ORIGINAL RESEARCH
Calculating Environmental Background Value:
A Comparative Study of Statistical Versus
Spatial Analyses
More details
Hide details
1
School of Resources and Civil Engineering, Suzhou University, Anhui, China
National Engineering Research Center of Coal Mine Water Hazard Control, Anhui, China
Submission date: 2017-11-28
Final revision date: 2018-01-24
Acceptance date: 2018-01-27
Online publication date: 2018-07-31
Publication date: 2018-11-20
Corresponding author
Sun Linhua
School of Resources and Civil Engineering, Suzhou University, Anhui 234000, China;
National Engineering Research Center of Coal Mine Water Hazard Controlling, Anhui 234000, China, 49# Bianhe Road, Suzhou City, Anhui Province, China, 234000 Suzhou, China
Pol. J. Environ. Stud. 2019;28(1):197-203
KEYWORDS
TOPICS
ABSTRACT
Local environmental background is important for environmental management. In this study,
lead concentrations of shallow groundwater samples from the urban area of Suzhou in Anhui
Province, China were measured and analyzed by statistical and spatial analyses for calculating the
environmental background value. The results show that the lead concentrations in the groundwater
range from 4.16-11.5 μg/L, and all of the samples were classified to be Class III or better according
to the groundwater quality standard of China. The samples have medium coefficient of variation and
low p-values of normal distribution test, suggesting that it may have been influenced by anthropogenic
activities, which was further demonstrated by the consistency of the distribution of the samples with high
lead concentrations and the areas with high density of transportation, as well as the high-low cluster of
the spatial autocorrelation analysis. The environmental background values have been calculated to be
3.74-8.62 and 3.48-10.3 μg/L with box plot and spatial autocorrelation analyses, respectively. The study
demonstrated that for calculating the environmental background value, the statistical and spatial methods
should be chosen according to the current state – especially pre-consideration about the distribution of the
elements or pollutants.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
CITATIONS (8):
1.
Estimation of background concentrations of macro- and trace elements in an aquatic plant as a basis for the passive biomonitoring of pollution
Ludmiła Polechońska, Agnieszka Klink
Science of The Total Environment
2.
Neutron activation analysis and machine learning models for elemental characterization of archaeological pottery
Wael M. Badawy, Vladimir Yu. Koval, Maksim V. Bulavin, Mohamed A. Soliman
The European Physical Journal Plus
3.
Estimation of natural background levels of heavy metals and major variables in groundwater to ensure the sustainable supply of safe drinking water in Fereidan, Iran
Vahab Amiri, Nassim Sohrabi, Razyeh Lak, Gholamreza Tajbakhsh
Environment, Development and Sustainability
4.
Assessing heavy metal pollution hazard in sediments of Lake Mariout, Egypt
Sherif Ahmed Abu El-Magd, T.H. Taha, H.H. Pienaar, P. Breil, R.A. Amer, Ph Namour
Journal of African Earth Sciences
5.
An integrated statistical-graphical approach for the appraisal of the natural background levels of some major ions and potentially toxic elements in the groundwater of Urmia aquifer, Iran
Vahab Amiri, Mohammad Nakhaei, Razyeh Lak, Peiyue Li
Environmental Earth Sciences
6.
Water chemistry and estimation of local geochemical background values of elements in headwater streams of Ken–Betwa catchment of Yamuna River, India
Harish Kumar, Amrita Sarkar, Utsa Singh, Nisha Singh, Sumit Jain, Archisman Dutta
Environmental Earth Sciences
7.
Occurrence of palladium and platinum in human scalp hair of adolescents living in urban and industrial sites
F. Lo Medico, D. Varrica, M.G. Alaimo
Science of The Total Environment
8.
Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8/9 to establish background levels
Magboul M. Sulieman, Fuat Kaya, Ali Keshavarzi, Abdullahi M. Hussein, Abdullah S. Al-Farraj, Eric C. Brevik
CATENA