China is suffering severe ambient air pollution in recent decades and particulate matter (PM) has become the major pollutant, especially for PM2.5 and PM10, which have highly raised scholars and policy-makers’ attention in last few years. The existing research has focused on the characteristics of PM2.5 and PM10, respectively, or analyzed the correlation between the two pollutants, while the ratio of PM2.5 to PM10 has been taken less consideration. In this study, daily mean PM2.5 and PM10 mass concentrations in 31 provincial capitals from 2014 to 2016 were used to present the temporal variations and spatial distribution of PM2.5/PM10 ratios among eight economic regions. And then, statistical method and correlation analysis were adopted to investigate the relationship between the ratios and AQI, the rate of change on the ratios, and the impact of meteorological parameters on the ratios. The results indicated that PM2.5/PM10 ratios showed an increasing trend from northwest to southeast due to different economic development and industrial types. The highest values were observed in winter among all regions, and the ratios on weekends were higher than that of on weekdays in most of the regions. Besides, domestic heating in northern China had a significant contribution to the ratios. Moreover, ratios had less changes, and the rate of change was stable in summer. As for air quality, the higher the ratio, the larger the possibility of high AQI so that the air pollution will be more severe. In terms of meteorological factors, the results demonstrated that relative humidity, precipitation, and pressure were the most important factors and had significantly positive impacts, while sunshine duration, temperature, and wind speed had negative effects on the ratios. The findings could identify the pollution sources among PM10 and be helpful for making regulation locally to reduce emission which considers anthropogenic sources and meteorological diffusion simultaneously.

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Background Soil erosion is a severe problem in the karst watershed, and analysis of soil erosion at the watershed scale is urgently needed. Methods This study tried to estimate the soil erodibility factor (K-factor) using the Erosion Productivity Impact Calculator (EPIC) nomograph and evaluate the spatial distribution of the predicted K-factor in a karst watershed. Soil properties and K-factors of five land use types (NF: natural mixed forest, CF: cypress forest, EF: economic forest, ST: stone dike terrace, VF: vegetable land) in the Xialaoxi small watershed were compared and key factors affecting erodibility were analyzed. Results Results showed that (1) The erodibility K-factor was unevenly distributed within different site types and strongly influenced by anthropogenic activities. The soil K-factors of sample sites subjected to frequent human disturbance (ST, VF) were high, ranging from 0.0480-0.0520 t hm2 h/(MJ mm hm2), while the soil K-factors of natural site types (NF, CF, and EF) were low, ranging from 0.0436-0.0448 t hm2 h/(MJ mm hm2). (2) The soil texture in the Xialaoxi watershed was mostly loamy, and that of the agricultural areas frequently disturbed by agricultural practices (ST, VF) was silty loam. (3) Soil carbon fractions were affected by land use types. Soil organic carbon storage of NF and CF had strong spatial heterogeneity. The soil organic carbon (SOC) and labile organic carbon (LOC) of the two were significantly higher than those of the disturbed EF and cultivated land soil. (4) There was a synergistic effect between the soil properties and the K-factor. K was significantly negatively related to sand fractions (2-0.05 mm) and non-capillary porosity, while positively related to silt content (0.05–0.002 mm). Overall, changes in bulk density (BD), total porosity (TP), non-capillary porosity (NCP), texture, and organic matter content caused by natural restoration or anthropogenic disturbance were the main reasons for soil erodibility. Natural care (sealing) and construction of stone dike planting practices were effective ways to reduce soil erosion in small karst watershed areas of western Hubei.

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IntroductionIn order to solve the inhibition of alkaline environment on plants growth at the initial stage of Eco-restoration of vegetation concrete technology, introducing AMF into vegetation concrete substrate is an effective solution. MethodsIn this study, Glomus mosseae (GM), Glomus intraradices (GI) and a mixture of two AMF (MI) were used as exogenous inoculation agents. Festuca elata and Cassia glauca were selected as host plants to explore the relationship between the physiological characteristics of plants and the content of substrate cement under exogenous inoculation of AMF.ResultsThe experiment showed that, for festuca elata, the maximum mycorrhizal infection rates of inoculation with GM, MI were when the cement contents ranged 5–8% and that of GI inoculation was with the cement contents ranging 5–10%. Adversely, for Cassia glauca, substrate cement content had little effect on the root system with the exogenous inoculation of AMF. Compared with CK, the effects of AMF inoculation on the physiological characteristics of the two plants were different. When the cement content was the highest (10% and 8% respectively), AMF could significantly increase(p<0.05) the intercellular CO2 concentration (Ci) of Festuca elata. Moreover, for both plants, single inoculation was more effective than mixed inoculation. When the cement content was relatively low, the physiological characteristics of Cassia glauca were promoted more obviously by the inoculation of GI. At higher cement content level, inoculation of GM had a better effect on the physiological characteristics of the two plants. ConclusionThe results suggest that single inoculation of GM should be selected to promote the growth of Festuca elata and Cassia glauca in higher alkaline environment.

The goal of this research is to investigate strategies to increase the erosion resistance of the slope surface during the early stages of vegetation concrete construction, as well as to offer a scientific foundation for improving vegetation concrete formulation. Simulated rainfall experiments were carried out at 2 different slope gradients (50° and 60°), 2 different rainfall intensities (60 and 120 mm·h−1), and 4 treatments (CK-no additive, 0.4% P-polyacrylamide, 4% C-biochar, and 0.4% F-palm fiber). PAM, palm fiber, and biochar significantly reduced the initial runoff time of the vegetation concrete slope by an average of 47.03%, 46.41%, and 22.67%, respectively (p < 0.05). The runoff rate of each slope under different conditions increased with the expansion of rainfall duration and then fluctuated and stabilized, whereas the erosion rate decreased and then fluctuated and stabilized. PAM and palm fiber both increased runoff rates while decreasing erosion rates, but biochar increased both runoff rates and erosion rates. The runoff reduction benefits of PAM, palm fiber, and biochar were −69.84~−1.97%, −68.82~−14.28% and −63.70~−6.80%, respectively, while the sediment reduction benefits were 69.21~94.07%, −96.81~−50.35%, and 36.20~60.47%, respectively. PAM and palm fiber both have obvious sediment reduction benefits and can be used in the ecological restoration of high and steep slopes in areas with heavy rainfall.

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Particulate matter with a diameter of less than 2.5 µm (PM 2.5 ) has a significant impact on air pollution, atmospheric visibility, and human health. The most basic and important step of regional air pollution control is to obtain air pollution data in different seasons from both satellite sensors and ground-level observations. The aim of this paper is to accurately estimate the PM 2.5 concentration in the Beijing-Tianjin-Hebei urban area in different seasons by establishing a seasonal geographically and temporally weighted regression (S-GTWR) model that integrates multiple complex factors. Using a greedy algorithm, the model results were optimized by selecting the characteristic variables that contributed to the accuracy of the model in different seasons. The measured and estimated PM 2.5 concentrations were compared and the cross-validation results were used as a basis for evaluating the accuracy of the model. The results showed that the accuracy of the S-GTWR model that combined the optimal characteristic variables was higher than that of the geographically weighted regression (GWR) model and the kriging method. The mean prediction error (ME), relative prediction error (RPE), and root mean square error (RMSE) of the S-GTWR model were small, and the coefficient of determination (R 2 ) of the model exceeded 0.86 for each season. The accuracy of the S-GTWR model in estimating the PM 2.5 concentration was highest in summer and lowest in winter. In addition, the proposed model can accurately estimate PM 2.5 concentrations in areas without monitoring sites. The results can provide a scientific basis for the study of pollution control and PM 2.5 exposure in large urban agglomerations.