Integrated system for population dose calculation and decision making on protection measures in case of an accident with air emissions in a nuclear power plant

Accumulation, potential risk and source identification of toxic metal elements in soil: a case study of a coal-fired power plant in Western China

Coal-fired power plants (CPP) usually release massive numerous amounts of potentially toxic metal(loid)s (PTMs) into nearby ecosystems. There have been relatively few studies targeted on the ecological influences of PTMs related to the CPP in arid area. In this work, the distribution pattern, source apportionment and environmental risks of As, Cd, Cr, Hg, Pb and a couple of seldom monitored PTMs (Se, Zn, Co, Cu, Fe, Mn and Ni) in the soils near a coal electricity integration base were investigated in Hami, a city in northwestern China. Nemerow synthesis pollution index, geo-accumulation index and ecological risk index were used to assess pollution state of these PTMs in soils, and ordinary Kriging interpolation was used to analyze the spatial distribution for these elements. Methods of CA, PCA, CA and PAM were carried out for quantitative source analysis. The research outcome includes: (1) the contents of individual PTMs in most samples were greater than the background values, the pollution degrees of Se, Pb, Hg, Cd and As were significant, and some areas exceeded the warning threshold value; (2) the main sources of these PTMs were natural sources (35%), coal mine sewage (11%), atmospheric release during coal combustion (21%), dust generated from coal and combustion products (33%); (3) attention should be paid to the open-pit coal mines, shaft coal mines and ash dumps where the contents of metal elements were significantly polluted; and (4) wind is the main driving forces of PTMs migration in arid areas.

Distribution and health risk assessment of potentially toxic elements in soils around coal industrial areas: A global meta-analysis

Coal production and utilization are recognized as two principal sources of potentially toxic elements in the environment. Here the published literature (2008-2018) was searched to collect data on As, Ni, Cd, Cu, Cr, Hg, Pb and Zn concentrations in soils near different types of coal industrial areas such as coal mines, thermal power plants, coal chemical plants, coal mining cities and coal waster piles. The contamination levels of soils and associated health risks were assessed using global reference materials and multiple contamination indices. The results revealed that average concentrations of potentially toxic elements varied widely, yet most of them exceeded global averages in background soils and upper continental crust concentrations. Spatial distribution analysis suggested the concentrations of potentially toxic elements varied according to coalification and combustion conditions. Higher concentrations were found in Southeast Asia, South Europe, and North Africa compared with other regions. Assessment of the geoaccumulation index revealed that contamination levels of Cd and Hg were higher than those of other elements. In particular, Ni, Cd, Zn, and Hg were most likely to accumulate in soils near coal mining areas, while Cd and Hg tended to accumulate near coal chemical plants. Regarding non-carcinogenic risks, oral ingestion was the major pathway of exposure to potentially toxic elements in coal industry-associated soils, followed by dermal contact and inhalation. Tolerable non-carcinogenic risk of potentially toxic elements and relatively high carcinogenic risks of As were observed. Children were most vulnerable to non-carcinogenic risks, while the carcinogenic risks estimated for adult and children populations were similar. Accordingly, As should be designated as top candidates for priority control to protect human health in the vicinity of coal industry-associated areas. This study provides timely information for developing control and management strategies to reduce soil contamination by potentially toxic elements in different types of coal industrial areas.

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.