Changes in fruit yield, biochemical attributes, and leaf minerals of different cucumber (Cucumis sativus L.) cultivars under foliar application of silicon nanoparticles.

Silicon nanoparticles (Si NPs) have an eminent role in improving plant yield through improving yield. The present study was conducted to find the effect of Si NPs on plant yield, biochemical attributes, and minerals of different cucumber cultivars. The greenhouse experiment with foliar application of Si NPs (100, 200, and 300 mg L-1) was carried out on cucumber cultivars (Emilie, Mirsoltan, Mitio, and Viola). The application of Si NPs at 300 mg L-1 led to the highest fruit yield, with a 17% increase in fruit production compared to the control. Fruit firmness differed by 31% between Emilie and Si NPs at 100 mg L-1 and Mito at 300 mg L-1. Plants experiencing Si NPs at 300 mg L-1 had the greatest chlorophyll (Chl) a+b. Compared to the other cultivars, Mito had a greater fruit yield and Chl content. The Si NPs increased TSS by 11% while lowering TA by 24% when compared to the control at 300 mg L-1. Foliar application of Si NPs reduced the value of TSS/TA. The largest value of K was reached in the Mito cultivar with Si NPs at 200 mg L-1, with a 22% increase in comparison to the control, indicating that Si NPs considerably boosted the K content. The Si NPs at 200 mg L-1 significantly increased leaf N and P in the Mito cultivar by 16 and 50%, respectively. By using agglomerative hierarchy clustering (AHC), Emilie and Mito were located in two separate clusters, whilst Viola and Mirsoltan were grouped in one cluster. In conclusion, Si NPs at 200-300 mg L-1 enhanced fruit yield, and Mito showed the highest yield when compared to other cultivars.

Pine needles as bioindicator and biomagnetic indicator of selected metals in the street dust, a case study from southeastern Iran.

Among the different approaches currently being used to evaluate the contamination level of street dust, the magnetic susceptibility of dust and urban tree leaves has received little attention. The key objectives of this study were: (i) to investigate the feasibility of using pine needles as a bioindicator and biomagnetic indicator for estimating the concentration of selected metals in street dust, and (ii) to predict the contamination level of street dust by selected metals using magnetic susceptibility. Street dust and pine tree needle samples were taken from 60 locations in three adjacent cities in Kerman province (Kerman, Rafsanjan, and Sirjan), southeastern Iran. The total concentrations of selected metals, including Cu, Zn, Fe, Mn, Ni, and Pb, and the magnetic susceptibility (χlf and χhf) values of both pine tree needles and street dust samples were determined. Among the three cities studied, samples from Kerman showed the highest magnetic susceptibility and metal concentration values. This could be attributed to the larger size and much higher population density of this city, with more industrial activities and urban traffic than the other two cities investigated. The results also showed that the concentrations of metals in pine needles were strongly correlated (p < 0.01) with those in street dust. The magnetic susceptibility of pine needles and the concentrations of Fe, Pb, Zn, Cu, Ni, and Mn in street dust showed a statistically significant correlation (p < 0.01). A strong and statistically significant correlation (p < 0.01) was also found between magnetic susceptibility and the concentration of metals in pine needles. In conclusion, strong relationships between magnetic properties and metal concentrations of pine needles with those of street dust samples seem to make pine needles a good bioindicator and biomagnetic estimator of the contamination level of metals in street dust.

Prediction and variability mapping of some physicochemical characteristics of calcareous topsoil in an arid region using Vis-SWNIR and NIR spectroscopy.

Site-specific management of soils needs continuous measurements of soil physicochemical characteristics. In this study, Vis-NIR spectroscopy with two spectroscopic instruments, including charge-coupled device (CCD) and indium-gallium-arsenide (InGaAs) spectrometers, was adopted to estimate some physicochemical characteristics of a calcareous topsoil in an arid climate. Partial least squares (PLS) as linear and artificial neural networks (ANN) as nonlinear multivariate techniques were utilized to enhance the accuracy of prediction. The best predictive models were then used to extract the variability maps of physicochemical characteristics. Diffuse reflectance spectra of 151 samples, collected from the calcareous topsoil, were acquired in the visible and short-wavelength near-infrared (Vis-SWNIR) (400-1100 nm) and near-infrared (NIR) (950-1650 nm) spectral ranges using CCD and InGaAs spectrometers, respectively. The results showed that NIR spectral data of the InGaAs spectrometer was necessary to reach the best predictions for all selected soil properties. The best predictive models based on the optimum spectral range could allow us the excellent predictions of sand (RPD = 2.63) and silt (RPD = 2.52), and very good estimations of clay (RPD = 2.35) and electrical conductivity (EC) (RPD = 2.224) by ANN and very good prediction of calcium carbonate equivalent (CCE) (RPD = 2.01) by PLS. The CCD device, however, resulted in acceptable predictions of sand (RPD = 2.13, very good) and clay (RPD = 1.66, fair) by ANN, and silt (RPD = 1.78, good), EC (RPD = 1.84, good) and CCE (RPD = 1.67, fair) by PLS. Similar variability was attained between pairs of predicted maps by best models and reference-measured maps for all studied soil properties. For clay, sand, silt, and CCE, the Vis/SWNIR-predicted and equivalent reference-measured maps had acceptable similarities, indicating the potential application of low-cost CCD spectrometers for prediction and the variability mapping of these parameters.

Biomagnetic monitoring of atmospheric heavy metal pollution using pine needles: the case study of Isfahan, Iran.

Atmospheric deposition particles are fine-sized having a high adsorption capacity. Therefore, they can easily transfer the contamination to other areas. Plants can absorb certain pollutants using their leaves and then accumulate them in their biomass. In this study, the spatial and temporal variability of air pollution was assessed using pine needles as the bioindicators of atmospheric pollution. The magnetic susceptibility (MS) at low and high frequencies (χlf, χhf) and the concentration of selected heavy metals of pine needles (Pinus mugo) were estimated in order to address the possible relationships between needles' MS and the heavy metal concentration in the city of Isfahan, central Iran. In addition, the relationship between the heavy metal concentration of pine needles and that of the atmospheric dust was examined using the published data. Tree pine needles were monthly sampled, from April to December 2015 (T1-T9), during 9 months, from 30 different sites in the Isfahan city. There were two treatments including washed + unwashed (WU) and washed + washed (WW). The heavy metal total concentrations including Zn, Fe, Cu, Co, Pb, and Ni were measured. The mean concentrations of Fe, Zn, Pb, Cu, Ni, and Co were 80.4, 3.9, 1.8, 1.4, 0.6, and 0.3 mg kg-1, respectively. The results revealed that the concentration of heavy metals and MS in the pine needles followed the order Fe > Zn > Pb > Cu > Ni > Co. Also, the heavy metal concentration in the pine needles with different treatments had the following trend: WU > WW. It was shown that there was a significant correlation (p < 0.01) between the heavy metal concentrations and the leaf MS values of the pine needles and the concentration of heavy metals in atmospheric dust. Besides, similar trends were detected for the spatial variability of heavy metals and the pine needles' MS. In general, it could be concluded that the biomagnetic approach could serve as a comparatively fast and low-cost method to detect highly polluted urban areas with selected heavy metals, particularly the areas which are under the influence of anthropogenic and other traffic-related sources.

Environmental impact assessment of industrial activities on heavy metals distribution in street dust and soil.

Street dust and soil are important materials for evaluating the contaminants level in industrial areas. Detailed size-resolved distribution of metal(loid)s in street dusts and soils influenced by industrial activities has rarely been investigated. This study was carried out to understand how industrialization might affect the size distribution of metal(loid)s concentration and contamination level in the street dust and soil from Murcia, southern Spain. An industrial and a natural areas were selected and surface soil and street dust samples were taken. They were fractionated into eleven size classes and total concentrations of Pb, Zn, Cu, Cd, Cr, Ni, As and Fe were determined in both the bulk samples and their fractions. Enrichment factor, geoaccumulation index, and mass loading of different heavy metal(loids) were calculated. The results indicated that the street dust from natural and industrial areas had almost the same particle size distribution, both containing higher percentage of coarse-sized particles than the soil. Industrialization seems to have only slightly affected the concentration of most elements studied in the soil. In contrast, the concentrations of the heavy elements in bulk industrial dust samples and all their size fractions were extremely higher than those from the natural area. This means that the industrial activities only affected the size dependency of the concentration (contamination level) of certain elements (Pb, Zn, Cu, Cd, and Cr) in the street dust, but not in the soil.

Provenance and environmental risk of windblown materials from mine tailing ponds, Murcia, Spain.

Atmospheric particulates play a vital role in the transport of potentially toxic metals, being an important exposure pathways of people to toxic elements, which is faster and can occur in a much larger scale than water, soil and biota transport. Windblown materials in abandoned tailing ponds have not been well examined. The objectives of this investigation were: to study the major physical and geochemical properties of the materials eroded by wind inside the tailing ponds, and to understand the relative contribution of different sources to its heavy metals concentration. Study area is located in Cartagena-La Union mining district (SE Spain), where metallic mining of Fe, Pb and Zn has been developed for more than 2500 years. Wind-eroded particulates were monthly collected at 3 different heights (20, 50, and 80 cm) from the ground for a period of a full year using 4 dust collectors. Four tailing samples and 4 surface soil samples from the surrounding hills were also taken. Dust, soil, and tailing samples were examined for pH, particle size distribution, electrical conductivity, calcium carbonate content, Pb, Cu, Zn, Cd, Mn, Co, Ni, Ti and Zr concentrations. The results indicated that very coarse textured, slightly saline, and almost neutral wind-eroded deposits were generated with a very high temporal variability throughout the year. They also showed that the concentration of Cd, Mn, Pb and Zn, in the dust samples is extraordinarily high (18, 1254, 1831, and 5747 mg kg-1 respectively), whereas Co, Ni, and Cu had concentrations into the range of background concentrations found in the Earth's crust (3.8, 12, and 60 mg kg-1 respectively). Besides, the concentration of both categories of heavy metals in the dust samples was higher than that in tailing and less than that of the soils. The barren surfaces of tailing ponds and also the surface soils of the surrounding area seem to be the major contributors to the dust collected. Therefore, abandoned mines as well as their tailing ponds should be rehabilitated by proper technologies and then well stabilized and/or covered by appropriate plant vegetation to control the transfer, particularly by air, of environmentally hazardous materials to other areas.

Particle Size Distribution of Heavy Metals and Magnetic Susceptibility in an Industrial Site.

This study was conducted to explore the relationships between magnetic susceptibility and some soil heavy metals concentrations in various particle sizes in an industrial site, central Iran. Soils were partitioned into five fractions (< 28, 28-75, 75-150, 150-300, and 300-2000 µm). Heavy metals concentrations including Zn, Pb, Fe, Cu, Ni and Mn and magnetic susceptibility were determined in bulk soil samples and all fractions in 60 soil samples collected from the depth of 0-5 cm. The studied heavy metals except for Pb and Fe displayed a substantial enrichment in the < 28 µm. These two elements seemed to be independent of the selected size fractions. Magnetic minerals are specially linked with medium size fractions including 28-75, 75-150 and 150-300 µm. The highest correlations were found for < 28 µm and heavy metals followed by 150-300 µm fraction which are susceptible to wind erosion risk in an arid environment.

Biomagnetic monitoring of heavy metals contamination in deposited atmospheric dust, a case study from Isfahan, Iran.

Tree leaves are considered as one of the best biogenic dust collectors due to their ability to trap and retain particulate matter on their surfaces. In this study, the magnetic susceptibility (MS) and the concentration of selected heavy metals of plane tree (Platanus orientalis L.) leaves and deposited atmospheric dust, sampled by an indirect and a direct method, respectively, were determined to investigate the relationships between leaf magnetic parameters and the concentration of heavy metals in deposited atmospheric dust. The objective was to develop a biomagnetic method as an alternative to the common ones used for determining atmospheric heavy metal contaminations. Plane tree leaves were monthly sampled on the 19th of May to November, 2012 (T1-T7), for seven months from 21 different sites in the city of Isfahan, central Iran. Deposited atmospheric dust samples were also collected using flat glass surfaces from the same sites on the same dates, except for T1. MS (χlf, χhf) values in washed (WL) and unwashed leaves (UL) as well as Cu, Fe, Mn, Ni, Pb, and Zn concentrations in UL and deposited atmospheric dust samples were determined. The results showed that the MS content with a biogenic source was low with almost no significant change during the sampling period, while an increasing trend was observed in the MS content of UL samples due to the deposition of heavy metals and magnetic particles on leaf surfaces throughout the plant growth. The latter type of MS content could be reduced through washing off by rain. Most heavy metals examined, as well as the Tomlinson pollution load index (PLI) in UL, showed statistically significant correlations with MS values. The correlation between heavy metals content in atmospheric dust deposited on glass surfaces and leaf MS values was significant for Cu, Fe, Pb, and Zn. Moreover, the similarity observed between the spatial distribution maps of leaf MS and deposited atmospheric dust PLI provided convincing evidence regarding the suitability of the biomagnetic approach as a relatively rapid and inexpensive method for identifying highly polluted urban areas with selected heavy metals, especially those subjected to anthropogenic and other traffic related sources.