摘要
生态风险评价指标在时间尺度上的表征效果是不一致的,因而有必要基于生态风险评价指标的时间尺度特征分析,探索生态风险动态评价方法.本文以辽宁省5个典型煤矿城市为研究对象,采用学习向量量化神经网络(learning vector quantization,LVQ)定量分析生态风险评价指标的重要性,进而明晰其时间尺度特征,并提出煤矿城市风险"长期-短期"时间二维动态表征方法.结果表明:单位产值工业SO2去除量、单位产值工业粉尘去除量、城市园林绿地面积覆盖率、降水量、子系统协调度、矿业从业人数百分比、污染治理项目本年度完成投资等为长时间尺度指标,其余指标偏向反映生态风险的短期特征;长、短时间尺度指标相结合,能够反映煤矿城市两个时间维度上的生态风险动态水平.其中,阜新市现状风险值最大,抚顺市短期风险上升幅度最高,朝阳市长期风险上升幅度最高.基于LVQ的评价指标时间尺度特征分析,对于煤矿城市生态风险的动态防范与综合管理具有重要指示意义.
Because the ability of selected indicators in assessing ecological risk at different temporal scales is not the same,it is necessary to clear the definite comparability of such indicators at temporal scale to explore a new method for dynamic assessing the ecological risk. In this case,five mining cities in Liaoning Province were selected as the study area,with the application of learning vector quantization（ LVQ） neural network,the significance of the indicators for the ecological risk assessment was quantitatively analyzed to clarify their characteristics at temporal scale. The expression with two-dimension（ long-term and short-term） of temporal scale was put forward as a new method to assess the ecological risk for mining cities. The results showed that the amount of industrial SO2 removed per output value,the amount of industrial dust removed per output value,coverage rate of urban green space,precipitation,coordination degree among subsystems,percentage of mining practitioners,and current year investment on pollution abatement projects were effective at longterm temporal scale,while the other indicators acted at short-term temporal scale. With the combination of long-term and short-term temporal scales,the dynamic assessment of ecological risk for mining cities could be expressed on two-dimension of temporal scale. It was found that Fuxin City got the highest ecological risk in current status,with the risk increasing most in Fushun City at the short-term temporal scale as well as in Chaoyang City at the long-term temporal scale. The method adopted in this study might act as a significant guidance in dynamic controlling and integrative management of ecological risk for mining cities.
出处
《应用生态学报》
CAS
CSCD
北大核心
2015年第3期867-874,共8页
Chinese Journal of Applied Ecology
基金
国家自然科学基金项目(41271195)资助
关键词
生态风险动态评价
“长期-短期”时间二维尺度
学习向量量化神经网络
煤矿城市
dynamic assessment of ecological risk
long-short term temporal dimension
learning vector quantization neural network
coal-mining city
