中国近地面NO_2污染分布特征及其社会经济影响因素分析
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摘要
基于2017年中国NO_2环境监测站点数据,综合运用全局莫兰指数(Global Moran's I)和热点分析(Getis-Ord G_i~*)方法对中国NO_2污染空间分布特征进行分析,并应用地理加权回归模型(GWR)探讨NO_2污染空间分布的社会经济影响因素。结果表明,(1)2017年,NO_2质量浓度年均值为31.28μg·m~(-3)。NO_2浓度分布在东西方向上大致以胡焕庸线为界,东部地区高于西部地区;南北方向上大致以长江为界,北部地区高于南部地区。(2)NO_2的季节变化规律为冬季>秋季>春季>夏季。秋冬季节,NO_2高污染区由京津冀、河南、陕西等地扩大至山西中部、新疆东南部以及内蒙古的包头、呼和浩特、乌兰察布等地区,其高值与中高值区域面积占比之和分别为13.47%与24.00%,显著高于春夏季。(3)NO_2质量浓度存在以京津冀及周边河南、山西等地区为主的高值集聚,低值区主要分布在在云南、西藏、广西、海南一带。(4)利用地理加权回归模型(GWR)分析NO_2的空间分布与社会经济因素之间的关系,经计算调整后的R~2为0.74,该模型能解释NO_2空间分布的74%,拟合效果较好。在该模型中,城镇化率、森林覆盖率、第二产业占比以及人均电力消费量对NO_2质量浓度影响较大,城镇化率和第二产业占比与NO_2质量浓度呈正相关关系,森林覆盖率和人均电力消费量与NO_2质量浓度呈负相关关系。另外,城镇化率是对NO_2影响最显著的因素,城镇化率的提高对NO_2的影响程度由西向东逐渐递减。(5)NO_2与人均私家车保有量的相关系数r为0.403,华北、东南沿海、东三省及西部新疆、西藏地区,人均私家车保有量与NO_2空间分布情况基本一致,河南、陕西、湖北以及川渝地区则出现了人均私家车保有量与NO_2质量浓度不匹配的情况。
Based on NO_2 monitoring data in 2017, this paper analyzes the spatial distribution of NO_2 pollution in China by using Global Moran's I and hotspot analysis methods. Also, social and economic factors affecting NO_2 spatial distribution are discussed by using geographic weighted regression(GWR) model. The results show that:(1) In 2017, the annual average mass concentration of NO_2 is 31.28 μg·m~(-3). Spatial distribution of NO_2 is divided by the Hu Line along the east-west direction, with NO_2 concentration in the east greater than in the west. Along the south and north direction, NO_2 distribution is divided by the Yangtze River, with NO_2 in the north of Yangtze River greater than the south.(2) In terms of overall seasonal variation, the order of NO_2 concentration follows: winter>autumn>spring>summer. In autumn and winter, heavy pollution area of NO_2 spreads from the region of Beijing-TianjinHebei, Henan and Shaanxi to vaster area including central Shanxi, southeast Xinjiang, and cities in Inner Mongolia such as Baotou, Hohhot, and Ulanqab. In addition, the proportions of area in which NO_2 concentration falls into medium-high or high range are 13.47% and 24.00% in autumn and in winter, respectively, significantly higher than that in spring and in summer.(3) The area with high NO_2 concentration is mainly distributed in Beijing-Tianjin-Hebei region and surrounding areas such as Henan province and Shanxi province, while the low NO_2 concentration area is mainly positioned in Yunnan, Tibet, Guangxi and Hainan.(4) The socio-economic factors influencing NO_2 concentration are calculated by geographically weighted regression(GWR) model, with an adjusted R~2 of 0.74 presented, which means the model can explain 74% of the spatial distribution of NO_2 with a well fit. The results show that four indices mainly impact NO_2 mass concentration: rate of urbanization, forest coverage, proportion of the industrial sector, and electric power consumption. Among them, forest coverage and electric power consumption are negatively correlated with NO_2 mass concentration, while rate of urbanization and industrial sector ratio are positively correlated with NO_2 mass concentration. The urbanization rate is the most significant factor that impacts NO_2 concentration, with the impact gradually decreasing from west to east. And(5) the correlation coefficient r between NO_2 and private car ownership per capita is 0.403. The distribution of private car ownership per capita is in good consistent with the spatial distribution of NO_2 in north China, southeast coastal areas, the three northeastern provinces and Xinjiang and Tibet regions, while the two do not match each other well in Henan, Shaanxi, Hubei, Sichuan and Chongqing regions.
Based on NO_2 monitoring data in 2017, this paper analyzes the spatial distribution of NO_2 pollution in China by using Global Moran's I and hotspot analysis methods. Also, social and economic factors affecting NO_2 spatial distribution are discussed by using geographic weighted regression(GWR) model. The results show that:(1) In 2017, the annual average mass concentration of NO_2 is 31.28 μg·m~(-3). Spatial distribution of NO_2 is divided by the Hu Line along the east-west direction, with NO_2 concentration in the east greater than in the west. Along the south and north direction, NO_2 distribution is divided by the Yangtze River, with NO_2 in the north of Yangtze River greater than the south.(2) In terms of overall seasonal variation, the order of NO_2 concentration follows: winter>autumn>spring>summer. In autumn and winter, heavy pollution area of NO_2 spreads from the region of Beijing-TianjinHebei, Henan and Shaanxi to vaster area including central Shanxi, southeast Xinjiang, and cities in Inner Mongolia such as Baotou, Hohhot, and Ulanqab. In addition, the proportions of area in which NO_2 concentration falls into medium-high or high range are 13.47% and 24.00% in autumn and in winter, respectively, significantly higher than that in spring and in summer.(3) The area with high NO_2 concentration is mainly distributed in Beijing-Tianjin-Hebei region and surrounding areas such as Henan province and Shanxi province, while the low NO_2 concentration area is mainly positioned in Yunnan, Tibet, Guangxi and Hainan.(4) The socio-economic factors influencing NO_2 concentration are calculated by geographically weighted regression(GWR) model, with an adjusted R~2 of 0.74 presented, which means the model can explain 74% of the spatial distribution of NO_2 with a well fit. The results show that four indices mainly impact NO_2 mass concentration: rate of urbanization, forest coverage, proportion of the industrial sector, and electric power consumption. Among them, forest coverage and electric power consumption are negatively correlated with NO_2 mass concentration, while rate of urbanization and industrial sector ratio are positively correlated with NO_2 mass concentration. The urbanization rate is the most significant factor that impacts NO_2 concentration, with the impact gradually decreasing from west to east. And(5) the correlation coefficient r between NO_2 and private car ownership per capita is 0.403. The distribution of private car ownership per capita is in good consistent with the spatial distribution of NO_2 in north China, southeast coastal areas, the three northeastern provinces and Xinjiang and Tibet regions, while the two do not match each other well in Henan, Shaanxi, Hubei, Sichuan and Chongqing regions.
引文
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YE W F,MA Z Y,HA X Z,2018.Spatial-temporal patterns of PM2.5concentrations for 338 Chinese cities[J].Science of the Total Environment,631-632:524-533.
ZHANG X,ZHANG W,LU X,et al.,2018.Long-term trends in NO2columns related to economic developments and air quality policies from 1997 to 2016 in China[J].The Science of the total environment,639:146-155.
陈姗姗,束炯,徐建中,2010.中国若干典型城市对流层NO2时空分布特征[J].长江流域资源与环境,19(12):1484-1490.CHEN S S,SHU J,XU J Z,2010.Tropospheric NO2 pollution analysis of some typical cities in China[J].Resources and Environment in the Yangtze Basin,19(12):1484-1490.
程念亮,李云婷,张大伟,等,2016.2013-2014年北京市NO2时空分布研究[J].中国环境科学,36(1):18-26.CHENG N L,LI Y T,ZHANG D W,et al.,2016.Spatial and temporal distribution of NO2 during 2013-2014 in Beijing[J].China Environmental Science,36(1):18-26.
刁贝娣,曾克峰,苏攀达,等,2016.中国工业氮氧化物排放的时空分布特征及驱动因素分析[J].资源科学,38(9):1768-1779.DIAO B D,ZENG K F,SU P D,et al.,2016.Temporal-spatial distribution characteristics of provincial industrial NOx emissions and driving factors in China from 2006 to 2013[J].Resources Science,38(9):1768-1779.
段杰雄,翟卫欣,程承旗,等,2018.中国PM2.5污染空间分布的社会经济影响因素分析[J].环境科学,39(5):2498-2504.DUAN J X,ZHAI W X,CHENG C Q,et al.,2018.Socio-economic factors influencing the spatial distribution of PM2.5 concentrations in China:An exploratory analysis[J].Environmental Science,39(5):2498-2504.
黄文珂,2012.多元回归建模过程中共线性的诊断与解决方法[D].哈尔滨:哈尔滨工业大学.HUANG W K,2012.The diagnosis process solution in multicollinearity of multiple regression model[D].Harbin:Harbin Institute of Technology.
李令军,王英,2011.基于卫星遥感与地面监测分析北京大气NO2污染特征[J].环境科学学报,31(12):2762-2768.LI L J,WANG Y,2011.The characterization of NO2 pollution in Beijing based on satellite and conventional observation data[J].Acta Scientiae Circumstantiae,31(12):2762-2768.
李龙,施润和,陈圆圆,等,2013.基于OMI数据的中国NO2时空分布与人类影响分析[J].地球信息科学学报,15(5):688-694.LI L,SHI R H,CHEN Y Y,et al.,2013.Spatio-temporal characteristics of NO2 in China and the anthropogenic influences analysis based on OMI data[J].Journal of Geo-Information Science,15(5):688-694.
李霄阳,李思杰,刘鹏飞,等,2018.2016年中国城市臭氧浓度的时空变化规律[J].环境科学学报,38(4):1263-1274.LI X Y,LI S J,LI P F,et al.,2018.Spatial and temporal variations of ozone concentrations in China in 2016[J].Acta Scientiae Circumstantiae,38(4):1263-1274.
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