利用位于北京市顺义气象局45 m气象塔上36m高度的湍流观测资料，对该区域2008年11月1日至2009年10月31日共365d的二氧化碳通量(CO_2)的时间变化和各方位的分布特征进行了分析研究，并计算了CO_2年排放量。结果表明，CO_2受交通因素和居民日常生活排放的影响较小，冬季耗能取暖会显著增加CO_2的排放量；受供暖排放和植物生长季节光合作用的影响,冬季的CO_2通量值在全天绝大多数时刻均高于其他季节，其日平均值为15.6 mumol m~(-2)s~(-1)，显著高于春、夏、秋季的日平均值5.6、5.7和8.8 mumol m~(-2)s-1(t-test, P<0.001)。各方向CO_2通量值的大小与其源区内土地利用/覆盖方式以及建筑物的使用功能和使用性质密切相关，住宅楼、饭店、工厂、旅馆等人工建筑面积占比例越大，CO_2排放量越大；而植被覆盖比例较高的方向CO_2值较小。观测点周边区域是CO_2的排放源，且年平均排放量达到13.6 kg m~(-2)a-1，但低于同一时期北京市内高密度住宅区域的CO_2年排放量。
英文摘要:
Over the last two decades,the eddy covariance (EC) technique has been applied to measure the flux of CO_2 in various ecosystems. Among terrestrial ecosystems, urban areas play an important role because most of the anthropogenic emissions of CO_2 originate in these areas. A better understanding of CO_2 exchange between urban ecosystems and the atmosphere is important for quantifying urban contributions to the global carbon cycle as well as for evaluating the urban climate impact on and response to global climate changes. In the past two decades, studies of CO_2 levels in urban environment mainly focused on the concentration in the ambient and emission inventory based on energy consumption data in China. To date,CO_2 monitoring in Chinese cities using EC techniques is rare because it is difficult to find a representative urban surface and install EC instruments on the available tower. We investigated CO_2 fluxes over a suburban, low-density residential area (Shunyi) in the north east Beijing. Suburban areas are growing rapidly in China and are potentially an important land-use category for anthropogenic CO_2 emissions. We collected the EC measurements from the meteorological tower of Shunyi Meteorology Center from November 1,2008 to October 31,2009. The half-hourly CO_2 fluxes were calculated by computing the mean covariance of CO_2 fluctuations with the fluctuating vertical velocity observed by the EC system. Turbulence Knight2 (TK2) software package was employed for quality assurance and quality control of the EC data. TK2 provides a quality flag for each half-hourly flux data,and the highest quality data when the quality flag was less than 3 were used to analyze the daily variation and spatial distribution characteristics of CO_2 flux during different seasons, the summer daytime CO_2 flux as a function of photosynthetically active radiation (PAR),and the nighttime CO_2 flux as a function of soil temperature at 10 cm depth during the measurement period. The data gaps were filled using the back propagation neural network to calculate the annual total CO_2 emissions. Our results indicate that CO_2 emissions were less affected by road vehicles and household activities at the Shunyi measurement area. Fuel consumption for heating during winter significantly increased CO_2 emissions ; and because of the carbon uptake by plants for photosynthesis during the growing season, the average daily flux measured in winter was significantly higher than that in other seasons. Patterns of spatial variation of CO_2 flux showed that areas with a high proportion of man-made surfaces had higher CO_2 values,while vegetation covered areas had lower CO_2 values. The consistently positive CO_2 flux throughout the year indicates that the analyzed suburban surface is a net source of CO_2 to the atmosphere. The total annual CO_2 emission from our study area was 13.6 kg m~(-2) a~(-1) ; this value is higher than the emissions from suburban residential areas in foreign cities. We infer that this high value is because of the low vegetation coverage and higher number of commercial and industrial buildings in our study area. Our data will be added to the global database of CO_2 fluxes and it can be used for future planning of urban development with regard to reducing the CO_2 emission.