Source identification and spatial distribution of metals in soils in a typical area of the lower Yellow River, eastern China

Identifying potentially contaminated areas with MaxEnt model for petrochemical industry in China

The presence of heavy metal and organic pollutants in wastewater effluents, flue gases, and even solid wastes from petrochemical industries renders improper discharges liable to posing threats to the ecological environment and human health. It is beneficial for pollution control to find out the regional distribution of contaminated sites. This study explored the relationship between the petrochemical contaminated areas and natural, socio-economic, and traffic factors. Ten indicators were selected as input variables, and the MaxEnt model was conducted to identify the potentially contaminated areas. Moreover, among these 10 variables, the factors that have the great impact on the results were determined according to the contribution of variables. The results showed that the MaxEnt model performed well with AUC of 0.981 ± 0.004, and 90% of the measured contaminated sites was located in areas with medium and high probability of contamination in the prediction results. The map of potentially contaminated areas indicated that the areas with high probability of contamination were distributed in Yangtze River Delta, Beijing, Tianjin, southern Guangdong, Fujian coastal areas, central Hubei and northeast Hunan, central Sichuan, and southwest Chongqing. The responses of variables presented that high probability of petrochemical contamination tended to appear in cities with developed economy, dense population, and convenient transportation. This study presents a novel way to identify the potentially contaminated areas for petrochemical sites and provides a theoretical basis to formulate future management strategies.

Predicting the spatial pollution of soil heavy metals by using the distance determination coefficient method

Identifying the spatial distribution of heavy metal concentrations is a prerequisite for soil contamination assessment and control. In this study, soil surface samples (0-20 cm) were collected in Wuwei, China, and heavy metal concentrations were determined. The LUR (land use regression) model was used to simulate the spatial distribution of seven heavy metal concentrations in the study area, considering various factors, and the results were compared with ordinary kriging (OK) interpolation. Based on A Distance Decay REgression Selection Strategy (ADDRESS), the distance-coefficient of determination (DCD) was proposed to more easily and accurately select the optimal buffer of the relevant covariates. The simulation results showed that the adj R² values of the LUR models of the remaining heavy metals were all above 0.6, and the empirical comparison showed that LUR was better than OK. The variables that showed a more significant impact on soil heavy metal concentrations in the research area included road length, normalized difference vegetation index (NDVI), and soil sample nutrients around the sample site. In the research area, the concentration of heavy metals in the soil was greatly affected by motorway, primary roads, and secondary roads in the range of 1.2-2.1 km (r > 0.5), while building and As, Cu, Pb and Zn in the range of 3.6-4.8 km had a significant correlation (r > 0.5). This study provides scientific evidence and basic information for the prevention and control of heavy metal contamination and human health risk assessment management in arid zones.

Characterizing the variation of particles in varied sizes from a container truck in a port area

The transportation of container trucks in urban areas not only frequently causes traffic jams but also produces severe air pollution. With regard to this consideration, measurements of particle concentrations and traffic volume on different polluted days were carried out to discover the varied characteristics of particles from container truck transportation in the port area. Based on the original data, descriptive statistics were performed firstly to reveal the statistical characteristics of particle number concentrations (PNC). The Kolmogorov-Smirnov test as well as the Anderson-Darling test was adopted to identify the "best-fit" distributions on PNC data while the corresponding maximum likelihood estimation was conducted to estimate the parameters of the identified distribution. Additionally, the Pearson correlation analysis and principal component analysis were performed respectively to reveal the relationships between traffic volume and PNC. The results showed that on a hazy day, PNC levels in the morning were generally higher than those in the afternoon, while on a non-hazy day, the results were opposite. Particles in all sizes on a non-hazy day and larger than 0.5 μm on a hazy day were verified to fit the lognormal distribution. In contrast to the particles below 2 μm, the particles above 2 μm exhibited higher correlations with the traffic flow of a container truck in the morning on a hazy day. These results indicate the importance of reducing air pollution from a container truck and provide policymakers with a foundation for possible measures in a port city.

Spatial distribution and source analysis of heavy metals in soils influenced by industrial enterprise distribution: Case study in Jiangsu Province

Heavy metal pollution is frequent in China and has received increasing attention globally. This study investigated the influence of Chinese industrialization and urbanization on soil environmental quality. Soil samples from Jiangsu Province were collected, the Cd, Pb, Cr, Cu, Zn, Hg, and As contents were measured, and their spatial variability structure, spatial distribution pattern, and pollution degree were analyzed. The mean values of Hg, Cd, Pb, Cr, Cu, and As were all higher than the background values in Jiangsu Province. Cr and As levels represented moderate pollution, Cu, Zn, Cd, and Pb represented mild pollution, Cr and As represented slight pollution, and Hg was not a pollutant. Spatial distribution patterns were both zonal and concentrated in nature. High concentrations of heavy metals were distributed in developed cities and industrial parks along the Yangtze River. Soil heavy metal pollution showed a decreasing trend from south to north, consistent with the economic gradient. Industrialization had the greatest influence on the spatial heterogeneity of heavy metal pollution. Cr, Cu, Zn, and As were affected by both natural and anthropogenic sources, while Cd and Pb were mainly affected by the latter. Hg was mainly derived from industrial activities such as petrochemical production. There was spatial consistency between industrial enterprise distribution and soil heavy metal pollution with a tendency toward composite pollution accumulated by multiple elements in the soil surrounding industries.

An integrated approach to identify quantitative sources and hazardous areas of heavy metals in soils

Identifying quantitative sources and hazardous areas of heavy metals is a crucial issue for soil management. For this purpose, an integrated approach composed of finite mixture distribution modeling (FMDM), positive matrix factorization (PMF) and sequential Gaussian co-simulation (SGCS) was proposed. FMDM was used to establish background standards and pollution thresholds. PMF supported by FMDM background standards was applied to estimate the source apportionment. Hazardous areas of single metals were delineated using SGCS with FMDM pollution thresholds and uncertainty analysis, and overall hazardous areas were defined by the presence of multiple metals. This integrated approach was applied to a dataset of seven metals as a case study. FMDM indicated that the distributions of Cr, Cu, Ni, and Zn were fitted to two-dimensional mixture distributions, representing a background distribution and a moderately polluted distribution. The distributions of Cd, Hg, and Pb were composed of a three-component lognormal mixture distribution, corresponding to the background, moderate, and high pollution distributions. Three sources were apportioned. Cr, Cu, Ni, and Zn were dominated by parent materials. Parent materials contributed 52.6%, 45.8%, and 81.9% of Cd, Hg, and Pb concentrations, respectively. Human emissions from coal combustion, industrial work and traffic had significant influences on Hg, Cd, and Pb, with contributions of 49.8%, 26.9%, and 15.6%, respectively. Agricultural practices were exclusively associated with 20.5% of Cd. Overall, hazardous areas exceeding moderate pollution thresholds covered 17.4% of the total area, corresponding to urban areas and industrial sites, whereas overall hazardous areas above high pollution thresholds were limited to 0.01% of the total area.

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

Absolute principal component score/multiple linear regression (APCS/MLR) and positive matrix factorization (PMF) were applied to a dataset consisting of 10 heavy metals in 300 surface soils samples. Robust geostatistics were used to delineate and compare the factors derived from these two receptor models. Both APCS/MLR and PMF afforded three similar source factors with comparable contributions, but APCS/MLR had some negative and unidentified contributions; thus, PMF, with its optimal non-negativity results, was adopted for source apportionment. Experimental variograms for each factor from two receptor models were built using classical Matheron's and three robust estimators. The best association of experimental variograms fitted to theoretical models differed between the corresponding APCS and PMF-factors. However, kriged interpolation indicated that the corresponding APCS and PMF-factor showed similar spatial variability. Based on PMF and robust geostatistics, three sources of 10 heavy metals in Guangrao were determined. As, Co, Cr, Cu, Mn, Ni, Zn, and partially Hg, Pb, Cd originated from natural source. The factor grouping these heavy metals showed consistent distribution with parent material map. 43.1% of Hg and 13.2% of Pb were related to atmosphere deposition of human inputs, with high values of their association patterns being located around urban areas. 29.6% concentration of Cd was associated with agricultural practice, and the hotspot coincided with the spatial distribution of vegetable-producing soils. Overall, natural source, atmosphere deposition of human emissions, and agricultural practices, explained 81.1%, 7.3%, and 11.6% of the total of 10 heavy metals concentrations, respectively. Receptor models coupled with robust geostatistics could successfully estimate the source apportionment of heavy metals in soils.