| 兰-白城市群主要大气污染物网格化排放清单及来源贡献 |
| 摘要点击 2412 全文点击 704 投稿时间：2020-07-13 修订日期：2020-10-29 |
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| 中文关键词 兰-白城市群 主要大气污染物 网格化清单 来源贡献 不确定性分析 |
| 英文关键词 Lan-Bai metropolitan area major criteria air pollutants gridded emission inventories source contribution uncertainty analysis |
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| 中文摘要 |
| 甘肃兰-白城市群为我国西北地区重要的重工业基地，大气污染物排放总量较大.研究高空间分辨率的污染物排放清单对于区域空气质量预报预警、减排方案模拟研究及大气污染防治等具有重要的科学意义.本文以兰州和白银为主要研究区域，基于研究区域污染源排放及统计年鉴等数据资料，建立了兰（2015年）-白（2016年）城市群7种（类）主要大气污染物网格化排放清单，并对其空间排放特征以及排放源贡献进行了详尽地讨论分析.结果表明，兰-白城市群7种主要污染物年排放量分别为：NOx 2.22×105 t、NH3 4.53×104 t、VOCs 7.74×104 t、CO 5.62×105 t、PM10 4.95×105 t、PM2.5 1.91×105 t和SO2 1.37×105 t.其中CO的排放量最大，NH3的排放量最小.本清单与北大和清华MEIC清单对比结果表明，交通源排放3个清单一致性较高，CO排放总量和其工业源排放与北大和清华MEIC清单排放源相差30%~40%，推测原因主要为清单计算过程中排放因子、分辨率和数据年份的差异.本清单网格化空间分布显示除NH3外的其他6种（类）污染物，排放主要集中在市区，排放源中工业非燃烧过程源均为最大贡献占比，NH3的主要贡献源是氮肥的施用及禽畜排放，其污染分布受耕地分布等因素影响较大.因此，减少工业非燃烧过程源、整合优质高效电力供应、使用清洁能源、严格控制工地扬尘、工业粉尘和做好城区绿化等，能有效地降低兰-白城市群NOx、VOCs、CO、PM10、PM2.5和SO2这6种（类）主要污染物的排放.NH3的减排则主要可从控制氮肥的使用及减少禽畜排放两方面考虑.本研究还利用蒙特卡洛法分析了排放清单的不确定性，NH3的不确定性最大为-31%~30%，CO的不确定性最小为-18%~16%，清单整体可信度较高. |
| 英文摘要 |
| Lan-Bai Metropolitan Area in Gansu province is an important heavy-industry base with the highest level of total air pollutant emissions in Northwest China. It is significant to study the high-resolution pollutant emission inventory to forecast regional air quality and to simulate pollutant emission reduction, as well as provide early warnings and forecasts, and to control air pollution. Taking Lanzhou and Baiyin as the main research areas, this study established the gridded emission inventories of seven major criteria air pollutants in the Lan-Bai Metropolitan Area based on emission data and statistical yearbooks of 2015-2016. The spatial pollution characteristics and emission source contributions were also studied. The results showed that the total annual emissions of seven major criteria air pollutants in the Lan-Bai Metropolitan Area were as followings:NOx 2.22×105 t, NH3 4.53×104t, VOCs 7.74×104t, CO 5.62×105 t, PM10 4.95×105 t, PM2.5 1.91×105 t, and SO2 1.37×105 t. Among them, annual CO emissions were the highest, while the annual emissions of NH3 were the lowest. The comparison of this gridded emission inventories with the Peking and Tsinghua University's MEIC inventories, found that the consistency of the three inventories for traffic source was relatively high, but for the total emissions and industrial source emissions of CO, a 30%-40% difference was found when compared with emissions in the Peking and Tsinghua University's inventories. The main differences were from the collected emission factors and the different resolution and years for collected data. The industrial non-combustion process sources, accounting for the largest proportion, were mainly concentrated in urban areas for the other six major criteria air pollutants except for NH3. The main contributing sources of NH3 were from the use of nitrogen fertilizers and livestock emissions, so its spatial pollution distribution was mainly affected by farmland distribution and other factors. It can be concluded that countermeasures, such as controlling industrial non-combustion process sources, integrating high-quality and high-efficiency power supply, using clean energy, strict dust emission control on construction sites and industrial production facilities, as well as urban greening could effectively reduce the emissions of six major criteria air pollutants including NOx, VOCs, CO, PM10, PM2.5, and SO2 in the Lan-Bai Metropolitan Area. The reduction of NH3 emission mainly depends on reducing the use of nitrogen fertilizer and controlling livestock emissions in the rural regions of Lan-Bai Metropolitan Area. This paper also used Monte Carlo uncertainty analysis to evaluate uncertainty in the gridded emission inventories, in which the maximum uncertainty was -31%-30% for NH3, the uncertainty of CO at -18%-16% was minimal. Therefore, the overall credibility was high for the established gridded emission inventories in this study. |
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