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China achieved comprehensive poverty eradication under the current standards in 2020, but eliminating absolute poverty does not mean the end of poverty alleviation and reduction; relative poverty will exist for a long time and has become the subject of poverty study. In this paper, the social poverty line (SPL) index is utilized to establish the relative poverty standard, and CHFS2017 is used to compare the regional distribution of relative poverty in China. The results show that the relative poverty in rural areas is more serious than that in urban areas. The rural relative poverty rate in five provinces and cities including Beijing is over 60%, and the rural relative poverty rate in Qinghai is low. The urban relative poverty rate in many provinces and cities of the central and western regions is below 40%, and the relatively high relative poverty rate in the eastern region has drawn attention to the issue of the income distribution. Moreover, a logit model for binary is employed for the influencing factor analysis of the relative poverty of urban and rural residents. The results show that the education year has a negative effect on the relative poverty of urban and rural residents. Happiness has a positive effect on urban residents, government financial expenditure and financial support for agriculture have different effects on rural residents and urban residents. Therefore, we put forward aiming at relative poverty in the rural areas of the central and western regions to reduce financial pressure and increase the benefits of poverty reduction.

The seasonal and spatial eukaryotic phytoplankton composition in the Fenhe River was investigated based on the 18S rDNA V4 region. The relationship between phytoplankton functional groups and environmental factors was explored to effectively capture the responses of these taxa to environmental gradients and their effects on ecosystem function. Our results indicated that the Chlorophyta and Bacillariophyta had higher relative abundance than other taxa, and their diversity and richness indices in spring were higher than those in other seasons. The linear discriminant analysis effect size (LEfSe) analyses detected that the potential seasonal biomarkers included Desmodesmus, Cyclotella, Pseudoschroederia, Discostella, Scenedesmus, Monoraphidium, and Nannochloropsis; the spatial biomarkers included Amphora, Neochloris, Hindakia, Pseudomuriella, Coccomyxa, Chloroidium, Scherffelia, Chromochloris, and Scotinosphaera. The systemic evolution and distribution characteristics of the first 50 representative sequences showed that the dominant genus included Desmodesmus in spring, Pseudopediastrum in summer, Mychonastes in autumn, and Monoraphidium in winter. Main seasonal variation of phytoplankton functional groups was as follows: spring (J + F + C + X1) → summer (J + F + X1 + X2) → autumn (J + F + X1 + C) → winter (X1 + J + B + X2). Pearson correlation, redundancy analysis, and variance partitioning analysis showed temperature and phosphate were the determining factors causing the changes of phytoplankton functional groups and community composition in the Fenhe River., (© 2021. The Author(s).)

A typical area, Gaomi City in China, was chosen to discuss the enrichment process of groundwater fluorine in sea water intrusion area. The groundwater had fluorine levels of 0.09-10.99 mg/L, with an average concentration of 1.38 mg/L. The high-fluorine groundwater was mainly distributed in the unconsolidated Quaternary sediments, where concentrations in 83.6% of the samples exceeded the national limit of 1.0 mg/L. The groundwater in the Quaternary sediments also had higher levels of Cl - , TDS, Mg 2+ , and pH and lower levels of Ca 2+ , Co, Ni, and Cu than that in the bedrock. The groundwater fluorine levels in the Quaternary sediments are positively correlated with Cl - , TDS, Mg 2+ , pH, and negatively correlated with Ca 2+ , γCa 2+ /γMg 2+ , Co, Ni, Cu. Geochemical indices of Cl - and TDS indicate sea water intrusion in the Quaternary high-fluorine groundwater area (F -  > 1.0 mg/L), while they do not indicate any intrusion in the bedrock area. The chemical weathering of minerals was intensified with the intrusion of sea water. Cation exchange was confirmed to occur in the Quaternary sediments and was promoted by sea water intrusion. Cation exchange consumes part of groundwater Ca 2+ and permits more F - dissolving. Consequently, in the Quaternary sediments, the groundwater was supersaturated with CaF 2 minerals and undersaturated with MgF 2 minerals when F -  > 1.0 mg/L, while CaF 2 and MgF 2 minerals both are undersaturated when F -  < 1.0 mg/L. Thus, the chemical weathering of minerals and cation exchange caused by sea water intrusion are the crucial processes controlling the groundwater fluorine levels, which should be considered when the groundwater fluorine enrichment mechanism is discussed along coastal zones., (© 2020, National Ground Water Association.)

Many source apportionment models have been applied to identify pollution sources, and differences often exist in the diagnostic results. The reasons causing these differences have not been fully elucidated. In this study, three receptor models, principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and factor analysis-nonnegative constraints (FA-NNC), were compared and applied for the analysis of 16 EPA priority polycyclic aromatic hydrocarbons (PAHs) adsorbed in street dust samples from Harbin City (China). The differences in the results were caused by different calculation approaches, including matrix decomposition, variable grouping extraction, and nonnegative constraints, especially between PCA-MLR and the other two models. PCA-MLR has no nonnegative constraints, making PCA-MLR less similar to the real world than the other two. Both PMF and FA-NNC have a nonnegative constraint process, which may be the main reason why their results were much more similar to each other than to those of PCA-MLR. PCA-MLR distinguishes variables into several groups that have the greatest variances from each other, whereas the other two methods find similarities among variables and extract them. In the case study of Harbin City, the contributions of mobile and industrial sources ranged from 47 to 69%, and the contributions of coal and other sources ranged from 30 to 52%. The recognized types of pollution sources were generally equivalent, but the proportional contributions were different. PCA-MLR performed best in calculating contributions, whereas PMF and FA-NNC were better in terms of source diagnosis.

This study used the officially released data by the Chinese air quality monitoring network to analyze the pollution characteristics of six air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ) for 29 cities in the Central Plains Economic Zone (CPEZ; China) in 2015. During 2015, serious particulate matter (PM) pollution often occurred, and the concentrations of PM 2.5 and PM 10 were 77 μg m -3 and 128 μg m -3 , respectively. Air pollutants were at higher concentrations in the northern cities than those in the southern region of the CPEZ, and the correlation among the cities indicated that there was regional pollution in CPEZ. Generally, PM, SO 2 , NO 2 , and CO showed similar seasonal characteristics and the highest and lowest concentrations appeared in winter and summer, respectively. In addition, we used the HYSPLIT model and trajStat model to identify the potential source contribution function and concentration-weighted trajectory of Zhengzhou, the central city of CPEZ. More serious air pollution occurred when air masses were transported from the west of the CPEZ. Shaanxi Province, Hubei Province, Anhui Province and the northwest of the CPEZ were found to be the main exogenous sources of total PM with contributions of > 100 μg m -3 PM 2.5 and > 180 μg m -3 PM 10 . Therefore, the concentrations of PM in 2015 at Zhengzhou were probably influenced by both long-distance transmission and local emissions.

The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes. Our model utilizes three detection branches, in which feature maps with a size of 16 × 16, 32 × 32, and 64 × 64 are used, respectively, to optimize the detection for large-, medium-, and small-scale objects. By means of a multitask loss function, our model can be trained end-to-end. The experimental results show that our model achieves state-of-the-art performance in terms of precision and recall rate, and the detection speed (up to 33 fps) is fast, which can meet the real-time requirements of industry., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2019 Jiangfan Feng et al.)

Due to extensive pollution and the relatively weak flow replacement in urban rivers, determining how to fully utilize the self-purification abilities of water bodies for water quality protection has been a complex and popular topic of research and social concern. Organic pollution is an important type of urban river pollution, and COD (chemical oxygen demand) is one of the key pollution factors. Currently, there is a lack of research on the relationship between COD degradation and the flow characteristics of urban rivers. In this paper, COD degradation experiments were conducted in an annular flume with Jinjiang River water at controlled flow velocities and the COD degradation coefficients under different hydraulic conditions were analyzed. A good correlation was observed between the degradation coefficient and hydraulic conditions. According to dimensional analysis, the relationship between the COD degradation coefficient and hydraulic conditions such as the flow velocity, water depth, Reynolds number ( Re ), and Froude number ( Fr ) was established as K COD = 86400 u h F r 0.8415 R e - 1.2719 + 0.258 . The COD degradation coefficients of the Chishui River in Guizhou Province ranged from 0.175-0.373 1/d based on this formula, and the field-measured values varied from 0.234-0.463 1/d. The error in the formula ranged from 5.4-25.3%. This study provides a scientific basis for the prediction of the COD degradation coefficients of urban rivers.

To investigate the differences in characteristics of heavy metals associated with different levels of ambient air quality, we collected road dust samples from Beihai (BH) and Shanghai (SH). The mean concentrations of Ni, Cr, Zn, and Cu in BH samples were one to four times the background concentrations in soil, whereas the concentrations of Cd, Cr, Mn, Zn, Cu, and Pb in SH were one to three times the background concentrations. The geo-accumulation index (I geo ) indicated widespread moderate contamination by Zn and high contamination near industrial areas by Ni and Cr in BH, whereas in SH was partly moderately contaminated by Pb, Cu, and Zn. The potential ecological risk index ([Formula: see text]) indicated the low risk posed by all metals in both BH and SH. However, special attention should be given to the maximal [Formula: see text] values, such as considerable risk for Hg ([Formula: see text] = 148.7) and high risk for Ni (254.1) in BH, respectively. According to the health risk assessment results, there were no non-carcinogenic or carcinogenic risks (CR) posed by heavy metals in road dust collected from BH and SH. Non-carcinogenic risks due to Cr for children in both BH (0.36) and SH (0.24) were relatively high compared to other metals, and a maximal CR value for Cr (2.7 × 10 -6 ) was found to pose a potential carcinogenic risk near the industrial area in BH. Compared with those in developed cities, the health risks in BH related to Cu, Pb, and Zn from motor vehicle emissions were relatively low, but those related to Ni and Cr from local industrial activity in road dust were relatively high.