Exploring the spatial heterogeneity and temporal homogeneity of ambient PM10 in nine core cities of China

We focus on the causes of fluctuations in wintertime PM10 in nine regional core cities of China using two machine learning models, Random Forest (RF) and Recurrent Neural Network (RNN). RF and RNN both show high performance in predicting hourly PM10 using only gaseous air pollutants (SO2, NO2 and CO) as inputs, showing the predominance of the secondary inorganic aerosol and implying the existence of thermodynamic equilibrium between gaseous air pollutants and PM10. Also, we find the following results. The correlation of gaseous air pollutants and PM10 were more relevant than that of meteorological conditions and PM10. CO was the predominant factor for PM10 in the Beijing-Tianjin-Hebei Plain and the Yangtze River Delta while SO2 and NO2 were also important features for PM10 in the Pearl River Delta and Sichuan Basin. The spatial heterogeneity and temporal homogeneity of PM10 in China are revealed. The long-range transported PM10 was substantiated to be insignificant, except in the sandstorms. The severity of PM10 was attributable to the lopsided shift of thermodynamic equilibrium and the phenology of indigenous flora.

Adjacent-Track InSAR Processing for Large-Scale Land Subsidence Monitoring in the Hebei Plain

Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed the small baseline subset (SBAS) and NSBAS technique to monitor the land subsidence in the Hebei Plain (45,000 km2). The 166 Sentinel-1A data of adjacent-track 40 and 142 collected from May 2017 to May 2019 were used to generate the average deformation velocity and deformation time-series. A novel data fusion flow for the generation of land subsidence velocity of adjacent-track is presented and tested, named as the fusion of time-series interferometric synthetic aperture radar (TS-InSAR) results of adjacent-track using synthetic aperture radar amplitude images (FTASA). A cross-comparison analysis between the two tracks results and two TS-InSAR results was carried out. In addition, the deformation results were validated by leveling measurements and benchmarks on bedrock results, reaching a precision 9 mm/year. Twenty-six typical subsidence bowls were identified in Handan, Xingtai, Shijiazhuang, Hengshui, Cangzhou, and Baoding. An average annual subsidence velocity over −79 mm/year was observed in Gaoyang County of Baoding City. Through the cause analysis of the typical subsidence bowls, the results showed that the shallow and deep groundwater funnels, three different land use types over the building construction, industrial area, and dense residential area, and faults had high spatial correlation related to land subsidence bowls.

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China.

LAND SUBSIDENCE MONITORING WITH MULTI-TRACK SAR DATA

In this study, we investigated wide-area land subsidence in Hebei Plain using 78 C-band Sentinel-1A SAR images acquired from May 2017 to May 2018 covering two tracks. High-precision time series retrieval was performed by NSBAS technology based on a single track. The offset deformation values of two tracks calculated in this paper were −0.09 mm/year, and the multi-track deformation rate was successfully merged. Using the cross-validation of redundant observations in the multi-track overlap area to evaluate the accuracy, and it was found that 90% pixel difference between the two track overlap areas was within 9 mm. The standard deviation was 5.38 mm, and the subsidence trend of the overlap area was consistent. Twenty-four subsidence bowls were extracted, and the maximum subsidence rate reached −62 mm/year.

Pollution, Source, and Relationship of Trace Metal(loid)s in Soil-Wheat System in Hebei Plain, Northern China

To study the complex migration and transformation of trace metal(loid)s in a soil–wheat system, 225 pairs of surface soil and wheat samples were collected from the Taihang Mountains front plain, Hebei Province, northern China. The concentrations and pools (F1, water-soluble; F2, exchangeable; F3, carbonate-bound; F4, humic acid-bound; F5, Fe–Mn oxide-bound; F6, organic matter-bound; and F7, residual) of Cu, Pb, Zn, Cr, Ni, Cd, and Hg, and the soil properties of the samples were analyzed. The sum of the F1, F2, F3, and F4 proportions of Cd was higher than that of the other trace metal(loid)s, implying that Cd has greater mobility. We found a significant correlation (p < 0.01) between pools of trace metal(loid)s and the corresponding elements in wheat and a significant correlation (p < 0.01) between pools of trace metal(loid)s and pH, cation exchange capacity, clay, and total organic carbon. The results of principle component analysis (PCA)indicated that Cr, Ni and As mainly come from natural sources and Cu, Pb, Zn, and Cd from mixed groups related to farming and industry, Hg come from the coal burning. In addition, the total target hazard quotients showed the presence of harmful levels of trace metal(loid)s in wheat.