scholarly journals Statistical Prediction of Winter Haze Days in the North China Plain Using the Generalized Additive Model

2017 ◽  
Vol 56 (9) ◽  
pp. 2411-2419 ◽  
Author(s):  
Zhicong Yin ◽  
Huijun Wang
Keyword(s):  
North China Plain ◽  
North China ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Predictive Ability ◽  
Statistical Prediction ◽  
The North ◽  
Tropospheric Biennial Oscillation ◽  
The North China Plain ◽  
Haze Days

AbstractWinter (December–February) haze days in the North China Plain (WHDNCP) have recently dramatically increased. In addition to human activities, climate change and variability also contributed to the severe situation and supported the possibility of seasonal predictions. In this study, using the generalized additive model (GAM), the sea surface temperature around the Alaska Gulf and sea ice area of the Beaufort Sea were selected as the predictors to establish a statistical prediction model (SPM). The difference between the current and previous year of WHDNCP (WDY) was predicted first and was then added to the observation of the previous year to obtain the final predicted WHDNCP. For WDY prediction, the root-mean-square error of the SPM using GAM was 3.01 days. In addition to the annual variation, the tropospheric biennial oscillation features and the dramatically increasing trend after 2010 were both captured successfully. Furthermore, for the final predicted WHDNCP anomalies, the long-term trend and turning points were simulated well, and the percentage of the same mathematical sign was 91.7%. Independent prediction tests were performed for 2014 and 2015, and the forecast bias was 0.86 and 0.19 days, respectively. To assess the predictive ability, recycling independent tests (including real-time hindcasts for the period 2005–15) were also applied, and the percentage of the same sign was 100%.

scholarly journals Observation of atmospheric peroxides during Wangdu Campaign 2014 at a rural site in the North China Plain

2016 ◽  
Vol 16 (17) ◽  
pp. 10985-11000 ◽  
Author(s):  
Yin Wang ◽  
Zhongming Chen ◽  
Qinqin Wu ◽  
Hao Liang ◽  
Liubin Huang ◽  
...  
Keyword(s):  
Biomass Burning ◽  
North China Plain ◽  
North China ◽  
Chemical Mechanism ◽  
Rural Site ◽  
Direct Production ◽  
The North ◽  
The North China Plain ◽  
The Impact ◽  
Haze Days

Abstract. Measurements of atmospheric peroxides were made during Wangdu Campaign 2014 at Wangdu, a rural site in the North China Plain (NCP) in summer 2014. The predominant peroxides were detected to be hydrogen peroxide (H2O2), methyl hydroperoxide (MHP) and peroxyacetic acid (PAA). The observed H2O2 reached up to 11.3 ppbv, which was the highest value compared with previous observations in China at summer time. A box model simulation based on the Master Chemical Mechanism and constrained by the simultaneous observations of physical parameters and chemical species was performed to explore the chemical budget of atmospheric peroxides. Photochemical oxidation of alkenes was found to be the major secondary formation pathway of atmospheric peroxides, while contributions from alkanes and aromatics were of minor importance. The comparison of modeled and measured peroxide concentrations revealed an underestimation during biomass burning events and an overestimation on haze days, which were ascribed to the direct production of peroxides from biomass burning and the heterogeneous uptake of peroxides by aerosols, respectively. The strengths of the primary emissions from biomass burning were on the same order of the known secondary production rates of atmospheric peroxides during the biomass burning events. The heterogeneous process on aerosol particles was suggested to be the predominant sink for atmospheric peroxides. The atmospheric lifetime of peroxides on haze days in summer in the NCP was about 2–3 h, which is in good agreement with the laboratory studies. Further comprehensive investigations are necessary to better understand the impact of biomass burning and heterogeneous uptake on the concentration of peroxides in the atmosphere.

scholarly journals Seasonal prediction of winter haze days in the north central North China Plain

2016 ◽  
Vol 16 (23) ◽  
pp. 14843-14852 ◽  
Author(s):  
Zhicong Yin ◽  
Huijun Wang
Keyword(s):  
Prediction Model ◽  
North China Plain ◽  
North China ◽  
Additive Model ◽  
Continuous Increase ◽  
North Central ◽  
The North ◽  
Predictive Skill ◽  
Haze Pollution ◽  
Haze Days

Abstract. Recently, the winter (December–February) haze pollution over the north central North China Plain (NCP) has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression (MLR) and the generalized additive model (GAM). By analyzing the associated increment of atmospheric circulation, seven leading predictors were selected to predict the upcoming winter haze days over the NCP (WHDNCP). After cross validation, the root mean square error and explained variance of the MLR (GAM) prediction model was 3.39 (3.38) and 53 % (54 %), respectively. For the final predicted WHDNCP, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent prediction tests for 2014 and 2015 also confirmed the good predictive skill of the new schemes. The predicted bias of the MLR (GAM) prediction model in 2014 and 2015 was 0.09 (−0.07) and −3.33 (−1.01), respectively. Compared to the MLR model, the GAM model had a higher predictive skill in reproducing the rapid and continuous increase of WHDNCP after 2010.

scholarly journals Analysis of a winter regional haze event and its formation mechanism in the North China Plain

2013 ◽  
Vol 13 (11) ◽  
pp. 5685-5696 ◽  
Author(s):  
X. J. Zhao ◽  
P. S. Zhao ◽  
J. Xu ◽  
W. Meng, ◽  
W. W. Pu ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
The Other ◽  
Plain Area ◽  
The North ◽  
The North China Plain ◽  
Secondary Formation ◽  
Regional Haze ◽  
Haze Episode ◽  
Haze Days

Abstract. A regional haze episode occurred in the Beijing, Tianjin and Hebei province (BTH) area in the North China Plain (NCP) from 16 to 19 January 2010. Data were collected and analyzed during the time frame of 14 through 23 January 2010 to include the haze event. The increase of secondary inorganic pollutants (SO42−, NO3−, NH4+) in PM2.5 was observed simultaneously at four sites, especially in the plain area of the BTH, which could be identified as a common characteristic of pollution haze in east China. The sulfate and nitrate in PM2.5 were mainly formed through the heterogeneous reaction process in the urban area. The organic matter (OM) increased more significantly at the Chengde (CD) site than the other three sites in the plain area. The secondary organic aerosols only existed during haze days at CD but in both haze and non-haze days at the other three sites, which suggested the greater regional impact of secondary formation process during the haze episode. The secondary formation of aerosol was one important formation mechanism of haze. The strong temperature inversion and descending air motions in the planetary boundary layer (PBL) allowed pollutants to accumulate in a shallow layer. The weak surface wind speed produced high pollutants concentration within source regions. The accumulation of pollutants was one main factor in the haze formation. The enhanced southwest wind in the last period of this episode transported pollutants to the downwind area and expanded the regional scope of the haze.

scholarly journals Seasonal Prediction of Winter Haze Days in the North-Central North China Plain

2016 ◽  
Author(s):  
Zhicong Yin ◽  
Huijun Wang
Keyword(s):  
Prediction Model ◽  
North China Plain ◽  
North China ◽  
Additive Model ◽  
Continuous Increase ◽  
North Central ◽  
The North ◽  
Predictive Skill ◽  
Haze Pollution ◽  
Haze Days

Abstract. Recently, the winter (December–February) haze pollution over the North-Central North China Plain (NCP) has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression (MLR) and the generalized additive model (GAM) approaches. By analyzing the associated increment of atmospheric circulation, seven leading predictors were selected to predict the upcoming winter haze days over the NCP (WHDNCP). After cross validation, the root mean square error and explained variance of the MLR (GAM) prediction model was 3.39 (3.38) and 53 % (54 %), respectively. For the final predicted WHDNCP, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent prediction tests for 2014 and 2015 also confirmed the good predictive skill of the new schemes. The predicted bias of the MLR (GAM) prediction model in 2014 and 2015 was 0.09 (−0.07) and −3.33 (−1.01), respectively. Compared to the MLR model, the GAM model had a higher predictive skill in reproducing the rapid and continuous increase of WHDNCP after 2010.

scholarly journals Observation of atmospheric peroxides during Wangdu Campaign 2014 at a rural site in the North China Plain

2016 ◽  
Author(s):  
Yin Wang ◽  
Zhongming Chen ◽  
Qinqin Wu ◽  
Hao Liang ◽  
Liubin Huang ◽  
...  
Keyword(s):  
Biomass Burning ◽  
North China Plain ◽  
North China ◽  
Chemical Mechanism ◽  
Rural Site ◽  
Direct Production ◽  
The North ◽  
The North China Plain ◽  
The Impact ◽  
Haze Days

Abstract. Measurements of atmospheric peroxides were made during Wangdu Campaign 2014 at Wangdu, a rural site in the North China Plain (NCP) in summer 2014. The predominant peroxides were detected to be hydrogen peroxide (H2O2), methyl hydroperoxide (MHP) and peroxyacetic acid (PAA). The observed H2O2 reached up to 11.3 ppbv, which was the highest value compared with previous observations in China at summer time. A box model simulation based on the Master Chemical Mechanism and constrained by the simultaneous observations of physical parameters and chemical species was performed to explore the chemical budget of atmospheric peroxides. Photochemical oxidation of alkenes was found to be the major secondary formation pathway of atmospheric peroxides, while contributions from alkanes and aromatics were of minor importance. The comparison of modelled and measured peroxide concentrations revealed an underestimation during biomass burning events and an overestimation on haze days, which were ascribed to the direct production of peroxides from biomass burning and the heterogeneous uptake of peroxides by aerosols, respectively. The strengths of the primary emissions from biomass burning were on the same order of the known secondary production rates of atmospheric peroxides during the biomass burning events. The heterogeneous process on aerosol particles was suggested to be the predominant sink for atmospheric peroxides. The atmospheric lifetime of peroxides on haze days in summer in the NCP was about 2–3 hours, which is in good agreement with the laboratory studies. Further comprehensive investigations are necessary to better understand the impact of biomass burning and heterogeneous uptake on the concentration of peroxides in the atmosphere.

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.

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