Statistical and spatial analysis for soil heavy metals over the Murray-Darling river basin in Australia

Assessment of benthic ecological status and heavy metal contamination in an estuarine intertidal mudflat in the Northern Bohai Sea

Evaluating the ecological quality and pollution status of coastal mudflats is crucial for environmental protection and management, particularly when these areas serve as major shellfish production hotspots. In this study, we assessed the benthic ecological quality and heavy metals pollution in Geligang, located in the Northern Bohai Sea using the macrobenthos diversity index and the heavy metal pollution index. The Shannon-Wiener index (H'), AZTI marine biotic index (AMBI), multivariate AMBI (M-AMBI) showed that the benthic ecological quality in Geligang is either good or high. The potential ecological risk index and geoaccumulation index highlighted that cadmium (Cd) and mercury (Hg) as the primary heavy metal pollutants in Geligang. Surprisingly, the biomass of the two dominant species other than these indices serve as reliable indicators of heavy metal pollution. This suggests that the biomass of Mactra veneriformis and Potamocorbula laevis could be used to assess heavy metal pollution levels in Geligang.

Ecological and human health risk from exposure to metal contaminated sediments in a subtropical river affected by anthropogenic activities: A case study from river Yamuna

Heavy metal enrichment in river sediments poses a significant risk to human and aquatic health. The Yamuna River faces severe challenges due to untreated industrial and domestic wastewater discharge. The study evaluates sediment metal content, ecological and human health risks, and potential sources. Results showed Cd and Pb exhibited moderate to severe contamination and displayed ecological risk based on contamination factor, enrichment factor, and potential ecological risk. According to synergistic indices (pollution load index, PINemerow, toxic risk index, contamination security index, mean probable effects level quotients, and probability of toxicity), the sediment in the Yamuna River doesn't seem to have a risk or enrichment from combined metals. Cd and Pb mainly originate from anthropogenic sources. Hazard index (< 1) and carcinogenic risk (2.2 × 10-7 to 4.7 × 10-5) assessments suggest metal didn't pose any risk to humans exposed to sediment. The present study aids in developing pollution control strategies for the Yamuna River.

Source-specific probabilistic health risk assessment of heavy metals in surface water of the Yangtze River Basin

The detrimental effects of heavy metal accumulation on both ecosystems and public health have raised widespread concern. Source-specific risk assessment is crucial for developing effective strategies to prevent and control heavy metal contamination in surface water. This study aims to investigate the contamination characteristics of heavy metals in the Yangtze River Basin, identifying the pollution sources, assessing the risk levels, and further evaluating the health risks to humans. The results indicated that the average concentrations of heavy metals were ranked as follows: zinc (Zn) > arsenic (As) > copper (Cu) > chromium (Cr) > cadmium (Cd) > nickel (Ni) > lead (Pb), with average concentrations of 38.02 μg/L, 4.34 μg/L, 2.53 μg/L, 2.10 μg/L, 1.17 μg/L, 0.84 μg/L, and 0.32 μg/L, respectively, all below the WHO 2017 standards for safe drinking water. The distribution trend indicates higher concentrations in the upper and lower reaches and lower concentrations in the mid-reaches of the river. By integrating the Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) receptor model and Positive Matrix Factorization (PMF) model, the main sources of heavy metals were identified as industrial activities (APCS-MLR: 41.3 %; PMF: 42.1 %), agricultural activities (APCS-MLR: 30.1 %; PMF: 27.4 %), and unknown mix sources (APCS-MLR: 29.1 %; PMF: 30.4 %). The calculation of the hazard index (HI) for both children and adults was <1, indicating no non-carcinogenic or carcinogenic risks. Based on the source-specific risk assessment, agricultural activities contribute the most to non-carcinogenic risks, while industrial activities pose the greatest contribution to carcinogenic risks. This study offers a reference for monitoring heavy metals and controlling health risks to residents, and provides crucial evidence for the utilization and protection of surface water in the Yangtze River Basin.

Integrated assessment of the pollution and risk of heavy metals in soils near chemical industry parks along the middle Yangtze River

Industrialization in riparian areas of critical rivers has caused significant environmental and health impacts. Taking eight industrial parks along the middle Yangtze River as examples, this study proposes a multiple-criteria approach to investigate soil heavy metal pollution and associated ecological and health risks posed by industrial activities. Aiming at seven heavy metals, the results show that nickel (Ni), cadmium (Cd), and copper (Cu) exhibited the most significant accumulation above background levels. The comprehensive findings from Pearson correlation analysis, cluster analysis, principal component analysis, and industrial investigation uncover the primary sources of Cd, arsenic (As), mercury (Hg), and lead (Pb) to be chemical processing, while Ni and chromium (Cr) are predominantly derived from mechanical and electrical equipment manufacturing. In contrast, Cu exhibits a broad range of origins across various industrial processes. Soil heavy metals can cause serious ecological and carcinogenic health risks, of which Cd and Hg contribute to >70 % of the total ecological risk, and As contributes over 80 % of the total health risk. This study highlights the importance of employing multiple mathematical and statistical models in determining and evaluating environmental hazards, and may aid in planning the environmental remediation engineering and optimizing the industry standards.

Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.