Iron Mining Area Research Articles

Integrating transcriptome and physiological analysis reveals the stress responses of rose petals to surface water in the iron-mining area

Mining activities in the iron area mining have caused serious pollution of surface water in the area, which in turn affects the normal growth and development of plants. As a restorative plant with heavy metal adsorption function, the stress response mechanism of rose to surface water in the iron-mining area remains unelucidated. In this study, we investigated the effects of surface water treatment on the phenotypic, physiological and molecular levels of the plant using rose as a material. The results showed that surface water in the Bayan Obo iron mining area caused darkening and soft rotting of rose petals, increased degree of petal damage, decreased the content of MDA (Malondialdehyde), increased the ratio of GSH/GSSG (glutathione/oxidized glutathione) and NADPH/NADP+(nicotinamide adenine dinucleotide phosphate/oxidized nicotinamide adenine dinucleotide phosphate), suggesting that the rose produces more GSH to prevent oxidative stress. Transcriptome analysis further revealed a large number of differentially expressed genes (DEGs) involved in the TCA (the tricarboxylic acid) cycle and the phenylpropanoid biosynthesis pathways under surface water stress, and these DEGs can promote the production of NADPH, which in turn positively regulates the content of GSH. Multiple peroxidase-related genes (RhPRXs) also exhibited a significant stress response in this process. In summary, our research results reflect that roses under surface water stress in the iron-mining area exhibit certain defense mechanisms at both physiological and transcriptional levels, and also provide a reference for cultivating effective rose resources for repairing pollution in mining areas and evaluating excessive iron pollution as a biological indicator.

Indicator species of former lead (Pb) ore mining areas

The main objective of this study was to determine whether species that are considered to be indicator species for former iron ore mining areas also have value as indicators for remnants of former lead ore mining areas. The study was conducted at an abandoned post-mining field dating from the sixteenth century with visible remains from the exploitation of lead ore deposits (post-mining mounds, PMM). In each of the 41 plots (21 on PMM and 21 in the surroundings of PMM), an inventory was conducted of all vascular plants growing in the vegetation layer indicating both their coverage in percent and analyzing them based on indicator species for former iron mining sites. Additionally, soil samples were taken from each, and chemical analyses were done: pH in H2O; contents of Ca, and Pb; and available forms of K, P, and Mg. Changes in the chemical properties of the soil impacted the diversity of the flora of the analyzed area. In the 21 research plots established on the PMM of the former lead ore mining area, as many as 18 species of mesophilic deciduous forest considered to be indicator species for former iron mining areas were identified. The analyses conducted indicate a strong preference for these species for the soil occurring on the lead PMM. Indicator species for former iron mining areas can also be a good indicator for former lead ore mining areas.

Role of heavy metals in species composition of moss: An insight study in contaminated mining sites of Eastern India

AbstractMining activities are significant contributors to heavy metal pollution in the environment, which poses risks to both the natural ecosystem and human health. Thus, the purpose of this study is to determine the extent to which mining‐affected regions of Eastern India have undergone heavy metal‐induced changes to ecosystem composition, specifically in soil heavy metal contamination and moss diversity. The relationship between the abundance of moss species and the heavy metal content of the soil in the study regions has been studied. A total of 48 soil samples were taken throughout the study areas, and 41 moss species spanning 14 families were identified. The identification process of mosses in sampling sites involves meticulously scrutinizing their morphological characteristics. Pottiaceae and Fissidentaceae were the most accountable families at the species level. The Shannon diversity index revealed variations in moss diversity among the regions. The concentrations of heavy metals were determined using AAS (Systronics, model No. 816), chromium (Cr), iron (Fe), and manganese (Mn) were the most noticeable metal in SP1 (Chromite mining areas in Jajpur), SP2(Iron mining area in Keojhar's Joda‐Barbil), and SP3(manganese mining areas in Sundargarh's Koira‐Joda), respectively. The metal concentrations were highest in the storage area and lowest at 1–3 km. Correlation analysis showed positive relationships among the metals and negative relationships between metals and mosses. Moss family, like Bruchiaceae, exhibited significant negative correlations with specific metals like Cr. These research findings help to understand the effects of mining on soil contamination, and moss diversity.

Sustained growth and phosphorus efficiency under iron excess in Paspalum densum, a C4 grass for mining site revegetation

Iron (Fe) is a strong phosphorus (P) chelator in Fe-rich environments. In Fe mining sites, the P deficiency might limit plant development. This study aims to understand how the interaction between the Fe excess and P deficit affects the P-use efficiency and photosynthesis in Paspalum densum, a C4 tropical native grass indicated for use in revegetation of iron mining areas. The plants were exposed to four treatments in Hoagland's nutrient solution: standard (100 % P and 0.019 mM Fe); Fe excess (100 % P and 7 mM Fe); P deficit (1 % P and 0.019 mM Fe); and the combination of P deficit and Fe excess (1 % P and 7 mM Fe). The interaction between Fe excess and P deficiency changed the P allocation in the root and root apoplast. In roots, P deficit caused a higher Fe concentration, and, in the leaf, lower levels of P were found in plants exposed to the Fe excess. The stomatal limitation was the main cause of net photosynthesis decline due to Fe excess in both P doses, however without affecting plant biomass or promoting oxidative damage. The iron excess increased the P-use efficiency (PUE) at the leaf level in P-deficient plants. The photosynthetic PUE and whole biomass PUE were higher due to P deficiency. Therefore, in an environment of Fe toxicity and P deficit, as in areas impacted by Fe mining, the adjustments in P allocation and use efficiency coupled with photosynthetic responses contribute to the P. densum performance during revegetation.

Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan.

PREDICTION OF SILICOSIS IN IRON AND STEEL WORKERS USING ARTIFICIAL INTELLIGENCE

Silicosis is one of the most common occupational lung diseases in Egypt, where its prevalence rate ranges from 18.5 % to 45.8% among workers exposed to free crystalline silica dust. Long-term contact to silica dust, which is prevalent in the iron and steel sector, can lead to silicosis, a crippling occupational lung disease. Periodic health tracking and worker medical evaluations are traditional methods for finding and managing the risk of silicosis; however, these techniques frequently have limitations in terms of accuracy and dependability. It is feasible to analyse sizable databases of worker health and exposure information using artificial intelligence. The prevalence and severity of silicosis in iron and steel workers may be lessened due to development of more precise and effective risk evaluation and management methods. This study aims to predict iron and steel workers liability to developing silicosis based on artificial intelligence. A case-control study design was used to fulfill the aim of this study. The study was conducted at the El Gedida Iron Mine area at Bahariya Oasis, Giza Governorate. A sample of 220 workers was included in this study. The participants received a questionnaire in addition to a standard medical examination. The researchers chose eight variables that influence silicosis based on a literature review then suggested an ensemble model based on the Feed Forward Neural Network, K-Nearest Neighbor, Fuzzy K-Nearest Neighbor, and Decision Tree. The experimental findings reveal that the suggested framework surpassed others in the automated prediction of silicosis, obtaining an accuracy of 97.2 percent. Keywords: Silicosis, K-Nearest Neighbor, Fuzzy K-Nearest Neighbor, Decision Tree DOI： https://doi.org/10.35741/issn.0258-2724.58.2.15

Genetic variation and molecular characterization of Zygophyllum coccineum L. ecotypes of the iron mining area of El-Wahat El-Bahariya in Egypt

Remediation and mitigation processes can recover the ecosystems affected by mining operations. Zygophyllum coccineum L. is a native indigenous xerophyte that grows in Egypt's Western Desert, particularly around the iron mining ore deposits, and accumulates high rates of potentially toxic elements (PTEs) in its succulent leaves. The present study evaluated the genetic variation and molecular responses of Z. coccineum to heavy metal stressful conditions in three sites. Results revealed that Z. coccineum bioaccumulation capacity was greater than unity and varied amongst the three locations. In response to heavy metal toxicity, Z. coccineum plants boosted their antioxidative enzymes activity and glutathione levels as a tolerance strategy. Anatomically, a compact epidermis, a thick spongy mesophyll with water storage cells, and a thicker vascular system were observed. Protein electrophoretic analysis yielded 20 fragments with a polymorphism rate of 85%. The antioxidant genes (CAT: catalase, POD: peroxidase and GST: polyphenol oxidase) showed greater levels of expression. In addition, DNA-based molecular genetic diversity analyses using Start Codon Targeted (SCoT) and Inter Simple Sequence Repeat (ISSR) markers yielded 54 amplified fragments (i.e. 24 monomorphic and 30 polymorphic), with 12 unique fragments and a polymorphism rate of 55.5%. The greatest PIC values were recorded for SCoT-6 (0.36) and for both of the 14 A and 44 B ISSR primers (0.25). Diversity index (DI) of all SCoT and ISSR amplified primers was 0.23. The present findings reveal the distinct heavy metal's adaption attributes of Z. coccineum, indicating its improved survival in severely arid mining environments.

Rehabilitation promotes rapid recovery of arbuscular mycorrhizal fungi in iron mining areas

Arbuscular mycorrhizal fungi (AMF) perform important ecological functions in the rehabilitation of degraded areas, acting through several mechanisms, such as the production of glomalin-related soil proteins (glomalin), which act as cementing agents and nutritional reserves and sequester carbon in soil. The aim of the present study was to determine whether a short rehabilitation time influenced the soil AMF community diversity and glomalin production in areas undergoing rehabilitation after iron mining. We analyzed the number of spores, mycorrhizal colonization, AMF species richness, and concentrations of easily extractable and total glomalin-related soil proteins. Species richness was reduced by approximately 50 % after mining activity. However, after the initial rehabilitation of degraded areas, we observed an increase in the number of spores, colonization, and AMF species. Families Glomeraceae and Acaulosporaceae were considered dominant in the studied areas. Glomus sp.1 and Acaulospora mellea were the most frequent morphotypes. The relationship between glomalin and the levels of dissolved organic carbon, total carbon, and total nitrogen in the soil was found to be an interesting and easily interpretable parameter of the influence of AMF on the storage of organic matter in the soil, indicating the degree of equilibrium and/or recovery of revegetated environments. Rehabilitation, even in early stages, favored improvements in AMF status and provided increases in total glomalin-related soil protein production, which may have contributed to the enrichment of carbon and nitrogen in the soil. These characteristics are indicative of the success of the rehabilitation actions carried out in these areas after mining activity.

Quantitative estimation for the impact of mining activities on vegetation phenology and identifying its controlling factors from Sentinel-2 time series

Quantitative assessment of the negative environmental effects of mining is vital for the eco-environmental restoration and management of mining areas. However, it remains unclear about the spatial dimension of the influence on the environment that comes from mining. Here, we constructed a new method to quantify the vegetation impact of mining activity using Sentinel-2 time series and Pareto principle, and applied it to the Liaoning Nanfen iron mining area (LNMA), the Inner Mongolia Sanheming iron mining area (IMMA), and the Sichuan Hongge iron mining area (SCMA) in China. Based on the unequal relationship of 80% of the consequences determined by 20% of the causes, the influence of mining activities on vegetation was quantified by the maximum phenological difference, the decay rate, the asymptotic value of exponential trend and the distance from the mine. Results showed that the impact of mining activities on vegetation phenology decayed exponentially along with the increase of the distance to the mines. The influence distance of mining activities on vegetation were 1566.95 m, 1959.67 m, and 1809.61 m for LNMA, IMMA and SCMA, respectively. Compared to areas 5 km away from the mining activity, the start of the growing season for vegetation surrounding the mining activity was delayed by 1.1 ± 0.4 days, 6.1 ± 1.9 days, and 1.5 ± 0.7 days for LNMA, IMMA and SCMA, respectively, while the length of the growing season was successively shortened by 1.0 ± 0.6 days, 5.4 ± 2.5 days, and 5.1 ± 3.9 days, respectively. Our investigation found that the dust pollution, decreases in groundwater levels, and waterborne pollution were the main factors that directly caused phenological changes around the mining area, and the distance and degree of their impact on phenology were closely related to drought and topography. This finding could provide a reference for the environmental restoration and management of mining areas and help to add insights on the assessment for the long-term impacts of mining activities on vegetation.

GIS-based impact assessment and spatial distribution of air and water pollutants in mining area

Mining is a significant part of the transforming economy, which is generally considered as essential as well as social evil at the same time. It is one of the potential contributors to air and water pollution and possesses long-term impact on their quality. Keeping in view the exponential mining activities, we have selected an iron mine area in Bailadila, Chhattisgarh, India, as a sampling site and investigated the impact of mining activities on the air as well as water quality by setting up seven air quality and thirty water quality monitoring stations. From the results obtained, it was observed that concentration of air pollutants such as SO2, NO2, PM2.5 and PM10 for the year 2015 lies in the range of 11.5-13.0µg/m3, 11.5-13.0µg/m3, 24.9-33.4ppm and 61.6-74.2ppm, respectively, while for the year 2018, it lies in the range of 10.3-11.7µg/m3, 10.5-14.7µg/m3, 18.3-50.8ppm and 23.7-60.7ppm, respectively. Furthermore, results obtained revealed that air pollutants such as SO2, NO2, PM2.5 and PM10 were within the permissible limits but they contributed towards the light air pollution (air pollution index: 25-50) at all the air monitoring stations. Moreover, PM10 was considered as criterion pollutant in the Bailadila, Chhattisgarh region. On the other hand, it was observed that groundwater quality was deteriorated in the subsequent years. Most of the water quality parameters were in the permissible limits except iron (Fe). Moreover, on the basis of water quality indexing, water quality was classified as "poor" in ~ 30% of the sites and "very poor" in ~ 34% sites. The water quality was "unhealthy for drinking" in 3% and 6% sites in the year 2015 and 2018, respectively.

Evaluation of metal contamination, flux and the associated human health risk from atmospheric dustfall in metal mining areas of Southern Jharkhand, India.

Metals can be apprehended in the atmospheric environment of copper and iron mining areas of Jharkhand, which falls in one of the most mineralized areas of India with extensive mining and industrial activities. The study was taken up to appraise the metal contamination in the atmospheric dust to evaluate the metal fluxes and associated health risk considering the seasonal variations. Sixty samples were analyzed for As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn using the inductively coupled plasma mass spectrometer (ICP-MS) and the contamination levels were assessed by various indices. The metal content of dustfall samples exceeded the average shale values for most of the metals. Higher metal concentrations were found in the locations in close vicinity of mining and industrial areas. The principal component analysis suggested both geogenic and anthropogenic sources for metals in the atmospheric dustfall. Human health risk as determined by hazard quotient (HQ) and hazard index (HI) suggested considerable risk to the child populace through the ingestion pathway for both the mining areas, higher being in iron mining areas. The metal flux and the health risk were higher in summers as compared to winters for both the mining areas. Consequently, the results advocate the necessity of periodic monitoring of the freefall dust of the mining areas and development of proper management strategies to reduce the metal pollution.

Genetic and symbiotic characterization of rhizobia nodulating legumes in a mining area in southeast Brazil

Identification and selection of nitrogen-fixing bacterial strains for inoculation into native leguminous tree species can assist in the recovery of degraded areas. Additionally, native strains from these areas are genetic resources adapted to these conditions and are thus suitable for selection. The aim of this study was to symbiotically and genetically characterize 18 bacterial strains from the Rhizobium and Bradyrhizobium genera isolated from Machaerium nyctitans, Platypodium elegans, and Ormosia arborea grown in a nursery in an iron mining area. Three experiments were conducted under axenic conditions in a greenhouse. The nodulation capacity of the strains was evaluated by the number (NN) and dry matter (NDM) of nodules. Symbiotic efficiency was evaluated based on the following parameters: SPAD index (SPAD), shoot dry matter (SDM), root dry matter (RDM), and total dry matter (TDM) of the plants, relative efficiency (RE), shoot nitrogen content (SNC), and total nitrogen content in the plant (TNC). The atpD and gyrB housekeeping genes and the nifH gene were sequenced for phylogenetic analysis, and the nodC and nodD symbiotic genes of the strains were amplified. Out of the 18 strains, 16 were authenticated by nodulation capacity in the species of origin. The SPAD variable allowed for the detection of differences between treatments before the SDM. Additionally, the SPAD index showed correlation with TNC, and the strain Bradyrhizobium sp., UFLA01-839, which may represent a new species, was outstanding in Machaerium nyctitans. The nifH, nodD, and nodC genes were detected in UFLA01-839.

Highly efficient density inversion of gravity data using nonlinear density polynomial fitting

The density inversion of gravity data is commonly achieved by discretizing the subsurface into prismatic cells and calculating the density of each cell. During this process, a weighting function is introduced to the iterative computation to reduce the skin effect during the inversion. Thus, the computation process requires a significant number of matrix operations, which results in low computational efficiency. We have adopted a density inversion method with nonlinear polynomial fitting (NPF) that uses a polynomial to represent the density variation of prismatic cells in a certain space. The computation of each cell is substituted by the computation of the nonlinear polynomial coefficients. Consequently, the efficiency of the inversion is significantly improved because the number of nonlinear polynomial coefficients is less than the number of cells used. Moreover, because representing the density change of all of the cells poses a significant challenge when the cell number is large, we adopt the use of a polynomial to represent the density change of a subregion with fewer cells and multiple nonlinear polynomials to represent the density changes of all prism cells. Using theoretical model tests, we determine that the NPF method more efficiently recovers the density distribution of gravity data compared with conventional density inversion methods. In addition, the density variation of a subregion with 8 × 8 × 8 prismatic cells can be accurately and efficiently obtained using our cubic NPF method, which also can be used for noisy data. Finally, the NPF method is applied to real gravity data in an iron mining area in Shandong Province, China. Convergent results of a 3D perspective view and the distribution of the iron ore bodies are acquired using this method, demonstrating the real-life applicability of this method.

Late Carboniferous biota from the Ljubija iron mine area, Bosnia and Herzegovina

The Olistostrome member of the Sana-Una Paleozoic complex of the Ljubija ore mine in Bosnia and Herzegovina contains limestone fragments of pebble to block size that have been examined paleontologically. The recovered conodont fauna of the first sample is characterized by the species Declinognathodus lateralis, Idiognathoides sulcatus sulcatus and Idiognathodus sp. confirming its mid-Bashkirian age. This report is the first on the occurrence of these taxa in the area. The second sample with chaetetid demosponges yields an abundant diversified microbiota consisting of cyanobacteria, algae and foraminifera. Chlorophyts are marked by the common siphonoclad occurrence of Donezella lutugini and D. lunaensis, whereas rhodophyts include rare representatives of Stacheia, Stacheoides, Pseudoungdarella and Masloviporidium. The presence of Asphaltinella horowitzi and Aphralysia carbonaria of unclear taxonomic position is also documented. Pseudostaffellids, eostaffellids and other foraminifera, mostly endothyrids are present. The examined associations of fossils point to the Bashkirian age of the primary rock that originated in a very shallow habitat most probably linked to a high-energy reef environment.

Multivariate linear regression models for predicting metal content and sources in leafy vegetables and human health risk assessment in metal mining areas of Southern Jharkhand, India.

The present study was intended to investigate the metal concentrations in the leafy vegetables, irrigation water, soil, and atmospheric dust deposition in the iron and copper mining areas of Southern Jharkhand, India. The study aimed to develop a multivariate linear regression (MVLR) model to predict the concentration of metals in leafy vegetables from the metals in associated environmental factors and assessment of the risk to the local population through the consumption of leafy vegetables and other allied pathways. The developed species-specific MVLR models were well fitted to predict the concentration of metals in the leafy vegetables. The coefficient of determination values (R2) was greater than 0.8 for all the species-specific models. Risk assessment was carried out considering multiple pathways of ingestion, inhalation, and dermal contact of vegetables, soil, water, and free-fall dust. Consumption of leafy vegetables was the major route of metal exposure to the local population in both the metal mining areas. The average hazard index (HI) value considering all the metals and pathways was calculated to be 5.13 and 12.1, respectively for iron and copper mining areas suggesting considerable risk to the local residents. Fe, As, and Cu were the major contributors to non-carcinogenic risk in the Iron mining areas while in the case of copper mining areas, the main contributors were Co, As, and Cu.

Dust Dispersion and Its Effect on Vegetation Spectra at Canopy and Pixel Scales in an Open-Pit Mining Area

Dust pollution is severe in some mining areas in China due to rapid industrial development. Dust deposited on the vegetation canopy may change its spectra. However, a relationship between canopy spectra and dust amount has not been quantitatively studied, and a pixel-scale condition for remote sensing application has not been considered yet. In this study, the dust dispersion characteristics in an iron mining area were investigated using the American Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA) regulatory model (AERMOD). Further, based on the three-dimensional discrete anisotropic radiative transfer (DART) model, the spectral characteristics of vegetation canopy under the dusty condition were simulated, and the influence of dustfall on vegetation canopy spectra was studied. Finally, the dust effect on vegetation spectra at the canopy scale was extended to a pixel scale, and the response of dust effect on vegetation spectra at the pixel scale was determined under different fractional vegetation covers (FVCs). The experimental results show that the dust pollution along a haul road was more severe and extensive than that in a stope. Taking dust dispersion along the road as an example, the variation of vegetation canopy spectra increased with the height of dust deposited on the vegetation canopy. At the pixel scale, a lower vegetation FVC would weaken the influence of dust on the spectra. The results derived from simulation spectral data were tested using satellite remote sensing images. The tested result indicates that the influence of dust retention on the pixel spectra with different FVCs was consistent with that created with the simulated data. The finding could be beneficial for those making decisions on monitoring vegetation under dusty conditions and reducing dust pollution in mining areas using remote sensing technology.

Evaluation of atmospheric dust deposition rates and their mineral characterization in copper and iron mining areas, Singhbhum, India

ABSTRACT Atmospheric dust can play a very important role in the polluted atmosphere. This has a direct impact on human health, global warming, climate change, visibility, precipitation, cloud formation, and so on. To evaluate the atmospheric dustfall rate and their mineralogical aspects, three separate sites were selected, namely mining, suburban, and control for dust sampling. Dustfall samples were collected at monthly intervals from copper and iron mining areas, in Singhbhum, India. The average atmospheric dustfall rate varied from 7.51 to 28.58 g/m2/month, and 7.40 to 26.37 g/m2/month during the summer and winter seasons, respectively, in the copper mining areas. At the same time, an average atmospheric dustfall rate varied from 7.23 to 76.99 g/m2/month during summer season and 6.48 to73.92 g/m2/month during the winter season in the iron mining area. The major minerals identified by X-ray diffraction (XRD) analysis of dustfall samples from copper mining area were quartz, kaolinite, pyrite, albite, and magnesio hornblende. However, in the case of iron mining area, the major minerals found were quartz, cristobalite, hematite, magnetite, biotite, albite, ilmenete, pyrite, rutile, and dolomite. Overall, the intensity of dust pollution is greater in the vicinity of mining and industrial sites of the copper and iron mining areas. Implications: The study has been conducted in the copper and iron mining areas of East and West Singhbhum districts of Jharkhand state, respectively. The aim of the present study was twofold, namely, (i) to evaluate the dustfall rates (ii) and to characterize the mineralogy of atmospheric dust. East and West Singhbhum are the significantly industrialized areas of India known for the mining of copper and iron ores, steel production, power generation, and other related activities. In order to improve local people’s living conditions, there is an urgent need for baseline data of dust pollution and its general characteristics based on scientific disclosures to allow policy recommendations and their implementation. Therefore, the study falls within the scope of the journal. The atmospheric dustfall rates were found to be higher during the summer season due to increased dispersion caused by the high wind speed during the summer season. During the winter season, lower rates were observed due to monsoonal rainfall washout and higher relative humidity, which reduces dust resuspension. However, the present study considered the extent of dustfall rates and their mineral characteristics. An immediate need arises to regularly monitor the dust pollution and to implement suitable dust control system like wet dust suppression and airborne dusts capture for dust abatement.

Prevalence and epidemiological characteristics of human immunodeficiency virus-1 infection in an iron mining area with intense migratory flow in Pará State, Brazilian amazon, 2005-2014.

Acquired immunodeficiency syndrome (AIDS) caused by human immunodeficiency virus (HIV) is a major global public health problem. The aim of this study is to determine the prevalence of HIV-1 infection in four municipalities of Pará State (Marabá, Parauapebas, Curionópolis, and Canaã dos Carajás), in northern, Brazil. The municipalities are located in the Carajás Complex iron mining area. The employment opportunities result in extensive migratory flow of people. A total of 4771 serum samples were obtained from 2005 to 2014 and were sent to Evandro Chagas Institute, Belém-Pará, where they were tested by enzyme-linked immunosorbent assay, with reactive samples confirmed by Western blot analysis. The samples were from individuals from 23 Brazilian states and the Federal District, mainly Maranhão (39.53%) and other municipalities of Pará (34.25%). The total positivity rate was 0.48% (23/4771). The rate was 0.47% (14/2975) in males and 0.50% (9/1796) in females. Of these, 0.33% (14/4275) were from asymptomatic individuals whose serum were collected during the serological survey, 1.81% (9/497) were from cases featuring clinical symptoms including fever/diarrhea/jaundice, which were included in febrile, diarrheal, and icteric syndromes analyzed during the study. The findings indicated the presence of HIV-1 infection in the general population studied. The majority of cases (60.9%, 14 of 23 positive cases) were asymptomatic.

Effects of the watershed on the seasonal variation of the surface water quality of a post-restoration coastal wetland: The case of the Nador lagoon (Mediterranean sea, Morocco)

The Nador lagoon is a coastal wetland which is subject to both the watershed pressures and land runoffs coming from domestic, agricultural, industrial and mining pollution. In 2017, 19 stations encompassing the entire lagoon and pollution sources, including 13 at the lagoon level and 6 at the watershed level, were sampled during wet and dry seasons. Descriptive and multivariate statistical analyses were used to assess its environmental status through physio-chemical, nutrients and trace metals parameters. The results show that seasonal variations significantly affected the concentrations of Fe, Cu, Cr, and Cd in the lagoon. The highest mean concentrations were recorded for Mn, Zn, Cr and Pb during the wet season and for Fe, Cu, Ni and Cd during the dry season. The average concentrations of Pb, Cd, Cr, Zn and Mn in the lagoon were lower than the USEPA 2016 standards on saltwater aquatic life preservation while the average concentrations of Ni and Cu are higher. In order to preserve this wetland, the implementation of an environmental management plan is required focusing on the rehabilitation of the iron mining area, the extension of the sewerage network, the control of agricultural effluents and the upgrading of industrial pollution controls.

Fe, Rather Than Soil Organic Matter, as a Controlling Factor of Hg Distribution in Subsurface Forest Soil in an Iron Mining Area.

To identify whether the iron (Fe) mining area in the Jiulongjiang River basin (JRB) has an influence on the mercury in the forest soil, the spatial distribution patterns of mercury’s behavior on different controlling factors were analyzed, and a potential ecological risk assessment was done. A total of 107 soil samples were collected from two forest soil profiles, one profile near the Fe mining area and the other far from it. The soil near the mining area had a moderate potential ecological risk with high Fe content rich in the upper layer of soil (<70 cm), whereas soil collected far from the mining area had a low potential ecological risk. These results indicated that the rise of iron content in the soil near the mining area was beneficial to the enrichment of mercury, probably causing damage to the forest ecosystem. Both soil organic carbon (SOC) and Fe content have strong positive correlations with THg content, controlling the mercury behavior in the upper layer (<70 cm) and a lower layer (>70 cm) of soil, respectively. The high Fe content in the upper layer of soil will compete for the adsorption of mercury by SOC, leading to the poor correlation between SOC and THg.