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] Spatial distribution of heavy metals in soil, water, and vegetables of farms in Sanandaj, Kurdistan, Iran
    Afshin Maleki
    Hassan Amini
    Shahrokh Nazmara
    Shiva Zandi
    Amir Hossein Mahvi
    Journal of Environmental Health Science and Engineering, 12
  • [22] The spatial distribution and source apportionment of heavy metals in soil of Shizuishan, China
    Bai, Yiru
    Zhang, Yuhan
    Liu, Xu
    Wang, Youqi
    ENVIRONMENTAL EARTH SCIENCES, 2023, 82 (21)
  • [23] Sources analysis and risk assessment of heavy metals in soil in a polymetallic mining area in southeastern Hubei based on Monte Carlo simulation
    Wang, Jing
    Wang, Bo
    Zhao, Qibin
    Cao, Jinnan
    Xiao, Xiao
    Zhao, Di
    Chen, Zhenya
    Wu, Di
    ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY, 2025, 290
  • [24] Spatial distribution of heavy metals and their potential sources in the soil of Yellow River Delta: a traditional oil field in China
    Xiongyi Miao
    Yupei Hao
    Fawang Zhang
    Shengzhang Zou
    Siyuan Ye
    Zhouqing Xie
    Environmental Geochemistry and Health, 2020, 42 : 7 - 26
  • [25] Analysis of Physio-chemical Parameters and Distribution of Heavy Metals in Soil and Water of Ex-Mining Area of Bestari Jaya, Peninsular Malaysia
    Ashraf, Muhammad Aqeel
    Maah, Mond Jamil
    Yosoff, Ismail
    ASIAN JOURNAL OF CHEMISTRY, 2011, 23 (08) : 3493 - 3499
  • [26] Assessment of air pollution around coal mining area: Emphasizing on spatial distributions, seasonal variations and heavy metals, using cluster and principal component analysis
    Pandey, Bhanu
    Agrawal, Madhoolika
    Singh, Siddharth
    ATMOSPHERIC POLLUTION RESEARCH, 2014, 5 (01) : 79 - 86
  • [27] Environmental Implications of the Soil-to-Groundwater Migration of Heavy Metals in Mining Area Hotspots
    Veskovic, Jelena
    Onjia, Antonije
    METALS, 2024, 14 (06)
  • [28] Effect of Heavy Metals on Soil Enzyme Activity at Different Field Conditions in Middle Spis Mining Area (Slovakia)
    Angelovicova, Lenka
    Lodenius, Martin
    Tulisalo, Esa
    Fazekasova, Danica
    BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY, 2014, 93 (06) : 670 - 675
  • [29] Distribution of heavy metals in soil and accumulation in plants at an agricultural area of Umudike, Nigeria
    Ogbonna, Princewill C.
    Odukaesieme, Chibuike
    Teixeira da Silva, Jaime A.
    CHEMISTRY AND ECOLOGY, 2013, 29 (07) : 595 - 603
  • [30] 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 - +