Source Apportionment and Model Applicability of Heavy Metal Pollution in Farmland Soil Based on Three Receptor Models

doi:10.21203/rs.3.rs-1103826/v1

Download PDF

Research Article

Source Apportionment and Model Applicability of Heavy Metal Pollution in Farmland Soil Based on Three Receptor Models

https://doi.org/10.21203/rs.3.rs-1103826/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The identification of the source of heavy metal pollution and its quantification are the prerequisite of soil pollution control. The apcs-mlr, UNMIX and PMF models were employed to apportion pollution sources of Cu, Zn, Pb, Cd, Cr and Ni of the farmland soil in vicinity of an abandoned iron and steel plant. The sources, contribution rates and applicability of the models were evaluated. The potential ecological risk index revealed greatest ecological risk from Cd. The results of source apportionment illustrated that apcs-mlr and UNMIX models can verify each other for accurate allocation of pollution sources. The industrial sources were the main sources of pollution (32.41% ~ 38.42%), followed by agricultural sources (29.35% ~ 31.65%), traffic emission sources (21.03% ~ 21.51%), and natural sources of pollution were accounted for smallest proportion (11.2% ~ 14.42%). The PMF model was easily affected by outliers, its fitting degree was not ideal, and it was unable to get more accurate results of source analysis. The combination of multiple models can effectively improve accuracy of pollution source analysis of soil heavy metals. These results can provide a scientific basis for further remediation of heavy metal pollution in farmland soil.

Environmental Engineering

Toxicology

Heavy metals

Source apportionment

APCS-MLR

UNMIX

PMF.

Table 1. Descriptive statistical analysis of soil heavy metals and soil properties (mgkg^-1)

Element	Min	Max	Mean	SD^a	CV^b(%)	Background of Zhejiang^c	Screening value^d	Standard exceeding rate（%）
pH	3.94	5.37	4.74	0.29	6.21	——	——	——
SOM^e	16.76	66.06	41.65	9.52	22.85	——	——	——
Cu	10.80	57.00	30.26	8.39	27.72	17.6	50.00	3.96
Zn	141.40	532.20	271.50	71.71	26.41	70.6	200.00	86.14
Pb	59.50	361.60	151.41	58.30	38.50	23.7	80.00	97.03
Cd	0.11	0.95	0.37	0.15	40.17	0.07	0.30	70.30
Cr	17.30	159.00	47.81	10.05	21.03	52.9	250.00	0.00
Ni	6.99	71.60	39.07	15.25	39.03	24.60	60.00	0.99

Notes: ^aSD stands for standard deviation.

^bCV stands for coefficient of variation, which is the ratio of standard deviation to mean.

^c Soil heavy metal background value of Zhejiang province(Wang et al., 2007).

^dSoil environmental quality risk control standard for soil contamination of agricultural land in China (GB15618-2018).

^eSOM means soil organic matter (gkg^-1).

Table 2. Fitting degree r² values of three receptor models

Element	APCS-MLR	UNMIX	PMF-1 Full data	PMF-2 eliminates outliers
Cu	0.785	0.880	0.984	0.994
Zn	0.820	0.876	0.402	0.448
Pb	0.924	0.948	0.119	0.213
Cd	0.895	0.914	1.000	1.000
Cr	0.983	0.999	0.403	0.610
Ni	0.906	0.917	0.998	0.995

Table 3. Correlation analysis of soil heavy metal content and properties in the study area

Element	pH	SOM	Cu	Zn	Pb	Cd	Cr	Ni
pH	1.000
SOM	-0.228^*	1.000
Cu	-0.020	0.430^**	1.000
Zn	-0.225^*	0.437^**	0.422^**	1.000
Pb	-0.030	0.130	0.346^**	0.405^**	1.000
Cd	-0.190	0.487^**	0.170	0.546^**	0.060	1.000
Cr	0.130	0.090	0.308^**	-0.080	0.010	-0.236^*	1.000
Ni	0.020	0.395^**	0.567^**	0.277^**	0.090	0.266^**	0.204^*	1.000

^*Correlation is significant at the 0.05 level (two-tailed).

^**Correlation is significant at the 0.01 level (two-tailed).

Table 4. Potential ecological risk index of heavy metals in soil

Statistics							RI
Statistics	Cu	Zn	Pb	Cd	Cr	Ni	RI
Min	3.07	2.00	12.55	47.14	0.65	2.57	92.37
Max	16.19	7.54	76.29	407.14	9.79	26.32	489.68
Mean	8.60	3.85	31.94	160.01	2.56	10.69	217.65
SD	2.37	1.01	12.24	63.96	1.24	3.38	67.89

Download PDF

Version 1

posted

You are reading this latest preprint version

Source Apportionment and Model Applicability of Heavy Metal Pollution in Farmland Soil Based on Three Receptor Models

Status:

Version 1

Abstract

Figures

Full Text

Tables

Status:

Version 1