Homology and heterogeneity of soil trace elements of coal power production bases in arid and semi-arid areas of Northwest China.

Coal power activities could cause regional fluctuations of trace elements, but the distribution information of these trace elements in arid and semi-arid areas is insufficient. In this study, the soil trace elements (As, B, Be, Cd, Co, Cr, Cu, Fe, Ga, Ge, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Ti, Tl, and Zn) of Ningdong Coal Power Production Base in China were monitored. Results showed that the concentrations of B, Tl, Mn, Pb, Cr, K, Cu, and Co exceeded background values. The maximum risk index reached 265.66, while the trace elements posed a cancer risk to children. Combining correlation analyses (CA), principal component analysis (PCA), and positive matrix factorization (PMF) techniques, it indicated that trace elements were mainly coming from coal combustion (34.15%), livestock farming (17.44%), traffic emissions (12.42%), and natural factors (35.99%). This study reveals the sources and potential ecological risks of soil trace elements in the Ningdong Coal and Power Production Base. It provides a scientific basis for developing targeted environmental management measures and reducing human health risks.

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions.

The identification technology for coal and coal-measure rock is required across multiple stages of coal exploration, mining, separation, and tailings management. However, the construction of identification models necessitates substantial data support. To this end, we have established a near-infrared spectral dataset for coal and coal-measure rock, which includes the reflectance spectra of 24 different types of coal and coal-measure rock. For each type of sample, 11 sub-samples of different granularities were created, and reflectance spectra were collected from sub-samples at five different detection azimuths, 18 different detection zeniths, and under eight different light source zenith conditions. The quality and usability of the dataset were verified using quantitative regression and classification machine learning algorithms. Primarily, this dataset is used to train artificial intelligence-based models for identifying coal and coal-measure rock. Still, it can also be utilized for regression studies using the industrial analysis results contained within the dataset.

Study on the mesomechanical behavior and damage process of coal samples with hole defects under eccentric loading.

When using end shield shearer to recover end slope coal resources, the stability of the overlying rock slope of the end slope is controlled by leaving coal pillars. Due to the influence of the self weight of the overlying rock layer, the coal pillar will be subjected to eccentric loads, and the influence of eccentric loads needs to be considered in the design of the coal pillar size. With the help of PFC discrete element software, uniaxial compression tests were carried out on coal sample containing hole defects under different degrees of eccentric loads based on the calibration of micro mechanical parameters. The results show that the peak stress, cracking stress and dilatancy stress of coal sample decrease in a linear function law with the increase of load eccentricity coefficient. The evolution of the number of microscopic cracks during uniaxial compression under eccentric load can be divided into four stages: the calm stage before crack initiation I, the stable propagation stage II, the unstable propagation and penetration stage III, and the post failure stage IV. The distribution of macroscopic cracks is jointly influenced by the relative position of the loading area and the hole defect. When the hole defect is within the loading area, the hole plays a guiding role in the evolution of coal sample cracks, and the macroscopic crack runs through the edge of the loading area and the hole. When the hole defect is located outside the loading zone, the degree of eccentric load is large, weakening the guiding effect of the hole defect on the crack, and the macroscopic crack does not pass through the hole defect.

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network.

In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems.

Study on the secondary oxidation behavior and microscopic characteristics of oxidized coal gangue.

Spontaneous combustion of coal gangue (CG) hills has caused varieties of secondary disasters that seriously endanger the ecological environment of the world. The emission law of index gases and their oxidation kinetics during the secondary oxidation process of CG with different ranks of oxidation were studied by using the temperature programmed device and online mass spectrometer (MS). Fourier transform infrared spectroscopy (FTIR) was used to reveal the changes of the CG internal active functional groups. The results showed that the energy required for the combustion of CG with low rank of pre-oxidation was significantly lower than that of the raw sample. However, as the oxidation rank increased, due to amounts of volatile components were released in the process of oxidation reaction, the CG in the combustion process of the emission of index gases and its oxygen consumption rate gradually reduced; the rapid oxidation stage shifted to the direction of the high temperature. In this study, the risk of spontaneous combustion of CG after oxidation at 80 â„ƒ under 3% oxygen concentration was the strongest. The results of this paper are of great guiding significance for exploring the spontaneous combustion characteristics of CG hills and their prevention by traditional covering method.

Characterization of the migration of organic contaminants in laboratory-scale groundwater polluted by underground coal gasification.

Underground coal gasification (UCG) is a promising technology, but the groundwater pollution caused by UCG is a potential risk to the environment. The measured results of the stratum in the combustion cavity resulting from UCG had proven that the combustion cavity would be filled with some UCG residues and caving rocks when UCG was finished. The pollutants in underground water around the combustion cavity include organic pollutants, inorganic pollutants, and ammonia nitrogen, and one of the primary organic pollutants is phenol. The migration and diffusion characteristics of organic pollutants (taking phenol as a representative) in the groundwater of the combustion cavity were investigated by breakthrough experiments and numerical simulations. The results show that the hydraulic conductivity of the coarse UCG residues is much higher than that of fine residues, and the hydraulic conductivity of the UCG residues with the size of - 0.15 mm and 0.15-0.3 mm are 4.68 × 10-6 m/s and 1.91 × 10-4 m/s respectively. The dispersivity λ for the migration of organic pollutants will be influenced significantly by the size of UCG residues in fractures of the combustion cavity, while the distribution coefficient Kd will not. The dispersivity of organic pollutants in the fine UCG residues is more significant than that in the coarse residues, and the λ for the two kinds of residues are 3.868 cm and 1.765 cm, respectively. The shape of the migration path slightly affects the pollutant concentration distribution along the path, but the width of a path has a more pronounced influence on the concentration distribution. In this research, the influence was formulated by a new technical term, MPWIT, which is related to transverse dispersion. Specifically, while the transverse dispersion values account for 20% and 10% of the longitudinal dispersion, respectively, the corresponding MPWIT values are 39.48 mm and 33.96 mm.

The refinery of the future.

Fossil fuels-coal, oil and gas-supply most of the world's energy and also form the basis of many products essential for everyday life. Their use is the largest contributor to the carbon dioxide emissions that drive global climate change, prompting joint efforts to find renewable alternatives that might enable a carbon-neutral society by as early as 2050. There are clear paths for renewable electricity to replace fossil-fuel-based energy, but the transport fuels and chemicals produced in oil refineries will still be needed. We can attempt to close the carbon cycle associated with their use by electrifying refinery processes and by changing the raw materials that go into a refinery from fossils fuels to carbon dioxide for making hydrocarbon fuels and to agricultural and municipal waste for making chemicals and polymers. We argue that, with sufficient long-term commitment and support, the science and technology for such a completely fossil-free refinery, delivering the products required after 2050 (less fuels, more chemicals), could be developed. This future refinery will require substantially larger areas and greater mineral resources than is the case at present and critically depends on the capacity to generate large amounts of renewable energy for hydrogen production and carbon dioxide capture.

Effects of indoor air pollution from household solid fuel use on the risk of gastrointestinal and liver diseases in middle aged and elderly adults.

Solid fuels are widely used in China and increase the concentrations of indoor air pollutants. Nevertheless, there is limited longitudinal evidence linking solid fuel use and Gastrointestinal (GI) and liver diseases. This study aimed to prospectively investigate the association between household solid fuel use and the risk of GI and liver diseases in middle aged and elderly adults. This work was based on the China Health and Retirement Longitudinal Study (CHARLS). Longitudinal data incorporate with cross-sectional data were analyzed. Compared with individuals using clean fuel for cooking, solid fuel users were observed to have higher risk of GI diseases (OR in 2011, 2013, 2015, 2018 wave separately: 1.37, 95 % CI: 1.24-1.50, P < 0.001; 1.24, 95 % CI: 1.11-1.39, P < 0.001; 1.18, 95 % CI: 1.06-1.33, P < 0.001; 1.23, 95 % CI: 1.04-1.45, P < 0.05). The associations between solid fuel use and liver diseases were not significant in most of the groups. Participants transforming from solid to clean cooking fuels had lower risk of GI and liver diseases than persistent solid fuel users. Moreover, biomass cooking fuel users were at a significant higher risk of both liver and GI diseases compared with clean fuel users. Overall, household solid fuel use, especially for cooking, was related to higher risk of GI and liver diseases, while switching from solid to clean fuels could reduce this risk. Using biomass for cooking was identified to be more associated with the increasing risk of GI and liver diseases than cooking with coal.

Air Quality and Health Implications of Coal Power Retirements Attributed to Industrial Electricity Savings in China.

The coal-dominated electricity system, alongside increasing industrial electricity demand, places China into a dilemma between industrialization and environmental impacts. A practical solution is to exploit air quality and health cobenefits of industrial energy efficiency measures, which has not yet been integrated into China's energy transition strategy. This research examines the pivotal role of industrial electricity savings in accelerating coal plant retirements and assesses the nexus of energy-pollution-health by modeling nationwide coal-fired plants at individual unit level. It shows that minimizing electricity needs by implementing more efficient technologies leads to the phaseout of 1279 hyper-polluting units (subcritical, <300 MW) by 2040, advancing the retirement of these units by an average of 7 years (3-16 years). The retirements at different locations yield varying levels of air quality improvements (9-17%), across six power grids. Reduced exposure to PM2.5 could avoid 123,100 pollution-related cumulative deaths over the next 20 years from 2020, of which ∼75% occur in the Central, East, and North grids, particularly coal-intensive and populous provinces (e.g., Shandong and Jiangsu). These findings provide key indicators to support geographically specific policymaking and lay out a rationale for decision-makers to incorporate multiple benefits into early coal phaseout strategies to avoid lock-in risk.

High temperature melting of municipal solid waste incineration (MSWI) fly ash and co-disposal technology with blast furnaces.

With the development of municipal solid waste incineration technologies, the disposal of fly ash has become a difficult problem that many countries need to solve. High-temperature melting is a promising disposal technology. Based on this, a new process for collaborative treatment of fly ash in metallurgical blast furnaces had been proposed in this study. To explore the impact of disposal of fly ash on blast furnace production, by simulating the high-temperature reducing environment of blast furnaces, the melting changes of water-washed fly ash (W-FA), and the effects of W-FA injection on coal combustion and products (slag, iron) composition were studied. The results showed that W-FA, as a flux, could be sprayed into the blast furnace separately or mixed with coal. But when injected along with coal, W-FA would suppress the combustion of coal. After melting, the removal rates of S, P, Cl, and Pb in W-FA were 21%, 30%, 86%, and 89%, respectively. The removal rates of K, Na, and Zn were close to 100%, and Cr was basically not removed. When the proportion of W-FA to coal was less than 1%, in addition to controlling the alkalinity of the slag, the impact of W-FA on the composition of iron and slag was minimal. The successful execution of this work will not only achieve the reduction, harmless and resourceful utilization of fly ash, but also save investment and operating costs of disposal facilities, with both environmental and social benefits.

Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite.

Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and rapid monitoring, analysis, and assessment to control environmental risks associated with large CSW dumpsites. We constructed a new composite model (PLS-FL) that uses partial least squares regression (PLSR) and fuzzy logic inference (FLI) to accurately predict heavy metal concentrations in soils and assess pollution risk levels. The potential application of the PLS-FL was tested through a gully type CSW case study. We compared 20 modeling strategies using the PLS-FL: five types heavy metals (Cd, Zn, Pb, Cr and As) * four spectral transformation methods (first derivative (FD), second derivative (SD), reverse logarithm (RL), and continuum removal (CR)) * one variable selection method (competitive adaptive reweighted sampling (CARS)). The results showed that the combination of derivative transformation and CARS was recommended for estimation, with R2C > 0.80 and R2P > 0.50. When comparing the PLSR model with four traditional machine learning methods (Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), and KNN), the PLSR model demonstrated the highest average prediction accuracy. Additionally, the FLI process no longer relies on human perception and expert opinion, enhancing the model's objectivity and reliability. The evaluation results revealed that the heavy metal contamination areas of the CSW dumpsite are concentrated at the bottom of the gully, with more severe contamination in the north. Furthermore, a high-risk zone exists in the interim storage area for CSW to the east of the dump. These findings align with the initial detections at the sampling sites and highlight the need for targeted monitoring and control in these areas. The application of the model will empower regulators to quickly assess the overall situation of large-scale heavy metal pollution and provide scientific program and data support for continuous large-scale pollution risk monitoring and sustainable risk management.

Preparation and characterization of high-ash coal slime-based soil amendment as well as investigations of its adsorption performance and mechanisms towards heavy metals in soil.

In this study, high-ash coal slime-based mineral soil amendment (MSA) was prepared via the hydrothermal method using high-ash coal slime as raw material, supplemented with activator calcium oxide and additive KOH solution. After hydrothermal treatment at 230 °C for 5 h, the original crystalline phase (quartz and kaolinite) of the high-ash slime was completely transformed into hydrotalcite zeolite, tobermorite, and silicate of potassium aluminosilicate, which has the largest specific surface area. The adsorption of Pb2+ and Cd2+ was adherent to the kinetic equation of secondary adsorption and Freundlich models, and the removal of Pb2+ and Cd2+ reached up to 362.58 mg g-1 and 64.67 mg g-1. The successive releases of SiO2 and CaO from MSA conformed to the Elovich equation, whereas the releases of SiO2 in Cd-containing environments and CaO in Pb- and Cd-containing environments more closely conformed to the power function; the releases of K2O all conformed to the first-order kinetic equation. The presence of Pb2+ and Cd2+ in the environment promotes the release of potassium and calcium elements with MSA's ion-exchange ability, and attenuates the release of silicon elements. Combining Pb2+ and Cd2+ with silicon resulted in the intolerant precipitation of 3PbO·2SiO2 and Cd2SiO4. The mineral precipitation mechanism is the most important mechanism of MSA in immobilizing heavy metals, accounting for 72.7%-80.5% of the total adsorption. Further contaminated soil immobilization experiments also showed that the application of MSA significantly reduced the bioavailability of soil heavy metals. When the MSA addition amount was 1.6%, the residual state increased by 63.58%. In conclusion, preparing MSA may effectively utilize coal-based solid waste with high added value.

Mechanisms of secondary biogenic coalbed methane formation in bituminous coal seams: a joint experimental and multi-omics study.

Coal seam microbes, as endogenous drivers of secondary biogenic gas production in coal seams, might be related to methane production in coal seams. In this study, we carried out anaerobic indoor culture experiments of microorganisms from three different depths of bituminous coal seams in Huainan mining area, and revealed the secondary biogas generation mechanism of bituminous coal seams by using the combined analysis of macro-genome and metabolism multi-omics. The results showed that the cumulative mass molar concentrations (Molality) of biomethane production increased with the increase of the coal seam depth in two consecutive cycles. At the genus level, there were significant differences in the bacterial and archaeal community structures corresponding to the three coal seams 1#, 6#, and 9#(p < 0.05). The volatile matter of air-dry basis (Vad) of coal was significantly correlated with differences in genus-level composition of bacteria and archaea, with correlations of R bacterial = 0.368 and R archaeal = 0.463, respectively. Functional gene analysis showed that the relative abundance of methanogenesis increased by 42% before and after anaerobic fermentation cultivation. Meanwhile, a total of 11 classes of carbon metabolism homologues closely related to methanogenesis were detected in the liquid metabolites of coal bed microbes after 60 days of incubation. Finally, the fatty acid, amino acid and carbohydrate synergistic methanogenic metabolic pathway was reconstructed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The expression level of mcrA gene within the metabolic pathway of the 1# deep coal sample was significantly higher than that of the other two groups (p < 0.05 for significance), and the efficient expression of mcrA gene at the end of the methanogenic pathway promoted the conversion of bituminous coal organic matter to methane. Therefore, coal matrix compositions may be the key factors causing diversity in microbial community and metabolic function, which might be related to the different methane content in different coal seams.

Characteristics of PM2.5 bounded carbonaceous aerosols, carbon dioxide and its stable carbon isotopes (δ13C) in rural households in northwest China: Effect of different fuel combustion.

In order to fully understand the carbon emission from different fuels in rural villages of China, especially in the typical atmospheric pollution areas. The characteristics of carbonaceous aerosols and carbon dioxide (CO2) with its stable carbon isotope (δ13C) were investigated in six households, which two households used coal, two households used wood as well as two households used biogas and liquefied petroleum gas (LPG), from two rural villages in Fenwei Plain from March to April 2021. It showed that the fine particulate matter (PM2.5) emitted from biogas and LPG couldn't be as lower as expected in this area. However, the clean fuels could relatively reduce the emissions of organic carbon (OC) and element carbon (EC) in PM2.5 compare to the solid fuels. The pyrolyzed carbon (OP) accounted more total carbon (TC) in coal than the other fuels use households, indicating that more water-soluble OC existed, and it still had the highest secondary organic carbon (SOC) than the other fuels. Meantime, the coal combustions in the two villages had the highest CO2 concentration of 527.6 ppm and 1120.6 ppm, respectively, while the clean fuels could effectively reduce it. The average δ13C values (-26.9‰) was much lighter than almost all the outdoor monitoring and similar to the δ13C values for coal combustion and vehicle emission, showing that they might be the main contributors of the regional atmospheric aerosol in this area. During the sandstorm, the indoor PM2.5 mass and CO2 were increasing obviously. The indoor cancer risk of PAHs for adults and children were greater than 1 × 10-6, exert a potential carcinogenic risk to human of solid fuels combustion in rural northern China. It is important to continue concern the solid fuel combustion and its health impact in rural areas.

China's "coal-to-gas" policy had large impact on PM1.0 distribution during 2016-2019.

Particulate matter with an aerodynamic diameter of less than 1 µm (PM1.0) can be extremely hazardous to human health, so it is imperative to accurately estimate the spatial and temporal distribution of PM1.0 and analyze the impact of related policies on it. In this study, a stacking generalization model was trained based on aerosol optical depth (AOD) data from satellite observations, combined with related data affecting aerosol concentration such as meteorological data and geographic data. Using this model, the PM1.0 concentration distribution in China during 2016-2019 was estimated, and verified by comparison with ground-based stations. The coefficient of determination (R2) of the model is 0.94, and the root-mean-square error (RMSE) is 8.49 µg/m3, mean absolute error (MAE) is 4.10 µg/m3, proving that the model has a very high performance. Based on the model, this study analyzed the PM1.0 concentration changes during the heating period (November and December) in the regions where the "coal-to-gas" policy was implemented in China, and found that the proposed "coal-to-gas" policy did reduce the PM1.0 concentration in the implemented regions. However, the lack of natural gas due to the unreasonable deployment of the policy in the early stage caused the increase of PM1.0 concentration. This study can provide a reference for the next step of urban air pollution policy development.

Edge-functionalized coal-derived graphene oxide in bacterial nanocellulose hydrogel for active wound healing.

This work presents a comparison of physicochemical and in vitro active wound healing properties of two distinct Graphene Oxides (GOs) from graphite and coal. These GOs are incorporated in Bacterial Nanocellulose (BNC) to form hydrogels. The performance and limitations of the loading fraction of both GOs in BNC are controlled by the processing technology and the source materials from which GOs are derived. Edge functionalization with C-GO offers the advantage of facilitating face-to-edge assembly in the hydrogel leading to better dispersion than the face-to-face assembly of basal functionalized G-GO. The latter leads to more aggregation of G-GO, resulting in a lower optimal loading fraction. Our investigation into the antibacterial properties of the BNC and BNC/GO hydrogels against gram-negative E. coli revealed inhibitory effects of the BNC/GO hydrogels that intensified with an increase in the concentration of GO. Furthermore, an in vitro wound scratch assay demonstrated that BNC/C-GO hydrogels promote better cell migration, confirming their superior biocompatibility and suitability as active wound dressings, albeit limited by loading fraction due to agglomeration. These findings shed light on the performance and limitations of GOs for diverse applications, emphasizing the significance of exploring the influence of different methods and source materials of GOs.

Seasonal variations and sources of atmospheric EPFRs in a megacity in severe cold region: Implications for the influence of strong coal and biomass combustion.

Environmentally persistent free radicals (EPFRs) can pose exposure risks by inducing the generation of reactive oxygen species. As a new class of pollutants, EPFRs have been frequently detected in atmospheric particulate matters. In this study, the seasonal variations and sources of EPFRs in a severe cold region in Northeastern China were comprehensively investigated, especially for the high pollution events. The geomean concentration of EPFRs in the total suspended particle was 6.58 × 1013 spins/m3 and the mean level in winter was one order of magnitude higher than summer and autumn. The correlation network analysis showed that EPFRs had significantly positive correlation with carbon component, K+ and PAHs, indicating that EPFRs were primarily emitted from combustion and pyrolysis process. The source appointment by the Positive Matrix Factorization (PMF) model indicated that the dominant sources in the heating season were coal combustion (48.4%), vehicle emission (23.1%) and biomass burning (19.4%), while the top three sources in the non-heating season were others (41.4%), coal combustion (23.7%) and vehicle emissions (21.2%). It was found that the high EPFRs in cold season can be ascribed to the extensive use of fossil fuel for heating demand; while the high EPFRs occurred in early spring were caused by the large-scale opening combustion of biomass. In summary, this study provided important basic information for better understanding the pollution characteristics of EPFRs, which suggested that the implementation of energy transformation and straw utilization was benefit for the control of EPFRs in severe cold region.

Multivariate analysis applied to X-ray fluorescence to assess soil contamination pathways: case studies of mass magnetic susceptibility in soils near abandoned coal and W/Sn mines.

Determining the origin and pathways of contaminants in the natural environment is key to informing any mitigation process. The mass magnetic susceptibility of soils allows a rapid method to measure the concentration of magnetic minerals, derived from anthropogenic activities such as mining or industrial processes, i.e., smelting metals (technogenic origin), or from the local bedrock (of geogenic origin). This is especially effective when combined with rapid geochemical analyses of soils. The use of multivariate analysis (MVA) elucidates complex multiple-component relationships between soil geochemistry and magnetic susceptibility. In the case of soil mining sites, X-ray fluorescence (XRF) spectroscopic data of soils contaminated by mine waste shows statistically significant relationships between magnetic susceptibility and some base metal species (e.g., Fe, Pb, Zn, etc.). Here, we show how qualitative and quantitative MVA methodologies can be used to assess soil contamination pathways using mass magnetic susceptibility and XRF spectra of soils near abandoned coal and W/Sn mines (NW Portugal). Principal component analysis (PCA) showed how the first two primary components (PC-1 + PC-2) explained 94% of the sample variability, grouped them according to their geochemistry and magnetic susceptibility in to geogenic and technogenic groups. Regression analyses showed a strong positive correlation (R2 > 0.95) between soil geochemistry and magnetic properties at the local scale. These parameters provided an insight into the multi-element variables that control magnetic susceptibility and indicated the possibility of efficient assessment of potentially contaminated sites through mass-specific soil magnetism.

Ecological risk assessment of heavy metals in desulfurized seawater discharged from a coal-fired power plant in Qingdao.

Despite the prevalence of discharge of large volumes of heavy-metal-bearing seawater from coal-fired power plants into adjacent seas, studies on the associated ecological risks remain limited. This study continuously monitored concentrations of seven heavy metals (i.e. As, Cd, Cr, Cu, Hg, Pb, and Zn) in surface seawater near the outfall of a coal-fired power plant in Qingdao, China over three years. The results showed average concentrations of As, Cd, Cr, Cu, Hg, Pb, and Zn of 2.63, 0.33, 2.97, 4.63, 0.008, 0.85, and 25.00 µg/L, respectively. Given the lack of data on metal toxicity to local species, this study investigated species composition and biomass near discharge outfalls and constructed species sensitivity distribution (SSD) curves with biological flora characteristics. Hazardous concentrations for 5% of species (HC5) for As, Cd, Cr, Cu, Hg, Pb, and Zn derived from SSDs constructed from chronic toxicity data for native species were 3.23, 2.22, 0.06, 2.83, 0.66, 4.70, and 11.07 µg/L, respectively. This study further assessed ecological risk of heavy metals by applying the Hazard Quotient (HQ) and Joint Probability Curve (JPC) based on long-term heavy metal exposure data and chronic toxicity data for local species. The results revealed acceptable levels of ecological risk for As, Cd, Hg, and Pb, but unacceptable levels for Cr, Cu, and Zn. The order of studied heavy metals in terms of ecological risk was Cr > Cu ≈ Zn > As > Cd ≈ Pb > Hg. The results of this study can guide the assessment of ecological risk at heavy metal contaminated sites characterized by relatively low heavy metal concentrations and high discharge volumes, such as receiving waters of coal-fired power plant effluents.