浅谈重金属污染排放统计中三种核算方法的优缺点及适用性条件

通过对重金属污染排放统计中使用的产排污系数、物料衡算和监测数据3种核算方法的优缺点进行分析和讨论,归纳总结3种方法的适用性条件。

白洋淀内不同土地利用类型土壤重金属分布特征与污染评价

【目的】白洋淀作为雄安新区生态共同体的重要组成部分,在“白洋淀生态环境治理和保护规划”中,淀区内陆地将被淹没。为科学评价淹没后陆地对白洋淀水体的环境影响,从淀区不同土地利用类型土壤重金属元素分布特征及污染程度评价两个方面开展了研究工作。【方法】以淀区内现有陆地0~20 cm土壤为研究对象,以砷、汞、镉、铬、铅、镍、铜、锌重金属元素为研究要素,以统计分析、污染负荷指数法和地质累计指数法评价为分析手段,对淀区内陆面重金属元素统计特征和污染程度进行分析和评价。【结果】淀区内陆地土壤重金属元素背景值显著高出区域背景值,污染程度总体为轻度污染;农业用地类型土壤重金属污染元素最多,铜、镉、汞是本区陆地土壤中背景值高、污染贡献最为显著的元素。【结论】淀内陆地土壤重金属元素含量均低于国家标准(GB 15618—2018)和(GB 36600—2018)中土壤筛选或管控的标准,环境承载力仍有一定缓冲容量;重金属元素间的相关性特征主要由地质背景因素控制,但也明显受到土地利用类型改变的影响。

Source and hazard identification of heavy metals in soils of Changsha based on TIN model and direct exposure method

A total of 153 soil samples were collected from Changsha City, China, to analyze the contents of As, Cd, Cr, Cu, Hg, Mn, Ni, Pb and Zn. A combination of sampling data, multivariate statistical method, geostatistical analysis, direct exposure method and triangulated irregular network （TIN） model was successfully employed to discriminate sources, simulate spatial distributions and evaluate children＇s health risks of heavy metals in soils. The results show that not all sites in Changsha city may be suitable for living without remediation. About 9.0% of the study area provided a hazard index （HI）1.0, and 1.9% had an HI2.0. Most high HIs were located in the southern and western areas. The element of arsenic and the pathway of soil ingestion were the largest contribution to potential health risks for children. This study indicates that we should attach great importance to the direct soil heavy metals exposure for children＇s health.

Statistical Approach to Identify Environmental Factors in Controlling Heavy Metal Concentrations in Sediment

Surface sediment samples were collected from 35 locations in Sulaibikhat Bay, Kuwait. Co, Cr, Cu, Ni, Pb and Zn concentrations were determined. Grain sizes, TOC （total organic carbon）, carbonate, mineralogical and environmental data were also determined. Multiple linear regression is applied to the data from the sediment sequential extractions to assess the relative importance of mineralogical and sedimentological factors in controlling heavy metal concentrations in individual chemical fractions （exchangeable, reducible, oxidizable, residual） under different environmental conditions. The analysis shows that grain size, TOC, calcium carbonate and minerals clearly influence heavy metal concentrations. For the exchangeable fraction, clay, grain size and the mineral pyrite are the main factors, whereas for the reducible fraction, TOC is the main factor influencing concentrations ofZn, Pb, Ni, Cu and Cr. For the oxidizable fraction, modelling shows that TOC is the main factor influencing Zn, Ni, Cu, Cr and Co concentrations. The residual fraction concentrations of Zn, Ni, Cr and Co were best predicted by the abundance of sand, with sand content having a negative effect on heavy metal concentrations in this fraction. The statistical techniques in environmental data interpretation are quite useful in cutting down the volume of the data and identifying identical classes which are statistically distinct.

Risk Assessment of Heavy Metal Pollution in Sediments of the Fenghe River by the Fuzzy Synthetic Evaluation Model and Multivariate Statistical Methods

Concentrations of heavy metals in 74 sediment samples from the Fenghe River, which originates from the north of the Qinling Mountains and flows through Xi'an, Shaanxi Province, China, were characterized by employing geographic information system(GIS)mapping, fuzzy synthetic assessment, and multivariate statistical analysis to determine the enrichment characteristics of heavy metals as well as their potential risks of pollution to sediments. Al, Cd, and Co were the major pollutants, with a high enrichment factor(EF) value. Heavy metal concentrations from samples near the paper plant were maintained at a high level. Significant enrichment of Al, Ba, Cr, Ni, Pb, and Co was found in the midstream and downstream, while high concentration of Cu occurred in the headwater stream. Based on the cluster and principal component analyses, sediment metals mainly came from the paper plants, agronomic practices, natural sources, and tourism, with a contribution of 51.59%, 23.01%, 14.21%, and 9.88%, respectively. Sediment pollution assessment explored using fuzzy theory based on the entropy method and toxicity coefficient showed that 26, 32, and 11 sites fell into Class III(slightly polluted), Class IV(moderately polluted), and Class V(heavily polluted), respectively, and their scores of membership degree in the polluted level were on the rise, suggesting a relatively high degree of sediment metal pollution in the study area. Closely related to the excessive industrial and agricultural applications, metal pollution in sediment is necessary to be addressed in the Fenghe River.

Source Apportionment of Heavy Metals in Soils Using Multivariate Statistics and Geostatistics

The main objectives of this study were to introduce an integrated method for effectively identifying soil heavy metal pollution sources and apportioning their contributions, and apply it to a case study. The method combines the principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geostatistics. The case study was conducted in an area of 31 km2 in the urban-rural transition zone of Wuhan, a metropolis of central China. 124 topsoil samples were collected for measuring the concentrations of eight heavy metal elements (Mn, Cu, Zn, Pb, Cd, Cr, Ni and Co). PCA results revealed that three major factors were responsible for soil heavy metal pollution, which were initially identified as "steel production", "agronomic input" and "coal consumption". The APCS technique, combined with multiple linear regression analysis, was then applied for source apportionment. Steel production appeared to be the main source for Ni, Co, Cd, Zn and Mn, agronomic input for Cu, and coal consumption for Pb and Cr. Geostatistical interpolation using ordinary kriging was finally used to map the spatial distributions of the contributions of pollution sources and further confirm the result interpretations. The introduced method appears to be an effective tool in soil pollution source apportionment and identification, and might provide valuable reference information for pollution control and environmental management.