00 to 17:00 when they basically rose to the level of 9:00 am at Caihe community.【Conclusion】The peak values of annual changes in concentrations of PM_(2.5)and PM_1were delayed by one month at Banshan site compared to those at Caihe sites.The diurnal and annual variation patterns of atmospheric particulate matter concentrations in typical urban forest areas and urban areas were significantly different in Hangzhou.The mean concentrations of four different types of airborne particles were both higher than those of the community.The concentrations of PM_(2.5)and PM_1at the Banshan and Caihe have highly statistically significant and statistically significant,respectively.
引文
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[2]Cao J,Xu H,Xu Q,et al. Fine Particulate Matter Constituents and-Cardiopulmonary Mortality in a Heavily Polluted Chinese City[J].Environmental Health Perspectives,2012,120(3):373-378.
[3]Sun Z,Mu Y,Liu Y,et al. A comparison study on airborne parti-clesduring haze days and non-haze days in Beijing[J]. Science ofthe Total Environment,2013,456-457(7):1-8.
[4]刘新春,陈红娜,赵克蕾,等.乌鲁木齐大气细颗粒物(PM2. 5)有机碳和元素碳污染特征分析[J].生态环境学报,2016,25(5):821-828.
[5]方叠.中国主要城市空气污染对人群健康的影响研究[D].南京:南京大学,2014.
[6]李小飞,张明军,王圣杰,等.中国空气污染指数变化特征及影响因素分析[J].环境科学,2012,33(6):1936-1943.
[7]Markkuk K. 2015. China’s choking cocktail[J]. Nature,526(7574):497-499.
[8]中华人民共和国环境保护部. 2014中国环境状况公报[EB/OL].http://www. zhb. gov. cn/gkml/hbb/qt/201506/t20150604_302942. htm[2016-05-17].
[9]国务院办公厅.国务院关于印发大气污染防治行动计划的通知[EB/OL]. http://www. gov. cn/zwgk/2013-09/12/content_2486773. htm[2016-05-17].
[10]王晓磊,王成.城市森林调控空气颗粒物功能研究进展[J].生态学报,2014,34(8):1910-1921.
[11]赵晨曦,王玉杰,王云琦,等.细颗粒物(PM2. 5)与植被关系的研究综述[J].生态学杂志,2013,32(8):2203-2210.
[12]Cheng M T,orng C L,Lin Y C. Characteristics of AtmosphericAerosol and Acidic Gases from Urban and Forest Sites in Central Tai-wan[J]. Bulletin of Environmental Contamination and Toxicology,2007,79(6):674-677.
[13]Scott K I,Mcpherson E G,Simpson J R. Air pollutant uptake bySacramento’s urban forest[J]. Journal of Arboriculture,1998,24(4):224-234.
[14]郭二果,王成,郄光发,等.北京西山典型游憩林空气悬浮颗粒物季节变化规律[J].东北林业大学学报,2010,38(10):55-57.
[15]任晓旭,陈勤娟,董建华,等.杭州城区空气负离子特征及其与气象因子的关系[J].环境保护科学,2016,42(3):109-112.
[16]王成,郭二果,郄光发.北京西山典型城市森林内PM2. 5动态变化规律[J].生态学报,2014,34(19):5650-5658.
[17]吴志萍,王成,侯晓静,等. 6种城市绿地空气PM2. 5浓度变化规律的研究[J].安徽农业大学学报,2008,35(4):494-498.
[18]赵军平,罗玲,郑亦佳,等. G20峰会期间杭州地区空气质量特征及气象条件分析[J].环境科学学报,2017,37(10):3885-3893.
[19]SUN Y L,WANG Z F,WILD O,et al.‘APEC Blue’:SecondaryAerosol Reductions from Emission Controls in Beijing[J]. ScientificReports,2016,doi:10. 1038/srep20668.
[20]MENG R,ZHAO F R,SUN K,et al. Analysis of the 2014‘APECBlue’in Beijing Using More than One Decade of Satellite Observa-tions:Lessons Learned from Radical Emission Control Measures[J].Remote Sens,2015,7(11):15224-15243.
[21]杜荣光,齐冰,徐宏辉,等.杭州市PM(2. 5)中碳气溶胶污染特征[J].环境化学,2013,32(12):2400-2401.
[22]包贞,冯银厂,焦荔,等.杭州市大气PM2. 5和PM10污染特征及来源解析[J].中国环境监测,2010,26(3):44-48.
[23]陈瑞,童方平,李贵,等.森林类型对大气中PM2. 5值的影响[J].湖南林业科技,2016,43(2):44-49.
[24]刘萌萌.林带对阻滞吸附PM2. 5等颗粒物的影响研究[D].北京:北京林业大学,2014.
[25]王磊,黄利斌,万欣,等.城市森林对大气颗粒物(尤其PM2. 5)调控作用研究进展[J].南京林业大学学报(自然科学版),2016,40(5):148-154.
[26]洪盛茂,焦荔,包贞.杭州市区可吸入颗粒物的污染特征分析[J].中国科学院研究生院学报,2007,24(5):571-577.
[27]常雷刚.杭州4所综合医院植物群落PM2. 5浓度及植物景观研究[D].临安:浙江农林大学,2014.
[28]付志民,孙在,贾峥,等.杭州市超细微粒数浓度和粒径分布特征[J].环境科学研究,2012,25(3):259-264.
[29]赵冰清,王云琦,赵晨曦,等.重庆缙云山4种典型林分的大气颗粒物浓度差异及不同大气条件影响研究[J].北京林业大学学报,2015,37(8):76-82.