The relationships between vegetation and climatic factors are extremely complex and nonlinear. To identifying these relationships across the drylands in northern China, we analysed the driving effects of air temperature, precipitation, actual evapotranspiration, and reference crop evapotranspiration (i.e., potential evapotranspiration) on the seasonal variability of the leaf area index (LAI) in various locations including Horqin, Hulun Buir, Otindag, Mu Us, Tengger, and Junggar by using convergent cross mapping and multivariate empirical dynamic modelling based on meteorological datasets of 421 stations and remote sensing datasets at 8‐day intervals during 2000 to 2014. Over the study period, the strengths of the driving effects of the climatic factors on LAI were related to their own seasonality. Except in Junggar, the sensitivity of LAI to the climatic factors weakened with decreases in the aridity index, and these relationships changed over different seasons, but LAI was most vulnerable to the effects of climatic factors in the early growing season. Our results show that vegetation in the eastern drylands (e.g., Horqin, Hulun Buir, and Otindag) of northern China is more sensitive to seasonal climatic variations than that in the western drylands (e.g., Mu Us and Tengger), which suggests that vegetation in the eastern drylands may face greater risks of degradation under climate change. On the basis of our results, we recommend the application of differential management strategies for the drylands in northern China to prevent ongoing desertification and to achieve the target of land degradation neutrality by 2030.

Forestland dynamics can affect the ecological security of a country and even the global environment, and therefore it is of great practical significance to understand the characteristics of temporal and spatial variations of forestland. Taking Jiangxi Province as the study area, this study first explored the driving mechanism of the natural environment and social economy on deforestation and afforestation using a simultaneous equation model. The results indicate that population size, topographic and geomorphologic factors, climate, and location play leading roles in influencing forestland density fluctuations. Specifically, the population size, economic development level, gross value of forestry production, climate conditions, and government policies are key influencing factors of afforestation. Deforestation is mainly influenced by agricultural population, non-agricultural economy, forestry production, forestry density, location, transportation, and climate. In addition, this study simulated the spatial distribution of land use and analyzed the spatial characteristics and variation trends of forestland area and quality under the Representative Concentration Pathways (RCPs) climate scenarios from 2010 to 2030 using the Conversion of Land Use and its Effects (CLUE) model. The results indicate that forestland declines

Abstract:Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK) and geographically weighted regression Kriging (GWRK) methods were employed using precipitation data from the period 1980-2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM), normalized difference vegetation index (NDVI), solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

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The presence of antibiotic sulfadiazine (SFD) poses threats to the ecosystem and human health, and traditional wastewater treatment processes are not ideal for sulfadiazine removal. Therefore, it is urgent to develop treatment processes with high efficiency targeting sulfadiazine. This study investigated the degradation and mineralization mechanisms of SFD by ozone-based catalysis processes including ozone/persulfate (PS) and ozone/peroxymonosulfate (PMS). The degradation, mineralization and byproducts of SFD were monitored by HPLC, TOC and LC/MS, respectively. SFD was efficiently removed by two ozone-based catalysis processes. Ozone/PMS showed high efficiency for SFD removal of 97.5% after treatment for 1 min and TOC reduction of 29.4% after treatment for 20 min from wastewater effluents. SFD degradation was affected by pH, oxidant dosage, SFD concentration and anions. In the two ozone-based catalysis processes, hydroxyl radicals (OH•) and sulfate radicals (SO4•−) contributed to the degradation of SFD. The degradation pathways of SFD under the two processes included hydroxylation, the opening of the pyrimidine ring and SO2 extrusion. The results of this study demonstrate that the two ozone-based catalysis processes have good potential for the elimination of antibiotics from water/wastewater effluents.