2015~2020年厦漳泉地区大气氨排放清单及分布特征 |
摘要点击 2731 全文点击 1574 投稿时间:2022-02-04 修订日期:2022-03-21 |
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中文关键词 大气氨 排放清单 时空分布 排放密度 厦漳泉地区 |
英文关键词 atmospheric NH3 emission inventory tempo-spatial distribution emission density Xiamen-Zhangzhou-Quanzhou (XZQ) region |
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中文摘要 |
收集厦漳泉地区各类大气氨排放源的区县活动水平,基于排放因子法建立了2017年大气氨排放清单(1 km×1 km),同时对2015~2020年大气氨排放趋势进行了分析.结果表明,厦漳泉2017年大气氨排放量为27.40 kt,其中畜禽养殖、农田生态系统、人体排放、燃料燃烧和废物处理占排放总量的比例分别为42.48%、22.04%、14.71%、7.08%和5.69%.大气氨排放密度顺序为:厦门(1.94 t ·km-2)>泉州(1.07 t ·km-2)>漳州(0.95 t ·km-2),高值区主要分布于人口集中的沿海城区和畜禽养殖业与种植业发达的内陆乡镇地区,氨排放量月度分布与温度变化规律一致.受不同地市经济结构和发展水平的影响,2015~2020年泉州市氨排放表现为下降,而厦门和漳州两市氨排放变化存在一定的波动,氨排放强度整体呈现随人均GDP上升而下降的趋势. |
英文摘要 |
Based on the district and county activity level data of different types of atmospheric ammonia (NH3) emission sources in the Xiamen-Zhangzhou-Quanzhou (XZQ) Region and the modified emission factors, an ammonia emission inventory with a spatial resolution of 1 km×1 km in 2017 was established. In addition, the annual variations in NH3 emission from 2015 to 2020 in this region were analyzed. The results showed that the emission of NH3 in the XZQ Region in 2017 was 27.40 kt with livestock and poultry breeding, farmland ecosystem, human emission, fuel combustion, and waste treatment accounting for 42.48%, 22.04%, 14.71%, 7.08%, and 5.69% of the total emission, respectively. The order of emission density of NH3 was Xiamen (1.94 t·km-2)>Quanzhou (1.07 t·km-2)>Zhangzhou (0.95 t·km-2). High values of emission density were mainly concentrated in the coastal urban areas with a concentrated population and the inland township areas with developed livestock and poultry breeding and planting industries. The monthly variation in NH3 emissions was consistent with the pattern of temperature change, with high values in summer. Due to the different economic structure and development level in different cities, NH3 emissions in Quanzhou City showed a decline from 2015 to 2020, whereas there were fluctuations in the trends of ammonia emissions in Xiamen and Zhangzhou cities. The relationship between NH3 emission intensity and per capita GDP was significantly negative. |
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