Historically, Romania is known as a mining site of mineral substances, including gold, silver, copper, lead, zin, uranium, manganese, salt, and coal, whereby their long periods of exploitation and extraction affected human health and the environment in various ways. In Moldova Nouă southwest region of Romania, we investigated the environmental impacts of mining activities on air quality over 2021. We quantified PM emission rates through in situ monitoring, dispersion modelling, and horizontal and vertical fluxes. Statistical metrics, including the fraction within factor 2 (FAC2), mean bias (MB), mean gross error (MGE), normalized mean bias (NMB), normalized mean gross error (NMGE), coefficient of efficiency (COE), index of agreements (IOAs), and Taylor diagram signifying standards deviation (SD), root mean squared error (RMSE), and correlation coefficient (R), were used to evaluate the reliability of modelling results against observation. Results conclude that PM dispersion agrees with MB, MGE, NMB, NMGE, COE, IOA, and Taylor diagram and moderately with FAC2 metrics. PM hotspot was investigated in the vicinity of the tailings ponds of 115.5 µg m annual mean, 563.7 µg m daily mean, 63.3 µg m s annual horizontal flux, and 3.0 µg m s annual vertical flux. PM dispersion was identified to expand to Moldova Nouă City and nearby country Serbia. Findings concluded that a windy air mass accumulation across the overburdened dumps and ponds causes the increase of PM in the air, resulting in the region's pollution. Therefore, results recommend adopting a strategic mitigation measure for residents, policymakers, stakeholders, and urban planners.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s10661-023-12199-1 | DOI Listing |
Microbiome
December 2024
College of Life Sciences, Shihezi University, Shihezi, Xinjiang, 832003, China.
Huan Jing Ke Xue
December 2024
Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China.
To study the pollution characteristics and ecological risks of heavy metals in tailings ponds is an important prerequisite for protecting the surrounding environment and human health. The total amount and morphology of five "toxic" heavy metals (As, Cr, Cd, Pb, and Hg) in Kafang tin tailings pond were determined. Based on the aforementioned, we analyzed the distribution characteristics of heavy metals.
View Article and Find Full Text PDFHuan Jing Ke Xue
December 2024
China National Environmental Monitoring Centre, Beijing 100012, China.
To analyze the source apportionment, potential ecological risk, and health risk of heavy metals in soils surrounding a manganese tailings pond in Chongqing, a positive matrix factorization (PMF) model, potential ecological risk index, and health risk assessment model were used. Further, all three models were combined to explore the risks of heavy metals in soils by different pollution sources to determine the priority control factors. The results showed that except for the Cr concentration, the average values of Mn, Cd, As, Pb, Cu, Zn, and Ni concentration were higher than their corresponding background values.
View Article and Find Full Text PDFJ Hazard Mater
November 2024
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Heavy metals (HMs) release from lead (Pb)-zinc (Zn) tailings poses significant environmental risks to surrounding areas. Furthermore, with the natural weathering and frequently happened acid rain events, the release of HMs could be elevated. This study conducted a series of laboratory column experiments with thermodynamics and hydrogeochemical analysis to investigate the environmental behavior of HMs release in Pb-Zn tailings under natural weathering conditions and acid rain events.
View Article and Find Full Text PDFEnviron Monit Assess
October 2024
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China.
To solve the difficult problems of tailings dam instability and environmental pollution, multisource information perception, prediction and early warning technology for tailings dams are investigated. Taking a tailings pond in China as an example, a three-dimensional visualization intelligent management platform based on the spatiotemporal fusion of multisource big data is established to realize intelligent real-time monitoring, prediction and early warning of tailings dams. A monitoring system for air-space-ground integration was developed via high-resolution optical image recording, unmanned aerial vehicles (UAVs), radar, video surveillance and displacement sensors.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!