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.
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页数:14
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