https://ira.lib.polyu.edu.hk/bitstream/10397/81757/1/Palansooriya_Soil_Amendments_Immobilization.pdf

Soil amendments for immobilization of potentially toxic elements in contaminated soils: A critical review.

Assessment of heavy metal pollution and human health risk in urban soils of steel industrial city (Anshan), Liaoning, Northeast China.

Trace metal contamination in urban soils of China.

The paper provides a complex, critical assessment of heavy metal soil pollution using different indices. Pollution indices are widely considered a useful tool for the comprehensive evaluation of the degree of contamination. Moreover, they can have a great importance in the assessment of soil quality and the prediction of future ecosystem sustainability, especially in the case of farmlands. Eighteen indices previously described by several authors (Igeo, PI, EF, Cf, PIsum, PINemerow, PLI, PIave, PIVector, PIN, MEC, CSI, MERMQ, Cdeg, RI, mCd and ExF) as well as the newly published Biogeochemical Index (BGI) were compared. The content, as determined by other authors, of the most widely investigated heavy metals (Cd, Pb and Zn) in farmland, forest and urban soils was used as a database for the calculation of all of the presented indices, and this shows, based on statistical methods, the similarities and differences between them. The indices were initially divided into two groups: individual and complex. In order to achieve a more precise classification, our study attempted to further split indices based on their purpose and method of calculation. The strengths and weaknesses of each index were assessed; in addition, a comprehensive method for pollution index choice is presented, in order to best interpret pollution in different soils (farmland, forest and urban). This critical review also contains an evaluation of various geochemical backgrounds (GBs) used in heavy metal soil pollution assessments. The authors propose a comprehensive method in order to assess soil quality, based on the application of local and reference GB.

/pdf/pollution-indices-as-useful-tools-for-the-comprehensive-1ezc2tqs9q.pdf

Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination–A review

Heavy metals are natural constituents of soils and their concentration varies depending on parental materials. In the last years, the content of heavy metal in soils has increased due to human activities as: distribution of fertilizers, pesticides, industries, waste disposal and air pollution. Due to these activities the life capacity of soils decreased; especially where the natural background is already high because of natural parental material richness in heavy metal. As a matter of fact it is very important to distinguish between the natural background values and anthropogenic inputs, and to understand that the background values change from area to area and with the scale of the area investigated. There is currently a wide variety of methods used to evaluate soil contamination. To evaluate the soil contamination rate different indexes like Enrichment Factor (EF) and geoaccumulation index (Igeo) can be applied. These indexes are used to assess the presence and intensity of anthropogenic contaminant deposition on surface soil.

https://www.longdom.org/open-access/the-importance-of-enrichment-factor-ef-and-geoaccumulation-index-igeo-to-evaluate-the-soil-contamination-2329-6755-1000237.pdf

The Importance of Enrichment Factor (EF) and Geoaccumulation Index (Igeo) to Evaluate the Soil Contamination

Assessment of heavy metals contamination in urban topsoil from Changchun City, China

. The aim of the present study is to evaluate the potential ecological risk and trend of soil heavy-metal pollution around a coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy-metal pollution. The potential ecological risk in the order of ER(Cd) > ER(Pb) > ER(Cu) > ER(Cr) > ER(Zn) have been obtained, which showed that Cd was the most important factor leading to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, then the fixed number of years exceeding the standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metals and the relationship between sampling points and variables. These findings provided some useful insights for making appropriate management strategies to prevent or decrease heavy-metal pollution around a coal gangue dump in the Yangcaogou coal mine and other similar areas elsewhere.

/pdf/potential-ecological-risk-assessment-and-prediction-of-soil-1z12l6ihws.pdf

Potential ecological risk assessment and prediction of soil heavy-metal pollution around coal gangue dump

Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China.

Rainfall links atmospheric and surficial processes and is one of the most important hydrologic variables. We apply support vector regression (SVR), which has a high generalization capability, to construct a rainfall forecasting model. Before construction of the model, a self-adaptive data analysis methodology called ensemble empirical mode decomposition (EEMD) is used to preprocess a rainfall data series. In addition, the phase-space reconstruction method is implemented to design input vectors for the forecasting model. The proposed hybrid model is applied to forecast the monthly rainfall at a weather station in Changchun, China as a case study. To demonstrate the capacity of the proposed hybrid model, a typical three-layer feed-forward artificial neural network model, an auto-regressive integrated moving average model, and a support vector regression model are constructed. Predictive performance of the models is evaluated based on normalized mean squared error (NMSE), mean absolute percent error (MAPE), Nash–Sutcliffe efficiency (NSE), and the coefficient of correlation (CC). Results indicate that the proposed hybrid model has the lowest NMSE and MAPE values of 0.10 and 14.90, respectively, and the highest NSE and CC values of 0.91 and 0.83, respectively, during the validation period. We conclude that the proposed hybrid model is feasible for monthly rainfall forecast and is better than the models currently in common use.

Wenxi Lu

Papers

Assessment of heavy metals contamination in urban topsoil from Changchun City, China

Potential ecological risk assessment and prediction of soil heavy-metal pollution around coal gangue dump

Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China.

Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction

Identifying the release history of a groundwater contaminant source based on an ensemble surrogate model