森林植被对信阳市城郊PM2.5等颗粒物污染的影响
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摘要
选择森林植被盖度存在明显差异的信阳市中心城区-近郊区-远郊区分别设立监测点开展大气颗粒物污染同步定位观测,比较分析了3个监测点TSP、PM10、PM2.5和PM1等4种粒径颗粒物质量浓度日变化和季节变化特征及其影响因素。结果表明,3个监测点4种颗粒物质量浓度日变化特征基本一致,峰值和最低值出现的时间基本同步。上午颗粒物污染比下午严重。3个监测点的颗粒物污染均表现为夏季最轻,秋季次之,冬季污染最严重。森林植被具有强大的削减PM2.5等颗粒物污染的功能,监测点颗粒物质量浓度与森林植被盖度呈负相关。在日变化和季节变化中,森林植被盖度最高的生态站监测点4种颗粒物质量浓度均明显低于其他2个监测点;同样,森林植被盖度较高的浉河景观带监测点4种颗粒物质量浓度均明显低于缺林少绿的体彩广场监测点。影响颗粒物污染的主要气象因子是气温和气压。PM2.5等4种颗粒物质量浓度与日均气温均呈极显著负相关,与日均气压均呈极显著正相关。
The Xinyang central city-near suburb-far suburbs, where there were obvious differences in forest vegetation coverage were chosen to set up monitoring points to carry out simultaneous location observation of atmospheric particulate matter pollution, and the diurnal and seasonal variation characteristics and influencing factors of mass concentration of four kinds of particulate matter, such as TSP, PM10, PM2.5 and PM1, were compared and analyzed in three monitoring sites.The results showed that the daily variation characteristics of the mass concentration of 4 particles in 3 monitoring points were basically consistent, and the time of peak and lowest value was basically synchronized. Particulate pollution in the morning was more serious than in the afternoon. The pollution of particulate matter in 3 monitoring points was the lightest in summer,the second in autumn and the worst in winter. Forest vegetation had a strong function of reducing particulate matter pollution such as PM2.5, and the concentration of particulate matter in the monitoring point was negatively correlated with the canopy of forest vegetation. In the diurnal and seasonal variations, the concentration of 4 kinds of particulate matter in the Ecological station monitoring site with the highest forest cover was significantly lower than that of other 2 monitoring points; In the same way, the mass concentrations of four kinds of particulate matter in the monitoring sites of Shihe River landscape zone with higher forest vegetation coverage were significantly lower than those in the Ticai square, where there was a lack of greening.The main meteorological factors affecting particulate matter are temperature and air pressure. The mass concentrations of 4 kinds of particulate matter were significantly negatively correlated with daily average temperature, and were significantly positively correlated with daily air pressure PM2.5.
The Xinyang central city-near suburb-far suburbs, where there were obvious differences in forest vegetation coverage were chosen to set up monitoring points to carry out simultaneous location observation of atmospheric particulate matter pollution, and the diurnal and seasonal variation characteristics and influencing factors of mass concentration of four kinds of particulate matter, such as TSP, PM10, PM2.5 and PM1, were compared and analyzed in three monitoring sites.The results showed that the daily variation characteristics of the mass concentration of 4 particles in 3 monitoring points were basically consistent, and the time of peak and lowest value was basically synchronized. Particulate pollution in the morning was more serious than in the afternoon. The pollution of particulate matter in 3 monitoring points was the lightest in summer,the second in autumn and the worst in winter. Forest vegetation had a strong function of reducing particulate matter pollution such as PM2.5, and the concentration of particulate matter in the monitoring point was negatively correlated with the canopy of forest vegetation. In the diurnal and seasonal variations, the concentration of 4 kinds of particulate matter in the Ecological station monitoring site with the highest forest cover was significantly lower than that of other 2 monitoring points; In the same way, the mass concentrations of four kinds of particulate matter in the monitoring sites of Shihe River landscape zone with higher forest vegetation coverage were significantly lower than those in the Ticai square, where there was a lack of greening.The main meteorological factors affecting particulate matter are temperature and air pressure. The mass concentrations of 4 kinds of particulate matter were significantly negatively correlated with daily average temperature, and were significantly positively correlated with daily air pressure PM2.5.
引文
[1]徐祥德，汤绪．城市化环境气象学引论[M]．北京：气象出版社，2002.243-244．
[2]BROOK R D,FRANKLIN B,CASCIO W,et al.Air pollution and cardiovascular disease:A statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association[J].Circulation,2004,109(21):2655-2671．
[3]白志鹏，蔡斌彬，董海燕，等．灰霾的健康效应[J]．环境污染与防治，2006,28(3):198-201．
[4]杨复沫，贺克斌，马永亮，等．北京大气PM2.5中微量元素的浓度变化特征与来源[J]．环境科学，2003,24(6):33-37．
[5]宋少洁，吴烨，蒋靖坤，等．北京市典型道路交通环境细颗粒物元素组成及分布特征[J]．环境科学学报，2012,32(1):66-73．
[6]STRAND L B,BARNETT A G,TONG S L.Maternal exposure to ambient temperature and the risks of preterm birth and stillbirth in Brisbane,Australia[J].American journal of epidemiology,2012,175(2):99-107．
[7]程渊．我国大气污染及生物治理措施探讨[J]．林业资源管理，2009,6(3):92-94．
[8]王华，鲁绍伟，李少宁，等．可吸入颗粒物和细颗粒物基本特征、监测方法及森林调控功能[J]．应用生态学报，2013,24(3):869-877．
[9]聂蕾，邓志华，陈奇伯．城市森林对大气颗粒物净化效果研究[J]．西部林业科学，2016,45(5):119-123．
[10]冯万富，周继良，张建设，等．信阳市不同生态功能区PM10和PM2.5污染特征[J]．西北林学院学报，2018,33(2):269-275．
[11]王跃，王莉莉，赵广娜，等．北京冬季PM2.5重污染时段不同尺度环流形势及边界层结构分析[J]．气候与环境研究，2014,19(2):173-184．
[12]刘娜，冯新斌，MATTHEW L，等．贵阳市大气颗粒物（PM2.5）污染特征及气象参数的影响[J]．地球与环境，2014,42(3):311-315．
[13]CHEN B,LI S N,YANG X B,et al.Characteristics of atmospheric PM2.5in stands and non-forest cover sites across urbanrural areas in Beijing,China[J].Urban ecosyst,2016,19(2):867-883．
[14]陈波，鲁绍伟，李少宁．北京城市森林不同天气状况下PM2.5浓度变化[J]．生态学报，2016,36(5):1391-1399．
[15]蒋维楣，曹文俊，蒋瑞宾．空气污染气象学教程[M]．北京：气象出版社，1993．
[16]陈波，李少宁，鲁绍伟，等．北京大兴南海子公园PM2.5和PM10质量浓度变化特征[J]．生态科学，2016,35(2):104-110．
[17]刘大锰，马永胜，高少鹏，等．北京市区春季燃烧源大气颗粒物的污染水平和影响因素[J]．现代地质，2005,19(4):627-633．
[18]ZHANG H L,WANG Y G,HU J L,et al.Relationships between meteorological parameters and criteria air pollutants in three megacities in China[J].Environmental research,2015,140:242-254.
[2]BROOK R D,FRANKLIN B,CASCIO W,et al.Air pollution and cardiovascular disease:A statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association[J].Circulation,2004,109(21):2655-2671．
[3]白志鹏，蔡斌彬，董海燕，等．灰霾的健康效应[J]．环境污染与防治，2006,28(3):198-201．
[4]杨复沫，贺克斌，马永亮，等．北京大气PM2.5中微量元素的浓度变化特征与来源[J]．环境科学，2003,24(6):33-37．
[5]宋少洁，吴烨，蒋靖坤，等．北京市典型道路交通环境细颗粒物元素组成及分布特征[J]．环境科学学报，2012,32(1):66-73．
[6]STRAND L B,BARNETT A G,TONG S L.Maternal exposure to ambient temperature and the risks of preterm birth and stillbirth in Brisbane,Australia[J].American journal of epidemiology,2012,175(2):99-107．
[7]程渊．我国大气污染及生物治理措施探讨[J]．林业资源管理，2009,6(3):92-94．
[8]王华，鲁绍伟，李少宁，等．可吸入颗粒物和细颗粒物基本特征、监测方法及森林调控功能[J]．应用生态学报，2013,24(3):869-877．
[9]聂蕾，邓志华，陈奇伯．城市森林对大气颗粒物净化效果研究[J]．西部林业科学，2016,45(5):119-123．
[10]冯万富，周继良，张建设，等．信阳市不同生态功能区PM10和PM2.5污染特征[J]．西北林学院学报，2018,33(2):269-275．
[11]王跃，王莉莉，赵广娜，等．北京冬季PM2.5重污染时段不同尺度环流形势及边界层结构分析[J]．气候与环境研究，2014,19(2):173-184．
[12]刘娜，冯新斌，MATTHEW L，等．贵阳市大气颗粒物（PM2.5）污染特征及气象参数的影响[J]．地球与环境，2014,42(3):311-315．
[13]CHEN B,LI S N,YANG X B,et al.Characteristics of atmospheric PM2.5in stands and non-forest cover sites across urbanrural areas in Beijing,China[J].Urban ecosyst,2016,19(2):867-883．
[14]陈波，鲁绍伟，李少宁．北京城市森林不同天气状况下PM2.5浓度变化[J]．生态学报，2016,36(5):1391-1399．
[15]蒋维楣，曹文俊，蒋瑞宾．空气污染气象学教程[M]．北京：气象出版社，1993．
[16]陈波，李少宁，鲁绍伟，等．北京大兴南海子公园PM2.5和PM10质量浓度变化特征[J]．生态科学，2016,35(2):104-110．
[17]刘大锰，马永胜，高少鹏，等．北京市区春季燃烧源大气颗粒物的污染水平和影响因素[J]．现代地质，2005,19(4):627-633．
[18]ZHANG H L,WANG Y G,HU J L,et al.Relationships between meteorological parameters and criteria air pollutants in three megacities in China[J].Environmental research,2015,140:242-254.