• 关键词：兰州盆地  大气污染物  网格化排放清单  排放贡献  空间分布特征

• 基金项目：国家自然科学基金面上项目（No.41371453，D010901）；甘肃省民生科技计划项目（No.1503FCMA003）；甘肃省科技支撑项目（No.144NKCA039）

• 张凯
• 兰州大学, 甘肃省环境污染预警与控制重点实验室, 兰州 730000

• 于周锁
• 兰州市环境监测站, 兰州 730000

• 高宏
• 兰州大学, 甘肃省环境污染预警与控制重点实验室, 兰州 730000

• 黄韬
• 兰州大学, 甘肃省环境污染预警与控制重点实验室, 兰州 730000

• 马建民
• 兰州大学, 甘肃省环境污染预警与控制重点实验室, 兰州 730000

• 章晓冬
• 兰州大学, 甘肃省环境污染预警与控制重点实验室, 兰州 730000

• 王亚男
• 兰州大学, 甘肃省环境污染预警与控制重点实验室, 兰州 730000

• 摘要：基于所搜集的兰州盆地各类人为污染源排放大气污染物的活动水平数据及其排放因子，采用"自下而上"的方法建立了2009年兰州盆地（石油化工城市）1 km×1 km的7种（类）大气污染物网格化排放清单，并对其来源和空间分布特征进行了分析研究.结果显示：2009年兰州盆地NO_x、SO₂、VOCs、CO、PM₁₀、PM_2.5和NH₃的排放总量分别为1.2×10⁵、8.8×10⁴、4.3×10⁴、4.1×10⁵、9.6×10⁴、4.2×10⁴和1.4×10⁴ t；工业燃烧排放是兰州盆地NO_x和SO₂的主要贡献源，分别占其总排放量的85.70%和52.55%；工业非燃烧过程排放是VOCs的最大贡献源，占总排放量的81.25%；工业点源和工业非燃烧过程排放是CO的两大贡献源，分别占其总排放量的33.97%和28.32%；PM₁₀和PM_2.5主要来源于工业非燃烧过程，贡献分别为51.09%和55.12%；氮肥使用和禽畜养殖是NH₃排放最大的贡献源，分别占其总排放量的39.20%和30.70%.空间分布特征表现为：以工业源为主要排放源的NO_x、SO₂、VOCs、CO、PM₁₀、PM_2.5主要分布在工业和人口最为集中的兰州盆地市区一带，NH₃的排放则主要集中在榆中县和皋兰县交界的农村地区.同时，还对2014年工业燃烧源和道路移动源的7种（类）大气污染物排放量进行了估算，并与2009年进行了排放比较研究.结果表明，2014年工业污染源的7种（类）污染物排放量与2009年相比平均增幅不高，最高不超过30%，但移动源污染物排放量却大幅增加，增幅将近1倍.此外，基于排放因子及活动水平的不确定性，本研究对排放清单的结果进行了不确定性分析，并通过蒙特卡罗模拟对各污染物的排放量进行了评估.本排放清单的建立，不仅填补了兰州盆地大气污染物网格化排放清单的空白，还可为兰州盆地大气污染物排放清单更新、区域环境过程、大气复合污染成因及大气污染预警技术等相关研究提供基本方法手段及基础数据.

• Abstract：In this paper, the 1 km×1 km gridded emission inventories in Lanzhou valley, a petrochemical industrialized city in northwestern China, were developed for seven kinds of air pollutants including NO_x, SO₂, VOCs, CO, PM₁₀, PM_2.5 and NH₃ using a bottom-up approach based on industrial activity data and emission factors. The sources and spatial distributions of air pollutants were also identified and investigated. Results showed that the total emissions of NO_x, SO₂, VOCs, CO, PM₁₀, PM_2.5 and NH₃ of 2009 in Lanzhou were 1.2×10⁵, 8.8×10⁴, 4.3×10⁴, 4.1×10⁵, 9.6×10⁴, 4.2×10⁴ and 1.4×10⁴ t, respectively. Industrial combustion was the largest SO₂ and NO_x emission sources, contributing to 85.70% and 52.55% of total emissions. The industrial non-combustion process was the largest VOCs source and accounted for 81.25% of total VOCs emissions. For CO, industrial point source and industrial non-combustion source were the main contributors to CO, accounting for 33.97% and 28.32% of the total CO emission, respectively. PM₁₀ and primary PM_2.5 could be attributed to industrial non-combustion source, contributing to 51.09% and 55.12% of total PM₁₀ and primary PM_2.5 emissions, respectively. Moreover, the livestock feed and nitrogen-fertilizer applications were the main sources contributing to significant NH₃ emissions at 69.94%. The emission levels of NO_x, SO₂, VOCs, CO, PM₁₀ and PM_2.5 were higher in the industrial urban area with higher population density than those in rural area. In comparison, higher NH₃ emission was found in the rural area near the border of Yuzhong and Gaolan. Emissions of industry combustion source and traffic source in 2014 were also estimated and compared with their emissions in 2009. Results showed that industry combustion emissions increase slightly from 2009 to 2014 by at most 30%, while traffic source almost doubled in these five years. In addition, the uncertainties of gridded emission inventories were evaluated based on the uncertainties of emission factors and industrial activity data, and the emissions of these pollutions were tested by Monte Carlo technique. The establishment of these gridded emission inventories could not only fill the knowledge gaps of gridded emission inventories of air pollutants in Lanzhou valley, but give the supporting information for further studies on atmospheric environmental process, source appointment and forecasting and warning of air pollution.

• Key words：Lanzhou valley  air pollutants  gridded emission inventories  emission contributions  pollution spatial distribution

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