Anthropogenic Sources Dominate Foliar Chromium Dust Deposition in a Mining-Based Urban Region of South Africa

Source and risk assessment of heavy metals in mining-affected areas in Jiangxi Province, China, based on Monte Carlo simulation

In recent years, heavy metal contamination of soils has become a major concern in China due to the potential risks involved. To assess environmental pollution and human health risks in a typical heavy metal polluted site in Jiangxi Province, a thorough evaluation of the distribution, pollution levels, and sources of heavy metals in soils of the Yangmeijiang River watershed was conducted in this study. Positive matrix factorization and Monte Carlo simulation were used to evaluate the ecological and human health risks of heavy metals. The research findings indicate that heavy metal pollution was the most severe at the depth of 20-40 cm in soils, with local heavy metal pollution resulting from mining and sewage irrigation. The high-risk area accounted for 91.11% of the total area. However, the pollution level decreased with time due to sampling effects, rainfall, and control measures. Leaf-vegetables and rice were primarily polluted by Cd and Pb. The main four sources of heavy metals in soils were traffic emission, metal smelting, agricultural activities and natural sources, mining extraction, and electroplating industries. Heavy metals with the highest ecological risk and health risk are Cd and As, respectively. The non-carcinogenic and carcinogenic risks of children were 7.0 and 1.7 times higher than those of adults, respectively. Therefore, children are more likely to be influenced by heavy metals compared to adults. The results obtained by the risk assessments may contribute to the identification of specific sources of heavy metals (e.g., traffic emissions, metal smelting, mining excavation, and electroplating industries). Additionally, the environmental impacts and biotoxicity associated with various heavy metals (e.g., Cd and As) can also be reflected. These outcomes may serve as a scientific basis for the pollution monitoring and remediation in the mining-affected areas.

Synergy of different leaf traits determines the particulate matter retention capacity and its susceptibility to rain wash-off

Rainfall plays a crucial role in the removal of particulate matter (PM) from plant leaves, influencing PM retention and the environmental behaviour of harmful substances that accumulate in PM. This study examined the PM retention capacity, particle size distributions, and wash-off rates of leaf surface PM from three common green tree species in northern China during two natural rainfall events (light rain: 8.3 mm; heavy rain: 54.2 mm), to investigate the relationship between the leaf traits, PM retention capacity, and PM wash-off process. Our results found that leaf morphometric characteristics, such as leaf size, length, width, and aspect ratio (length-to-width), had a negative and significant correlations with the PM retention capacity, but had no significant correlation with the leaf surface PM wash-off rate. Smaller leaves with low aspect ratios exhibited greater stability under external disturbances than large leaves with high aspect ratios, resulting in a higher PM retention capacity and lower wash-off rate. Ridges and grooves enhanced the PM retention capacity by increasing the leaf roughness. Rainfall could wash off all particle size ranges of leaf surface PM without altering their mechanical composition. Larger particles were more easily washed off. Euonymus japonicus, with its small leaf size and low aspect ratio, exhibited the highest PM retention capacity. Its curled leaf shape also hindered light rain from washing off leaf surface PM. Forsythia suspensa, with denser grooves and ridges compared with Prunus serrulata, exhibited a rougher leaf surface and higher PM retention capacity. However, this roughness may reduce wettability, making it easier for runoff to form on the leaf surface and dislodge leaf surface PM, resulting in F. suspensa having the highest wash-off rate. Our results highlight the synergy of different leaf traits on PM retention capacity and the PM stability after rainfall.

A biochemical and morphological study with multiple linear regression modeling-based impact prediction of ambient air pollutants on some native tree species of Haldwani City of Kumaun Himalaya, Uttarakhand, India

The current study was conducted around the province of Haldwani City, Uttarakhand, India, to understand the seasonal variation of ambient air pollutants (PM_2.5, PM₁₀, SO₂, and NO₂) and their impact on four tree species, i.e., neem (Azadirachta indica), mountain cedar (Toona ciliate), bottlebrush (Callistemon citrinus), and guava (Psidium guajava) during 2020-2021. Multiple linear regression (MLR)-based prediction analysis showed that the selected air quality variables (PM_2.5, PM₁₀, SO₂, and NO₂) had a significant impact on the biochemical responses of selected tree spp. including, pH, ascorbic acid (AA), total chlorophyll content (T. Chl.), relative water content (RWC), and dust deposition potential. In this, the coefficient of variance (R²) of the developed models was in the range of 0.70-0.98. The ambient air pollutants showed significant seasonal variations as depicted by using the air pollution tolerance index (APTI) and anticipated performance index (API). The tree species from polluted sites observed more pollution tolerance than the tree species from the control site. Regression analysis showed a significant positive association between the biochemical characteristics and APTI, with the highest influence by AA (R² = 0.961) followed by T. Chl., RWC, and pH. The APTI and API score was observed as maximum for A. indica and minimum for C. citrinus. The impact of air pollutants on the morphology of foliar surface was investigated by the scanning electron microscopy (SEM) and recorded various dust deposition patterns, stomatal blockages, and damage of guard cells in the trees growing along the polluted site (S2). The present study can assist environmental managers to examine the pollution-induced variables and develop an effective green belt for combating air pollution in polluted areas.