针对PM_(2.5)土地利用回归(Land Use Regression,简称LUR)模型地理要素选取不规范、代表性不明确的问题,本文从地理要素的精度、易获取程度、广泛应用程度及地理要素与PM_(2.5)的经验相关性4个评价指标出发,结合层次分析法(Analytic Hie...针对PM_(2.5)土地利用回归(Land Use Regression,简称LUR)模型地理要素选取不规范、代表性不明确的问题,本文从地理要素的精度、易获取程度、广泛应用程度及地理要素与PM_(2.5)的经验相关性4个评价指标出发,结合层次分析法(Analytic Hierarchy Process简称AHP)和熵值法,对京津冀地区PM_(2.5)LUR模型构建时各备选地理要素的权重进行综合度量。结果显示,京津冀地区污染企业、交通网络、地表覆盖等优选地理要素的综合权重分别为20%、19%、18%,地理要素与PM_(2.5)的经验相关性和数据精度等优先评价指标的综合权重占××的比例分别次为49%、26%。该方法得出的评价结果符合客观实际,能达到科学选取地理要素的目的,对评估地理要素的代表性和分析LUR的异同性及地方主要污染要素具有重要的参考价值。展开更多
Based on GISS-E2-R model simulations, the changes in PM2.5 and ozone concentrations during 2016– 35 are analyzed over the Jing-Jin-Ji region under different future emissions scenarios: 2.6, 4.5, 6.0, 8.5 Representati...Based on GISS-E2-R model simulations, the changes in PM2.5 and ozone concentrations during 2016– 35 are analyzed over the Jing-Jin-Ji region under different future emissions scenarios: 2.6, 4.5, 6.0, 8.5 Representative Concentration Pathways scenarios(RCP2.6, RCP4.5, RCP6.0, and RCP8.5), compared to the baseline periods of 1851–70(pre-industrial) and 1986–2005(present day). The results show that PM2.5 increases under all emissions scenarios, with the maximum value occurring in the southeastern part of the region under most scenarios. As for ozone, its concentration is projected to increase during 2016–35 under all emissions scenarios, compared to the baseline periods. The temporal evolutions of PM2.5 and ozone show PM2.5 reaching a peak during 2020–40, while ozone will likely increase steadily in the future.展开更多
文摘针对PM_(2.5)土地利用回归(Land Use Regression,简称LUR)模型地理要素选取不规范、代表性不明确的问题,本文从地理要素的精度、易获取程度、广泛应用程度及地理要素与PM_(2.5)的经验相关性4个评价指标出发,结合层次分析法(Analytic Hierarchy Process简称AHP)和熵值法,对京津冀地区PM_(2.5)LUR模型构建时各备选地理要素的权重进行综合度量。结果显示,京津冀地区污染企业、交通网络、地表覆盖等优选地理要素的综合权重分别为20%、19%、18%,地理要素与PM_(2.5)的经验相关性和数据精度等优先评价指标的综合权重占××的比例分别次为49%、26%。该方法得出的评价结果符合客观实际,能达到科学选取地理要素的目的,对评估地理要素的代表性和分析LUR的异同性及地方主要污染要素具有重要的参考价值。
基金support from the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY201306019)the National Natural Science Foundation of China (Grant No. 41275078)+1 种基金the Grant Projects of China Clean Development Mechanism Fund (Grant No. 121312)the Climate Change Foundation of China Meteorological Administration (Grant No. CCSF201339)
文摘Based on GISS-E2-R model simulations, the changes in PM2.5 and ozone concentrations during 2016– 35 are analyzed over the Jing-Jin-Ji region under different future emissions scenarios: 2.6, 4.5, 6.0, 8.5 Representative Concentration Pathways scenarios(RCP2.6, RCP4.5, RCP6.0, and RCP8.5), compared to the baseline periods of 1851–70(pre-industrial) and 1986–2005(present day). The results show that PM2.5 increases under all emissions scenarios, with the maximum value occurring in the southeastern part of the region under most scenarios. As for ozone, its concentration is projected to increase during 2016–35 under all emissions scenarios, compared to the baseline periods. The temporal evolutions of PM2.5 and ozone show PM2.5 reaching a peak during 2020–40, while ozone will likely increase steadily in the future.
