Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils

Quantitative source apportionment of heavy metal(loid)s in the agricultural soils of an industrializing region and associated model uncertainty

Heavy metal(loid)s are natural constituents of the Earth's crust, and apportionment of their sources in surface soils is a challenging task. This study evaluated the application of positive matrix factorization (PMF) model, assisted with regression modeling and geospatial mapping, in the quantitative source apportionment of heavy metal(loid)s in the agricultural soils of Handan, a region covering >12,000 km². Obvious enrichment of As, Cd, Cu, Pb, and Zn was found in the surface soils, with Cd alone accounted for 73 % of the overall potential ecological risk. PMF model revealed that Cd (56.9 %) and Pb (47.8 %) in the region's agricultural soils were predominantly contributed by industrial sources, Fe (71.8 %), Cr (60.0 %), V (52.9 %), Cu (50.7 %), Ni (42.2 %), and Mn (41.4 %) were primarily of lithogenic origin, while Co (54.1 %), As (42.9 %), and Zn (40.0 %) mainly came from the mixed sources of natural background, agricultural sources, and vehicle emissions. Uncertainty analysis showed that the contributions of pollution sources to the soil heavy metal(loid)s estimated by PMF model had considerable variations. While quantitative source apportionment of heavy metal(loid)s in soils could be achieved with PMF based on their spatial distributions, combination with emission inventory and reactive transport are probably necessary to obtain more accurate results.

Hydrodynamic impact on trace metals in sediments in the cascade reservoirs, North China

Cascade reservoirs facilitate the effective use of water resources and help to alleviate existing problems of water shortage in drought-prone regions. However, the geochemical behavior and controlling mechanisms of trace metals in response to the operation of cascade reservoirs are relatively unknown. Here, trace metals (As, Cr, Cu, Li, Ni, Pb and Zn) from thirty sediment cores from cascade reservoirs (Panjiakou and Daheiting Reservoirs) in China were evaluated. Multiple methods including geochemical baseline, geostatistical analysis, factor analysis (FA), and positive matrix factorization (PMF), were combined to assess pollution status, identify and quantify potential anthropogenic sources, and determine the influence of hydrodynamic conditions on trace metals distribution. The results indicate that minor enrichment of trace metals appeared in both cascade reservoirs. However, trace metal concentrations exhibited spatial heterogeneity between two cascade reservoirs, and diverse hotspots of different metals were unexpectedly observed. This can be explained by the following three aspects: (1) Metal hotspots were detected upstream of the cascade dams via geostatistical analysis and FA, particularly for naturally sourced metals (As and Li) where dam interception resulted in higher concentrations in the upstream reservoir. (2) PMF analysis identified agricultural, industrial, and natural sources to account for 23.44%, 41.61%, and 34.95%, respectively, to the metal concentrations in the downstream reservoir. Anthropogenic emissions were the dominant factors influencing the spatial variability of Cu, Pb, and Zn between the cascade reservoirs, with higher concentrations observed in the downstream reservoir. (3) The hydrological regime also influenced the redistribution of human-derived metals, where slower flow velocities at river bends resulted in higher deposition of metal-bearing particles. This study shed light on the spatial distribution of trace metals in response to the construction and operation of cascade reservoirs, and it suggests that trace metal hotspots should be monitored to prevent potential contamination in sediments.

Heavy metal(loid)s in the topsoil of urban parks in Beijing, China: Concentrations, potential sources, and risk assessment

Urban parks play an important role in the urban ecosystem and are also used by residents for recreation. The environmental quality of urban park soils might influence human health following long-term exposure. To assess potential sources and pollution risks of heavy metal(loid)s in the topsoil of urban parks, we subjected metal concentrations in soil samples from 121 parks in the Beijing urban area to geostatistical analyses, conditional inference tree (CIT) analyses, ecological risk and human health risk assessment. CIT effectively explained the influence of human activity on the spatial variation and accumulation of soil metal(loid)s and identified the contributions of natural and anthropogenic inputs. The main factors influencing the accumulation of heavy metal(loid)s, including urbanization duration, park age, per capita GDP, industrial output, and coal consumption, were evaluated by CIT. Except for Cr and Ni, the average concentrations of the metal(loid)s tested (Cu, Pb, Zn, Hg, As, and Cd) were higher than the background values. In the urban parks, Ni and Cr derived mostly from soil parent materials. Concentrations of Cu, Zn, Pb, Cd, As, and Hg were strongly associated with human influences, including industrial, agricultural, and traffic activity. After assessing health and ecological risks, we conclude that heavy metal(loid)s in the soil of Beijing urban parks pose no obvious health risk to humans, and the ecological risk is also low.

A partition computing-based positive matrix factorization (PC-PMF) approach for the source apportionment of agricultural soil heavy metal contents and associated health risks

Apportion soil heavy metal sources across large-scale regions is a challenging task. The present study developed a modified receptor model to estimate the contributions of various sources to soil heavy metals and the associated health risks at a large scale. A positive matrix factorization model based on a partition computing approach was employed; the entire study area was divided into several zones for the source apportionment and then calculated together, termed partition computing-PMF (PC-PMF). The agricultural soil in Tianjin, China, was chosen for the case study. The PC-PMF results showed that irrigation, atmospheric deposition and sludge application were the main anthropogenic sources, with contributions of 26.60 %, 19.56 % and 2.86 %, respectively. We subsequently combined PC-PMF with a human health risk assessment model (HHRA) to obtain the human health risk of every source category. The natural background was regarded as a major factor influencing human health in the study area, with contributions of 38.03 % for the noncarcinogenic risk and 28.68 % for the carcinogenic risk. The results indicated that PC-PMF performed better at the source apportionment of soil heavy metals than PMF. This study provides a good example of how the spatial variability can be utilized to reduce the uncertainty in source apportionment.

Source apportionment of soil contamination based on multivariate receptor and robust geostatistics in a typical rural–urban area, Wuhan city, middle China

In this study topsoil samples were collected from 57 sites of Dongxihu District which is a typical Chinese urban–rural combination area, to analyze the causes and effects of 6 heavy elements. (Ni, Pb, As, Cu, Cd, and Hg) Pollution of Enrichment factor, multivariate statistics, geostatistics were adopted to study the spatial pollution pattern and to identify the priority pollutants and regions of concern and sources of studied metals. Most importantly, the study area was creatively divided into central urban, semi-urbanized, and rural areas in accordance with the characteristics of urban development and land use. The results show that the pollution degree of potential ecological risk assessment is Hg>Ni>Cu>As>Cd>Pb, and semi-urban regions> city center> rural areas. Results based on the proposed integrated source identification method indicated that As was probably sourced from agricultural sources (33.99%), Pb was associated with atmospheric deposition (50.11%), Cu was related to industrial source 1 (45.97%), Cd was mainly derived from industrial source 2 (42.97%) and Hg come mainly from industrial source 3 (56.22%). The pollution in semi-urban areas in urbanization need more attention.

Identifying quantitative sources and spatial distributions of potentially toxic elements in soils by using three receptor models and sequential indicator simulation

Determining the reliable source contribution and spatial distribution of potentially toxic elements (PTEs) is a focal issue for soil regulation and remediation. For this purpose, three receptor models, US-EPA positive matrix factorization (EPAPMF), weighted alternating least squares positive matrix factorization (WALSPMF), and non-negative constrained absolutely principle analysis (NCAPCA), were used to a dataset consisting of ten PTEs (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, and Zn) for source apportionment. Hazardous areas of ten PTEs were delineated using sequential indicator simulation (SIS) and uncertainty analysis. Three factors for ten PTEs were derived by three receptor models with a one-to-one correspondence between the factors. To obtain more appropriate results, the three receptor models were combined to calculate the ensemble-average source contributions. As, Co, Cr, Cu, Mn, and Ni were derived from a natural source with ensemble-average contributions higher than 85.72%. Cd, Hg, Pb, and Zn were contributed by both parent material and anthropogenic influence. More than half of Hg concentrations were associated with atmospheric deposition caused by human emissions. The concentrations of 28.04% for Cd, 20.74% for Hg, 43.49% for Pb, and 23.71% for Zn were associated with human inputs including agriculture practice, industrial activities, and vehicle emissions. The maps of spatial distribution generated by the SIS indicated that parent materials controlled the spatial distributions of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. 27.1% and 32.1% of the total area for Cd and Hg were identified as hazardous areas exceeding 1.5 times background values of Shandong province.

An integrated approach to quantifying ecological and human health risks from different sources of soil heavy metals

Heavy metals (HMs) in soil cause adverse effects on ecosystem and human health. Quantifying ecological risk and human health risk (HHR) from sources can determine priority sources and help to mitigate the risks. In this research, geostatistics and positive matrix factorization (PMF) were used to identify and quantify the sources of soil HMs; and then ecological risk and HHR from different sources under woodland, construction land and farmland were quantitatively calculated by combining the potential ecological risk index (RI) and HHR assessment models with PMF model. Taking Jiedong District as an example, four sources were quantitatively apportioned, which were agricultural practices (23.08%), industrial activities (29.10%), natural source (22.87%) and traffic emissions (24.95%). For ecological risk, industrial activities were the greatest contributor, accounting for about 49.71%, 48.11% and 47.15% under construction land, woodland and farmland, respectively. For non-carcinogenic risk, agricultural practices were the largest source under woodland and farmland, while industrial activities were the largest source under construction land. As for carcinogenic risk, no matter which kind of land use, agricultural practices were the largest source. In addition, the health risks of children, including non-carcinogenic and carcinogenic risks, were higher than those of adults, and the trends in health risks for children and adults were similar. The integrated approach was useful to evaluate ecological risk and HHR quantification from sources under different land use, thereby providing valuable suggestions for reducing pollution and protecting human health from the sources.

Ecological risk assessment of mercury and chromium in greenhouse soils

Very little information is available about Hg and Cr evolution in greenhouse soils. This paper presents the results of determining Hg and Cr in greenhouse soils in a semi-arid region in the southern Iberian Peninsula (Almería, Spain), and assessing the enrichment level and the Potential Ecological Risk Index (PERI) according to crop age. Hakanson's approach was used to evaluate the PERI. To investigate the behaviour of Hg and Cr in greenhouse soils over time, samples were grouped into values in soils for blocks according to crop age: 0 years, 5-10 years, 10-20 years, more than 20 years. The results showed that 74% of GS exceeded the obtained background level (37.1 μg kg^-1) for Hg, with 43% (48.9 mg kg^-1) for Cr. Temporal patterns indicated that these elements are accumulating in greenhouse soils and this trend was very significant for Hg. After more than 20 intensive crop-farming years, concentrations and the PERI had clearly increased. Although the ecological risk was moderate, our observations suggest that the farming practices performed in the last 35 years have allowed these metals to accumulate. In fact, the 15% of the studied soils presented a considerable potential risk and were the soils that had been used longer.

Correction of in-situ portable X-ray fluorescence (PXRF) data of soil heavy metal for enhancing spatial prediction

Heavy metal data measured by portable X-ray fluorescence (PXRF), especially by in-situ PXRF, are usually affected by multiple soil factors, such as soil moisture (SM), soil organic matter (SOM), and soil particle size (SPZ). Thus, a correction may be needed. However, traditionally-used correction methods, such as non-spatial linear regression (LR), cannot effectively correct the spatially non-stationary influences of the related soil factors on PXRF analysis. Moreover, these correction methods are not robust to outliers. In this study, robust geographically weighted regression (RGWR) was used to correct in-situ and ex-situ PXRF data of soil Pb in a peri-urban agricultural area of Wuhan City, China. The accuracy of the corrected PXRF data by RGWR was compared with those by non-spatial and spatial but non-robust methods (i.e., LR and GWR). In addition, to find an appropriate method of using the corrected PXRF data for more accurate spatial prediction, we compared robust ordinary kriging with the corrected PXRF data as part of hard data (ROK-CPXRF) and robust ordinary cokriging with the corrected PXRF data as auxiliary soft data (RCoK-CPXRF). Results showed that (i) RGWR obtained higher correction accuracy than LR and GWR on both the in-situ and ex-situ PXRF data; (ii) the accuracy of the RGWR-corrected in-situ PXRF data was increased nearly to that of the RGWR-corrected ex-situ PXRF data; (iii) given the same amount of sample data, ROK-CPXRF obtained higher prediction accuracy than RCoK-CPXRF. It is concluded that the methods suggested in this study may largely promote the application of in-situ PXRF technique for rapid and accurate soil heavy metal investigation in large-scale areas.

An unconstrained ordination- and GIS-based approach for identifying anthropogenic sources of heavy metal pollution in marine sediments

A new method consisting of enrichment factor (EF) determination, nonmetric multidimensional scaling (NMS), and the geographic information system (GIS) technique was firstly developed to identify anthropogenic heavy metal sources in marine sediments of Hong Kong. Firstly, the EF was determined to differentiate between heavy metals originating from human and natural sources. Subsequently, NMS was applied to identify various source patterns of heavy metals, and the NMS score was calculated and spatially interpolated using GIS technology to evaluate the spatial influences of anthropogenic impacts in different areas. The concentrations of heavy metals in sediments of Hong Kong substantially exceeded their background values, demonstrating anthropogenic pollution. Two different types of human sources could be identified via NMS, one representing the industrial pollution discharges in the period from the 1960s to the 1980s before pollution control was introduced and one representing sewage discharge before the Tolo Harbour Action Plan in the mid-1980s.

Street Dust Heavy Metal Pollution Source Apportionment and Sustainable Management in A Typical City-Shijiazhuang, China

Street dust is repeatedly raised by the wind as a secondary suspension, helping heavy metals therein to enter the human body through the respiratory system, harming human health. A detailed investigation was conducted to determine levels and sources of Cd (cadmium), Cr (chromium), Cu (copper), Pb (lead), Zn (zinc), Ni (nick), and Hg (mercury) contamination in street dust from Shijiazhuang, China. The average concentrations of these metals were: Cd, 1.86 mg·kg⁻¹; Cr, 131.7 mg·kg⁻¹; Ni, 40.99 mg·kg⁻¹; Cu, 91.06 mg·kg⁻¹; Pb, 154.78 mg·kg⁻¹, Hg, 0.29 mg·kg⁻¹; and Zn, 496.17 mg·kg⁻¹—all of which were greater than the local soil reference values. The concentrations of the heavy metals were mapped for the three Shijiazhuang ring roads, with the results showing significant differences between each ring. Application of enrichment factors and geoaccumulation indexes showed that there was significant enrichment and accumulation of Cd, Pb, Zn, and Hg. Multivariate statistical analyses showed that Cd, Pb, Zn, and Hg levels were mainly controlled by human activities, while Cr, Ni, and Cu levels were associated with natural sources. Absolute principal component scores with multiple linear regression (APCS-MLR) were applied to facilitate source apportionment. The results showed that the mixed (traffic and industry) group contributed 53.55%, 59.7%, and 62.25% of the Cd, Pb, and Zn concentration, respectively, while the natural sources group contributed 58.01%, 65.09%, and 66.91% of the Cu, Ni, and Cr concentration, respectively. The burning coal group was found to be responsible for 63.38% of the Hg present in the samples. These results provide a useful theoretical basis for Shijiazhuang authorities to address heavy metal pollution management.

Pollution, Source, and Relationship of Trace Metal(loid)s in Soil-Wheat System in Hebei Plain, Northern China

To study the complex migration and transformation of trace metal(loid)s in a soil–wheat system, 225 pairs of surface soil and wheat samples were collected from the Taihang Mountains front plain, Hebei Province, northern China. The concentrations and pools (F1, water-soluble; F2, exchangeable; F3, carbonate-bound; F4, humic acid-bound; F5, Fe–Mn oxide-bound; F6, organic matter-bound; and F7, residual) of Cu, Pb, Zn, Cr, Ni, Cd, and Hg, and the soil properties of the samples were analyzed. The sum of the F1, F2, F3, and F4 proportions of Cd was higher than that of the other trace metal(loid)s, implying that Cd has greater mobility. We found a significant correlation (p < 0.01) between pools of trace metal(loid)s and the corresponding elements in wheat and a significant correlation (p < 0.01) between pools of trace metal(loid)s and pH, cation exchange capacity, clay, and total organic carbon. The results of principle component analysis (PCA)indicated that Cr, Ni and As mainly come from natural sources and Cu, Pb, Zn, and Cd from mixed groups related to farming and industry, Hg come from the coal burning. In addition, the total target hazard quotients showed the presence of harmful levels of trace metal(loid)s in wheat.

Improved heavy metal mapping and pollution source apportionment in Shanghai City soils using auxiliary information

Soil heavy metal pollution can be a serious threat to human health and the environment. The accurate mapping of the spatial distribution of soil heavy metal pollutant concentrations enables the detection of high pollution areas and facilitates pollution source apportionment and control. To make full use of auxiliary soil properties information and show that they can improve mapping, a synthesis of the Bayesian Maximum Entropy (BME) theory and the Geographically Weighted Regression (GWR) model is proposed and implemented in the study of the Shanghai City soils (China). The results showed that, compared to traditional techniques, the proposed BME-GWR synthesis has certain important advantages: (a) it integrates heavy metal measurements and auxiliary information on a sound theoretical basis, and (b) it performs better in terms of both prediction accuracy and implementation flexibility (including the assimilation of multiple data sources). Based on the heavy metal concentration maps generated by BME-GWR, we found that the As, Cr and Pb concentration levels are high in the eastern part of Shanghai, whereas high Cd concentration levels were observed in the northwestern part of the city. Organic carbon and pH were significantly correlated with most of the heavy metals in Shanghai soils. We concluded that Cd pollution is mainly the result of agricultural activities, and that the Cr pollution is attributed to natural sources, whereas Pb and As have compound pollution sources. Future studies should investigate the implementation of BME-GWR in the case of space-time heavy metal mapping and its ability to integrate human activity information and soil category variables.