Estimation of Emission Inventory with High-Resolution of Anthropogenic PM2.5 in Wuhan Metropolitan Area from 2017 to 2023 and Its Spatial-Temporal Evolution
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摘要:
武汉城市圈的高时空分辨率大气细颗粒物（PM2.5）排放清单研究的暂时缺乏，制约着区域PM2.5污染的精确模拟和防控. 本研究采用排放因子法，融合高德地图兴趣点数据以及人口、路网和土地利用类型等代用指标，构建了2017-2023年该区域人为源排放PM2.5的高空间分辨率（1 km×1 km）排放清单，评估其不确定性，揭示其时空演变规律. 结果表明，武汉城市圈PM2.5排放总量在2018年达峰值（164.59 kt），2020年因疫情降至137.15 kt，2023年反弹至149.97 kt. 各源类排放PM2.5的不确定性为-31.7%~42.2%，化石燃料燃烧源（-13.2%~35.8%）和工艺过程源（-15.2%~34.3%）不确定性较高，扬尘源不确定性最低（-8.2%~15.4%）. 工艺过程源和扬尘源为主要贡献源，占PM2.5总排放的46.5%~52.6%和26.7%~31.8%. 区域内PM2.5排放强度在城市中心区为600~800 t/km2，是郊区、农村区域排放强度的40~50倍. 本研究可为改进大气化学数值模拟精度提供可靠的高精度清单数据支撑.
Abstract:The lack of high spatio-temporal resolution emission inventories for atmospheric fine particulate matter (PM2.5) in the Wuhan metropolitan area limits the accurate simulation and control of regional PM2.5 pollution. In this study, the emission factor method was adopted, integrating point of interest data from Amap as well as relevant allocation indices including population, road network and land use type, etc., to construct a high-spatial-resolution (1 km×1 km) emission inventory of anthropogenic PM2.5 emissions in the region from 2017 to 2023. Its uncertainty was evaluated, and its temporal and spatial evolution patterns were revealed. Results show that the total PM2.5 emissions peaked in 2018 at 164.59 kt, dropped to 137.15 kt in 2020 due to the pandemic, and rebounded to 149.97 kt in 2023. The uncertainty of PM2.5 emissions from various source categories ranged from -31.7% to 42.2%, fossil fuel combustion sources (-13.2% to 35.8%) and process sources (-15.2% to 34.3%) have high uncertainty, while dust sources have the lowest uncertainty (-8.2% to 15.4%). Industrial and dust sources were the main contributors, accounting for 46.5%-52.6% and 26.7%-31.8% of total PM2.5 emissions, respectively. The emission intensity of PM2.5 in urban central area was 600-800 t/km2, which was 40-50 times that in suburban and rural areas. This study can provide reliable high-precision emission inventory data support for improving the accuracy of atmospheric chemistry numerical simulations.
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图 2 各二级源活动水平数据推算指标
Fig. 2. Indicators for estimating activity level data of secondary emission sources
图 3 武汉城市圈2017-2023年人为源PM2.5排放量
a. 人为源排放总量；b. 化石燃料燃烧源排放量；c. 工艺过程源排放量；d. 生物质燃烧源排放量；e. 移动源排放量；f. 扬尘源排放量；g. 餐饮源排放量
Fig. 3. Anthropogenic PM2.5 emissions in Wuhan metropolitan area from 2017 to 2023
图 4 武汉城市圈2017、2020和2023年各二级源PM2.5排放量变化
Fig. 4. Changes in PM2.5 emissions from secondary sources in Wuhan metropolitan area in 2017, 2020 and 2023
图 5 武汉城市圈2017-2023年人为源PM2.5排放强度分布的演变
图a~g分别对应2017、2018、2019、2020、2021、2022和2023年
Fig. 5. Evolution of the distribution of anthropogenic PM2.5 emission intensity in the Wuhan metropolitan area from 2017 to 2023
图 6 武汉城市圈2017-2020年(a)和2020-2023年(b)PM2.5排放强度变化
Fig. 6. Changes in PM2.5 emission intensity in the Wuhan metropolitan area from 2017 to 2020 (a) and from 2020 to 2023 (b)
表 1 化石燃料燃烧源PM2.5排放因子
Table 1. Emission factors of PM2.5 for fossil fuel combustion sources
燃料 EF(1) 原煤 7.35 g/kg 洗精煤 2.97 g/kg 其他洗煤 2.97 g/kg 型煤 2.97 g/kg 柴油 0.5 g/kg 燃料油 0.62 g/kg 煤油 0.9 g/kg 天然气 0.03 g/m3 液化石油气 0.17 g/m3 其他气体 0.03 g/m3 注：(1)参考前人研究中估算方法部分关于排放因子的选取( Xiong et al., 2016 ; 闫东杰等, 2019; 张雪纯等, 2022)，本研究排放因子来自《城市大气污染物排放清单编制技术手册(T-CSES 144-2024)》、《大气细颗粒物一次源排放清单编制技术指南（试行）(000014672/2014-01379)》和《PM2.5排放量核算技术规范（火电厂、水泥工业企业）（征求意见稿）》.
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表 2 工艺过程源PM2.5排放因子
Table 2. Emission factors of PM2.5 for process source
产品 $ EF $ 烧结矿(2) 2.52 g/kg 球团矿(2) 1.8 g/kg 生铁(2) 5.25 g/kg 钢(2) 8.26 g/kg 水泥(3) 21.61 g/kg 石灰(3) 1.4 g/kg 炼焦(3) 5.2 g/kg 原油生产(3) 0.1 g/kg 化肥(3) 1.86 g/kg 碳(3) 1.44 g/kg 注：(2)数据来自高玉冰等(2021)的研究. (3)参考前人研究中估算方法部分关于排放因子的选取( Xiong et al., 2016 ; 张雪纯等, 2022)，本研究排放因子来自《城市大气污染物排放清单编制技术手册(T-CSES 144-2024)》和《PM2.5排放量核算技术规范（火电厂、水泥工业企业）（征求意见稿）》.
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表 3 生物质锅炉源和生物质炉灶源PM2.5排放因子和燃烧效率
Table 3. Emission factors and combustion efficiency of PM2.5 for biomass boiler sources and biomass cooker sources
生物质 $ EF $ $ CE $ 生物质成型燃料 1.15 g/kg(4) — 玉米 6.87 g/kg(4) 0.92(5) 水稻 9.14 g/kg(4) 0.93(5) 小麦 8.24 g/kg(4) 0.92(5) 油菜籽 7.46 g/kg(4) 0.804(5) 大豆 11.2 g/kg(4) 0.68(5) 马铃薯 7.15 g/kg(4) 0.68(5) 花生 9.05 g/kg(4) 0.82(5) 棉花 4.87 g/kg(4) 0.804(5) 甘蔗 5.26 g/kg(4) 0.68(5) 芝麻 3.59 g/kg(4) 0.804(5) 甜菜 7.15 g/kg(4) 0.804(5) 烟草 7.15 g/kg(4) 0.804(5) 大麻 7.15 g/kg(4) 0.804(5) 薪柴 5.22 g/kg(4) — 牲畜粪便 8.22 g/kg(4) 0.2(6) 注：(4)数据来自 Wu et al. (2020) 的研究.(5)数据来自何敏等(2015)的研究.(6)数据来自Zhou et al. (2017) 的研究.
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表 4 生物质开放燃烧源PM2.5排放因子和燃烧效率
Table 4. Emission factors and combustion efficiency of PM2.5 for biomass open combustion sources
生物质 $ EF $ $ CE $ 玉米 11.7 g/kg(7) 0.92(8) 水稻 5.67 g/kg(7) 0.93(8) 小麦 7.58 g/kg(7) 0.92(8) 油菜籽 6.79 g/kg(7) 0.804(8) 大豆 6.79 g/kg(7) 0.68(8) 马铃薯 6.79 g/kg(7) 0.68(8) 花生 6.79 g/kg(7) 0.82(8) 棉花 11.7 g/kg(7) 0.804(8) 甘蔗 6.79 g/kg(7) 0.68(8) 芝麻 6.79 g/kg(7) 0.804(8) 甜菜 6.79 g/kg(7) 0.804(8) 烟草 6.79 g/kg(7) 0.804(8) 大麻 6.79 g/kg(7) 0.804(8) 草地 5.4 g/kg(7) 0.25(8) 灌木丛 7.9 g/kg(7) 0.95(8) 注：(7)数据来自 Wu et al.(2020) 的研究.(8)数据来自何敏等(2015)的研究.
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表 5 道路移动源PM2.5排放系数(g/km)
Table 5. Emission factors of PM2.5 from road mobile sources (g/km)
燃料 车型/种类 无控 国1 国2 国3 国4 汽油 重型载货汽车 0.10 0.03 0.02 0.01 0.01 中型载货汽车 0.10 0.03 0.02 0.01 0.01 轻型载货汽车 0.12 0.04 0.03 0.02 0.01 微型载货汽车 0.12 0.04 0.03 0.02 0.01 大型载客汽车 0.10 0.03 0.02 0.01 0.01 中型载客汽车 0.10 0.03 0.02 0.01 0.01 小型载客汽车 0.004 0.003 0.003 0.001 0.001 微型载客汽车 0.004 0.003 0.003 0.001 0.001 摩托车 0.31 0.17 0.09 0.09 0.09 柴油 重型载货汽车 2.00 1.00 0.40 0.30 0.06 中型载货汽车 0.60 0.60 0.13 0.09 0.02 轻型载货汽车 0.30 0.20 0.07 0.05 0.03 微型载货汽车 0.30 0.20 0.07 0.05 0.03 大型载客汽车 2.00 1.00 0.40 0.30 0.06 中型载客汽车 0.60 0.60 0.13 0.09 0.02 小型载客汽车 0.30 0.20 0.07 0.05 0.03 微型载客汽车 0.30 0.20 0.07 0.05 0.03 注：数据来自《道路机动车大气污染物排放清单编制技术指南（试行）(000014672/2014-01379)》.
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表 6 各排放源空间分配参数
Table 6. Parameters for spatial allocation of emission sources
二级源 分配参数 电力供热源 电力、热力和燃气生产及供应企业的点源数量 工业锅炉源 工业企业的点源数量 钢铁源 钢铁厂的点源数量 石化与化工源 石化厂和化工厂的点源数量 建材源 其他金属冶炼厂、水泥厂和石灰厂的点源数量 其他工业源 其他工业企业的点源数量 生物质锅炉源 生物质能源企业的点源数量 民用燃烧和生物质炉灶源 农村人口密度 生物质开放燃烧源 开放燃烧火点的点源数量 非道路移动源 河流长度 道路移动和道路扬尘源 道路长度 堆场扬尘源 石材厂的点源数量 工地扬尘源 土地利用类型：建筑用地面积 土壤扬尘源 所有网格的数量 餐饮源 餐饮企业的点源数量
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表 7 不同区域PM2.5源排放贡献率与本研究对比
Table 7. Contribution of PM2.5 source emissions from different regions compared to this study
年份 区域 源贡献率 研究来源 化石燃料燃烧源 工艺过程源 生物质燃烧源 移动源 扬尘源 餐饮源 2017-2023 武汉
城市圈5.4%~7.4% 46.5%~52.6% 4.2%~7.5% 4.7%~5.7% 26.7%~31.8% 2.9%~4.9% 本研究 2017-2020 珠三角 5%~10% 18%~20% 10% 18%~20% 8%~10% — Zhang et al., 2024b 2017-2019 江苏省 10% 60% 20% 10% — — Gu et al., 2023 2023 成都市 15.4% 6.5% 3.5% 25.6% 8.5% — Zhang et al., 2024c 2018 西安市 20.6% 3.1% 24.4% 10.5% 12.5% — Cao and Cui, 2021 2018 广州市 3% 19% 10% 10% 45% — Zhang et al., 2023 2017 长三角 5% 40% 15% 10% 30% — An et al., 2021
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表 8 95%置信区间下各类源PM2.5排放的不确定性
Table 8. Uncertainties in PM2.5 emissions from various sources under 95% confidence interval
二级源 不确定性 电力供热源 -23.1%~35.8% 工业锅炉源 -19.7%~30.3% 民用燃烧源 -13.2%~25.6% 钢铁源 -21.6%~34.3% 石化与化工源 -20.1%~31.2% 建材源 -18.3%~32.4% 其他工业源 -15.2%~28.9% 生物质锅炉源 -16.8%~17.5% 生物质炉灶源 -17.7%~18.2% 生物质开放燃烧源 -9.8%~16.8% 道路移动源 -31.7%~42.2% 非道路移动源 -17.5%~20.4% 道路扬尘源 -8.2%~13.7% 土壤扬尘源 -14.8%~15.4% 工地扬尘源 -13.3%~12.1% 堆场扬尘源 -10.9%~13.3% 餐饮源 -13.2%~26.7%
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