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伴随模式在追踪污染事件重点源区中的应用 被引量:4
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作者 王超 安兴琴 +1 位作者 翟世贤 孙兆彬 《中国环境科学》 EI CAS CSSCI CSCD 北大核心 2017年第4期1283-1290,共8页
本研究利用GRAPES-CUACE气溶胶伴随模式,对2015年11月27日~12月2日北京市一次高浓度PM_(2.5)污染过程进行了敏感性分析,显示了伴随模式在追踪重点排放源区及关注敏感排放时段等方面的优越性.研究结果表明:本次污染事件所关注的北京市PM_... 本研究利用GRAPES-CUACE气溶胶伴随模式,对2015年11月27日~12月2日北京市一次高浓度PM_(2.5)污染过程进行了敏感性分析,显示了伴随模式在追踪重点排放源区及关注敏感排放时段等方面的优越性.研究结果表明:本次污染事件所关注的北京市PM_(2.5)峰值浓度是北京市本地排放源和周边省市排放源共同作用的结果.从累积敏感系数来看.目标时刻前23h内,本地源贡献占主导,PM_(2.5)峰值浓度对本地排放源响应迅速,目标时刻前5h,本地源对峰值浓度的贡献达到最大,逐时敏感系数峰值为9.4μg/m^3.周边源贡献表现为周期性波动,逐时敏感系数在目标时刻前9,29,43h,出现3次峰值,分别为6.66,6.24,1.74μg/m^3,伴随着偏南风,周边源在目标时刻前1~57h内持续不断地向北京市输送污染物.不同距离的周边源对目标时刻PM_(2.5)峰值浓度的影响时段和程度不一样,目标时刻前72h内,北京、天津、河北及山西排放源对目标时刻PM_(2.5)峰值浓度的累积贡献比例分别为31%、9%、56%及4%;从逐时敏感系数来看,天津源贡献的主要时段为目标时刻前1~33h,逐时敏感系数峰值出现在目标时刻前9h,为2.10μg/m^3,山西源贡献的主要时段为目标时刻前17~33h,逐时敏感系数峰值出现在目标时刻前27h,为0.71μg/m^3,河北源贡献的主要时段为目标时刻前1~57h,逐时敏感系数呈现周期性波动,出现3次峰值,分别为4.55,5.31,1.59μg/m^3. 展开更多
关键词 伴随方法 污染个例 污染源追踪 敏感性分析
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The dynamic impact of income and income distribution on food consumption among adults in rural China 被引量:5
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作者 LI Lei zhai shi-xian BAI Jun-fei 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2021年第1期330-342,I0001,I0002,共15页
Previous studies have demonstrated that income has a significant effect on food demand in rural China. However, little research has focused on the dynamic impact of income and income distribution on food demand in rur... Previous studies have demonstrated that income has a significant effect on food demand in rural China. However, little research has focused on the dynamic impact of income and income distribution on food demand in rural China. Using China Health and Nutrition Survey data, this study employs a consistent two-step quadratic almost ideal demand system model, with addressed problems of endogeneity of total expenditure and zero shares, to estimate the food demand elasticities among adults in rural areas with regard to the different income strata. The results show that changes in income and income strata have significant effects on food demand in rural areas. Except for grains, all other food groups, including vegetables, oils and fats, animal products, and other foods, have positive income elasticities, and the rise in the income strata will lead to declining income elasticities for grains, vegetables, oils and fats, and animal products. Based on the estimated income elasticities, the food consumption projections indicate that reducing income inequality in rural society can improve the living standard of low-income people in terms of nutrient intakes. 展开更多
关键词 income redistribution food demand ENDOGENEITY censored demand system rural China
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