County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China
County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China
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青藏高原县级畜禽粪便焚烧颗粒物和气体排放清单
DOI：
10.1016/j.jclepro.2022.133051
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发表时间：
2022-07
影响因子：
11.1
通讯作者：
Junji Cao
中科院分区：
文献类型：
--
作者：
Bin Zhang;Zhenxing Shen;Jian Sun;Kun He;Haijiang Zou;Qian Zhang;Jianjun Li;Hongmei Xu;Suixin Liu;Kin-Fai Ho;Junji Cao
青藏高原畜禽粪便焚烧排放对区域和全球气候环境影响巨大，但目前尚缺乏多种污染物的排放清单。本研究根据2019年QTP的实地测量排放因子和详细活动数据，制定了县级颗粒物和气体污染排放清单。结果表明，空气动力学直径≤ 2.5 μ m的颗粒物排放总量结果表明，大气中PM2.5、有机碳（OC）、元素碳（EC）、8种离子、51种多环芳烃（PAHs）、108种挥发性有机物（VOCs）、CO和CO2的含量分别为41.1，23.1，2.36，3.15，1.13、6.55、135和4963 Gg。青海东北部和西藏西北部的共和、祁连、尼玛和刚察等县由于牲畜数量较多，是污染物排放的主要贡献者（占总排放量的11.9%~14.6%）。羊粪燃烧的贡献率（平均67.1%）略高于牦牛粪燃烧（32.9%），这是由于较高的EFs和消耗。在时间分布上，采暖季的排放量占总排放量的70%~82%。虽然人口少得多，但由于氧气少和燃烧技术差，QTP显示出比平原地区更高的排放密度。与现有的排放清单相比，所有污染物排放清单的不确定性大大降低（42.7 - 163%），与EC作为一个例外，因为它的高系数的变异系数的EFs比其他污染物。所发展的排放清单，可以帮助制定有针对性的空气污染控制措施，通过提供详细的资料，多类型的空气污染物的时空分布的QTP区域。同时，相对较高的分辨率和精度清单可以为区域和全球尺度的污染物传输和源解析等大气模拟提供更好的基础。·编制了QTP中动物粪便燃烧的多污染物排放清单。·青海东北部和西藏西北部的排放量最高。·燃烧羊粪取暖是QTP最大的排放源。·QTP具有相对高的排放密度，因为氧气较少且燃烧技术较差。
Emissions from animal dung burning in the Qinghai–Tibetan Plateau (QTP) influenced heavily on the regional and global climate and environment, from which the emission inventory of multiple pollutants is not yet available. This study developed a county-level emission inventory of particles and gases pollution based on field measured emission factors and detailed activity data for the QTP in 2019. The results revealed that the total emissions of particulate matter with an aerodynamic diameter of ≤2.5 μm (PM 2.5 ), organic carbon (OC), elemental carbon (EC), eight ions, fifty-one polycyclic aromatic hydrocarbons (PAHs) in gaseous and particulate phases, 108 volatile organic compounds (VOCs), CO , and CO 2 were 41.1, 23.1, 2.36, 3.15, 1.13, 6.55, 135, and 4963 Gg, respectively. Gonghe, Qilian, Nyima, and Gangcha Counties in northeast of Qinghai and northwestern Tibet were the major contributors of all pollutants contributed (accounting for 11.9%–14.6% of the total emission) due to their higher livestock numbers. Sheep dung burning contributed a little more (average of 67.1%) than yak dung burning (32.9%) due to the high EFs and consumptions. For temporal distribution , the heating season contributed majority of the total emissions (70%–82%). Although with much less population, the QTP showed even higher emission densities than those in plain areas due to less oxygen and poor combustion technology. Compared with existing emission inventories, the uncertainties of all pollutant emission inventories were considerably reduced (42.7–163%), with EC as an exception because of its high coefficients of variation of EFs than the other pollutants. The developed emission inventory can help formulate targeted air pollution control measures for the QTP region by providing detailed information on the spatiotemporal distribution of multitype air pollutants . Meanwhile, the relative high resolution and accuracy inventory could serve a better basis for atmospheric modeling such as pollution transportation and source apportionment in both regional and global scales. • Multiple pollutants emission inventory for animal dung burning in the QTP was developed. • The emissions displayed the highest of northeast of Qinghai and northwestern Tibet. • Sheep dung burning for heating was the largest emission source in QTP. • The QTP had relative high emission densities for less oxygen and poor combustion technology.
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影响因子：
6.3
作者：
Zhou Ying;Xing Xiaofan;Lang Jianlei;Chen Dongsheng;Cheng Shuiyuan;Wei Lin;Wei Xiao;Liu Chao
通讯作者：
Liu Chao
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影响因子：
11.2
作者：
Peng Liqun;Zhang Qiang;Yao Zhiliang;Mauzerall Denise L.;Kang Sicong;Du Zhenyu;Zheng Yixuan;Xue Tao;He Kebin
通讯作者：
He Kebin
DOI：
--
发表时间：
2011
期刊：
--
影响因子：
--
作者：
B. Zhao
通讯作者：
B. Zhao
影响因子：
6.3
作者：
Zhao, Y.;Nielsen, C. P.;Hao, J.
通讯作者：
Hao, J.
影响因子：
5.5
作者：
Fei Yu;Qun Wang;Qishe Yan;Nan Jiang;Junhua Wei;Zhi-Qin Wei;Shasha Yin
通讯作者：
Fei Yu;Qun Wang;Qishe Yan;Nan Jiang;Junhua Wei;Zhi-Qin Wei;Shasha Yin