中文摘要：

采用自下而上的清单编制方法,搜集各农业环节（秸秆燃烧、整地、收割、谷物处理、化肥施用、农机排放、风蚀）排放因子、作物面积和耕作方式等信息,编制了2010年东北地区县级尺度的农业一次颗粒物（PM_（10）和PM_（2.5））排放清单,并分析了农业源颗粒物排放的时空分布特征.结果表明：1）2010年东北地区农业源一次颗粒物PM_（10）总排放量54.6万t,PM_（2.5）总排放量35.6万t;2）东北地区农业源一次颗粒物PM_（10）排放量最大的农业活动环节是秸秆燃烧,占农业源总排放量的比例为60%,秸秆燃烧排放PM_（2.5）占PM_（2.5）农业源排放量的87%,整地环节是一次颗粒物排放的第2大农业排放源,对农业源排放PM_（10）和PM_（2.5）总量的贡献率分别是27%和6%;3）PM_（10）和PM_（2.5）的排放强度空间分布表明,东北地区农业源颗粒物排放区域集中在黑龙江省东北部和中部地区,吉林省中部和辽宁省中部地区;4）PM_（10）和PM_（2.5）排放的时间变化特征显示,PM_（10）农业源排放年变化曲线中,5月份和9、10月份是农业源排放一次颗粒物较多的月份,PM_（2.5）排放集中在9、10月份;5）本研究估算的污染物排放清单的不确定性为184.3%.未来的工作将侧重于典型农业区本土排放因子测定,从而有效减小排放清单的不确定性.

英文摘要：

Agricultural PM10 and PM2.5 inventories on the county-level scale in northeastern China were developed in this study. Based on bottom-Up method, we firstly collect the agricultural activity data, including conventional farming operations （i.e., straw burning, land preparation, harvest, grain processing, fertilizer, agricultural machinery, and wind erosion）, emission factors of these operations and crop area at each county. And then, county-level PM10and PM2.5 emission inventories from agricultural operations in 2010 were developed and spatio-temporal distributions were analyzed. The results showed that the magnitude of agricultural PM10 and PM2.5 emissions in northeastern China were 54.6＆#215;104 tons and 35.6＆#215;104 tons, respectively. The PM10 and PM2.5 emissions from straw burning were highest, which accounted for 60% and 87% of the total emissions, respectively. In addition, land preparing occupied 27% and 6% for PM10 and PM2.5, respectively. The spatial distribution of agricultural PM emissions, concentrated on three plains,i.e., the Sanjiang Plain （in the northeast of Heilongjiang）, the Songnen Plain （in the middle of Heilongjiang and Jilin Provinces）, the Liaohe Plain （in the middle of Liaoning Provinces）. Based on temporal variations, agricultural PM10 emissions mainly occurred in May, September and October, which contributed 88% of total emissions. The PM2.5 was mainly released in September and October. The uncertainty of emission inventory was 184.3%. More information on local emission factors will be helpful to decrease the emission inventory uncertainty.