Incorporating environmental capacity considerations to prioritize control factors for the management of heavy metals in soil

Identifying spatial drivers of soil heavy metal pollution risk integrating positive matrix factorization, machine learning, and multi-scale geographically weighted regression

Soil heavy metal (HMs) contamination poses significant ecological and health risks, yet the spatial drivers of HMs pollution remain poorly understood. This study integrates pollution risk assessment, positive matrix factorization, machine learning, and multi-scale geographically weighted regression to develop a framework for identifying the spatial drivers of soil HMs contamination risk in Yangtze River New City, China. Analysis of 7152 samples revealed that although average HMs concentrations were below national standards, As, Cd, Cr, Cu, Hg, and Ni exceeded local background levels. Four key factors were identified as drivers of HMs contamination: natural sources (30.36 %, influenced by soil type), mixed agricultural and transportation sources (29.56 %, driven by cropland, aquaculture, and road density), human activities (12.68 %, including population density and community activities), and industrial sources (27.42 %, linked to factories and enterprises). Regional variations indicated that industrial activities, transportation, and human activities primarily influenced health risks, while agriculture and natural factors had a greater impact on ecological and environmental capacity risks. These findings underscore the importance of considering spatial heterogeneity in HMs pollution risk assessments and offer insights for developing targeted, region-specific policies to mitigate pollution risks of soil HMs.

Associations between prenatal exposure to metal mixtures and infant reproductive hormones during mini-puberty

BACKGROUND

The reproductive hormone homeostasis is crucial for child development. Exposure to metals during pregnancy may have adverse effects on offspring health. However, the association between prenatal exposure to metals and infant reproductive hormone levels remains unknown.

METHODS

This study involved 812 mother-infant pairs from Wuhan, China, assessing prenatal exposure to 13 metals in maternal plasma during early pregnancy and measuring five reproductive hormones in urine samples of one-month-old infants. Generalized linear models were employed to investigate the associations between individual metal exposures and urinary hormone levels in infants. Additionally, weighted quantile sum (WQS) regression and quantile g-computation were employed to evaluate the impact of metal mixtures.

RESULTS

Most of the detected metals in maternal plasma were associated with lower levels of follicle-stimulating hormone (FSH) and luteinizing hormone (LH) in infants. Each interquartile range (IQR) increase in manganese (Mn), barium (Ba), thallium (Tl), vanadium (V), cobalt (Co), nickel (Ni), and lead (Pb) was significantly associated with an approximately 9.87 % to 38.24 % decrease in FSH or LH. WQS and quantile g-computation models confirmed a significant association between prenatal metal mixtures and reduced FSH and LH in male infants, and WQS indicated a significant association between metal mixtures and decreased FSH in female infants.

CONCLUSIONS

Maternal exposure to mixed metals during early pregnancy was associated with lower levels of FSH and LH in infants, suggesting that prenatal exposure to metals may disrupt the balance of infant reproductive hormones. Further research is warranted to confirm these associations and explore the underlying mechanisms.

Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.67, 63.41, 37.39, 1.28, 23.93, 14.48, 125.08, and 467.45 mg/kg, respectively. The geoaccumulation index identified Cd as the dominant metal, indicating heavy to extreme contamination in soils. The QI revealed that over 99% of the areas were polluted for Ni and Cd with less uncertain regions whereas Cr showed a significant portion of areas with uncertain pollution status. The positive matrix factorization (PMF) model identified three major sources: agricultural (29%), vehicular emissions (25%), and industrial (46%). The probabilistic assessment of health hazards indicated that both carcinogenic and non-carcinogenic risks for adult male, adult female, and children were deemed unacceptable. Moreover, children faced a higher health hazard compared to adults. For adult male, adult female, and children, industrial operations contributed 48.4%, 42.7%, and 71.2% of the carcinogenic risks, respectively and these risks were associated with Ni and Cr as the main pollutants of concern. The study emphasizes valuable scientific insights for environmental managers to tackle soil pollution from HMs by effectively managing anthropogenic sources. It could aid in devising strategies for environmental remediation engineering and refining industry standards.

Assessment of soil environmental capacity for heavy metals in Shantou City, Guangdong Province, China: source analysis and enrichment evaluation

Most studies assessing soil environmental capacity (EC) often overlook the impact of heavy metal sources. Analyzing the sources of heavy metals (HMs) provides a better understanding of regional environmental capacity characteristics and their dynamic changes. The current study focuses on the surface soil of Shantou, using 511 soil samples to assess the soil environmental capacity. Results indicate that the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in Shantou's surface soil are notable, with lead moderately enriched and other metals lightly enriched. The principal component analysis (PCA) identifies five primary sources of heavy metals: mixed natural and agricultural sources, mixed agricultural and industrial sources, industrial sources, mining sources, and quarrying sources. The primary source contributing significantly to soil HM concentrations in Shantou City is a complex interplay between natural geological processes and extensive agricultural practices. In terms of static environmental capacity, Zn, Cr, Ni, Pb, Cu, As, Hg, and Cd are ranked in descending order. The overall environmental capacity for heavy metals in the soil is at a medium level, influenced by geological backgrounds. However, regions such as Yanhong Town, Guiyu Town, and Chendian Town face lower environmental capacities due to comprehensive human activities, posing certain risks. This study provides a scientific reference for forecasting, controlling soil heavy metal pollution, and improving soil quality and environmental capacity in Shantou City.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.