Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regre...Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression （MLR） equations were established based on the most significant variables for NO2 in heating season （R2 = 0.74）, and non-heating season （R2 = 0.61） in the whole study area; and PM10 in heating season （R2 = 0.72）, and non-heating season （R2 = 0.49）. Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations （resolution is 10 krn）. Intercepts of MLR equations were 0.050 （NOz, heating season）, 0.035 （NO2, non-heating season）, 0.068 （PM10, heating season）, and 0.092 （PM10, non-beating season） in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 （NO2, heating season）, 0.043 （NO2, non-heating season）, 0.087 （PM10, heating season）, and 0.096 （PMl0, non-heating season）. These intercept values might imply an area＇s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. Rz values increased 0.09 （heating season） and 0.18 （non-heating season） for NO2, and 0.08 （heating season） and 0.04 （non-heating season） for PMl0. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NOz compared with PM10.展开更多
SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR...SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR) model using regulatory monitoring data to predict the spatial distribution of air pollutant concentrations in Jinan, China. Traffic, land use and census data, and meteorological and physical conditions were included as candidate independent variables, and were tabulated for buffers of varying radii. SO2, NO2, and PM10 concentrations were most highly correlated with the area of industrial land within a buffer of 0.5 km (R2=0.34), the distance from an expressway (R2=0.45), and the area of residential land within a buffer of 1.5 km (R2=0.25), respectively. Three multiple linear regression (MLR) equations were established based on the most significant variables (p<0.05) for SO2, NO2, and PM10, and R2 values obtained were 0.617, 0.640, and 0.600, respectively. An LUR model can be applied to an area with complex terrain. The buffer radii of independent variables for SO2, NO2, and PM10 were chosen to be 0.5, 2, and 1.5 km, respectively based on univariate models. Intercepts of MLR equations can reflect the background concentrations in a certain area, but in this study the intercept values seemed to be higher than background concentrations. Most of the cities in China have a network of regulatory monitoring sites. However, the number of sites has been limited by the level of financial support available. The results of this study could be helpful in promoting the application of LUR models for monitoring pollutants in Chinese cities.展开更多
采集了浙江、福建、江苏、湖南、湖北、四川、重庆、黑龙江、河南9个省的稗(Echinochloa crus-galli(L.)P.Beauv.)及其变种的33份种子,分别播种在相同的环境下,获得33个种群,测定了种群的16个形态性状,筛选出重复性好的9条ISSR引物,从3...采集了浙江、福建、江苏、湖南、湖北、四川、重庆、黑龙江、河南9个省的稗(Echinochloa crus-galli(L.)P.Beauv.)及其变种的33份种子,分别播种在相同的环境下,获得33个种群,测定了种群的16个形态性状,筛选出重复性好的9条ISSR引物,从33个种群中扩增出了109个位点。基于这些形态性状和ISSR位点信息,对33个种群先进行主成分分析,在此基础上再进行模糊均值聚类分析,探讨了它们的形态和遗传变化特点,及其与形态—遗传—地理背景三者之间的关系。主要结论如下:(1)33个种群可以鉴别出形态性状相对一致的4组,能够识别出西来稗(E.crus-galli var.zelayensis(Kunth)Farw.)、无芒稗(E.crus-galli var.mitis(Pursh)Peterm.)、细叶旱稗(E.crus-galli var.praticola Ohwi);(2)基于109个位点信息对33个种群进行聚类分析得到了6组,部分组与形态聚类分组有一定的对应性;(3)33个稗草种群的遗传分化受地理背景因素的影响(r=0.684,n=33,P<0.001);形态变异也有较明显的遗传背景因素(r=0.425,n=33,P<0.02)。在相对一致的稻田生境中,可能存在着形态上的趋同适应,使遗传上分化的组间在形态上又往往有交叉过渡,致使稗原变种(E.crus-galli var.crus-galli)、西来稗、无芒稗、短芒稗(E.crus-galli var.breviseta(D9ll)Podp.)在形态上难以区别;(4)基于遗传和形态数据分析,发现细叶旱稗无论在形态上,还是遗传上,均形成了明显的一组,推测与该种长期适应于干旱生境有关,建议将细叶旱稗提升为种的水平,并将其命名为Echinochloa praticola(Ohwi)Guo S L,Lu Y L,Yin L P&Zou M Y。展开更多
基金supported by the Special Environmental Research Funds for Public Welfare (No. 200709048,200909005)the National Natural Science Foundation of China (No. 20677030)
文摘Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression （MLR） equations were established based on the most significant variables for NO2 in heating season （R2 = 0.74）, and non-heating season （R2 = 0.61） in the whole study area; and PM10 in heating season （R2 = 0.72）, and non-heating season （R2 = 0.49）. Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations （resolution is 10 krn）. Intercepts of MLR equations were 0.050 （NOz, heating season）, 0.035 （NO2, non-heating season）, 0.068 （PM10, heating season）, and 0.092 （PM10, non-beating season） in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 （NO2, heating season）, 0.043 （NO2, non-heating season）, 0.087 （PM10, heating season）, and 0.096 （PMl0, non-heating season）. These intercept values might imply an area＇s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. Rz values increased 0.09 （heating season） and 0.18 （non-heating season） for NO2, and 0.08 （heating season） and 0.04 （non-heating season） for PMl0. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NOz compared with PM10.
基金Project supported by the National Natural Science Foundation of China (No. 20677030)the Development Plan of Key National Fun-damental Research (No. 2011CB503801)the Special Research Funds for Science Development in Jinan (No. 200904015), China
文摘SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR) model using regulatory monitoring data to predict the spatial distribution of air pollutant concentrations in Jinan, China. Traffic, land use and census data, and meteorological and physical conditions were included as candidate independent variables, and were tabulated for buffers of varying radii. SO2, NO2, and PM10 concentrations were most highly correlated with the area of industrial land within a buffer of 0.5 km (R2=0.34), the distance from an expressway (R2=0.45), and the area of residential land within a buffer of 1.5 km (R2=0.25), respectively. Three multiple linear regression (MLR) equations were established based on the most significant variables (p<0.05) for SO2, NO2, and PM10, and R2 values obtained were 0.617, 0.640, and 0.600, respectively. An LUR model can be applied to an area with complex terrain. The buffer radii of independent variables for SO2, NO2, and PM10 were chosen to be 0.5, 2, and 1.5 km, respectively based on univariate models. Intercepts of MLR equations can reflect the background concentrations in a certain area, but in this study the intercept values seemed to be higher than background concentrations. Most of the cities in China have a network of regulatory monitoring sites. However, the number of sites has been limited by the level of financial support available. The results of this study could be helpful in promoting the application of LUR models for monitoring pollutants in Chinese cities.
文摘采集了浙江、福建、江苏、湖南、湖北、四川、重庆、黑龙江、河南9个省的稗(Echinochloa crus-galli(L.)P.Beauv.)及其变种的33份种子,分别播种在相同的环境下,获得33个种群,测定了种群的16个形态性状,筛选出重复性好的9条ISSR引物,从33个种群中扩增出了109个位点。基于这些形态性状和ISSR位点信息,对33个种群先进行主成分分析,在此基础上再进行模糊均值聚类分析,探讨了它们的形态和遗传变化特点,及其与形态—遗传—地理背景三者之间的关系。主要结论如下:(1)33个种群可以鉴别出形态性状相对一致的4组,能够识别出西来稗(E.crus-galli var.zelayensis(Kunth)Farw.)、无芒稗(E.crus-galli var.mitis(Pursh)Peterm.)、细叶旱稗(E.crus-galli var.praticola Ohwi);(2)基于109个位点信息对33个种群进行聚类分析得到了6组,部分组与形态聚类分组有一定的对应性;(3)33个稗草种群的遗传分化受地理背景因素的影响(r=0.684,n=33,P<0.001);形态变异也有较明显的遗传背景因素(r=0.425,n=33,P<0.02)。在相对一致的稻田生境中,可能存在着形态上的趋同适应,使遗传上分化的组间在形态上又往往有交叉过渡,致使稗原变种(E.crus-galli var.crus-galli)、西来稗、无芒稗、短芒稗(E.crus-galli var.breviseta(D9ll)Podp.)在形态上难以区别;(4)基于遗传和形态数据分析,发现细叶旱稗无论在形态上,还是遗传上,均形成了明显的一组,推测与该种长期适应于干旱生境有关,建议将细叶旱稗提升为种的水平,并将其命名为Echinochloa praticola(Ohwi)Guo S L,Lu Y L,Yin L P&Zou M Y。
