Spatial-temporal analysis and background value determination of molybdenum concentration in basins with high molybdenum geochemical background - A case study of the upper Yi River basin.

The environmental background value of the river section is important. It can be used to evaluate the effect of pollution control of the upstream of that river section, analyze the trend of environmental pollution, and assist the government to make decisions. Yi river is the main tributary of the Yellow River. In the headwaters of the Yi river, there are two very large molybdenum mines with a history of mining and smelting of many years. This area is also a region with a high molybdenum geochemical background. Using the collected regional molybdenum geochemical map, historical monitoring data, sampling data, remote sensing image, and spatial information of mineral enterprises, we found two reasons of why the molybdenum concentration is unusual in the basin. The first reason is the area is a high molybdenum region. The second reason is that the inherent solubility of molybdenum in the soil is changed due to human engineering activities. In this paper, we did a linear fitting on the soil samples and water samples collected from the natural areas and areas affected by human mining activities, and established a leaching model. By comparing the leaching capability of molybdenum in the soil of different areas, we found that the molybdenum release capability in areas affected by human mining was much higher than that in natural areas. Finally, this paper proposed a method to analyze the contribution rate of molybdenum concentration of this river section, using a combination of the leaching model and the D8 algorithm. The experimental results showed that the contribution rate of natural factors and human influence factors at the exit section of Yi River was 81.38% and 18.62% respectively. The background molybdenum concentration in this section was 0.16 mg/L.

A chemical status predictor. A methodology based on World-Wide sediment samples.

As a consequence of the limited resources of underdeveloped countries and the limited interest of the developed ones, the assessment of the chemical quality of entire water bodies around the world is a utopia in the near future. The methodology described here may serve as a first approach for the fast identification of water bodies that do not meet the good chemical status demanded by the European Water Framework Directive (WFD). It also allows estimating the natural background (or reference values of concentration) of the areas under study using a simple criterion. The starting point is the calculation the World-Wide Natural Background Levels (WWNBLs) and World-Wide Threshold Values (WWTVs), two indexes that depend on the concentration of seven elements present in sediments. These elements, As, Cd, Cr, Cu, Ni, Pb and Zn, have been selected taking into account the recommendations of the UNEP (United Nations Environment Programme) and USEPA (United States Environmental Protection Agency), that describe them as elements of concern with respect to environmental toxicity. The methodology has been exemplified in a case study that includes 134 sediment samples collected in 11 transitional water bodies from 7 different countries and 4 different continents. Six of the water bodies considered met the good chemical status demanded by the WFD. The rest of them exceeded the reference WWTVs, at least for one of the elements. The estuaries of the Nerbioi-Ibaizabal (Basque Country) and Cavado (Portugal), the sea inlet of Río San Pedro (Spain), the Sepetiba Bay (Brazil) and the Yucateco lagoon (Mexico) belong to that group.

A methodology for evaluating the relative pollution level of metal pollution in surface sediments of rivers based on the statistical results of relevant literatures covering world-wide rivers.

Due to the intervention of human activities, the background values of riverbed sediment exhibit spatiotemporal variability, which can affect the accuracy of risk assessment results. Using risk assessment that do not rely on background values is an executable alternative to avoid such problems. In this study, a relative pollution level assessment (RPLA) method which was based on the statistical results of relevant literatures was proposed. This method includes a four-step data processing procedure to extract the evaluation indexes of relative pollution degree of pollutants in environment and a series of relative pollution status assessment methods to evaluate the overall relative pollution level and regional difference of world-wide rivers. To demonstrate how to use RPLA method, 310 relevant literatures covering world-wide rivers were selected. And the ambient background value (x̅), the world-wide threshold values (WWTV) and the relative pollution grades (LEVEL I ∼ IV) of 9 target metals (Cr, Ni, Cu, Zn, As, Cd, Pb, Sb and Tl) in riverbed surface sediments of world-wide rivers were extracted and used for evaluation. Moreover, the stability and applicability of RPLA method were evaluated. Results show that the evaluation results of RPLA method are robust and comparable with traditional evaluation method.

Multipath diffusion process and spatial accumulation simulation of Cd in lead-zinc mining areas.

This study combined process simulation and actual measurement to construct a multipath diffusion and spatial accumulation model of Cd in a typical lead-zinc mining area through accuracy and root mean square error(RMSE) analysis. The results indicated that (1) the diffusion of Cd was in a quadratic inverse proportional relationship with the distance from the pollution source within watershed. The average annual atmospheric Cd sedimentation in study area was 0.71 * 10-6 g and the contribution of runoff diffusion to Cd exceeded 80%. (2) With the increase in the concentration range of Cd content (k) carried by unit runoff sediment, the model accuracy and RMSE showed decreasing trends. However, when the lower and upper limits of k were 10% and 90%, the model accuracy reached 75%. (3) Two sub-watersheds with same dominant wind direction but different runoff directions were selected to verify the model accuracy, indicating that the model construction method can precisely simulate the spatial accumulation of Cd in similar mining areas. The results provide a scientific basis for the prevention of heavy metal diffusion in lead-zinc mines. Future research should focus on the migration pathways of heavy metals through vertical infiltration caused by rainfall to further optimise the model structure and accuracy.

Spatial response relationship between mining and industrial activities and eco-environmental risks in mineral resource-based areas.

Mining and industrial activities in mineral resource-based areas are important sources of potentially toxic elements (PTEs) in the soil, which lead to spatial heterogeneity in regional eco-environmental risks. In this study, we analysed the spatial response relationship between mining and industrial activities and eco-environmental risks using Anselin local Moran's I index and bivariate local Moran's I index. The results showed that the proportions of moderate, moderate to strong, and strong pollution of PTEs in the study area reached 30.9%. The high clusters of PTEs ranged from 5.4 to 13.6%, and were mainly distributed around cities. The influence of different types of metal mines on eco-environmental risks was nonferrous metal mines > precious metal mines > ferrous metal mines. In turn, that of different pollution enterprises was manufacturing industry > other industries > power and thermal industries. Our research demonstrates that there was a significant spatial response relationship between densities of mines and enterprises and eco-environmental risk level. High-density metal mines (5.3/100 km2) and high-density pollution enterprises (10.3/100 km2) resulted in the local high risk. Consequently, this study provides a basis for regional eco-environmental risk management of mineral resource-based areas. With the gradual depletion of mineral resources, high-density pollution enterprise area should be paid more attention to, which would pose a threat not only to the environment but also to population health.

Forecasting China's natural gas external dependence under the "Dual Carbon" goals by a new grey model.

As a low-carbon and cost-effective clean energy source, natural gas plays an important role in achieving China's "Dual Carbon" target. In this article, a new three-parameter discrete grey prediction model is used to simulate and forecast the production and consumption of natural gas in China from the perspective of background value optimization. Then the minimum mean absolute percentage error as the objective function from the perspective of fractional order cumulative generation in the real number field. Last, a fractional order in the real number field three parameter discrete grey prediction model TDGM(1,1,z,r(R)) is constructed under the condition of optimal background value. Then we use the model to simulate and predict China's Natural Gas External Dependence (NGED) under the "Dual Carbon" target. The results show that the performance of the new model is better than that of the traditional model GM(1,1) and DGM(1,1), thus proving the practicability and effectiveness of the new model. Put forward relevant policy suggestions according to the prediction results of China's NGED, and provide decision-making reference for the Chinese government to achieve the "Dual Carbon" goals.

Use of artificial neural network to evaluate cadmium contamination in farmland soils in a karst area with naturally high background values.

In recent years, the naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil Cd content and bioavailability among different parent materials, the previous land classification scheme based on total soil Cd content as the classification standard, has certain shortcomings. This study aims to explore the factors influencing soil Cd bioavailability in typical karst areas of Guilin and to suggest a scientific and effective farmland use management plan based on the prediction model. A total of 9393 and 8883 topsoil samples were collected from karst and non-karst areas, respectively. Meanwhile, 149 and 145 rice samples were collected together with rhizosphere soil in karst and non-karst areas, respectively. The results showed that the higher CaO level in the karst area was a key factor leading to elevated soil pH value. Although Cd was highly enriched in karst soils, the higher pH value and adsorption of Mn oxidation inhibited Cd mobility in soils. Conversely, the Cd content in non-karst soils was lower, whereas the Cd level in rice grains was higher. To select the optimal prediction model based on the correlation between Cd bioaccumulation factors and geochemical parameters of soil, artificial neural network (ANN) and linear regression prediction models were established in this study. The ANN prediction model was more accurate than the traditional linear regression model according to the evaluation parameters of the test set. Furthermore, a new land classification scheme based on an ANN prediction model and soil Cd concentration is proposed in this study, making full use of the spatial resources of farmland to ensure safe rice consumption.

Forecasting CO2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China.

Accurate predictions of CO2 emissions have important practical significance for determining the best measures for reducing CO2 emissions and accomplishing the target of reaching a carbon peak. Although some existing models have good modeling accuracy, the improvement of model specifications can provide a more accurate grasp of a system's future and thus help relevant departments develop more effective targeting measures. Therefore, considering the shortcomings of the existing grey Bernoulli model, in this paper, the traditional model is optimized from the perspectives of the accumulation mode and background value optimization, and the novel grey Bernoulli model NFOGBM(1,1,α,ß) is constructed. The effectiveness of the model is verified by using CO2 emissions data from seven major industries in Shaanxi Province, China, and future trends are predicted. The conclusions are as follows. First, the new fractional opposite-directional accumulation and optimization methods for background value determination are effective and reasonable, and the prediction performance can be enhanced. Second, the prediction accuracy of the NFOGBM(1,1,α,ß) is higher than that of the NGBM(1,1) and FANGBM(1,1). Third, the forecasting results show that under the current conditions, the CO2 emissions generated by the production and supply of electricity and heat are expected to increase by 23.8% by 2030, and the CO2 emissions of the other six examined industries will decline.

Effects of the virgin forests to the dissolved organic matters in small forested watershed: a case study of the Tangwang River in Northeast China.

Making clear of the background values of different watersheds is an important mission for water resource management and assessment. The background values of the virgin forested watershed with less human activities sometimes have high COD values. In order to investigate this issue, this study take the Tangwang River watershed as a study case, analyzing the monitoring data of COD, BOD, and DO from the year of 2014 to 2018, and taking the water samples in the wet, normal, and dry season of 2019 to make clear the relationships between COD and DOM, the composition, and source of DOM. The non-degradation and terrigenous source DOM contributed to the most of COD values through the fitting analysis. The DOM in the Tangwang River mostly consisted of the humic-like acid and fulvic-like acid. Along with the river, the concentration of DOM had a little decrease, but the composition of DOM did not change obviously, so that the DOM coming from the virgin forest did not degrade in the river, and the river water only had the dilution effect with the tributaries fed in spatially. The organic matters in the virgin forest were easily washed away, so that the DOM concentration had an increase in the wet season temporally. The background COD values of the small virgin forested watershed were usually high as for the leaching effects of the organic matters from the forest, which was worthy noted when conducting the management project of this kind of watershed, in order to adjust measures to local conditions.

The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China.

High concentrations of potentially toxic elements (PTE) create global environmental stress due to the crucial threat of their impacts on the environment and human health. Therefore, determining the concentration levels of PTE and improving their prediction accuracy by sampling optimization strategy is necessary for making sustainable environmental decisions. The concentrations of five PTEs (Pb, Cd, Cr, Cu, and Zn) were compared with reference values for Shanghai and China. The prediction of PTE in soil was undertaken using a geostatistical and spatial simulated annealing algorithm. Compared to Shanghai's background values, the five PTE mean concentrations are much higher, except for Cd and Cr. However, all measured values exceeded the reference values for China. Pb, Cu, and Zn levels were 1.45, 1.20, and 1.56 times the background value of Shanghai, respectively, and 1.57, 1.66, 1.91 times the background values in China, respectively. The optimization approach resulted in an increased prediction accuracy (22.4% higher) for non-sampled locations compared to the initial sampling design. The higher concentration of PTE compared to background values indicates a soil pollution issue in the study area. The optimization approach allows a soil pollution map to be generated without deleting or adding additional monitoring points. This approach is also crucial for filling the sampling strategy gap.

Microplastic abundance and distribution in a Central Asian desert.

Microplastic pollution is widespread, affecting even the remotest places on Earth. However, observational data on microplastic deposition in deserts, which comprise 21% of the total land area, are relatively rare. The current study aims to address the knowledge gap in terms of microplastic distribution in Asian deserts. The Badain Jaran Desert in Central Asia is the second largest desert in China. We investigated microplastic distribution and deposition on dunes and lakes of this desert. Microplastics were extracted from surface sediments to determine their characteristics and polymer types by microscopic inspection and µ-FTIR. The abundance of microplastics (detection limit is approximately 40 µm) in the uninhabited area ranged from 0.7 ± 1.5 to 11.7 ± 15.5 items/kg, with an average of 6.0 ± 15.4 items/kg. Fragments and fibers accounted for 77% and 23% of the total microplastics, respectively. Epoxy resin (28%), polyethylene terephthalate (25%), phenoxy resin (25%), and polyamide (9%) were the main polymer components, whose sizes were concentrated at 50-200 µm. Back-trajectory modeling was then performed to explore the possible source direction of the microplastics. The results showed that the microplastics mainly originated from the populated areas southeast of the desert, indicating long-distance atmospheric transport and deposition in deserts. The desert-edge zone with some tourism activity contained more microplastics (8.2 ± 17.9 items/kg) than the non-tourism zone (0.9 ± 1.6 items/kg), indicating a potential contribution from tourism. The abundance in the non-tourism zone (0.9 items/kg) can be used as a reference for microplastic background values in the Central Asian deserts, as this value is critical for simulating and predicting global microplastic yields.

Forecasting the Number of New Coronavirus Infections Using an Improved Grey Prediction Model.

BACKGROUND: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. METHODS: The number of new coronavirus infections was characterized by "small data, poor information" in the short term. The grey prediction model provides an effective method to study the prediction problem of "small data, poor information". Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). RESULTS: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. CONCLUSION: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts forward specific suggestions.

Importance of Ssurface water background values for objectively assessing water quality in a unique basin.

Ignoring differences in surface water background values of different basins is a prominent shortcoming in water resource assessment and management systems in China. This study described the impact of this shortcoming on the results of water quality assessments by analysing water quality and the intensity of anthropogenic activities in 10 water resource basins in China. In particular, the Songhuajiang River Basin had a relatively undegraded natural environment with minimal anthropogenic activity. However, water quality assessment results showed the lowest water quality, and this basin is the most seriously affected by background values. The Huma River source water reserve was selected to conduct research of background value identification, concentration characteristics, migration and transformation laws, and output mechanism characteristics to enable the acknowledgment of the severe background value problem. Most data collected in basins with minimal anthropogenic pollution failed to meet Chinese water quality standard requirements, and high background values mainly resulted from litter leachate and soil erosion by rainfall or snowmelt runoff. A revised water quality assessment method was proposed in view of the particularity and heterogeneity of background values in the Songhuajiang River Basin. This study therefore clarified the negative impact of background values on water quality assessments, so that these values can be properly considered in Chinese water quality assessment and management systems.

Prediction and Optimization of Electrospun Polyacrylonitrile Fiber Diameter Based on Grey System Theory.

This paper provides a new method for predicting the diameter of electrospun nanofibers. Based on the grey system theory, the effects of polyacrylonitrile mass fraction, voltage, flow rate, and receiving distance on fiber diameter were studied. The GM(1,1) (grey model) model and DNGM(1,1) (The DNGM (1,1) model is based on the whitening differential equation using parameters to Directly estimate the approximate Non-homogeneous sequence Grey prediction Model) model were established to predict fiber diameter by a single-factor change, and the results showed high prediction accuracy. The multi-variable grey model MGM(1,n) (MGM(1,n) is a Multivariate Grey prediction Model) was used for prediction of fiber diameter when multiple factors change simultaneously. The results showed that the average modeling fitting error is 8.62%. The background value coefficients of the MGM(1,n) model were optimized, the average modeling fitting error was reduced to 1.01%, and the average prediction error was reduced to 1.33%. Combining the fractional optimization with the background-value coefficient optimization, the optimal background-value coefficient α and the order r were selected. The results showed that the average modeling fitting error is 0.85%, and the average prediction error is 0.38%. The results demonstrate that the grey system theory can effectively predict the diameter of polyacrylonitrile electrospinning fibers with high prediction accuracy. This theory can increase the control of nanofiber diameters in production.

Combined use of diffusive gradients in thin film, high-resolution dialysis technique and traditional methods to assess pollution and bioavailability of sediment metals of lake wetlands in Taihu Lake Basin.

The geochemical behavior of trace metals at the sediment/water interface in Taihu Lake, the third-largest fresh water lake in China, has been widely explored. However, information on metals in lake wetlands of the basin is lacking. Here, diffusive gradients in thin film (DGT), high-resolution dialysis technique (HR-Peeper) and traditional methods were jointly used to study the occurrence characteristics, pollution degree, bioavailability, and mobility of sediment metals in the northern lake wetlands of Jiaxing City in Taihu Lake Basin. The contents of Cr, Ni, Cu, Zn, As, Cd and Pb were 101, 52.8, 62.3, 184, 10.3, 0.4, and 39.8 mg/kg, respectively. The metals in the sediments were in an overall low enrichment level. The main form of Cd was acid-soluble (F1), and the other metals mainly existed in residual (F4) or oxidable (F3) forms. The mean DGT-labile contents (CDGT) of Cr, Ni, Cu, Zn, As, Cd and Pb were 1.3, 1.2, 9.3, 6.7, 13.4, 0.7, and 0.8 µg/L, respectively. CDGT-Cu and CDGT-As were significantly and positively related to the Cu and As contents in pore water (Csol). CDGT-Cr, CDGT-Cd, CDGT-Pb, and CDGT-Cu were significantly and positively related to CF1-Cr, CF1-Cd, CF1-Pb, and CF3-Cu, respectively. The stability of Cd was the worst with a mean risk assessment code of 40%, indicating a high risk of remobilization in the sediment. The remobilization risks of other metals were low or moderate. The CDGT/Csol ratio of Cd was also the largest, with a mean of 0.99, suggesting that the Cd resupplying ability from sediment solid to pore water was strong.

Relationship between lifespan indicators and elemental background values: A case study in Guangdong Province, China.

There was a significant difference in the distribution of centenarians at county-level in Guangdong Province, China. To carry out an integrated analysis on elemental background values and their relation to lifespan level, samples of environmental media such as drinking water, staple rice and soil were collected in ten counties (non-longevity and longevity areas) in Guangdong Province. Meanwhile, lifespan indicators were calculated based on census data in 2010: the percentage of population aged 80 to 90years (octogenarian index, 80-90%, OI%), the percentage of population aged 90 to 100years (longevity index, 90-100%, LI%), centenarians per one hundred thousand inhabitants (CH). Grey relational analysis (GRA) and stepwise multiple linear regression analysis (SMLR) were conducted. The major results show that the contents of Se, Mo, Ni, K and Zn in drinking water and rice and the content of Se in soil in longevity areas are significantly higher than those in non-longevity areas. Significantly positive correlation between elemental background values (Se, K, Mo, Ni and Zn) and three lifespan indicators shows an increasing trend in the order of OI, LI and CH. However, element Al in rice and soil shows a negative correlation with CH. In addition, the influence degree of elemental background values on LI and CH can be ranked as Se>Mo>K>Zn>Ni. The explanatory power of elemental background values (Se, K, Mo, Ni and Zn) to difference in LI/CH in longevity and non-longevity areas can be ranked: drinking water>rice>soil. The elemental background values (Se, K, Mo, Ni and Zn) have a positive impact on human lifespan, especially for those who live longer than 90years old.

Review of Heavy Metals Pollution in China in Agricultural and Urban Soils.

BACKGROUND: The concentrations of heavy metals in soil and potential risks to the environment and public health are receiving increased attention in China. OBJECTIVES: The objective of this paper is to review and analyze heavy metals soil contamination in urban and agricultural areas and on a national scale in China. METHODS: Initially, data on soil heavy metals concentration levels were gathered from previous studies and narratively analyzed. A further statistical analysis was performed using the geo-accumulation index (Igeo), Nemerow integrated pollution index (NIPI), mean, standard deviation (SD), skewness and kurtosis. Pollution levels were calculated and tabulated to illustrate overall spatial variations. In addition, pollution sources, remedial measures and impact of soil contamination as well as limitations are addressed. RESULTS: The concentration level of heavy metals was above the natural background level in most areas of China. The problem was more prevalent in urban soils than agricultural soils. At the national level, the soil in most of the southern provinces and Beijing were heavily polluted. Even though the pollution condition based on Igeo was promising, the Nemerow integrated pollution level was the most worrisome. The soils in about 53% of the provinces were moderately to heavily polluted (NIPI>2). The effects were noticed in terms of both public and ecological health risks. The major sources were waste gas, wastewater, and hazardous residuals from factories and agricultural inputs such as pesticides. Efforts have been made to reduce the concentrations and health risks of heavy metals, including policy interventions, controlling contamination sources, reducing the phytoavailability of heavy metals, selecting and rearing of grain cultivars with low risk of contamination, paddy water and fertilizer management, land use changes, phytoremediation and engineering techniques. CONCLUSIONS: China is experiencing rapid economic and technological advancements. This increases the risk of heavy metals contamination of soil. If serious attention is not paid to this problem, soil toxicity and biological accumulation will continue to threaten the sustainability of China's development. COMPETING INTERESTS: The authors declare no competing financial interests.

[Determination of Background Value and Potential Ecological Risk Assessment of Heavy Metals in Sediments of the Danjiangkou Reservoir].

The aim of this study was to explore the pollution level and potential ecological risk of heavy metals in sediments of the Danjiangkou Reservoir, and the study obtained the background value of heavy metals of the surface sediments using reference element and statistical methods. The results indicated that the background values of Cr, Ni, Cu, Zn, As, Cd, Hg and Pb were 41.18, 34, 32.13, 76.84, 10.46, 0.70, 0.07 and 27.11 mg·kg-1, respectively. In addition, the potential ecological risks of the eight heavy metals in sediments were assessed using the Hakanson ecological risk index method. The values of RI for these eight metals in surface sediments ranged from 29.49 to 214.11, with the mean value of 118.91, and the pollution level was low. Furthermore, the Cfi value was in the order of Cd> Hg> Zn> Cr> Pb> Ni> As=Cu with the mean value of 1.05-1.31, and the pollution level varied from low to intermediate. The Cfi values of Cd and Hg were higher than those of other heavy metals with the mean values of 1.31 and 1.24, respectively, which should be paid more attentions.

Establishing geochemical background levels of selected trace elements in areas having geochemical anomalies: The case study of the Orbetello lagoon (Tuscany, Italy).

The determination of background concentration values (BGVs) in areas, characterised by the presence of natural geochemical anomalies and anthropogenic impact, appears essential for a correct pollution assessment. For this purpose, it is necessary to establish a reliable method for determination of local BGVs. The case of the Orbetello lagoon, a geologically complex area characterized by Tertiary volcanism, is illustrated. The vertical concentration profiles of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were studied in four sediment cores. Local BGVs were determined considering exclusively samples not affected by anthropogenic influence, recognized by means of multivariate statistics and radiochronological dating ((137)Cs and (210)Pb). Results showed BGVs well-comparable with mean crustal or shale values for most of the considered elements except for Hg (0.87 mg/kg d.w.) and As (16.87 mg/kg d.w.), due to mineralization present in the catchment basin draining into the lagoon.

Development of a C3c-based ELISA method for the determination of anti-complementary potency of Bupleurum polysaccharides.

Traditionally, determination of inhibitory potency of complement inhibitors is performed by the hemolytic assay. However, this assay is not applicable to the lectin pathway, thus impeding the understanding of complement inhibitors against the overall function of the complement system. The main objective of our study was to develop a specific enzyme-linked immunosorbent assay (ELISA) as an alternative method to assess the anti-complement activity, particularly against the lectin pathway. By using respective coating substrates against different activation pathways, followed by capturing the stable C3c fragments, our ELISA method can be used to screen complement inhibitors against the classical pathway and the lectin pathway. The inhibitory effect of suramin on the classical pathway, as measured by our hemolytic assay is consistent with previous reports. Further assessment of suramin and Bupleurum polysaccharides against the lectin pathway showed a good reproducibility of the method. Comparison of the lectin pathway IC50 between Bupleurum smithii var. parvifolium polysaccharides (1.055 mg/mL) and Bupleurum chinense polysaccharides (0.98 mg/mL) showed that, similar to the classical and alterative pathway, these two Bupleurum polysaccharides had comparable anti-complementary properties against the lectin pathway. The results demonstrate that the described ELISA assay can compensate for the shortcomings of the hemolytic assay in lectin pathway.