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Two Ultraviolet Radiation Datasets that Cover China 被引量:2
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作者 Hui LIU Bo HU +42 位作者 Yuesi wang Guangren LIU Liqin TANG Dongsheng JI Yongfei BAI Weikai BAO Xin CHEN Yunming CHEN Weixin DING Xiaozeng HAN Fei HE Hui HUANG Zhenying HUANG Xinrong LI Yan LI Wenzhao LIU Luxiang LIN Zhu OUYANG Boqiang QIN Weijun SHEN Yanjun SHEN Hongxin SU Changchun SONG Bo SUN Song SUN Anzhi wang Genxu wang Huimin wang Silong wang youshao wang Wenxue WEI Ping XIE Zongqiang XIE Xiaoyuan YAN Fanjiang ZENG Fawei ZHANG Yangjian ZHANG Yiping ZHANG Chengyi ZHAO Wenzhi ZHAO Xueyong ZHAO Guoyi ZHOU Bo ZHU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2017年第7期805-815,共11页
Ultraviolet(UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. O... Ultraviolet(UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization's measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes. 展开更多
关键词 reconstructed ozone absolute ultraviolet estimates environments climatic cumulative sunshine meteorological
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Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China 被引量:1
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作者 Meilin WU youshao wang +3 位作者 Junde DONG Fulin SUN Yutu wang Yiguo HONG 《Frontiers of Earth Science》 SCIE CAS CSCD 2017年第1期114-126,共13页
A cruise was commissioned in the summer of 2009 to evaluate water quality in the Pearl River Estuary (PRE). Chemometrics such as Principal Component Analysis (PCA), Cluster analysis (CA) and Self-Organiz- ing M... A cruise was commissioned in the summer of 2009 to evaluate water quality in the Pearl River Estuary (PRE). Chemometrics such as Principal Component Analysis (PCA), Cluster analysis (CA) and Self-Organiz- ing Map (SOM) were employed to identify anthropogenic and natural influences on estuary water quality. The scores of stations in the surface layer in the first principal component (PC1) were related to NH4-N, PO4-P, NO2-N, NO3-N, TP, and Chlorophyll a while salinity, turbidity, and SiO3-Si in the second principal component (PC2). Similarly, the scores of stations in the bottom layers in PC1 were related to PO4-P, NO2-N, NO3-N, and TP, while salinity, Chlorophyll a, NH4-N, and SiO3-Si in PC2. Results of the PCA identified the spatial distribution of the surface and bottom water quality, namely the Guangzhou urban reach, Middle reach, and Lower reach of the estuary. Both cluster analysis and PCA produced the similar results. Self-organizing map delineated the Guangzhou urban reach of the Pearl River that was mainly influenced by human activities. The middle and lower reaches of the PRE were mainly influenced by the waters in the South China Sea. The information extracted by PCA, CA, and SOM would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions. 展开更多
关键词 principal component analysis self-organizing map estuarine water quality the Pearl River Estuary spatial variation
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