scholarly journals Cloud scavenging of abundant anthropogenic refractory particles at a mountain site in North China

2018 ◽  
Author(s):  
Lei Liu ◽  
Jian Zhang ◽  
Liang Xu ◽  
Qi Yuan ◽  
Dao Huang ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
Size Composition ◽  
Climate Forcing ◽  
Pollution Levels ◽  
Aerosol Cloud ◽  
The North ◽  
The North China Plain ◽  
Transmission Electron ◽  
Cloud Scavenging

Abstract. Aerosol-cloud interaction remains a major source of uncertainty in climate forcing estimate. Our knowledge about the aerosol-cloud interaction is particularly weak in heavily polluted conditions. In this study, cloud residual (cloud RES) and cloud interstitial (cloud INT) particles were collected during cloud events under different pollution levels from 22 July to 1 August, 2014 at Mt. Tai (1532 m above sea level) located in the North China Plain (NCP). Transmission electron microscopy (TEM) was used to investigate size, composition, and mixing state of individual cloud RES and INT particles. Our results show that S-rich particles were predominant (78 %) during clean periods (PM2.5 

scholarly journals Spatial Characteristics of PM2.5 Pollution among Cities and Policy Implication in the Northern Part of the North China Plain

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Yangjun Wang ◽  
Hongli Li ◽  
Jin Feng ◽  
Wu Wang ◽  
Ziyi Liu ◽  
...  
Keyword(s):  
Prevention And Control ◽  
North China Plain ◽  
North China ◽  
Recent Decade ◽  
Clustering Coefficient ◽  
Spatial Characteristics ◽  
Pollution Levels ◽  
The North ◽  
The North China Plain ◽  
And Control

In the recent decade, the North China Plain (NCP) has been among the region’s most heavily polluted by PM2.5 in China. For the nonattainment cities in the NCP, joint pollution control with related cities is highly needed in addition to the emission controls in their own cities. However, as the basis of decision-making, the spatial characteristics of PM2.5 among these cities are still insufficiently revealed. In this work, the spatial characteristics among all nonattainment cities in the northern part of the North China Plain (NNCP) region were revealed based on data mining technologies including clustering, coefficient of divergence (COD), network correlation model, and terrain and meteorology analysis. The results indicate that PM2.5 pollution of cities with a distance of less than 180 km exhibits homogeneity in the NCP region. Especially, the sub-region, composed of Xinxiang, Hebi, Kaifeng, Zhengzhou, and Jiaozuo, was strongly homogeneous and a strong correlation exists among them. Compared with spring and summer, much stronger correlations of PM2.5 between cities were found in autumn and winter, indicating a strong need for joint prevention and control during these periods. All nonattainment cities in this region were divided into city-clusters, depending on the seasons and pollution levels to further helping to reduce their PM2.5 concentrations effectively. Air stagnation index (ASI) analysis indicates that the strong correlations between cities in autumn were more attributed to the transport impacts than those in winter, even though there were higher PM2.5 concentrations in winter. These results provided an insight into joint prevention and control of pollution in the NCP region.

ROx Budgets and O3 Formation during Summertime at Xianghe Suburban Site in the North China Plain

2021 ◽  
Author(s):  
Min Xue ◽  
Jianzhong Ma ◽  
Guiqian Tang ◽  
Shengrui Tong ◽  
Bo Hu ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
The North ◽  
The North China Plain ◽  
Suburban Site

scholarly journals Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Groundwater Level ◽  
North China Plain ◽  
North China ◽  
Gradient Boosting ◽  
Fusion Model ◽  
The North ◽  
The North China Plain ◽  
Prediction Module

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.

scholarly journals The water-saving potential of using micro-sprinkling irrigation for winter wheat production on the North China Plain

2021 ◽  
Vol 20 (6) ◽  
pp. 1687-1700
Author(s):  
Li-chao ZHAI ◽  
Li-hua LÜ ◽  
Zhi-qiang DONG ◽  
Li-hua ZHANG ◽  
Jing-ting ZHANG ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Water Saving ◽  
Wheat Production ◽  
The North ◽  
The North China Plain
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