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91.
秦巴山区降雨侵蚀力时空变化特征 总被引:2,自引:0,他引:2
降雨侵蚀力时空变化特征的研究对区域土壤侵蚀风险评估及水土保持规划具有重要的意义。利用秦巴山区及周边地区共63个气象站1961~2016年的逐日降雨量数据计算各站的降雨侵蚀力,借助Kriging空间插值法、Mann-Kendall趋势检验、Pettitt突变检验等方法分析了秦巴山区降雨侵蚀力的时空变化特征。结果表明:秦巴山区年均降雨侵蚀力为3 696 MJ·mm/(hm2·h·a),年内变化呈单峰型,7月最大,占全年的26.6%;四季中,夏季最大,冬季最小。代际间,20世纪80年代的降雨侵蚀力最大,90年代最小。年际间,年降雨侵蚀力存在明显的阶段性,但未表现出显著的趋势性和突变性特征。秦巴山区多年平均降雨侵蚀力呈南高北低的分布格局,不同地区年均降雨侵蚀力变化于787~8 858 MJ·mm/(hm2·h·a)之间;整体而言,年降雨侵蚀力随纬度增加而减小,随海拔升高而减小。 相似文献
92.
93.
Xiang Li Ling Peng Yuan Hu Jing Shao Tianhe Chi 《Environmental science and pollution research international》2016,23(22):22408-22417
With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance. 相似文献
94.
Peipei Wang Lili Zhang Li Liu Ling Chen Hongwen Gao Lingling Wu 《Environmental science and pollution research international》2015,22(21):16423-16433
Toxicity evaluation is an important segment in sediment quality monitoring in order to protect aquatic organisms and human health. The purpose of this study is to assess the toxicity of sediments from three sediment cores in Yangtze River Estuary, China, using the zebrafish (Danio rerio) embryo tests. Fertilized zebrafish eggs were exposed to both whole sediments and sediment organic extracts prepared from collected sediments, in order to provide a comprehensive and realistic insight into the bioavailable toxicity potential of the sediments. As end points, development parameters (mortality, hatching rate, and abnormality) in the developing embryos were recorded during the 96-h exposure. The results showed that some samples increased mortality, inhibited the hatching of embryos, and induced morphological abnormalities. The embryonic toxicities presented serrated changes and irregular distribution with depth, which may be related to hydrodynamic effect and unstable environmental input. However, lethal and sub-lethal effects were more significant at the sub-surface sediments (10~40 cm), which indicated that the pollution is more serious in recent decades. 相似文献
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96.
微波消解-流动注射分光光度法测定总氮和总磷 总被引:2,自引:0,他引:2
以碱性过硫酸钾溶液为消解液,采用微波消解-流动注射分光光度法测定水中总氮和总磷。在碱性过硫酸钾溶液中氢氧化钠质量浓度为9.6g/L、微波功率为320W、消解管长度为16m的条件下,总氮和总磷的检出限分别为0.040m g/L和0.020mg/L,线性范围分别为0.040~3.500mg/L和0.020~2.500mg/L,相对标准偏差分别为1.6%和1.0%。微波消解-流动注射分光光度法应用于河水、湖水、化工废水等实际水样中总氮和总磷的测定,加标回收率分别为95.7%~98.1%和97.2%~102.2%。 相似文献
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98.
采用RAINS ASIA模型研究了"十一五"期间长三角地区实施脱硫措施前后硫沉降超临界负荷的变化.结果表明,在90%保证率下,长三角地区硫沉降的临界负荷(以S计,下同)总值为78.38万t/a;2005年该地区硫沉降的超临界负荷总值为27.98万t/a,有45.6%区域面积的硫沉降超过临界负荷;如果不实施脱硫措施,到2010年长三角地区硫沉降超临界负荷的情景将急剧恶化,硫沉降超临界负荷总值将增长18.4%,超临界负荷的区域面积将增加到48.7%;"十一五"脱硫计划如期实施后,2010年长三角地区硫沉降超临界负荷总值将在2005年的基础上下降27.4%,但仍有39.1%的区域面积超过临界负荷,须采取更严格的措施控制硫沉降量. 相似文献
99.
微波联合消解流动注射光度法测定水中总氮和总磷 总被引:7,自引:1,他引:6
利用微波联合消解水样,采用流动注射分析技术,建立了在线测定水中总氮和总磷的快速分析方法.优化了试验条件,在线性范围内,总氮和总磷的工作曲线线性关系良好,检出限分别为0.03 mg/L和0.01 mg/L,相对标准偏差分别为1.5%和1.2%,加标回收率分别为96.7%~103%和98.8%~102%. 相似文献
100.
Zhenhao Ling Liqing Wu Yonghong Wang Min Shao Xuemei Wang Weiwen Huang 《环境科学学报(英文版)》2022,34(4):259-285
Secondary organic aerosol(SOA) is a very important component of fine particulate matter(PM2.5) in the atmosphere. However, the simulations of SOA, which could help to elucidate the detailed mechanism of SOA formation and quantify the roles of various precursors, remains unsatisfactory, as SOA levels are frequently underestimated. It has been found that the performance of SOA formation models can be significantly improved by incorporating the emission and evolution of semivolatile and ... 相似文献