Estimating the spatial distribution of soil heavy metals in oil mining area using air quality data

被引:13
|
作者
Song, Yingqiang [1 ]
Kang, Lu [1 ]
Lin, Fan [1 ]
Sun, Na [1 ]
Aizezi, Aziguli [1 ]
Yang, Zhongkang [2 ]
Wu, Xinya [1 ]
机构
[1] Shandong Univ Technol, Sch Civil & Architectural Engn, Zibo 255000, Peoples R China
[2] Shandong Agr Univ, Key Lab Agr Environm Univ Shandong, Coll Resources & Environm, Tai An 271000, Peoples R China
关键词
Air quality; PM2.5; Heavy metals; Hybrid geostatistical method; Soil; YELLOW-RIVER DELTA; HEALTH-RISK; WETLAND SOILS; POLLUTION; SPECTROSCOPY; VEGETATION; PM2.5; WATER; PM10; LEAD;
D O I
10.1016/j.atmosenv.2022.119274
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Air quality is a vital environment variable which determines spatial accumulation of soil heavy metals. It is very important to estimate the contribution of air quality for soil heavy metals in oil mining area. For the end, we collected 116 samples from surface soil of oil mining in the Yellow River Delta (YRD) of China, and analyzed the content of As, Cr, Ni, Pb, and Zn. Furthermore, 40 monitoring stations data of air quality were collected in study area, including CO, NO2, SO2, O-3, PM2.5, and PM10. Spatial estimation and mapping of heavy metals in soil were carried out by hybrid geostatistical models, including multiple linear regression-ordinary kriging (MLROK), support vector machine-ordinary kriging (SVMOK) and random forest-ordinary kriging (RFOK). RFOK exhibited the highest estimation accuracy (R-2) for As (65.76%), Cr (77.85%), Ni (61.47%), Pb (74.64%), and Zn (71.35%) in comparison with other models. And relative R-2 of RFOK improved 30%, while MLROK and SVMOK increased over 100% for Zn (RIo = 121.90% and RIo = 121.64%) based on their original R-2 of machine learning models. In addition, mapping results by RFOK showed the high concentrations of heavy metals were focused in the central and northeastern (As), northern (Cr), northeastern and northwestern (Ni), central and eastern (Pb), and northern (Zn). Especially, compared with vegetation index and topographic factors, PM2.5 is the highest driving variable for As (18.34%) and Zn (12.91%), and CO is the most important variable for Cr (18.22%), Ni (14.28%). The above results indicated that there is a mechanism of sources-receptor relationship between air quality and soil heavy metals, that is, oil well and factory in study area discharge heavy metal particles into the atmosphere, and then enter the soil through atmospheric deposition and precipitation. Enlightened by this study, variable selection should be focused on important sources for the accumulation of heavy metals in study area, who must take decisions to prevent and to early warn heavy metals pollution in mine soil.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Effect of biochar on the bioavailability and transformation of heavy metals in soil of mining area
    Wang, Zhe
    Mi, Zhansheng
    Zheng, Chunli
    Li, Weiping
    Wang, Weida
    Wang, Huimin
    Huagong Jinzhan/Chemical Industry and Engineering Progress, 2019, 38 (06): : 2977 - 2985
  • [22] Spatial Distribution of Heavy Metals and the Environmental Quality of Soil in the Northern Plateau of Spain by Geostatistical Methods
    Santos-Frances, Fernando
    Martinez-Grana, Antonio
    Avila Zarza, Carmelo
    Garcia Sanchez, Antonio
    Alonso Rojo, Pilar
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2017, 14 (06):
  • [23] Spatial distribution of smelter emission heavy metals on farmland soil
    Xing, Weiqin
    Zheng, Yali
    Scheckel, Kirk G.
    Luo, Yongming
    Li, Liping
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2019, 191 (02)
  • [24] Spatial distribution of heavy metals in agricultural soil in Wutan, China
    Li, Yu-Hua
    Liu, Hong-Bin
    Wu, Wei
    2009 INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND INFORMATION APPLICATION TECHNOLOGY, VOL II, PROCEEDINGS, 2009, : 501 - +
  • [25] Spatial Distribution of Heavy Metals in Surface Soil of Zhejiang Pinghu
    Li Qiong
    Hao Chunming
    Liu Huilin
    MECHANICAL AND ELECTRONICS ENGINEERING III, PTS 1-5, 2012, 130-134 : 3773 - +
  • [26] Spatial distribution of smelter emission heavy metals on farmland soil
    Weiqin Xing
    Yali Zheng
    Kirk G. Scheckel
    Yongming Luo
    Liping Li
    Environmental Monitoring and Assessment, 2019, 191
  • [27] Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China
    Xianghua Xu
    Xidong Zhang
    Yuxuan Peng
    Renying Li
    Cuiying Liu
    Xiaosan Luo
    Yongcun Zhao
    Bulletin of Environmental Contamination and Toxicology, 2021, 106 : 33 - 39
  • [28] Spatial distribution, sources apportionment and risk assessment of heavy metals in the Changchun black soil area, China
    Li, Wenwen
    Zhang, Shuke
    Gao, Fan
    Chen, Zhihui
    Jiang, Jie
    Sun, Guo-Xin
    JOURNAL OF HAZARDOUS MATERIALS ADVANCES, 2024, 13
  • [29] Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China
    Xu, Xianghua
    Zhang, Xidong
    Peng, Yuxuan
    Li, Renying
    Liu, Cuiying
    Luo, Xiaosan
    Zhao, Yongcun
    BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY, 2021, 106 (01) : 33 - 39
  • [30] Distribution characteristics of soil heavy metals in farmland soil around the mine area
    Wu, Yinghao
    Chen, Jingying
    Gao, Bai
    PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY, 2016, 60 : 225 - 228