基于OMI数据的贵州省对流层NO_2浓度时空变化分析
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摘要
利用OMI卫星遥感数据提供的NO_2浓度产品及气象站点数据,分析了2005—2017年贵州省对流层NO_2柱浓度时空分布特征及其影响因素。结果表明:①贵州省NO_2柱浓度年均值较小,说明贵州省空气质量整体比较好,年变化为NO_2柱浓度先升高再降低的趋势,冬季最高、夏季最低,而月变化呈内凹型分布,一年中最大值大多出现在1月,7月出现最低值的次数最多;②空间分布呈西高东低、北高南低的特点;③在9个地市州中,六盘水市的NO_2柱浓度年均值最大,贵阳市位于第二,浓度最低的是黔东南州。④降水和温度对NO_2柱浓度都具有一定的负影响。
NO_2 concentration products and meteorological station data provided by OMI satellite remote sensing data were used to analyze the spatial and temporal distribution characteristics of NO2 column concentration in the troposphere and its influencing factors from 2005 to 2017 in Guizhou province. The results show that: ①In the year 2005—2017, the annual NO_2 column concentration in Guizhou Province was small, indicating that the overall air quality in Guizhou Province is better. The overall annual variation shows the trend of increase first and then decrease. Seasonal changes are expressed as: highest in winter and lowest in summer. The monthly variation shows an inner concave distribution, the maximum value of most of the year occurs in January, and the lowest number of times occurs in July. ②The spatial distribution is characterized by high in the west and north, low in the east and south. ③Among the nine cities, Liupanshui had the highest concentration of NO_2 column, Guiyang is the second, while Qiandongnan had the lowest. In addition, the annual change of Yunnan is the most gradual, while Chongqing has the largest change. ④The analysis found that precipitation and temperature have a certain impact on the concentration of NO_2 column.
NO_2 concentration products and meteorological station data provided by OMI satellite remote sensing data were used to analyze the spatial and temporal distribution characteristics of NO2 column concentration in the troposphere and its influencing factors from 2005 to 2017 in Guizhou province. The results show that: ①In the year 2005—2017, the annual NO_2 column concentration in Guizhou Province was small, indicating that the overall air quality in Guizhou Province is better. The overall annual variation shows the trend of increase first and then decrease. Seasonal changes are expressed as: highest in winter and lowest in summer. The monthly variation shows an inner concave distribution, the maximum value of most of the year occurs in January, and the lowest number of times occurs in July. ②The spatial distribution is characterized by high in the west and north, low in the east and south. ③Among the nine cities, Liupanshui had the highest concentration of NO_2 column, Guiyang is the second, while Qiandongnan had the lowest. In addition, the annual change of Yunnan is the most gradual, while Chongqing has the largest change. ④The analysis found that precipitation and temperature have a certain impact on the concentration of NO_2 column.
引文
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[11]张莹,袁金国,王莹莹,等.基于OMI数据的京津冀地区对流层NO2浓度遥感监测[J].长江流域资源与环境,2018(2):443-452.
[12]江文华,马建中,颜鹏,等.利用 GOME 卫星资料分析北京大气 NO2污染变化 [J].应用气象学报.2006,17(2):67-72.
[13]李莹.地基 DOAS 观测反演的 NO2柱总量与 SCIAMACHY 卫星 NO2数据的比较及 NO2时空分布研究[D].北京:北京大学,2006:11-34.
[14]张兴赢,张鹏,张艳,等.近 10 年中国对流层 NO2的变化趋势时空分布特征及其来源解析 [J].中国科学,2007,37(10):1 409-1 416.
[15]BOERSMA K F,EAKES H J,DIRKSEN R J,et al.An improved tropospheric NO2 column retrieval algorithm for the O-zone Monitoring Instrument[J].Atmos Meas Tech,2011,4(9):1905-1928.
[16]刘彩霞,边玮.天津市空气质量与气象因子相关分析[J].中国环境监测,2007,23(5):63-65.
[17]韦英英,杨苏勤,陈志泉,等.基于OMI卫星数据的长三角对流层NO2特征研究[J].环境科学与技术,2018,41(3):80-87.
[2] Environmental Protection Agency (EPA).National air quality and emissions trends report 1997[R].Washington DC:EPA,1998:18-20.
[3] 王跃启,江洪,张秀英,等.基于 OMI 卫星遥感数据的中国对流层 NO2时空分布[J].环境科学研究,2009,22(8):932-937.
[4] Chen D,Zhou B,Beirle S,et al.Tropospheric NO2column densities deduced from zenith-sky DOAS measurements in Shanghai,China,and their application to satellite validation[J].Atmos Chem Phys Discuss,2008,8(4):16 713-16 762.
[5] 周春艳,王桥,厉青,等.近10年长江三角洲对流层NO2柱浓度时空变化及影响因素[J].中国环境科学,2016(36):1 921-1 930.
[6] 李凯路,闫瑞峰.2006—2015 年山西省对流层NO2时空分布及影响因素[J].山西大学学报(自然科学版),2016,39(4):686-694.
[7] 赵瑞东,刘旻霞,李瑞,等.基于OMI数据太原市NO2时空变化及影响因素分析[J].环境科学与技术,2017(6):141-148.
[8] 胡春梓,袁金国.基于多源卫星遥感数据的河北省对流层NO2柱浓度时空变化[J].科技通报,2016,32(11) :30-36.
[9] 程念亮,李云婷,张大伟,等.2013—2014 年北京市NO2时空分布研究[J].中国环境科学,2016,36(1):18-26.
[10]李鹏,肖致美,陈魁,等.基于 OMI 数据天津市NO2浓度分布特征及其适用性[J].环境科学与技术,2016,39(1):183-187.
[11]张莹,袁金国,王莹莹,等.基于OMI数据的京津冀地区对流层NO2浓度遥感监测[J].长江流域资源与环境,2018(2):443-452.
[12]江文华,马建中,颜鹏,等.利用 GOME 卫星资料分析北京大气 NO2污染变化 [J].应用气象学报.2006,17(2):67-72.
[13]李莹.地基 DOAS 观测反演的 NO2柱总量与 SCIAMACHY 卫星 NO2数据的比较及 NO2时空分布研究[D].北京:北京大学,2006:11-34.
[14]张兴赢,张鹏,张艳,等.近 10 年中国对流层 NO2的变化趋势时空分布特征及其来源解析 [J].中国科学,2007,37(10):1 409-1 416.
[15]BOERSMA K F,EAKES H J,DIRKSEN R J,et al.An improved tropospheric NO2 column retrieval algorithm for the O-zone Monitoring Instrument[J].Atmos Meas Tech,2011,4(9):1905-1928.
[16]刘彩霞,边玮.天津市空气质量与气象因子相关分析[J].中国环境监测,2007,23(5):63-65.
[17]韦英英,杨苏勤,陈志泉,等.基于OMI卫星数据的长三角对流层NO2特征研究[J].环境科学与技术,2018,41(3):80-87.