河西走廊中段荒漠绿洲土壤生态化学计量特征

为探究荒漠绿洲土壤C、 N、 P和K的含量变化及生态化学计量特征,阐明其对环境因子的生态学响应,选择河西走廊中段张掖临泽荒漠绿洲10块样地,采集表层土壤样品,测定土壤C、 N、 P和K含量,揭示不同生境中土壤养分含量变化和化学计量比的分布特征以及与其他环境因子间的相关关系.结果表明：(1)土壤C在各样点分布不均匀且具有明显的异质性(R=0.761,P=0.06).其中,ω(C)在绿洲平均值最高,为12.85 g·kg^-1,过渡带次之,为8.65 g·kg^-1,荒漠最低,为4.1 g·kg^-1;(2)土壤K含量除在盐碱地含量较低外,在荒漠、过渡带和绿洲之间均无显著变化且含量较高;(3)土壤C∶N平均值为12.92,C∶P平均值为11.69,N∶P平均值为0.9,均低于全球土壤平均水平(13.33、 72.0、 5.9)和中国土壤平均水平(12、 52.7、 3.9);(4)土壤含水量为影响荒漠绿洲土壤C、 N、 P、 K和生态化学计量特征的最大影响因子,贡献率为86.9%,其次为土壤酸碱度和土壤孔隙度,贡献率分别为9.2%...     

A comparative assessment of land management approaches in Bhutan

Arable land in Bhutan is under serious threats of land degradation. Proper land management approach is needed to control soil erosion problems. This study is an attempt to characterize and document the conventional and the community-based land management approaches, applied in Chukha and Dagana districts, respectively. The study tried to make a comparative assessment of their social, economic and environmental impacts on the participating farmers. Farmers’ level of participation in the program, implementation of the soil erosion control measures (SECM) and subsequent social, economic and environmental impacts were found to be significantly influenced by the type of approach applied. The study found that community-based approach is by any means better suited for the study sites than the conventional approach. The social, economic and environmental attributes of respondents from the community-based approach were found to have improved significantly as compared to the conventional approach. The community-based approach was thus found to have a higher potential to meet the diverse requirements of rural subsistence farming communities in Bhutan than the conventional approach.     

基于风险熵的化工园区事故风险突变模型研究

为准确判断化工园区事故风险演化进程,基于风险熵理论分析化工园区系统风险状态,从影响园区系统风险熵的不同因素出发,根据化工园区公共区域和企业端的各自特点建立风险熵指标;利用尖点突变理论,用矩阵表征多参数多时刻的风险熵增和熵减值,建立化工园区熵增、熵减计算模型;根据计算结果判断事故风险状态。将所提出的方法应用于某化工园区,结果表明:在该园区某危化品储存企业丁二烯泄漏情况下园区系统风险熵发生突变,由泄漏引发二次事故的风险骤升,需要采取相应安全措施。分析结果与实际情况相符合。     

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.