地理学报 ›› 2014, Vol. 69 ›› Issue (s1): 57-60.doi: 10.11821/dlxb2014S010

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中国分省主要畜种产污系数数据集

周天墨1, 2, 诸云强1, 付强3, 胡卓玮2, 杨飞1   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室, 北京 100101;
    2. 首都师范大学资源环境与旅游学院, 北京 100048;
    3. 河南财经政法大学资源与环境学院, 郑州 450002
  • 收稿日期:2014-03-20 修回日期:2014-06-20 出版日期:2014-12-26 发布日期:2020-04-07
  • 作者简介:周天墨 (1989-), 女, 硕士研究生, 主要从事空间分析与应用研究。E-mail: dx-ztm@163.com
  • 基金资助:
    环保公益性行业科研专项重点项目 (201009017)

Pollutant coefficients data of livestock industry at provincial level in China

WANG Liang1, 3, XU Xinliang1, LIU Luo2, 3   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2014-03-20 Revised:2014-06-20 Published:2014-12-26 Online:2020-04-07
  • Supported by:
    National Scientific Special Program of Public welfare Industry for Environmental Protection, No.201009017

摘要: 为了提升中国分省畜禽产污量估算精度,在系统收集已有文献中各省畜禽产污系数的基础上,基于单位变换、畜禽当量转换、异常值修正、分阶段加权计算以及误差校正等一系列处理,形成了中国分省主要畜禽产污系数数据集。该数据集包含除港澳台外的中国所有省份六种主要畜禽 (奶牛、肉牛、猪、蛋鸡、肉鸡和役用牛) 的产污系数,可供全国畜禽污染核算、畜禽污染时空分异规律等研究使用。

关键词: 畜禽, 产污系数, 中国, 分省

Abstract: Remotely sensed dataset of grassland degradation in the Qinghai-Tibet Plateau (GLD_Tibet) is a production based on a related research about spatiotemporal changes of grassland degradation on the Qinghai-Tibet Plateau (QTP) over the periods of 1991-2000 and 2001-2012 using Sense's slope and Mann-Kendall trend test. During the 1990s, more than half (53.41%) of the grassland on the QTP was in some degree of degradation, but after 2000, more than three-fourths (78.62%) of the grassland had improved. The dataset provide scientific evidence for monitoring, assessment, and restoration management of alpine grasslands on the QTP, as well as the sustainable development and management of other grassland ecosystems.

Key words: Qinghai-Tibet Plateau, grassland degradation, remote sensing, spatial data