Impact of precursor gases and meteorological variables on satellite-estimated near-surface sulfate and nitrate concentrations over the North China Plain
Impact of precursor gases and meteorological variables on satellite-estimated near-surface sulfate and nitrate concentrations over the North China Plain
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前体气体和气象变量对卫星估算的华北平原近地表硫酸盐和硝酸盐浓度的影响
DOI：
10.1016/j.atmosenv.2018.11.030
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发表时间：
2019-02
影响因子：
5
通讯作者：
Chen Liangfu
中科院分区：
文献类型：
--
作者：
Si Yidan;Li Shenshen;Chen Liangfu
以往探讨前体排放和气象变量对pm2.5浓度影响的研究通常是在短期内和城市环境中进行的。基于卫星气溶胶光学深度（AOD）数据和全球三维化学输运模型（GEOS-Chem），结合云气溶胶激光雷达和红外探路者卫星观测（CALIPSO）的垂直剖面，首次获得了2006 - 2014年华北平原（NCP） 10 km分辨率的近地表硫酸盐和硝酸盐质量浓度。使用地面pm2.5成分浓度，我们基于卫星的模型得出硫酸盐的相关系数(r)为0.72，硝酸盐的相关系数(r)为0.70。其次，分析了研究期间臭氧监测仪（OMI）采集的大气化学成分及其前体气体的时空格局。硝酸盐（~ 30 μg/m3）和硫酸盐（~ 25 μg/m3）的最高水平集中在河北、山东、河南和北京，硝酸盐和no2的月变化呈较强的相关系数（0.67）。硝态氮和硝态氮的年际变化趋势分别为1.43%/yr和1.60%/yr。相反，硫酸盐和so2分别以3.5%/yr和0.76%/yr的速率下降，呈负相关（- 0.54）。第三，比较了前驱气体组分与相对湿度（RH）、行星边界层高度（PBLH）、温度（TEM）和风速（WS）的关系。结果表明，高no2水平通常伴随着稳定的大气条件（即低TEM、浅PBLH、低RH和轻度WS）。此外，硝态氮浓度往往依赖于NO2，它们与气象因子表现出相似的特征。PBLH和WS对硝酸盐浓度的影响最大，RH和WS对硝酸盐浓度的影响随季节而变化。在有利条件下，即高TEM、RH、PBLH和低WS， so2迅速转化为硫酸盐，并与硫酸盐浓度成反比波动。总体而言，高硝酸盐水平主要与人为原因（即大量排放）有关，而气象条件对硫酸盐浓度形成的影响大于排放。
Previous studies exploring the impact of precursor emissions and meteorological variables on PM2.5concentrations were typically conducted over a short period and in urban environments. In this analysis, near-surface sulfate and nitrate mass concentrations at a 10-km resolution over the North China Plain (NCP) from 2006 to 2014 were first obtained based on satellite aerosol optical depth (AOD) data and the global 3D chemical transport model (GEOS-Chem) in combination with vertical profiles from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Using ground-based PM2.5component concentrations, our satellite-based model yielded correlation coefficients (r) of 0.72 for sulfate and 0.70 for nitrate. Second, the spatiotemporal patterns of the chemical composition and corresponding precursor gases collected from the Ozone Monitoring Instrument (OMI) during the study period were analyzed. The highest levels of nitrate (∼30 μg/m3) and sulfate (∼25 μg/m3) were concentrated in Hebei, Shandong, Henan and Beijing, and the monthly variations of nitrate and NO2presented a strong correlation coefficient (0.67). Inter-annual changes of nitrate and NO2exhibited increasing trends of 1.43%/yr and 1.60%/yr, respectively. In contrast, sulfate and SO2decreased at rates of 3.5%/yr and 0.76%/yr, respectively, and presented a negative correlation (−0.54). Third, the relationships between the components or precursor gases and relative humidity (RH), planetary boundary layer height (PBLH), temperature (TEM) and wind speed (WS) were compared. The results showed that high levels of NO2were usually accompanied by stable atmospheric conditions (i.e., low TEM, shallow PBLH, low RH and mild WS). Moreover, the nitrate concentration was often dependent on NO2, and they showed similar characteristics with meteorological factors. PBLH and WS had the greatest influence on nitrate concentrations, whereas the effects of RH and WS varied seasonally. Under beneficial conditions, i.e., high TEM, RH and PBLH and low WS, SO2rapidly converted to sulfate and fluctuated inversely with the sulfate concentration. Overall, high nitrate levels were primarily associated with anthropogenic causes (i.e., extensive emissions), whereas meteorological conditions have a greater effect on the formation of sulfate concentrations than emissions.
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影响因子：
10.4
作者：
Son JY;Lee JT;Kim KH;Jung K;Bell ML
通讯作者：
Bell ML
影响因子：
5
作者：
Wang, Xinfeng;Wang, Wenxing;Wang, Zhe
通讯作者：
Wang, Zhe
影响因子：
--
作者：
C. Lee;R. Martin;A. van Donkelaar;G. O'Byrne;N. Krotkov;A. Richter;L. G. Huey;J. Holloway
通讯作者：
C. Lee;R. Martin;A. van Donkelaar;G. O'Byrne;N. Krotkov;A. Richter;L. G. Huey;J. Holloway
影响因子：
4.6
作者：
Wang WN;Cheng TH;Gu XF;Chen H;Guo H;Wang Y;Bao FW;Shi SY;Xu BR;Zuo X;Meng C;Zhang XC
通讯作者：
Zhang XC
影响因子：
5.5
作者：
Jianan Zou;Zirui Liu;B. Hu;Xiaojuan Huang;T. Wen;D. Ji;Jing-Yuan Liu;Yang Yang-Yang;Q. Yao
通讯作者：
Jianan Zou;Zirui Liu;B. Hu;Xiaojuan Huang;T. Wen;D. Ji;Jing-Yuan Liu;Yang Yang-Yang;Q. Yao