A Comparative Assessment on Soil Environment Quality Based on Chemical Analyses of Heavy Metals
Ning Liu1,2, Feng Tian2, Li Yang3, Wenqian Li2, Haiyan Fan1, Jin Xia1
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1Department of Environmental Science, Jinling College, Nanjing University, Nanjing 210089, China
2State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment,
Nanjing University, Nanjing 210023, China
3School of Geography and Biological Information, Nanjing University of Posts and Telecommunication,
Nanjing 210003, China
Submission date: 2015-04-06
Final revision date: 2015-06-21
Acceptance date: 2015-06-21
Publication date: 2015-09-21
Pol. J. Environ. Stud. 2015;24(5):2045–2054
This study investigated the concentrations of 11 metals in soils sampled in 1994 and 2014 from 17 cities throughout Anhui Province in China. Among the tested metals, Mn had the highest concentration and Hg the lowest. In the past 20 years, soil Cd, Co, Mn, and Cu concentrations demonstrated an increasing trend. In 1994, only Tongling City had a total metal concentration over 1,000 mg/kg, but in 2014, the seriously polluted cities also included Bengbu, Chizhou, Fuyang, Huannai, Huangshan, and Maanshan. Four assessment methods (two pollution indexes and two fuzzy mathematical models) were employed to investigate the soil environment quality of 17 cities. Environmental quality was determined to be Class I (excellent) or Class II (good) for each soil with single-factor index method, and most was identified as Class I for soils with the comprehensive index model. Different from the single-factor index method, the comprehensive index model concerned both the predominant index and average contribution of all pollution indices to integrated environmental quality. Using each of the two fuzzy mathematical methods (single-factor deciding and weighted average models), the environmental risks were determined to be Class I. However, divergence of the membership degree to each pollution class still occurred between the two methods. For fuzzy mathematical methods, membership functions were used to describe the limits between different pollution degrees, and different weights were allocated for the factors according to pollution contribution. Introduction of membership degree and weight of each factor to fuzzy mathematical models made the methods more reasonable in the field of environmental risk assessment.