Vol.31, NO.1, 2020
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Article Navigation >  Journal of Applied Meteorological Science  > 2020  >  31(1): 105-116
Ke Huabing, Gong Sunling, He Jianjun, et al. Assessment of open biomass burning impacts on surface PM2.5 concentration. J Appl Meteor Sci, 2020, 31(1): 105-116. DOI:  10.11898/1001-7313.20200110.
Citation: Ke Huabing, Gong Sunling, He Jianjun, et al. Assessment of open biomass burning impacts on surface PM2.5 concentration. J Appl Meteor Sci, 2020, 31(1): 105-116. DOI:  10.11898/1001-7313.20200110.
shu

Assessment of Open Biomass Burning Impacts on Surface PM2.5 Concentration

DOI: 10.11898/1001-7313.20200110
  • 1.

    Chinese Academy of Meteorological Sciences, Beijing 100081

  • 2.

    Nanjing University of Information Science & Technology, Nanjing 210044

  • Received Date: 2019-03-05
  • Rev Recd Date: 2019-12-27
  • Publish Date: 2020-01-31
    Abstract
  • Open biomass burning plays an important role in the formation of heavy pollution events during harvest seasons in China by releasing gases and particulate matters into the atmosphere. A better understanding of open biomass burning in China is required to assess its impacts on the air quality and especially on heavy haze pollution.By using datasets of MODIS fire spot, land cover, vegetation cover, biomass loading and emission factors, a biomass emission model is developed, which is then embedded to an air quality model (WRF-CUACE) to quantitatively assess impacts of biomass burning on surface PM2.5 concentration in China through sensitivity tests. Three simulation scenarios are designed to ensure that simulation results of revised scenarios are closer to actual atmospheric conditions according to the model evaluation. Results show that in October 2014, Northeast, South and Southwest China are regions of the largest contribution to biomass burning with the average monthly increased concentration of PM2.5 up to 30-60 μg·m-3, and even more than 100 μg·m-3 at local regions. In North, East and South China, biomass burning generally provides a contribution of PM2.5 concentration of 5-20 μg·m-3. In terms of the percentage of relative contribution, the value in Northeast China exceeds 50% in most regions. In South China, the relative contribution of biomass burning reaches 20%-50%, and even exceeds 60% in parts of Southwest China. While in North, Central and East China, the relative contribution of biomass burning is generally 10%-20%. In addition, the contribution of secondary aerosols in PM2.5 from biomass burning is also estimated. A group of sensitivity experiments are set up, with and without the gas emission from biomass burning. In Northeast China, the contribution concentration of secondary aerosols is only 0-10 μg·m-3, significantly lower than that in North, Central, East and South China, where the contribution concentration of secondary aerosols could reach 5-15 μg·m-3. In terms of the percentage of contribution to secondary aerosols in PM2.5 from biomass burning, the value in Northeast China is the lowest, which is less than 30% in most regions. And in South and Southwest China, the contribution percentage is relatively larger, which can reach 30%-50%. While in North, Central, East China and vast remote areas, the contribution percentage almost exceed 70%. Based on the above analysis, it is found that the percentage of secondary aerosols in PM2.5 from biomass burning drops when the biomass burning grows.
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  • Fig. 1  Vertical distribution of biomass burning emissions from vegetation type of forest

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    Fig. 2  Model domain (the second one) and location of observation stations

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    Fig. 3  The observed and simulated daily mean surface PM2.5 concentration of three scenarios at 10 stations during 1-31 Oct 2014

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    Fig. 4  Distributions of averaged surface PM2.5 concentration and contribution from biomass burning in Oct 2014

    (a)simulated PM2.5 concentration from SIM1, (b)simulated PM2.5 concentration from SIM3, (c)contribtution from biomass burning, (d)percentage of contribtution from biomass burning

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    Fig. 5  Contribution(a) and percentage of contribution(b) of secondary aerosols in PM2.5 from biomass burning

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    Table  1  Biomass loadings and emission factors for different land cover types

    植被类型 木质类燃料载荷/
    (kg·m-2)    		 草本类燃料载荷/
    (kg·m-2)    		 CO/(g·kg-1) PM2.5/(g·kg-1) OC/(g·kg-1) BC/(g·kg-1)
    常绿针叶林 28.61 4.79 118 13.0 7.8 0.20
    常绿阔叶林 19.45 5.17 92 9.7 4.7 0.52
    落叶针叶林 15.46 5.48 118 13.0 7.8 0.20
    落叶阔叶林 19.50 4.73 102 13.0 9.2 0.56
    混交林 19.98 7.93 102 13.0 9.2 0.56
    稠密灌丛 4.80 1.24 68 9.3 6.6 0.50
    稀疏灌丛 2.63 0.82 68 9.3 6.6 0.50
    木本稀树草原 12.51 3.07 68 9.3 6.6 0.50
    稀树草原 10.51 2.89 59 5.4 2.6 0.37
    草地 2.62 1.40 59 5.4 2.6 0.37
    永久湿地 8.34 10.14 59 5.4 2.6 0.37
    农田 0 0.66 111 5.8 3.3 0.69
    农田/自然植被混合 8.87 2.97 59 5.4 2.6 0.37
    贫瘠或稀疏植被 1.18 0.48 59 5.4 2.6 0.37
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    Table  2  Statistics of observed and simulated meteorological elements

    站点 要素 观测值 模拟值 平均偏差 均方根误差 相关系数
    北京 气温/℃ 14.1 13.8 -0.3 2.1 0.90
    相对湿度/% 64.0 56.8 -7.2 14.4 0.87
    风速/(m·s-1) 1.7 1.9 0.2 0.9 0.70
    济南 气温/℃ 17.1 17.9 0.7 1.6 0.94
    相对湿度/% 56.9 48.2 -8.8 12.2 0.92
    风速/(m·s-1) 2.8 2.9 0.1 1.2 0.70
    石家庄 气温/℃ 15.4 15.8 0.5 2.8 0.82
    相对湿度/% 68.9 52.2 -16.7 22.8 0.70
    风速/(m·s-1) 0.9 2.0 1.0 1.5 0.38
    合肥 气温/℃ 19.1 20.4 1.3 1.8 0.94
    相对湿度/% 73.6 57.5 -16.1 19.0 0.82
    风速/(m·s-1) 1.8 2.4 0.6 1.0 0.70
    南京 气温/℃ 19.0 19.8 0.8 1.2 0.97
    相对湿度/% 72.9 60.5 -12.4 14.7 0.90
    风速/(m·s-1) 2.4 2.3 -0.1 0.9 0.80
    上海 气温/℃ 20.2 20.7 0.5 1.1 0.95
    相对湿度/% 68.3 64.3 -4.0 8.2 0.88
    风速/(m·s-1) 2.6 3.2 0.6 1.2 0.79
    郑州 气温/℃ 17.8 19.2 1.3 2.0 0.92
    相对湿度/% 64.8 47.4 -17.4 20.7 0.85
    风速/(m·s-1) 1.7 2.5 0.8 1.3 0.63
    沈阳 气温/℃ 10.9 10.5 -0.4 2.3 0.94
    相对湿度/% 55.7 59.8 4.0 14.0 0.83
    风速/(m·s-1) 2.4 3.4 1.0 1.7 0.67
    长春 气温/℃ 8.6 9.2 0.6 1.9 0.96
    相对湿度/% 52.3 46.3 -6.0 14.8 0.77
    风速/(m·s-1) 2.8 3.3 0.4 1.2 0.84
    哈尔滨 气温/℃ 6.4 5.4 -0.9 2.1 0.95
    相对湿度/% 55.3 52.8 -2.5 14.3 0.79
    风速/(m·s-1) 2.7 3.4 0.7 1.4 0.81
    DownLoad: Download CSV

    Table  3  Statistics of PM2.5 between observation and simulation from SIM1

    站点 平均值/(μg·m-3) 相关系数 平均偏差/
    (μg·m-3)    		 均方根误差/
    (μg·m-3)    		 平均相对偏差/% 平均相对误差/%
    观测 模拟
    北京 116.1 81.6 0.84 -34.5 59.3 -31.8 35.1
    济南 71.6 53.4 0.46 -18.3 33.8 -27.4 37.7
    石家庄 161.2 83.3 0.77 -77.8 104.8 -56.4 56.5
    合肥 78.0 63.0 0.53 -15.0 35.0 -17.7 32.2
    南京 69.7 52.8 0.79 -16.9 25.1 -26.5 31.2
    上海 51.8 41.5 0.66 -19.9 21.9 -24.6 40.2
    郑州 113.9 68.6 0.75 -45.4 53.8 -49.2 49.2
    沈阳 88.1 63.1 0.66 -25.0 70.7 -14.3 40.4
    长春 155.4 51.4 0.62 -104.0 135.5 -85.7 85.8
    哈尔滨 147.6 33.7 0.55 -113.9 188.4 -83.0 83.0
    DownLoad: Download CSV

    Table  4  Statistics of PM2.5 between observation and simulation from SIM2

    站点 平均值/(μg·m-3) 相关系数 平均偏差/
    (μg·m-3)    		 均方根误差/
    (μg·m-3)    		 平均相对偏差/% 平均相对误差/%
    观测 模拟
    北京 116.1 86.6 0.84 -29.4 55.3 -28.0 32.8
    济南 71.6 55.4 0.45 -16.2 33.4 -24.0 37.1
    石家庄 161.2 87.5 0.76 -73.7 101.6 -52.7 54.6
    合肥 78.0 64.8 0.55 -13.2 33.9 -15.1 30.7
    南京 69.7 55.4 0.84 -14.3 21.6 -22.7 27.9
    上海 51.8 42.6 0.69 -17.8 20.7 -22.3 38.5
    郑州 113.9 70.3 0.75 -38.3 52.4 -46.8 46.8
    沈阳 88.1 69.5 0.69 -21.1 67.0 -5.1 36.8
    长春 155.4 58.5 0.77 -97.0 126.9 -77.0 77.1
    哈尔滨 147.6 39.5 0.73 -108.1 179.6 -76.2 76.3
    DownLoad: Download CSV

    Table  5  Statistics of PM2.5 between observation and simulation from SIM3

    站点 平均值/(μg·m-3) 相关系数 平均偏差/
    (μg·m-3)    		 均方根误差/
    (μg·m-3)    		 平均相对偏差/% 平均相对误差/%
    观测 模拟
    北京 116.1 97.9 0.85 -18.2 48.3 -17.9 28.7
    济南 71.6 60.5 0.41 -11.1 32.8 -15.9 36.7
    石家庄 161.2 95.8 0.76 -65.4 94.0 -45.2 48.4
    合肥 78.0 72.3 0.64 -5.7 29.2 -5.4 27.4
    南京 69.7 61.3 0.85 -8.4 17.8 -13.8 22.8
    上海 51.8 46.6 0.74 -5.2 18.6 -14.0 34.5
    郑州 113.9 78.1 0.71 -35.8 47.4 -37.2 38.6
    沈阳 88.1 93.4 0.57 5.3 67.5 21.3 40.1
    长春 155.4 91.2 0.73 -64.2 96.2 -43.0 47.9
    哈尔滨 147.6 71.1 0.79 -76.5 134.9 -45.8 50.7
    DownLoad: Download CSV
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    • Received : 2019-03-05
    • Accepted : 2019-12-27
    • Published : 2020-01-31
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