scholarly journals Evaluation of multiple surface irradiance-based clear sky detection methods at Xianghe—a heavy polluted site on the North China Plain

Author(s):  
Menqing LIU ◽  
Jinqiang ZHANG ◽  
Xiangao XIA
2013 ◽  
Vol 13 (1) ◽  
pp. 2725-2758 ◽  
Author(s):  
K. Kawamura ◽  
K. Okuzawa ◽  
S. G. Aggarwal ◽  
H. Irie ◽  
Y. Kanaya ◽  
...  

Abstract. Gaseous and particulate semi-volatile carbonyl compounds were determined every three hours in the atmosphere of Mount Tai (elevation, 1534 m) in the North China Plain during 2–5, 23–24 and 25 June, 2006 under a clear sky condition. Using two-step filter cartridge in a series, particulate carbonyls were first collected on a quartz filter and then gaseous carbonyls were collected on a quartz filter impregnated with O-benzylhydroxylamine (BHA). After the two-step derivatization with BHA and N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA), carbonyl derivatives were measured using a gas chromatography. The gaseous concentrations were obtained as follow: glycolaldehyde (range 0–1271 ng m−3, average 555 ng m−3), hydroxyacetone (0–707 ng m−3, 163 ng m−3), glyoxal (198–1396 ng m−3, 720 ng m−3), methylglyoxal (410–3170 ng m−3, 1376 ng m−3), n-nonanal (0–236 ng m−3, 71 ng m−3), and n-decanal (0–159 ng m−3, 31 ng m−3). These concentrations are among the highest ever reported in the urban and forest atmosphere. We found that gaseous carbonyls are more than 10 times more abundant than particulate carbonyls. Time-resolved variations of carbonyls did not show any a clear diurnal pattern, except for hydroxyacetone. We found that glyoxal, methylglyoxal and glycolaldehyde positively correlated with levoglucosan (a tracer of biomass burning), suggesting that a contribution from field burning of agricultural wastes (wheat crops) is significant for the bifunctional carbonyls in the atmosphere of Mt. Tai. Upward transport of the pollutants to the mountaintop from the low lands in the North China Plain is a major process to control the distributions of carbonyls in the upper atmosphere over Mt. Tai.


Author(s):  
Min Xue ◽  
Jianzhong Ma ◽  
Guiqian Tang ◽  
Shengrui Tong ◽  
Bo Hu ◽  
...  

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.


2021 ◽  
Vol 20 (6) ◽  
pp. 1687-1700
Author(s):  
Li-chao ZHAI ◽  
Li-hua LÜ ◽  
Zhi-qiang DONG ◽  
Li-hua ZHANG ◽  
Jing-ting ZHANG ◽  
...  

2021 ◽  
Vol 351 ◽  
pp. 129349
Author(s):  
Bisma Riaz ◽  
Qiuju Liang ◽  
Xing Wan ◽  
Ke Wang ◽  
Chunyi Zhang ◽  
...  

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