留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于统计学模型的矿区粉尘污染特征及影响因素的定量分析

赵洪宝 翟汝鹏 戈海宾 陈超男 刘绍强 荆士杰

赵洪宝, 翟汝鹏, 戈海宾, 陈超男, 刘绍强, 荆士杰. 基于统计学模型的矿区粉尘污染特征及影响因素的定量分析[J]. 矿业科学学报, 2024, 9(2): 243-257. doi: 10.19606/j.cnki.jmst.2024.02.011
引用本文: 赵洪宝, 翟汝鹏, 戈海宾, 陈超男, 刘绍强, 荆士杰. 基于统计学模型的矿区粉尘污染特征及影响因素的定量分析[J]. 矿业科学学报, 2024, 9(2): 243-257. doi: 10.19606/j.cnki.jmst.2024.02.011
ZHAO Hongbao, ZHAI Rupeng, GE Haibin, CHEN Chaonan, LIU Shaoqiang, JING Shijie. Quantitative analysis of dust pollution characteristics and influencing factors in mining areas based on statistical modelling[J]. Journal of Mining Science and Technology, 2024, 9(2): 243-257. doi: 10.19606/j.cnki.jmst.2024.02.011
Citation: ZHAO Hongbao, ZHAI Rupeng, GE Haibin, CHEN Chaonan, LIU Shaoqiang, JING Shijie. Quantitative analysis of dust pollution characteristics and influencing factors in mining areas based on statistical modelling[J]. Journal of Mining Science and Technology, 2024, 9(2): 243-257. doi: 10.19606/j.cnki.jmst.2024.02.011

基于统计学模型的矿区粉尘污染特征及影响因素的定量分析

doi: 10.19606/j.cnki.jmst.2024.02.011
基金项目: 

国家自然科学基金 51474220

中央高校基本科研业务费专项资金 BBJ2023004

详细信息
    作者简介:

    赵洪宝(1980—),男,山东德州人,教授,博士生导师,主要从事矿山岩体力学方面的教学与研究工作。Tel:13426079538,E-mail:hongbaozhao@126.com

  • 中图分类号: TD714

Quantitative analysis of dust pollution characteristics and influencing factors in mining areas based on statistical modelling

  • 摘要: 针对露天矿粉尘无秩序排放引发的生态环境退化问题,选取河曲露天煤矿粉尘产生区域为研究靶区,利用粉尘监测体系获取靶区TSP、PM10、PM2.5及环境指标数据,结合粉尘浓度对不同粒径粉尘分布差异性进行对比分析,引入空气质量浓度分指数法、Pearson关联矩阵分析法和灰色关联法对靶区内核心污染物、不同粒径粉尘内在关联性及环境指标与粉尘浓度的关联度进行深入探讨,基于单变量、多元线性和主成分得分-多元线性回归分析法对粒子变迁演化规律及环境指标对微粉尘权重的影响规律进行定量解析,同时运用均值误差法对MLR和PCA-MLR模型预测的精确度进行验证。结果表明:①区域1(采掘场)和3(煤场)不同粒径粉尘浓度均存在超过现行标准二级限值的情况,区域2(交通干道)仅存在超过一级限值的情况。②不同区域粉尘污染能力的强弱与IAQI评估结果一致,均为区域1>3>2;当不同区域TSP浓度一致时,域内粉尘污染能力的强弱顺序转变为区域2>3>1,且各区域核心污染物均为PM2.5。③不同区域粉尘浓度线性关系较为显著。④不同区域MLR模型演算出的多元线性方程的拟合度排序规律与粉尘浓度Pearson关联度趋于一致,且多变量拟合度优于单变量拟合度,结合MRE法检验出不同区域MLR模型预测精度区域3(3.02 %)>2(9.46 %)>1(10.75 %)。⑤区域1中TSP和PM10与气压呈强正相关,PM2.5与相对湿度呈强负相关;区域2中各粒径粉尘均与温度和风速呈强负相关;区域3中仅与温度呈强负相关。⑥微粉尘权重与环境指标的PCA-MLR模型相对于直接MLR模型,预测精确度提高了56.63 % 和13.41 %。
  • 图  1  不同区域多种粒径粉尘浓度

    Figure  1.  Variations of dust concentrations with different particle sizes in different regions

    图  2  不同区域微粉尘浓度的富含率

    Figure  2.  Enrichment rates of microdust concentrations in different regions

    图  3  不同区域空气质量分指数IAQI变化情况

    Figure  3.  Changes in institute for administrative quality improvement IAQI in different regions

    图  4  各区域不同粒径粉尘浓度Pearson关联度

    Figure  4.  Pearson correlation of dust concentration of different particle sizes in different region

    图  5  不同区域MLR残差正态P-P图

    Figure  5.  Normal P-P plot of MLR residuals for different regions

    图  6  不同区域粉尘浓度MLR方程拟合图

    Figure  6.  MLR equation fitting diagram of dust concentration in different regions

    图  7  不同区域TSP的真实值与MLR模型TSP浓度预测值的对比

    Figure  7.  Comparison of the true values of TSP in different regions with the predicted values of TSP concentrations from the MLR

    图  8  粉尘浓度与环境指标的Pearson相关度

    Figure  8.  Pearson correlation of dust concentration with environmental indicators

    图  9  不同粒径粉尘浓度与环境指标的灰色关联度弦图

    Figure  9.  Grey correlation chords between regional dust concentrations of different particle sizes and environmental indicators

    图  10  微粉尘权重MLR模型残差正态P-P图

    Figure  10.  Residual normal P-P diagram of MLR model with microdust weights

    图  11  PCA-MLR和直接MLR模型预测值同微粉尘权重真实值的对比

    Figure  11.  Comparison of the predicted values of PCA-MLR and direct MLR models with the true values of microdust weights

    表  1  不同回归模型参数检验

    Table  1.   Parameter tests of different regression models

    区域 模型 r r2 $r_{\mathrm{ad}}^2$ F p Sig.
    1 L PM10 0.751 0.564 0.549 36.287 0.000 0.000
    PM2.5 0.765 0.586 0.571 39.552 0.000 0.000
    N PM10 0.741 0.550 0.534 34.173 0.000 0.000
    PM2.5 0.782 0.612 0.598 44.085 0.000 0.000
    M PM10 0.749 0.562 0.546 35.864 0.000 0.000
    PM2.5 0.777 0.604 0.590 42.720 0.000 0.000
    S PM10 0.744 0.554 0.538 34.736 0.000 0.000
    PM2.5 0.785 0.616 0.602 44.948 0.000 0.000
    2 L PM10 0.878 0.771 0.763 94.169 0.000 0.000
    PM2.5 0.874 0.763 0.755 90.272 0.000 0.000
    N PM10 0.888 0.789 0.781 104.204 0.000 0.000
    PM2.5 0.732 0.536 0.520 32.355 0.000 0.000
    D PM10 0.886 0.786 0.778 102.563 0.000 0.000
    PM2.5 0.818 0.670 0.658 56.769 0.000 0.000
    M PM10 0.914 0.836 0.830 142.618 0.000 0.000
    PM2.5 0.830 0.689 0.678 62.156 0.000 0.000
    3 L PM10 0.905 0.819 0.813 126.924 0.000 0.000
    PM2.5 0.892 0.796 0.788 108.938 0.000 0.000
    U PM10 0.890 0.792 0.785 106.803 0.000 0.000
    PM2.5 0.872 0.760 0.752 88.715 0.000 0.000
    H PM10 0.890 0.792 0.785 106.803 0.000 0.000
    PM2.5 0.872 0.760 0.752 88.715 0.000 0.000
    Z PM10 0.890 0.792 0.785 106.803 0.000 0.000
    PM2.5 0.872 0.760 0.752 88.715 0.000 0.000
    下载: 导出CSV

    表  2  不同区域各模型拟合方程

    Table  2.   Equations for fitting each model in different regions

    类别 区域1 区域2 区域3
    模型 方程 模型 方程 模型 方程
    PM10 L yL=0.563x+295.11 L yL=1.721x-2.305 L yL=1.726x+42.443
    PM2.5 yL=0.887x+300.429 yL=2.399x+8.569 yL=2.399x+8.569
    PM10 N yN=-12169.28/x+499.23 N yN=-7462.49/x+233.92 U yU=89.74×1.008x
    PM2.5 yN=-7535.26/x+463.306 yN=-2769.095/x+185.089 yU=75.90×1.017x
    PM10 M yM=127.02x0.218 D yD=116.24log(x)-370.91 H yH=e(4.497+0.008x)
    PM2.5 yM=143.88x0.216 yD=88.28log(x)-215.01 yH=e(4.329+0.017x)
    PM10 S yS=e(6.153-32.02/x) M yM=1.381x1.045 Z yZ=89.74e0.008x
    PM2.5 yS=e(6.162-19.83/x) yM=5.886x0.781 yZ=75.90e0.017x
    下载: 导出CSV

    表  3  MLR模型预设性检验结果

    Table  3.   Results of MLR model preconditioning test

    区域 r r2 $r_{\mathrm{ad}}^2$ F p1 p2 Sig. U VIF
    1 PM10 0.807 0.651 0.625 25.201 0.000 0.080 0.015 1.679 2.429
    PM2.5 0.032 2.429
    2 PM10 0.907 0.823 0.810 62.976 0.000 0.080 0.005 1.601 3.920
    PM2.5 0.009 3.920
    3 PM10 0.915 0.837 0.825 69.280 0.000 0.080 0.014 1.604 4.647
    PM2.5 0.009 4.647
    下载: 导出CSV

    表  4  微粉尘权重MLR模型参数检验

    Table  4.   Parameter tests of MLR model for microdust weights

    项目 F p1 p2 Sig. U VIF
    ρ2.5 Fac1 11.89 0.024 0.94 0.022 2.447 1.000
    Fac2 0.048 1.000
    ρ10 Fac1 47.131 0.005 0.94 0.021 2.217 1.000
    Fac2 0.003 1.000
    下载: 导出CSV

    表  5  各模型的主成分回归拟合方程

    Table  5.   Principal component regression fitted equations for each model

    项目 拟合方程 R2
    ρ2.5 y2.5=0.004Fac1-0.008Fac2+0.283 0.558
    ρ10 y10=0.01Fac1-0.02Fac2+0.404 0.969
    下载: 导出CSV
  • [1] WANG Z M, ZHOU W, JISKANI I M, et al. Annual dust pollution characteristics and its prevention and control for environmental protection in surface mines[J]. Science of the Total Environment, 2022, 825: 153949. doi: 10.1016/j.scitotenv.2022.153949
    [2] LUO H T, ZHOU W, JISKANI I M, et al. Analyzing characteristics of particulate matter pollution in open-pit coal mines: implications for green mining[J]. Energies, 2021, 14(9): 2680. doi: 10.3390/en14092680
    [3] 佟瑞鹏, 崔鹏程, 杨校毅, 等. 基于蒙特卡洛方法的煤矿粉尘健康损害不确定性分析[J]. 矿业科学学报, 2017, 2(5): 467-474. http://kykxxb.cumtb.edu.cn/article/id/97

    TONG Ruipeng, CUI Pengcheng, YANG Xiaoyi, et al. Uncertainty analysis of health damage of coal mine dust using the Monte Carlo method[J]. Journal of Mining Science and Technology, 2017, 2(5): 467-474. http://kykxxb.cumtb.edu.cn/article/id/97
    [4] 常玲利, 邵龙义, 杨书申, 等. 大气污染综合治理攻坚行动前后北京市PM2.5质量浓度变化特征研究[J]. 矿业科学学报, 2019, 4(6): 539-546. http://kykxxb.cumtb.edu.cn/article/id/256

    CHANG Lingli, SHAO Longyi, YANG Shushen, et al. Study on variation characteristics of PM2.5 mass concentrations in Beijing after the action on comprehensive control of air pollution[J]. Journal of Mining Science and Technology, 2019, 4(6): 539-546. http://kykxxb.cumtb.edu.cn/article/id/256
    [5] WANG Z M, ZHOU W, JISKANI I M, et al. Dust pollution in cold region surface mines and its prevention and control[J]. Environmental Pollution, 2022, 292: 118293. doi: 10.1016/j.envpol.2021.118293
    [6] 任晓芬, 郭军霞, 郜玉聪, 等. 铁渣转运廊道粉尘分布规律及其影响因素模拟研究[J]. 安全与环境工程, 2022, 29(6): 184-191. https://www.cnki.com.cn/Article/CJFDTOTAL-KTAQ202206022.htm

    REN Xiaofen, GUO Junxia, GAO Yucong, et al. Simulation study on the distribution law of dust in iron slag transfer corridor and its influencing factors[J]. Safety and Environmental Engineering, 2022, 29(6): 184-191. https://www.cnki.com.cn/Article/CJFDTOTAL-KTAQ202206022.htm
    [7] WANG J Z, DU C F, CHEN Z, et al. Influence of vehicle and pavement characteristics on dust resuspension from soil pavement of open-pit mine[J]. Science of the Total Environment, 2023, 878: 163252. doi: 10.1016/j.scitotenv.2023.163252
    [8] TANG W J, CAI Q X. Dust distribution in open-pit mines based on monitoring data and fluent simulation[J]. Environmental Monitoring and Assessment, 2018, 190(11): 632. doi: 10.1007/s10661-018-7004-9
    [9] 张明浩, 赵廷宁, 肖辉杰. 内蒙古乌海粉尘浓度时空分布及影响因素探析[J]. 地学前缘, 2021, 28(4): 118-130. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY202104018.htm

    ZHANG Minghao, ZHAO Tingning, XIAO Huijie. Temporospatial distribution and influencing factor analysis of dust concentration in Wuhai, Inner Mongolia[J]. Earth Science Frontiers, 2021, 28(4): 118-130. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY202104018.htm
    [10] 蒋仲安, 曾发镔, 冯雪, 等. 高海拔隧道爆破后粉尘污染动力学模型及影响因素[J]. 煤炭学报, 2023, 48(1): 263-278. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202301020.htm

    JIANG Zhongan, ZENG Fabin, FENG Xue, et al. Dynamic model and influencing factors of dust pollution after blasting in high altitude tunnel[J]. Journal of China Coal Society, 2023, 48(1): 263-278. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202301020.htm
    [11] KOK J, MAHOWALD N, ALBANI S, et al. An improved dust emission model with insights into the global dust cycle's climate sensitivity[J]. Atmospheric Chemistry and Physics Discussions, 2014, 14(5): 6361-6425.
    [12] 张海霞, 程先富, 陈冉慧. 安徽省PM2.5时空分布特征及关键影响因素识别研究[J]. 环境科学学报, 2018, 38(3): 1080-1089. https://www.cnki.com.cn/Article/CJFDTOTAL-HJXX201803031.htm

    ZHANG Haixia, CHENG Xianfu, CHEN Ranhui. Analysis on the spatial-temporal distribution characteristics and key influencing factors of PM2.5 in Anhui Province[J]. Acta Scientiae Circumstantiae, 2018, 38(3): 1080-1089. https://www.cnki.com.cn/Article/CJFDTOTAL-HJXX201803031.htm
    [13] WU T, YANG Z, WANG A A, et al. A study on movement characteristics and distribution law of dust particles in open-pit coal mine[J]. Scientific Reports, 2021, 11: 14703. doi: 10.1038/s41598-021-94131-6
    [14] LI L, ZHANG R X, SUN J D, et al. Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm[J]. Journal of Environmental Health Science and Engineering, 2021, 19(1): 401-414. doi: 10.1007/s40201-021-00613-0
    [15] 阿尔祖娜·阿布力米提, 王敬哲, 王宏卫, 等. 新疆准东矿区土壤与降尘重金属空间分布及关联性分析[J]. 农业工程学报, 2017, 33(23): 259-266. doi: 10.11975/j.issn.1002-6819.2017.23.034

    AERZUNA Abulimiti, WANG Jingzhe, WANG Hongwei, et al. Spatial distribution analysis of heavy metals in soil and atmospheric dust fall and their relationships in Xinjiang Eastern Junggar mining area[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(23): 259-266. doi: 10.11975/j.issn.1002-6819.2017.23.034
    [16] 罗怀廷, 周伟, 刘宇, 等. 露天煤矿冬季坑底粉尘污染特征及影响因素[J]. 深圳大学学报: 理工版, 2020, 37(6): 637-644. https://www.cnki.com.cn/Article/CJFDTOTAL-SZDL202006013.htm

    LUO Huaiting, ZHOU Wei, LIU Yu, et al. Pollution characteristics and influencing factors of dust at the bottom of open-pit coal mine in winter[J]. Journal of Shenzhen University: Science and Engineering, 2020, 37(6): 637-644. https://www.cnki.com.cn/Article/CJFDTOTAL-SZDL202006013.htm
    [17] 赵洪宝, 刘绍强, 康钦容, 等. 基于数字分形原理的矿区粉尘时空分布与防治技术[J]. 矿业科学学报, 2022, 7(6): 710-719. doi: 10.19606/j.cnki.jmst.2022.06.008

    ZHAO Hongbao, LIU Shaoqiang, KANG Qinrong, et al. Temporal and spatial distribution and prevention of dust in mining area based on digital fractal principle[J]. Journal of Mining Science and Technology, 2022, 7(6): 710-719. doi: 10.19606/j.cnki.jmst.2022.06.008
    [18] HUANG R X, CHUN L H. Seasonal variation characteristics and forecasting model of PM2.5 in Changsha, central city in China[J]. Journal of Environmental & Analytical Toxicology, 2017, 7(1): 429-435.
    [19] 刘英, 许萍萍, 毕银丽, 等. 新疆戈壁煤矿露天开采对生态环境扰动定量分析[J]. 煤炭学报, 2023, 48(2): 959-974. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202302032.htm

    LIU Ying, XU Pingping, BI Yinli, et al. Quantitative analysis of coal mining disturbance on environment in Xinjiang Gobi Open-pit mining area[J]. Journal of China Coal Society, 2023, 48(2): 959-974. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202302032.htm
    [20] 李颖若, 汪君霞, 韩婷婷, 等. 利用多元线性回归方法评估气象条件和控制措施对APEC期间北京空气质量的影响[J]. 环境科学, 2019, 40(3): 1024-1034. https://www.cnki.com.cn/Article/CJFDTOTAL-HJKZ201903002.htm

    LI Yingruo, WANG Junxia, HAN Tingting, et al. Using multiple linear regression method to evaluate the impact of meteorological conditions and control measures on air quality in Beijing during APEC 2014[J]. Environmental Science, 2019, 40(3): 1024-1034. https://www.cnki.com.cn/Article/CJFDTOTAL-HJKZ201903002.htm
    [21] HOSSEINI S, MOUSAVI A, MONJEZI M. Prediction of blast-induced dust emissions in surface mines using integration of dimensional analysis and multivariate regression analysis[J]. Arabian Journal of Geosciences, 2022, 15(2): 163. doi: 10.1007/s12517-021-09376-2
    [22] LÜ B L, COBOURN W G, BAI Y Q. Development of nonlinear empirical models to forecast daily PM2.5 and ozone levels in three large Chinese cities[J]. Atmospheric Environment, 2016, 147: 209-223. doi: 10.1016/j.atmosenv.2016.10.003
    [23] HUERTAS J I, HUERTAS M E, CERVANTES G, et al. Assessment of the natural sources of particulate matter on the opencast mines air quality[J]. Science of the Total Environment, 2014, 493: 1047-1055. doi: 10.1016/j.scitotenv.2014.05.111
    [24] 吴益玲, 李成名, 戴昭鑫, 等. 城区空气质量指数时空分布特征及影响机制分析[J]. 测绘通报, 2020(4): 81-86. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB202004015.htm

    WU Yiling, LI Chengming, DAI Zhaoxin, et al. Spatial and temporal distribution characteristics and influencing mechanisms of air quality index in urban areas[J]. Bulletin of Surveying and Mapping, 2020(4): 81-86. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB202004015.htm
    [25] 张晓彬, 于渤. 基于皮尔森相关性分析和BP神经网络的北京城市雾霾治理对策[J]. 系统工程, 2023, 41(2): 26-34.

    ZHANG Xiaobin, YU Bo. Beijing urban haze control strategy based on Pearson correlation analysis and BP neural network[J]. Systems Engineering, 2023, 41(2): 26-34.
    [26] 王远, 杜翠凤, 靳文波, 等. 深凹露天矿复环流决定参数准则方程式的建立[J]. 煤炭学报, 2018, 43(5): 1365-1372. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201805021.htm

    WANG Yuan, DU Cuifeng, JIN Wenbo, et al. Establishment of criterion equation for decision parameter of recombination circulation in deep Sunken open-pit mine[J]. Journal of China Coal Society, 2018, 43(5): 1365-1372. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201805021.htm
    [27] 王亮, 廖晓雪, 查梦霞, 等. 基于主成分分析法的松软煤体煤尘润湿特性研究[J]. 煤炭科学技术, 2020, 48(2): 104-109.

    WANG Liang, LIAO Xiaoxue, ZHA Mengxia, et al. Study on wetting characteristics of coal dust in soft coal based on principal component analysis[J]. Coal Science and Technology, 2020, 48(2): 104-109.
    [28] 刘潇, 薛莹, 纪毓鹏, 等. 基于主成分分析法的黄河口及其邻近水域水质评价[J]. 中国环境科学, 2015, 35(10): 3187-3192.

    LIU Xiao, XUE Ying, JI Yupeng, et al. An assessment of water quality in the Yellow River Estuary and its adjacent waters based on principal component analysis[J]. China Environmental Science, 2015, 35(10): 3187-3192.
    [29] 王志明. 哈尔乌素露天煤矿冬季坑底粉尘污染特征及扩散规律[D]. 中国矿业大学, 2021.

    WANG Zhiming. Pollution characteristics and diffusion law of dust at the pit bottom in haerwusu open-pit coal mine in winter[D]. China University of Mining and Technology, 2021.
  • 加载中
图(11) / 表(5)
计量
  • 文章访问数:  61
  • HTML全文浏览量:  8
  • PDF下载量:  29
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-09-16
  • 修回日期:  2024-01-25
  • 刊出日期:  2024-04-30

目录

    /

    返回文章
    返回